diff --git a/.gitignore b/.gitignore
index 51e4b5ac01..ecba5b62b7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,9 +2,5 @@
 **.a
 **.mod
 driver/run/cosp2_test
-driver/data/inputs/UKMO/*_global.nc
-driver/data/outputs/UKMO/*kgo.v*.nc
 driver/data/outputs/UKMO/*.nc
-driver/data/outputs/UKMO/*.png
-!driver/data/outputs/cosp2_output_um.gfortran.kgo.nc
-!driver/data/outputs/UKMO/*.out
+**/.ipynb_checkpoints/
\ No newline at end of file
diff --git a/README.md b/README.md
index 8817f2de5f..7d01962677 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,19 @@
+# About this fork
+
+This is development code for Doppler velocity simulator, accomodating to EarthCARE's CPR. Therefore, the codes are subject to change. This is being prepared for merging into COSP2.
+
+The developing codes are stored in `dplrw_src`, and links are provided so that each can be referenced from its original directory.
+
+The description paper with example results are published as [Nakamuta et al. 2026, JAMES](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2025MS004958).
+
+## How to use this fork
+
+1. To make input dataset in NetCDF format, execute `exe.gt2input.out` in `driver/data/work`. This procedure is needed because vertical air motion and cumulus mass flux is required as additional input variables from the parent model. The inpud dataset is made by MIROC6.
+2. Run `make driver`.
+3. Get input data made by MIROC6 from [here](https://github.com/yuhi-n/ECdemo-data). Put `demo.tar.xz` in `driver/data` and unzip it `tar Jxvf demo.tar.xz`. (This also contains example output data.)
+4. Run `./cosp2_test cosp2_MIROCtest.txt`, and a result file is generated in `driver/data/MIROC_outputs/ctrl_MIROC-COSP2.nc`.
+5. Python notebooks for visualization are stored in `driver/check`.
+
 # About COSP
 
 The CFMIP Observation Simulator Package (COSP) takes the models representation of the
diff --git a/build/Makefile b/build/Makefile
index 0f1a7898c9..0580dbfff6 100644
--- a/build/Makefile
+++ b/build/Makefile
@@ -138,7 +138,7 @@ cosp_rttov_utilSTUB.o       : cosp_kinds.o
 
 
 # Example subcolumn generaton and mapping to optical properties, following COSP 1.4
-SUBCOL_OBJS = mo_rng.o scops.o prec_scops.o cosp_utils.o cosp_optics.o quickbeam_optics.o array_lib.o math_lib.o mrgrnk.o optics_lib.o cosp_errorHandling.o
+SUBCOL_OBJS = mo_rng.o scops.o prec_scops.o cosp_optics.o quickbeam_optics.o cosp_utils.o array_lib.o math_lib.o mrgrnk.o optics_lib.o cosp_errorHandling.o
 libsubcol.a: $(SUBCOL_OBJS) libcosp.a
 	ar -rvs libsubcol.a $(SUBCOL_OBJS)
 
@@ -150,7 +150,7 @@ optics_lib.o      : cosp_kinds.o cosp_errorHandling.o
 quickbeam_optics.o: cosp_kinds.o cosp_errorHandling.o cosp_constants.o cosp_config.o mrgrnk.o array_lib.o optics_lib.o math_lib.o quickbeam.o cosp_stats.o
 scops.o      : cosp_kinds.o cosp_errorHandling.o mo_rng.o
 prec_scops.o : cosp_kinds.o cosp_config.o
-cosp_utils.o : cosp_kinds.o cosp_config.o
+cosp_utils.o : cosp_kinds.o cosp_config.o quickbeam_optics.o
 cosp_optics.o: cosp_kinds.o cosp_config.o cosp_constants.o modis_simulator.o
 mo_rng.o     : cosp_kinds.o
 
diff --git a/driver/check/COSP2_global-map.ipynb b/driver/check/COSP2_global-map.ipynb
new file mode 100644
index 0000000000..172fc48830
--- /dev/null
+++ b/driver/check/COSP2_global-map.ipynb
@@ -0,0 +1,260 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "cf1bab3d-ca36-498f-983d-9ac15198597a",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import xarray\n",
+    "import pygmt\n",
+    "import pandas\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5292ce3c-d43f-4ffd-8151-ba2d81658dc3",
+   "metadata": {},
+   "source": [
+    "## This is global map figure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1050bc03-232b-4b98-a74b-e0018328d31d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# valid only for variables with following shape: (loc).\n",
+    "\n",
+    "vname='cltatlid'\n",
+    "vmin=0.\n",
+    "vmax=100."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2317395a-d769-479a-806a-eee0285f3d1e",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "### internal work"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b0efb38e-f221-4943-a019-581e93556deb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xarray.open_dataset(\"../data/MIROC_outputs/ctrl_MIROC-COSP2.nc\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "65ec4419-02fe-4e9a-9f3d-50513d55cb91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "\n",
+    "var=ds[vname].values\n",
+    "\n",
+    "lat=ds[\"latitude\"].values\n",
+    "lon=ds[\"longitude\"].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5df1e741-aa79-4bde-b5fa-e0bf3e5dcbbd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mask = (var >= vmin) & (var <= vmax)\n",
+    "data = np.column_stack([lon[mask], lat[mask], var[mask]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e77e1cd0-4ccf-4a6c-b700-5c1eb56a2fa9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "check_rows = np.isfinite(data).all(axis=1)\n",
+    "Nerror = np.where(~check_rows)[0]\n",
+    "\n",
+    "if Nerror.size > 0:\n",
+    "    raise ValueError(\"invalid data\")\n",
+    "\n",
+    "if data.size == 0:\n",
+    "    raise RuntimeError(\"no valid points\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4c550b99-7ca3-464f-a6f1-38b3f124d014",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = pygmt.Figure()\n",
+    "region = [-180, 180, -75, 75]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "4965afac-3f2c-448c-ace7-2886ffb00fdd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tbl_block = pygmt.blockmean(\n",
+    "    data=data,\n",
+    "    region=region,\n",
+    "    spacing=\"1.5d\"\n",
+    ")\n",
+    "\n",
+    "grid = pygmt.surface(\n",
+    "    data=tbl_block,\n",
+    "    region=region,\n",
+    "    spacing=\"1.5d\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "bddcfba4-0c3d-4060-ae86-64738174caa3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gxyz = pygmt.xyz2grd(\n",
+    "    data=data,\n",
+    "    region=region,\n",
+    "    spacing=\"1.5d\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "34824734-03a7-4643-98b7-b98c8f41b400",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig.coast(\n",
+    "    region=region,\n",
+    "    projection=\"W15c\",\n",
+    "    frame=\"afg\",\n",
+    "    land=\"lightgray\",\n",
+    "    water=\"white\"\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "4e557933-95ff-482e-a729-aa6b5996ab3e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig.grdimage(\n",
+    "    grid=gxyz,\n",
+    "    cmap=\"turbo\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "de1908e4-92e5-4591-9181-0c41b65aa48f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig.colorbar(frame='af+l\"Total Cloud Cover (%)\"')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1dd9302c-8fcd-4870-a667-d21bd295da05",
+   "metadata": {},
+   "source": [
+    "### result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "5b18919b-7518-469c-8b1b-c6d21c99d612",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "width": "80%"
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig.show(width=\"80%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe996e44-9a38-4f18-bd8d-50735fb5b8b6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee5ac3d9-4e0d-48f0-9b2e-481bda326725",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/driver/check/COSP2_zonal-mean.ipynb b/driver/check/COSP2_zonal-mean.ipynb
new file mode 100644
index 0000000000..bc07f697df
--- /dev/null
+++ b/driver/check/COSP2_zonal-mean.ipynb
@@ -0,0 +1,208 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "54f959e2-ee4f-4720-ad04-e37b8dcd8bde",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import xarray\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.colors import ListedColormap,LinearSegmentedColormap\n",
+    "from matplotlib.ticker import MultipleLocator\n",
+    "import matplotlib.ticker as mticker"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2f901612-cbe6-4dfe-acd8-12d0097973f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# valid only for variables with following shape: (levStat, loc).\n",
+    "\n",
+    "vname=\"clatlid\"\n",
+    "\n",
+    "vmin=0.\n",
+    "vmax=100."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e71e2f0-fc47-4140-98b4-e0d7492d835b",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "### internal work"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9a2a0996-b59b-4f5f-a74c-06ac6e3f075c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xarray.open_dataset(\"../data/MIROC_outputs/ctrl_MIROC-COSP2.nc\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3e978297-3afd-426e-bb95-6512458516f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lat = ds[\"latitude\"]\n",
+    "lon = ds[\"longitude\"]\n",
+    "lev = ds[\"levStat\"]\n",
+    "\n",
+    "var = ds[vname]\n",
+    "var = var.where((var >= vmin) & (var <= vmax))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2d26d842-eed2-4663-bab1-aef67eb24d3b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "var = var.assign_coords(latitude=(\"loc\", lat.data))\n",
+    "var_zonal = var.groupby(\"latitude\").mean(\"loc\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "96bbc0ad-da9c-4386-b9b5-426387b422a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colors=np.array(\n",
+    "   [\n",
+    "    [248,248,248,1],#white\n",
+    "    [160,210,255,1],\n",
+    "    [ 33,140,255,1],\n",
+    "    [  0, 65,255,1],\n",
+    "    [  0,185,  0,1],\n",
+    "    [250,245,  0,1],\n",
+    "    [255,153,  0,1],\n",
+    "    [255, 40,  0,1],\n",
+    "    [180,  0,104,1],\n",
+    "    [ 45, 45, 45,1],\n",
+    "   ],dtype=np.float64\n",
+    ")\n",
+    "colors[:,:3] /=256\n",
+    "color_listed=ListedColormap(colors)\n",
+    "color_linear=LinearSegmentedColormap.from_list('color_linear',colors=colors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7158e1b2-3ad4-407a-9dff-4d0311afa2c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(12,7))\n",
+    "\n",
+    "pcm = ax.pcolormesh(\n",
+    "    var_zonal.latitude,\n",
+    "    var_zonal.levStat,\n",
+    "    var_zonal,\n",
+    "    shading=\"auto\",\n",
+    "    vmin=vmin,\n",
+    "    vmax=vmax,\n",
+    "    cmap=color_linear\n",
+    ")\n",
+    "\n",
+    "ax.set_title(\"zonal mean: \"+vname, fontsize=32, pad=16)\n",
+    "\n",
+    "ax.xaxis.set_major_locator(MultipleLocator(30.))\n",
+    "ax.set_xlabel(\"latitude\",fontsize=22)\n",
+    "\n",
+    "ax.yaxis.set_major_locator(MultipleLocator(3000.))\n",
+    "ax.yaxis.set_major_formatter(\n",
+    "    mticker.FuncFormatter(lambda y, pos: f\"{y/1000:g}\")\n",
+    ")\n",
+    "ax.set_ylabel(\"level (km)\",fontsize=22)\n",
+    "ax.tick_params(axis='both', which='major', labelsize=18)\n",
+    "\n",
+    "cbar = fig.colorbar(pcm, ax=ax, pad=0.02)\n",
+    "cbar.set_label(\"\", fontsize=13)\n",
+    "cbar.ax.tick_params(labelsize=16)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97208430-407f-44b8-8f52-16c38e312aaa",
+   "metadata": {},
+   "source": [
+    "### Figure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "be27ca04-76ab-46e4-90fb-711aae66ad44",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9bb7d525-f727-4fbd-bc95-126111f52d0c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/driver/check/EC_check.ipynb b/driver/check/EC_check.ipynb
new file mode 100644
index 0000000000..dd8cd18dcf
--- /dev/null
+++ b/driver/check/EC_check.ipynb
@@ -0,0 +1,582 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "887c003d-0904-4443-b0a8-d2c2675e4263",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import xarray\n",
+    "import pygmt\n",
+    "import pandas\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.colors import ListedColormap,LinearSegmentedColormap\n",
+    "from matplotlib.ticker import MultipleLocator\n",
+    "from IPython.display import display"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4065b987-8ecd-4aa5-aa85-760c8a90cd14",
+   "metadata": {},
+   "source": [
+    "### config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "cb5587d5-bcc4-4023-b0ae-e28743fc0e76",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# variables\n",
+    "\n",
+    "#vname='Doppler velocity'\n",
+    "vname='radar reflectivity'\n",
+    "#vname='terminal velocity'\n",
+    "#vname='vertical air motion'\n",
+    "#vname='Ze-Doppler'\n",
+    "#vname='Ze-terminal'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5a951cf4-47ec-439a-bd6a-5b71e6ff3239",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# meridional zone\n",
+    "\n",
+    "zone='tropics'\n",
+    "#zone='mid-north'\n",
+    "#zone='pol-north'\n",
+    "#zone='mid-south'\n",
+    "#zone='pol-south'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "25a7ea7c-98c0-4b2d-8174-d2995d734cb4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# cloud regime\n",
+    "#ctype='ALL'\n",
+    "ctype='stratiform'\n",
+    "#ctype='convective'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "950f2697-2451-4634-816b-139f78f8e0ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# only for Doppler or terminal or air velocity\n",
+    "\n",
+    "zonal_mean_flag = False\n",
+    "#  True: zonal mean\n",
+    "# False: CFED"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "414f7d1c-1b87-4fce-a618-92cb0b56a08c",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "### internal process"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3295478a-ad57-45a5-924d-89965bf9a1cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Nlvdplr = 80\n",
+    "Nlvtemp = 57\n",
+    "NlvdBZe = 35\n",
+    "\n",
+    "temp = -79.0 + 2.0 * np.arange(Nlvtemp)\n",
+    "zgrd = -39.0 + 2.0 * np.arange(NlvdBZe)\n",
+    "vgrd =  -7.9 + 0.2 * np.arange(Nlvdplr)\n",
+    "\n",
+    "Nlvhght = 60\n",
+    "hght = 0.125 + 0.25 * np.arange(Nlvhght)\n",
+    "\n",
+    "Nlvglat = 144\n",
+    "glat = -89.375 + 1.25 * np.arange(Nlvglat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "125d2e01-a852-416d-a93c-86052f30bfdc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# target region\n",
+    "\n",
+    "if zone=='tropics':\n",
+    "    rgnID = 0\n",
+    "elif zone=='mid-north':\n",
+    "    rgnID = 1\n",
+    "elif zone=='pol-north':\n",
+    "    rgnID = 2\n",
+    "elif zone=='mid-south':\n",
+    "    rgnID = 3\n",
+    "elif zone=='pol-south':\n",
+    "    rgnID = 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "62a61e04-41b7-4dc0-af99-b8dfcc7b42b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# data access\n",
+    "\n",
+    "if vname=='Doppler velocity':\n",
+    "    if zonal_mean_flag:\n",
+    "        var=np.fromfile(\"../data/EarthCARE/vd_zonal_mean.grd\", dtype=np.float32)\n",
+    "        var = var.reshape( (5,8,Nlvhght,Nlvglat) )\n",
+    "    else:\n",
+    "        var=np.fromfile(\"../data/EarthCARE/cfed_Vd.bin\", dtype=np.float32)\n",
+    "elif vname=='radar reflectivity':\n",
+    "    var=np.fromfile(\"../data/EarthCARE/cfed_Ze.bin\", dtype=np.float32)\n",
+    "    zonal_mean_flag = False\n",
+    "elif vname=='terminal velocity':\n",
+    "    if zonal_mean_flag:\n",
+    "        var=np.fromfile(\"../data/EarthCARE/vd_zonal_mean.grd\", dtype=np.float32)\n",
+    "        var = var.reshape( (5,8,Nlvhght,Nlvglat) )\n",
+    "    else:\n",
+    "        var=np.fromfile(\"../data/EarthCARE/cfed_Vf.bin\", dtype=np.float32)\n",
+    "elif vname=='vertical air motion':\n",
+    "    if zonal_mean_flag:\n",
+    "        var=np.fromfile(\"../data/EarthCARE/vd_zonal_mean.grd\", dtype=np.float32)\n",
+    "        var = var.reshape( (5,8,Nlvhght,Nlvglat) )\n",
+    "    else:\n",
+    "        var=np.fromfile(\"../data/EarthCARE/cfed_Wa.bin\", dtype=np.float32)\n",
+    "elif vname=='Ze-Doppler':\n",
+    "    var=np.fromfile(\"../data/EarthCARE/pdf2_zv.bin\", dtype=np.float32)\n",
+    "    zonal_mean_flag = False\n",
+    "elif vname=='Ze-terminal':\n",
+    "    var=np.fromfile(\"../data/EarthCARE/cfed_zf.bin\", dtype=np.float32)\n",
+    "    zonal_mean_flag = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "5f3a14a2-0e88-40be-9248-240a590fd3a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if zonal_mean_flag:\n",
+    "    pass\n",
+    "else:\n",
+    "    if vname=='radar reflectivity':\n",
+    "        var = var.reshape( (5,5,Nlvtemp,NlvdBZe) )\n",
+    "        window = 2.0\n",
+    "        extent = [ -40.0, 30.0, 20.0, -80.0 ]\n",
+    "        iy = np.where((temp >= -80.0) & (temp <= 20.0))[0]\n",
+    "        ix = np.where((zgrd >= -40.0) & (zgrd <= 30.0))[0]\n",
+    "        xtics = 5.0 ; ytics = 10.0\n",
+    "        vmax = 0.2\n",
+    "        origin='upper'\n",
+    "    elif vname=='Ze-Doppler' or vname=='Ze-terminal':\n",
+    "        var = var.reshape( (5,5,Nlvdplr,NlvdBZe) )\n",
+    "        window = 2.0*0.2\n",
+    "        extent = [ -40.0, 30.0, -6.0, 6.0 ]\n",
+    "        iy = np.where((vgrd >=  -6.0) & (vgrd <=  6.0))[0]\n",
+    "        ix = np.where((zgrd >= -40.0) & (zgrd <= 30.0))[0]\n",
+    "        xtics = 5.0 ; ytics = 1.0\n",
+    "        vmax = 3\n",
+    "        origin='lower'\n",
+    "    else:\n",
+    "        var = var.reshape( (5,5,Nlvtemp,Nlvdplr) )\n",
+    "        window = 0.2\n",
+    "        extent = [ -6.0, 6.0, 20.0, -80.0 ]\n",
+    "        iy = np.where((temp >= -80.0) & (temp <= 20.0))[0]\n",
+    "        ix = np.where((vgrd >=  -6.0) & (vgrd <=  6.0))[0]\n",
+    "        xtics = 1.0 ; ytics = 10.0\n",
+    "        vmax = 2.0\n",
+    "        origin='upper'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "02c2d337-fb7c-4c23-a78b-111110b75c4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# cloud regime\n",
+    "if zonal_mean_flag:\n",
+    "    pass\n",
+    "else:\n",
+    "    if ctype=='ALL':\n",
+    "        sel = var[rgnID,0:3,:,:].sum(axis=0)\n",
+    "    elif ctype=='stratiform':\n",
+    "        sel = var[rgnID,3,:,:]\n",
+    "    elif ctype=='convective':\n",
+    "        sel = var[rgnID,2,:,:]\n",
+    "\n",
+    "# warm:0 upper:1 conv:2 strat:3 other:4 all:5 w/o warm:6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "5410286f-6ef3-4f4f-8545-9d9c00c979cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if zonal_mean_flag:\n",
+    "    pass\n",
+    "else:\n",
+    "    if vname=='Ze-Doppler' or vname=='Ze-terminal':\n",
+    "        smpl = sel.sum()\n",
+    "        smpl = smpl.T\n",
+    "        nrm = 1e-2 * window * smpl.sum()\n",
+    "    else:\n",
+    "        smpl = ( sel.sum(axis=1) * window )[:,None]\n",
+    "        nrm = np.broadcast_to(smpl,sel.shape)\n",
+    "    \n",
+    "    zero_check = (nrm < window)\n",
+    "    work = np.zeros_like(sel)\n",
+    "    \n",
+    "    work[~zero_check] = sel[~zero_check]/nrm[~zero_check]\n",
+    "    work[zero_check] = np.nan\n",
+    "    cfed = work[np.ix_(iy,ix)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c08110a-4bf0-420b-9d4d-5d05368ccc7b",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "43319578-dbc9-4500-88fa-5192ce01bbcf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not zonal_mean_flag:\n",
+    "    pass\n",
+    "else:\n",
+    "    if vname=='Doppler velocity':\n",
+    "        sumID = 0 ; numID = 1\n",
+    "    elif vname=='terminal velocity':\n",
+    "        sumID = 2 ; numID = 3\n",
+    "    elif vname=='vertical air motion':\n",
+    "        sumID = 4 ; numID = 5\n",
+    "\n",
+    "    if ctype=='ALL':\n",
+    "        totl = var[0:3,sumID,:,:].sum(axis=0)\n",
+    "        smpl = var[0:3,numID,:,:].sum(axis=0)\n",
+    "    elif ctype=='stratiform':\n",
+    "        totl = var[3,sumID,:,:]\n",
+    "        smpl = var[3,numID,:,:]\n",
+    "    elif ctype=='convective':\n",
+    "        totl = var[2,sumID,:,:]\n",
+    "        smpl = var[2,numID,:,:]\n",
+    "\n",
+    "    zero_check = (smpl < 1.0)\n",
+    "    zmn_work = np.zeros_like(smpl)\n",
+    "    \n",
+    "    zmn_work[~zero_check] = totl[~zero_check]/smpl[~zero_check]\n",
+    "    zmn_work[zero_check]  = np.nan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "843e2f9c-a5d2-42ba-be18-29d4a07ffe0e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not zonal_mean_flag:\n",
+    "    pass\n",
+    "else:\n",
+    "    window = 0.2\n",
+    "    xtics = 30.0 ; ytics = 2.0\n",
+    "    origin='lower'\n",
+    "\n",
+    "    extent = [ -90.0, 90.0, 0.0, 15.0 ]\n",
+    "    zmn = zmn_work"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e316738a-98f2-4d0c-ad2f-73b2184e6b36",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "### make figure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "21c37598-91ff-4b32-a1bb-cd565146fcc9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colors=np.array(\n",
+    "   [\n",
+    "    [248,248,248,1],#white\n",
+    "    [160,210,255,1],\n",
+    "    [ 33,140,255,1],\n",
+    "    [  0, 65,255,1],\n",
+    "    [  0,185,  0,1],\n",
+    "    [250,245,  0,1],\n",
+    "    [255,153,  0,1],\n",
+    "    [255, 40,  0,1],\n",
+    "    [180,  0,104,1],\n",
+    "    [ 45, 45, 45,1],\n",
+    "   ],dtype=np.float64\n",
+    ")\n",
+    "colors[:,:3] /=256\n",
+    "color_listed=ListedColormap(colors)\n",
+    "color_linear=LinearSegmentedColormap.from_list('color_linear',colors=colors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "e594689e-b171-4ff0-876b-ab02247e8ee0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tmp=np.array(\n",
+    "    [\n",
+    "    [  1, 64, 38,1],\n",
+    "    [ 71,108, 25,1],\n",
+    "    [154,125, 66,1],\n",
+    "    [206,185,156,1],\n",
+    "    [242,239,246,1],\n",
+    "    [236,196,225,1],\n",
+    "    [204,124,186,1],\n",
+    "    [164, 65,138,1],\n",
+    "    [101,  2, 75,1],\n",
+    "    ],dtype=np.float64\n",
+    ")\n",
+    "tmp[:,:3] /=256\n",
+    "near0=LinearSegmentedColormap.from_list('lower',colors=tmp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "e20b3b56-8732-400a-9ed0-8fb9d706bd6a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tmp=np.array(\n",
+    "    [\n",
+    "    [ 26, 51, 51,1],\n",
+    "    [ 35, 85,130,1],\n",
+    "    [ 61,144,199,1],\n",
+    "    [120,197,204,1],\n",
+    "    [231,255,232,1],\n",
+    "    ],dtype=np.float64\n",
+    ")\n",
+    "tmp[:,:3] /=256\n",
+    "lower=LinearSegmentedColormap.from_list('near0',colors=tmp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "036d42bd-7b01-4dd1-8832-103e506fd51b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N1 = 128\n",
+    "N2 = 256\n",
+    "\n",
+    "lower_linear = lower(np.linspace(0, 1, N1))\n",
+    "near0_linear = near0(np.linspace(0, 1, N2))\n",
+    "\n",
+    "hoge = np.vstack([lower_linear, near0_linear])\n",
+    "\n",
+    "zmn_color = ListedColormap(hoge)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "63251813-c7d4-4e45-a235-8341f6135216",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "if zonal_mean_flag:\n",
+    "    pass\n",
+    "else:\n",
+    "    plt.ioff()\n",
+    "    \n",
+    "    fig, ax = plt.subplots(figsize=(16,9))\n",
+    "    im = ax.imshow(cfed,\n",
+    "                    extent=extent,\n",
+    "                    interpolation='nearest',\n",
+    "                    origin=origin,\n",
+    "                    vmin=0, vmax=vmax,\n",
+    "                    cmap=color_linear,\n",
+    "                    aspect='auto')\n",
+    "    \n",
+    "    if vname=='Ze-Doppler' or vname=='Ze-terminal':\n",
+    "        ax.set_title(\"2d-PDF: \"+vname, fontsize=32, pad=16)\n",
+    "    else:\n",
+    "        ax.set_title(\"CFED: \"+vname, fontsize=32, pad=16)\n",
+    "\n",
+    "    if vname=='radar reflectivity' or vname=='Ze-Doppler' or vname=='Ze-terminal':\n",
+    "        ax.set_xlabel(\"reflectivity (dB Ze)\",fontsize=22)\n",
+    "    else:\n",
+    "        ax.set_xlabel(\"velocity (m/s)\",fontsize=22)\n",
+    "\n",
+    "    if vname=='Ze-Doppler' or vname=='Ze-terminal':\n",
+    "        ax.set_ylabel(\"velocity (m/s)\",fontsize=22)\n",
+    "    else:\n",
+    "        ax.set_ylabel(\"Temperature (degC)\",fontsize=22)\n",
+    "    \n",
+    "    ax.xaxis.set_major_locator(MultipleLocator(xtics))\n",
+    "    ax.yaxis.set_major_locator(MultipleLocator(ytics))\n",
+    "    ax.tick_params(axis='both', which='major', labelsize=18)\n",
+    "    \n",
+    "    cbar = fig.colorbar(im, ax=ax,\n",
+    "                        orientation='vertical',\n",
+    "                        pad=0.02,\n",
+    "                        fraction=0.045,\n",
+    "                        shrink=1.0)\n",
+    "    cbar.set_label(\"\", fontsize=13)\n",
+    "    cbar.ax.tick_params(labelsize=16)\n",
+    "    \n",
+    "    \n",
+    "    fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "59fc2653-f03d-4c20-9206-7589e180b816",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "if not zonal_mean_flag:\n",
+    "    pass\n",
+    "else:\n",
+    "    plt.ioff()\n",
+    "    \n",
+    "    fig, ax = plt.subplots(figsize=(16,9))\n",
+    "    im = ax.imshow(zmn,\n",
+    "                    extent=extent,\n",
+    "                    interpolation='nearest',\n",
+    "                    origin=origin,\n",
+    "                    vmin=-4.0, vmax=2.0,\n",
+    "                    cmap=zmn_color,\n",
+    "                    aspect='auto')\n",
+    "    \n",
+    "    ax.set_title(\"zonal mean: \"+vname, fontsize=32, pad=16)\n",
+    "\n",
+    "    ax.set_xlabel(\"latitude\",fontsize=22)\n",
+    "    ax.set_ylabel(\"height\",fontsize=22)\n",
+    "    \n",
+    "    ax.xaxis.set_major_locator(MultipleLocator(xtics))\n",
+    "    ax.yaxis.set_major_locator(MultipleLocator(ytics))\n",
+    "    ax.tick_params(axis='both', which='major', labelsize=18)\n",
+    "    \n",
+    "    cbar = fig.colorbar(im, ax=ax,\n",
+    "                        orientation='vertical',\n",
+    "                        pad=0.02,\n",
+    "                        fraction=0.045,\n",
+    "                        shrink=1.0)\n",
+    "    cbar.set_label(\"\", fontsize=13)\n",
+    "    cbar.ax.tick_params(labelsize=16)\n",
+    "    \n",
+    "    \n",
+    "    fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bfc0da8a-a5c5-40b8-a0f7-ac1496802524",
+   "metadata": {},
+   "source": [
+    "### results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "009c34ad-ecaf-426e-8917-5be596ed7295",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x900 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51b015e1-d4d4-4bf6-ab3e-e1df9e55e344",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/driver/check/MIROC_check.ipynb b/driver/check/MIROC_check.ipynb
new file mode 100644
index 0000000000..7930b45ddc
--- /dev/null
+++ b/driver/check/MIROC_check.ipynb
@@ -0,0 +1,262 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2ad48f61-e598-4a15-89f8-a0483fe2c9fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray\n",
+    "import pygmt\n",
+    "import pandas\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "dc10108f-956d-47bf-a02c-f42282da2ef1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xarray.open_dataset(\"../data/MIROC_inputs/ctrl_MIROCinput.nc\", decode_timedelta=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "eaf47183-d94f-49f7-a397-ce248957a526",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "xarray.Dataset {\n",
+      "dimensions:\n",
+      "\tlat = 128 ;\n",
+      "\tlon = 256 ;\n",
+      "\tlevel = 40 ;\n",
+      "\thydro = 9 ;\n",
+      "\n",
+      "variables:\n",
+      "\tfloat64 landmask(lat, lon) ;\n",
+      "\tfloat64 skt(lat, lon) ;\n",
+      "\tfloat64 orography(lat, lon) ;\n",
+      "\tfloat64 sunlit(lat, lon) ;\n",
+      "\tfloat64 pfull(level, lat, lon) ;\n",
+      "\tfloat64 height(level, lat, lon) ;\n",
+      "\tfloat64 height_half(level, lat, lon) ;\n",
+      "\tfloat64 T_abs(level, lat, lon) ;\n",
+      "\tfloat64 qv(level, lat, lon) ;\n",
+      "\tfloat64 rh(level, lat, lon) ;\n",
+      "\tfloat64 gwvel(level, lat, lon) ;\n",
+      "\tfloat64 tca(level, lat, lon) ;\n",
+      "\tfloat64 cca(level, lat, lon) ;\n",
+      "\tfloat64 mr_lsliq(level, lat, lon) ;\n",
+      "\tfloat64 mr_lsice(level, lat, lon) ;\n",
+      "\tfloat64 mr_ccliq(level, lat, lon) ;\n",
+      "\tfloat64 mr_ccice(level, lat, lon) ;\n",
+      "\tfloat64 fl_lsrain(level, lat, lon) ;\n",
+      "\tfloat64 fl_lssnow(level, lat, lon) ;\n",
+      "\tfloat64 fl_ccrain(level, lat, lon) ;\n",
+      "\tfloat64 fl_ccsnow(level, lat, lon) ;\n",
+      "\tfloat64 dtau_s(level, lat, lon) ;\n",
+      "\tfloat64 dtau_c(level, lat, lon) ;\n",
+      "\tfloat64 dem_s(level, lat, lon) ;\n",
+      "\tfloat64 dem_c(level, lat, lon) ;\n",
+      "\tfloat64 mr_ozone(level, lat, lon) ;\n",
+      "\tfloat64 phalf(level, lat, lon) ;\n",
+      "\tfloat64 gcumf(level, lat, lon) ;\n",
+      "\tfloat64 Reff(hydro, level, lat, lon) ;\n",
+      "\tfloat64 lon(lon) ;\n",
+      "\tfloat64 lat(lat) ;\n",
+      "\n",
+      "// global attributes:\n",
+      "}"
+     ]
+    }
+   ],
+   "source": [
+    "ds.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47c43a93-1549-4475-bb14-62e92a04287b",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4afebca7-6d9a-42e4-87f8-22381a3240cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "data=ds[\"tca\"]\n",
+    "data.load()\n",
+    "\n",
+    "zid=10\n",
+    "hid=1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f18b7b21-31f7-4d84-b9b7-8a2f0c188704",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "var = data.isel(level=zid)\n",
+    "#var = data.isel(level=zid,hydro=hid)\n",
+    "\n",
+    "#lat = ds[\"lat\"].values\n",
+    "#lon = ds[\"lon\"].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "faf8c56f-18c9-4d70-97b8-37c086594c59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "region = [ -180, 180, -75, 75 ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "663f115e-1c8f-42d8-b233-3b7331f2c602",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lon, lat = np.meshgrid(var.lon, var.lat)\n",
+    "ll = np.column_stack([lon.ravel(), lat.ravel(), var.values.ravel()])\n",
+    "\n",
+    "grid = pygmt.xyz2grd(\n",
+    "    data=ll,\n",
+    "    region=region,\n",
+    "    spacing=\"1.5d\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e06f7d7f-9135-4007-85f5-9e91f679b585",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = pygmt.Figure()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "67a53630-fc07-4cbf-b5fc-eabc5b57a137",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig.coast(\n",
+    "    region=region,\n",
+    "    projection=\"W15c\",   # Robinson 投影（15 cm 幅）\n",
+    "    land=\"lightgray\",\n",
+    "    water=\"white\",\n",
+    "    frame=\"af\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "9fce5808-e758-4045-a8bc-8ba746f09c6f",
+   "metadata": {},
+   "source": [
+    "pygmt.makecpt(cmap=\"turbo\", series=[float(var.min()), float(var.max())])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "34d714ca-db20-4b4b-8081-9f540e4ccd3d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig.grdimage(\n",
+    "    grid=grid,\n",
+    "    cmap=\"turbo\",\n",
+    "    shading=True,\n",
+    ")\n",
+    "\n",
+    "fig.colorbar(frame=\"af\", position=\"JBC+w10c/0.5c+h\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "431e1d02-99ff-4ca5-a28d-f4db72f1d58f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "width": "70%"
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig.show(width='70%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44e22c8f-d7a4-4712-b678-48be425e163f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a4076f67-bfdc-4c6f-963b-da9544d0da41",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/driver/check/dplrw_results.ipynb b/driver/check/dplrw_results.ipynb
new file mode 100644
index 0000000000..e842de13d9
--- /dev/null
+++ b/driver/check/dplrw_results.ipynb
@@ -0,0 +1,663 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "cf1bab3d-ca36-498f-983d-9ac15198597a",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import xarray\n",
+    "import pygmt\n",
+    "import pandas\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.colors import ListedColormap,LinearSegmentedColormap\n",
+    "from matplotlib.ticker import MultipleLocator\n",
+    "from IPython.display import display"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b0efb38e-f221-4943-a019-581e93556deb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xarray.open_dataset(\"../data/MIROC_outputs/ctrl_MIROC-COSP2.nc\")"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "bc8c7853-38c7-4cc9-8550-4bfe379c03d3",
+   "metadata": {},
+   "source": [
+    "print(ds.info())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8f95c55-be27-4e36-a6c7-05b04049a4d0",
+   "metadata": {},
+   "source": [
+    "### config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3a3345fc-48d9-4848-9c58-f15f9cd18508",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# variables\n",
+    "\n",
+    "vname='Doppler velocity'\n",
+    "#vname='radar reflectivity'\n",
+    "#vname='terminal velocity'\n",
+    "#vname='vertical air motion'\n",
+    "#vname='Ze-Doppler'\n",
+    "#vname='Ze-terminal'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "69ff28b8-67d2-44c3-9d8e-a911178c77d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# meridional zone\n",
+    "\n",
+    "zone='tropics'\n",
+    "#zone='mid-north'\n",
+    "#zone='pol-north'\n",
+    "#zone='mid-south'\n",
+    "#zone='pol-south'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3ffbf5f0-78b4-4c4b-92b4-9c4366d502d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# cloud regime\n",
+    "#ctype='ALL'\n",
+    "ctype='stratiform'\n",
+    "#ctype='convective'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "85cafa90-23d6-4e47-9192-3914b4f5c04e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# only for Doppler or terminal velocity\n",
+    "\n",
+    "zonal_mean_flag = True\n",
+    "#  True: zonal mean\n",
+    "# False: CFED"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "55690fe9-ec39-4e75-bb81-d63e519ddc1c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# only for terminal velocity\n",
+    "\n",
+    "weight_factor='reflectivity'\n",
+    "#weight_factor='mass'\n",
+    "#weight_factor='number'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d8737ab-22e8-48cb-a8a6-9836451780c2",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "### internal process"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "65ec4419-02fe-4e9a-9f3d-50513d55cb91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "# data access\n",
+    "\n",
+    "if vname=='Doppler velocity':\n",
+    "    if zonal_mean_flag:\n",
+    "        var=ds['dplrw_Z']\n",
+    "    else:\n",
+    "        var=ds['dplrw_T']\n",
+    "elif vname=='radar reflectivity':\n",
+    "    var=ds['Zef94_T']\n",
+    "    zonal_mean_flag = False\n",
+    "elif vname=='terminal velocity':\n",
+    "    if zonal_mean_flag:\n",
+    "        var=ds['vfall_Z']\n",
+    "    else:\n",
+    "        var=ds['vfall_T']\n",
+    "elif vname=='vertical air motion':\n",
+    "    if zonal_mean_flag:\n",
+    "        var=ds['gridw_Z']\n",
+    "    else:\n",
+    "        var=ds['gridw_T']\n",
+    "elif vname=='Ze-Doppler':\n",
+    "    var=ds['ZefVd_2']\n",
+    "    zonal_mean_flag = False\n",
+    "elif vname=='Ze-terminal':\n",
+    "    var=ds['ZefVd_2']\n",
+    "    zonal_mean_flag = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ddafada6-a47f-4209-9c0c-f6e4f927f017",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#grids\n",
+    "\n",
+    "lat=ds[\"latitude\"].values\n",
+    "lon=ds[\"longitude\"].values\n",
+    "\n",
+    "temp=ds[\"lvtemp_grid\"].values\n",
+    "zgrd=ds[\"lvdBZe_grid\"].values\n",
+    "vgrd=ds[\"lvdplr_grid\"].values\n",
+    "hght=ds[\"levStat\"].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f4bc95c9-dfba-446e-8f8f-5658a52f3b40",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# target region\n",
+    "west=0. ; east=360.\n",
+    "\n",
+    "if zone=='tropics':\n",
+    "    south=-20.0 ; north= 20.0\n",
+    "elif zone=='mid-north':\n",
+    "    south= 20.0 ; north= 65.0\n",
+    "elif zone=='pol-north':\n",
+    "    south= 65.0 ; north= 90.0\n",
+    "elif zone=='mid-south':\n",
+    "    south=-65.0 ; north=-20.0\n",
+    "elif zone=='pol-south':\n",
+    "    south=-90.0 ; north=-65.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3a89e0da-5511-44b5-87cf-207dc9218307",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# location mask\n",
+    "lon_mask = (lon >= west) & (lon <= east)\n",
+    "lat_mask = (lat >= south)&(lat <= north)\n",
+    "\n",
+    "mask = lon_mask & lat_mask\n",
+    "\n",
+    "index = np.where(mask)[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "0fff3516-6f6c-4fc1-8f69-f2f61f4cac2c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# cloud regime\n",
+    "if ctype=='ALL':\n",
+    "    regime=0\n",
+    "elif ctype=='stratiform':\n",
+    "    regime=1\n",
+    "elif ctype=='convective':\n",
+    "    regime=2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "aff2506a-5aae-4e4a-8212-d49d8eb9fef4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# moment\n",
+    "if weight_factor=='reflectivity':\n",
+    "    nmlz=1\n",
+    "elif weight_factor=='mass':\n",
+    "    nmlz=2\n",
+    "elif weight_factor=='number':\n",
+    "    nmlz=3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d969c24b-398a-4b8f-9795-bc36da1c0ad4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if zonal_mean_flag:\n",
+    "    pass\n",
+    "else:\n",
+    "    if vname=='terminal velocity':\n",
+    "        sel_work = var.isel(loc=index).isel(regimeID=regime).isel(nrmlzdID=nmlz)\n",
+    "    else:\n",
+    "        sel_work = var.isel(loc=index).isel(regimeID=regime)\n",
+    "\n",
+    "    if vname=='radar reflectivity':\n",
+    "        window = 2.0\n",
+    "        xtics = 5.0 ; ytics = 10.0\n",
+    "        vmax = 0.2\n",
+    "        origin='upper'\n",
+    "\n",
+    "        extent = [ -40.0, 30.0, 20.0, -80.0 ]\n",
+    "        zlower = np.searchsorted(vgrd,-40.0)\n",
+    "        zupper = np.searchsorted(vgrd, 30.0)\n",
+    "        tlower = np.searchsorted(temp,-80.0)\n",
+    "        tupper = np.searchsorted(temp, 20.0)\n",
+    "        sel = sel_work[tlower:tupper,zlower:zupper]\n",
+    "    elif vname=='Ze-Doppler' or vname=='Ze-terminal':\n",
+    "        window = 2.0*0.2\n",
+    "        xtics = 5.0 ; ytics = 1.0\n",
+    "        vmax = 3\n",
+    "        origin='lower'\n",
+    "\n",
+    "        extent = [ -40.0, 30.0, -6.0,  6.0 ]\n",
+    "        zlower = np.searchsorted(zgrd,-40.0)\n",
+    "        zupper = np.searchsorted(zgrd, 30.0)\n",
+    "        vlower = np.searchsorted(vgrd, -6.0)\n",
+    "        vupper = np.searchsorted(vgrd,  6.0)\n",
+    "        sel = sel_work[zlower:zupper,vlower:vupper]\n",
+    "    else:\n",
+    "        window = 0.2\n",
+    "        xtics = 1.0 ; ytics = 10.0\n",
+    "        vmax = 2.0\n",
+    "        origin='upper'\n",
+    "\n",
+    "        extent = [ -6.0, 6.0, 20.0, -80.0 ]\n",
+    "        vlower = np.searchsorted(vgrd, -6.0)\n",
+    "        vupper = np.searchsorted(vgrd,  6.0)\n",
+    "        tlower = np.searchsorted(temp,-80.0)\n",
+    "        tupper = np.searchsorted(temp, 20.0)\n",
+    "        sel = sel_work[tlower:tupper,vlower:vupper]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "c661218d-216c-4051-9143-720c20a8c947",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if zonal_mean_flag:\n",
+    "    pass\n",
+    "else:\n",
+    "    if vname=='Ze-Doppler' or vname=='Ze-terminal':\n",
+    "        smpl = sel.sum(dim='loc', skipna=True).values\n",
+    "        smpl = smpl.T\n",
+    "        nrm = window * smpl.sum()\n",
+    "        nrm = np.broadcast_to(nrm,smpl.shape)\n",
+    "    else:\n",
+    "        smpl = sel.sum(dim='loc', skipna=True).values\n",
+    "        nrm = window * smpl.sum(axis=1, keepdims=True)\n",
+    "        nrm = np.broadcast_to(nrm,smpl.shape)\n",
+    "\n",
+    "    zero_check = (nrm < window)\n",
+    "    cfed = np.zeros_like(smpl)\n",
+    "\n",
+    "    cfed[~zero_check] = smpl[~zero_check]/nrm[~zero_check]\n",
+    "    cfed[zero_check] = np.nan\n",
+    "    if vname=='Ze-Doppler' or vname=='Ze-terminal':\n",
+    "        cfed = cfed * 100."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7fca1b0-e94f-41f0-a093-10c8fcb33ad9",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "f5ac58f0-3955-4da5-8641-261d4b534b89",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(40, 72)\n"
+     ]
+    }
+   ],
+   "source": [
+    "if not zonal_mean_flag:\n",
+    "    pass\n",
+    "else:\n",
+    "    j_list = ( (lat + 90.0)//2.5 ).astype(int)\n",
+    "    Nlev = hght.size\n",
+    "    \n",
+    "    if vname=='terminal velocity':\n",
+    "        sel = var.isel(regimeID=regime).isel(nrmlzdID=nmlz)\n",
+    "    else:\n",
+    "        sel = var.isel(regimeID=regime)\n",
+    "    \n",
+    "    tmp = sel.sum(axis=1).values\n",
+    "    smpl = np.zeros((Nlev, 72), dtype=var.dtype)\n",
+    "    np.add.at(smpl, (slice(None), j_list), tmp)\n",
+    "    \n",
+    "    tmp = ( sel * vgrd[None,:,None] ).sum(axis=1).values\n",
+    "    totl = np.zeros((Nlev, 72), dtype=var.dtype)\n",
+    "    np.add.at(totl, (slice(None), j_list), tmp)\n",
+    "    \n",
+    "    zero_check = (smpl < 1.0)\n",
+    "    zmn_work = np.zeros_like(smpl)\n",
+    "    \n",
+    "    zmn_work[~zero_check] = totl[~zero_check]/smpl[~zero_check]\n",
+    "    zmn_work[zero_check]  = np.nan\n",
+    "    print(zmn_work.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "b2d2372b-bf50-4cd9-8387-bb066ec8d4f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not zonal_mean_flag:\n",
+    "    pass\n",
+    "else:\n",
+    "    window = 0.2\n",
+    "    xtics = 30.0 ; ytics = 2.0\n",
+    "    origin='lower'\n",
+    "\n",
+    "    extent = [ -90.0, 90.0, 0.0, 14.0 ]\n",
+    "    hlower = np.searchsorted(hght[::-1],     0.0)\n",
+    "    hupper = np.searchsorted(hght[::-1], 14000.0)\n",
+    "    zmn = zmn_work[hlower:hupper,:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df06376c-0eee-4d88-98dc-8d4982ab3afd",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "### make figure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "4f0215d7-c0c7-496d-9de9-77b79724dfdb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colors=np.array(\n",
+    "   [\n",
+    "    [248,248,248,1],#white\n",
+    "    [160,210,255,1],\n",
+    "    [ 33,140,255,1],\n",
+    "    [  0, 65,255,1],\n",
+    "    [  0,185,  0,1],\n",
+    "    [250,245,  0,1],\n",
+    "    [255,153,  0,1],\n",
+    "    [255, 40,  0,1],\n",
+    "    [180,  0,104,1],\n",
+    "    [ 45, 45, 45,1],\n",
+    "   ],dtype=np.float64\n",
+    ")\n",
+    "colors[:,:3] /=256\n",
+    "color_listed=ListedColormap(colors)\n",
+    "color_linear=LinearSegmentedColormap.from_list('color_linear',colors=colors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f7e346ae-df53-4cc6-affe-3ef1193dfc51",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tmp=np.array(\n",
+    "    [\n",
+    "    [  1, 64, 38,1],\n",
+    "    [ 71,108, 25,1],\n",
+    "    [154,125, 66,1],\n",
+    "    [206,185,156,1],\n",
+    "    [242,239,246,1],\n",
+    "    [236,196,225,1],\n",
+    "    [204,124,186,1],\n",
+    "    [164, 65,138,1],\n",
+    "    [101,  2, 75,1],\n",
+    "    ],dtype=np.float64\n",
+    ")\n",
+    "tmp[:,:3] /=256\n",
+    "near0=LinearSegmentedColormap.from_list('lower',colors=tmp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "45ecf13f-9b8e-4b94-b176-099acb1855f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tmp=np.array(\n",
+    "    [\n",
+    "    [ 26, 51, 51,1],\n",
+    "    [ 35, 85,130,1],\n",
+    "    [ 61,144,199,1],\n",
+    "    [120,197,204,1],\n",
+    "    [231,255,232,1],\n",
+    "    ],dtype=np.float64\n",
+    ")\n",
+    "tmp[:,:3] /=256\n",
+    "lower=LinearSegmentedColormap.from_list('near0',colors=tmp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "72913185-cab3-4fef-8434-98768042a687",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N1 = 128\n",
+    "N2 = 256\n",
+    "\n",
+    "lower_linear = lower(np.linspace(0, 1, N1))\n",
+    "near0_linear = near0(np.linspace(0, 1, N2))\n",
+    "\n",
+    "hoge = np.vstack([lower_linear, near0_linear])\n",
+    "\n",
+    "zmn_color = ListedColormap(hoge)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "0b29f7e6-d7a8-4ee1-bc10-62e4a3ae2209",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "if zonal_mean_flag:\n",
+    "    pass\n",
+    "else:\n",
+    "    plt.ioff()\n",
+    "    \n",
+    "    fig, ax = plt.subplots(figsize=(16,9))\n",
+    "    im = ax.imshow(cfed,\n",
+    "                    extent=extent,\n",
+    "                    interpolation='nearest',\n",
+    "                    origin=origin,\n",
+    "                    vmin=0, vmax=vmax,\n",
+    "                    cmap=color_linear,\n",
+    "                    aspect='auto')\n",
+    "\n",
+    "    if vname=='Ze-Doppler' or vname=='Ze-terminal':\n",
+    "        ax.set_title(\"2d-PDF: \"+vname, fontsize=32, pad=16)\n",
+    "    else:\n",
+    "        ax.set_title(\"CFED: \"+vname, fontsize=32, pad=16)\n",
+    "\n",
+    "    if vname=='radar reflectivity' or vname=='Ze-Doppler' or vname=='Ze-terminal':\n",
+    "        ax.set_xlabel(\"reflectivity (dB Ze)\",fontsize=22)\n",
+    "    else:\n",
+    "        ax.set_xlabel(\"velocity (m/s)\",fontsize=22)\n",
+    "\n",
+    "    if vname=='Ze-Doppler' or vname=='Ze-terminal':\n",
+    "        ax.set_ylabel(\"velocity (m/s)\",fontsize=22)\n",
+    "    else:\n",
+    "        ax.set_ylabel(\"Temperature (degC)\",fontsize=22)\n",
+    "    \n",
+    "    ax.xaxis.set_major_locator(MultipleLocator(xtics))\n",
+    "    ax.yaxis.set_major_locator(MultipleLocator(ytics))\n",
+    "    ax.tick_params(axis='both', which='major', labelsize=18)\n",
+    "    \n",
+    "    cbar = fig.colorbar(im, ax=ax,\n",
+    "                        orientation='vertical',\n",
+    "                        pad=0.02,\n",
+    "                        fraction=0.045,\n",
+    "                        shrink=1.0)\n",
+    "    cbar.set_label(\"\", fontsize=13)\n",
+    "    cbar.ax.tick_params(labelsize=16)\n",
+    "    \n",
+    "    \n",
+    "    fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "d13229d3-98c2-4419-b993-2f1b09654904",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "if not zonal_mean_flag:\n",
+    "    pass\n",
+    "else:\n",
+    "    plt.ioff()\n",
+    "    \n",
+    "    fig, ax = plt.subplots(figsize=(16,9))\n",
+    "    im = ax.imshow(zmn,\n",
+    "                    extent=extent,\n",
+    "                    interpolation='nearest',\n",
+    "                    origin=origin,\n",
+    "                    vmin=-4.0, vmax=2.0,\n",
+    "                    cmap=zmn_color,\n",
+    "                    aspect='auto')\n",
+    "\n",
+    "    ax.set_title(\"zonal mean: \"+vname, fontsize=32, pad=16)\n",
+    "\n",
+    "    ax.set_xlabel(\"latitude\",fontsize=22)\n",
+    "    ax.set_ylabel(\"height (km)\",fontsize=22)\n",
+    "    \n",
+    "    ax.xaxis.set_major_locator(MultipleLocator(xtics))\n",
+    "    ax.yaxis.set_major_locator(MultipleLocator(ytics))\n",
+    "    ax.tick_params(axis='both', which='major', labelsize=18)\n",
+    "    \n",
+    "    cbar = fig.colorbar(im, ax=ax,\n",
+    "                        orientation='vertical',\n",
+    "                        pad=0.02,\n",
+    "                        fraction=0.045,\n",
+    "                        shrink=1.0)\n",
+    "    cbar.set_label(\"\", fontsize=13)\n",
+    "    cbar.ax.tick_params(labelsize=16)\n",
+    "    \n",
+    "    \n",
+    "    fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7289897a-0a71-4d38-9147-403391c657ad",
+   "metadata": {},
+   "source": [
+    "### results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "11581c37-1a75-41ce-9f19-fe27c7436016",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x900 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18ad941f-537f-49ae-a500-5c499058d324",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/driver/check/input_check.ipynb b/driver/check/input_check.ipynb
new file mode 100644
index 0000000000..ecc4f0b59d
--- /dev/null
+++ b/driver/check/input_check.ipynb
@@ -0,0 +1,402 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2ad48f61-e598-4a15-89f8-a0483fe2c9fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray\n",
+    "import pygmt\n",
+    "import pandas\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "dc10108f-956d-47bf-a02c-f42282da2ef1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xarray.open_dataset(\"../data/inputs/UKMO/cosp_input.um_global.nc\", decode_timedelta=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "02b11a5e-09bc-4ce9-9424-70dc5f125412",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "xarray.Dataset {\n",
+      "dimensions:\n",
+      "\tlat = 36 ;\n",
+      "\tlon = 48 ;\n",
+      "\tlevel = 54 ;\n",
+      "\tbnds = 2 ;\n",
+      "\thydro = 9 ;\n",
+      "\n",
+      "variables:\n",
+      "\tint32 landmask(lat, lon) ;\n",
+      "\t\tlandmask:standard_name = land_binary_mask ;\n",
+      "\t\tlandmask:units = 1 ;\n",
+      "\t\tlandmask:um_stash_source = m01s00i030 ;\n",
+      "\t\tlandmask:source = Data from Met Office Unified Model ;\n",
+      "\t\tlandmask:um_version = 11.6 ;\n",
+      "\t\tlandmask:grid_mapping = latitude_longitude ;\n",
+      "\tint32 latitude_longitude() ;\n",
+      "\t\tlatitude_longitude:grid_mapping_name = latitude_longitude ;\n",
+      "\t\tlatitude_longitude:longitude_of_prime_meridian = 0.0 ;\n",
+      "\t\tlatitude_longitude:earth_radius = 6371229.0 ;\n",
+      "\tfloat64 cca(level, lat, lon) ;\n",
+      "\t\tcca:um_stash_source = m01s02i317 ;\n",
+      "\t\tcca:source = Data from Met Office Unified Model ;\n",
+      "\t\tcca:um_version = 11.6 ;\n",
+      "\t\tcca:grid_mapping = latitude_longitude ;\n",
+      "\tfloat64 level_height_bnds(level, bnds) ;\n",
+      "\tfloat64 sigma_bnds(level, bnds) ;\n",
+      "\tfloat64 dem_s(level, lat, lon) ;\n",
+      "\t\tdem_s:standard_name = stratiform_cloud_longwave_emissivity ;\n",
+      "\t\tdem_s:units = 1 ;\n",
+      "\t\tdem_s:um_stash_source = m01s02i376 ;\n",
+      "\t\tdem_s:source = Data from Met Office Unified Model ;\n",
+      "\t\tdem_s:um_version = 11.6 ;\n",
+      "\t\tdem_s:grid_mapping = latitude_longitude ;\n",
+      "\tfloat64 dem_c(level, lat, lon) ;\n",
+      "\t\tdem_c:standard_name = convective_cloud_longwave_emissivity ;\n",
+      "\t\tdem_c:units = 1 ;\n",
+      "\t\tdem_c:um_stash_source = m01s02i378 ;\n",
+      "\t\tdem_c:source = Data from Met Office Unified Model ;\n",
+      "\t\tdem_c:um_version = 11.6 ;\n",
+      "\t\tdem_c:grid_mapping = latitude_longitude ;\n",
+      "\tfloat64 dtau_s(level, lat, lon) ;\n",
+      "\t\tdtau_s:standard_name = atmosphere_optical_thickness_due_to_stratiform_cloud ;\n",
+      "\t\tdtau_s:units = 1 ;\n",
+      "\t\tdtau_s:um_stash_source = m01s02i375 ;\n",
+      "\t\tdtau_s:source = Data from Met Office Unified Model ;\n",
+      "\t\tdtau_s:um_version = 11.6 ;\n",
+      "\t\tdtau_s:grid_mapping = latitude_longitude ;\n",
+      "\tfloat64 dtau_c(level, lat, lon) ;\n",
+      "\t\tdtau_c:standard_name = atmosphere_optical_thickness_due_to_convective_cloud ;\n",
+      "\t\tdtau_c:units = 1 ;\n",
+      "\t\tdtau_c:um_stash_source = m01s02i377 ;\n",
+      "\t\tdtau_c:source = Data from Met Office Unified Model ;\n",
+      "\t\tdtau_c:um_version = 11.6 ;\n",
+      "\t\tdtau_c:grid_mapping = latitude_longitude ;\n",
+      "\tfloat64 fl_lsrain(level, lat, lon) ;\n",
+      "\t\tfl_lsrain:um_stash_source = m01s02i390 ;\n",
+      "\t\tfl_lsrain:source = Data from Met Office Unified Model ;\n",
+      "\t\tfl_lsrain:um_version = 11.6 ;\n",
+      "\t\tfl_lsrain:grid_mapping = latitude_longitude ;\n",
+      "\tfloat64 fl_lssnow(level, lat, lon) ;\n",
+      "\t\tfl_lssnow:standard_name = large_scale_snowfall_flux ;\n",
+      "\t\tfl_lssnow:units = kg m-2 s-1 ;\n",
+      "\t\tfl_lssnow:um_stash_source = m01s04i223 ;\n",
+      "\t\tfl_lssnow:source = Data from Met Office Unified Model ;\n",
+      "\t\tfl_lssnow:um_version = 11.6 ;\n",
+      "\t\tfl_lssnow:grid_mapping = latitude_longitude ;\n",
+      "\tfloat64 fl_ccrain(level, lat, lon) ;\n",
+      "\t\tfl_ccrain:um_stash_source = m01s02i389 ;\n",
+      "\t\tfl_ccrain:source = Data from Met Office Unified Model ;\n",
+      "\t\tfl_ccrain:um_version = 11.6 ;\n",
+      "\t\tfl_ccrain:grid_mapping = latitude_longitude ;\n",
+      "\tfloat64 height(level, lat, lon) ;\n",
+      "\t\theight:standard_name = height_above_reference_ellipsoid ;\n",
+      "\t\theight:units = m ;\n",
+      "\t\theight:um_stash_source = m01s15i101 ;\n",
+      "\t\theight:source = Data from Met Office Unified Model ;\n",
+      "\t\theight:um_version = 11.6 ;\n",
+      "\t\theight:grid_mapping = latitude_longitude ;\n",
+      "\tfloat64 height_half(level, lat, lon) ;\n",
+      "\t\theight_half:standard_name = height_above_reference_ellipsoid ;\n",
+      "\t\theight_half:units = m ;\n",
+      "\t\theight_half:um_stash_source = m01s15i102 ;\n",
+      "\t\theight_half:source = Data from Met Office Unified Model ;\n",
+      "\t\theight_half:um_version = 11.6 ;\n",
+      "\t\theight_half:grid_mapping = latitude_longitude ;\n",
+      "\tfloat64 level_height_0_bnds(level, bnds) ;\n",
+      "\tfloat64 sigma_0_bnds(level, bnds) ;\n",
+      "\tfloat64 mr_ccice(level, lat, lon) ;\n",
+      "\t\tmr_ccice:um_stash_source = m01s02i319 ;\n",
+      "\t\tmr_ccice:source = Data from Met Office Unified Model ;\n",
+      "\t\tmr_ccice:um_version = 11.6 ;\n",
+      "\t\tmr_ccice:grid_mapping = latitude_longitude ;\n",
+      "\tfloat64 mr_ccliq(level, lat, lon) ;\n",
+      "\t\tmr_ccliq:um_stash_source = m01s02i318 ;\n",
+      "\t\tmr_ccliq:source = Data from Met Office Unified Model ;\n",
+      "\t\tmr_ccliq:um_version = 11.6 ;\n",
+      "\t\tmr_ccliq:grid_mapping = latitude_longitude ;\n",
+      "\tfloat64 mr_lsice(level, lat, lon) ;\n",
+      "\t\tmr_lsice:units = 1 ;\n",
+      "\t\tmr_lsice:grid_mapping = latitude_longitude ;\n",
+      "\tfloat64 mr_lsliq(level, lat, lon) ;\n",
+      "\t\tmr_lsliq:units = 1 ;\n",
+      "\t\tmr_lsliq:grid_mapping = latitude_longitude ;\n",
+      "\tfloat64 mr_ozone(level, lat, lon) ;\n",
+      "\t\tmr_ozone:standard_name = mass_fraction_of_ozone_in_air ;\n",
+      "\t\tmr_ozone:units = 1 ;\n",
+      "\t\tmr_ozone:um_stash_source = m01s02i260 ;\n",
+      "\t\tmr_ozone:source = Data from Met Office Unified Model ;\n",
+      "\t\tmr_ozone:um_version = 11.6 ;\n",
+      "\t\tmr_ozone:grid_mapping = latitude_longitude ;\n",
+      "\tfloat64 pfull(level, lat, lon) ;\n",
+      "\t\tpfull:standard_name = air_pressure ;\n",
+      "\t\tpfull:units = Pa ;\n",
+      "\t\tpfull:um_stash_source = m01s00i408 ;\n",
+      "\t\tpfull:source = Data from Met Office Unified Model ;\n",
+      "\t\tpfull:um_version = 11.6 ;\n",
+      "\t\tpfull:grid_mapping = latitude_longitude ;\n",
+      "\tfloat64 phalf(level, lat, lon) ;\n",
+      "\t\tphalf:standard_name = air_pressure ;\n",
+      "\t\tphalf:units = Pa ;\n",
+      "\t\tphalf:um_stash_source = m01s00i407 ;\n",
+      "\t\tphalf:source = Data from Met Office Unified Model ;\n",
+      "\t\tphalf:um_version = 11.6 ;\n",
+      "\t\tphalf:grid_mapping = latitude_longitude ;\n",
+      "\tfloat64 psfc(lat, lon) ;\n",
+      "\t\tpsfc:standard_name = surface_air_pressure ;\n",
+      "\t\tpsfc:units = Pa ;\n",
+      "\t\tpsfc:um_stash_source = m01s00i409 ;\n",
+      "\t\tpsfc:source = Data from Met Office Unified Model ;\n",
+      "\t\tpsfc:um_version = 11.6 ;\n",
+      "\t\tpsfc:grid_mapping = latitude_longitude ;\n",
+      "\tfloat64 qv(level, lat, lon) ;\n",
+      "\t\tqv:standard_name = specific_humidity ;\n",
+      "\t\tqv:units = kg kg-1 ;\n",
+      "\t\tqv:um_stash_source = m01s00i010 ;\n",
+      "\t\tqv:source = Data from Met Office Unified Model ;\n",
+      "\t\tqv:um_version = 11.6 ;\n",
+      "\t\tqv:grid_mapping = latitude_longitude ;\n",
+      "\tfloat64 Reff(hydro, level, lat, lon) ;\n",
+      "\t\tReff:long_name = effective_radius_of_hydrometeor ;\n",
+      "\t\tReff:units = m ;\n",
+      "\t\tReff:grid_mapping = latitude_longitude ;\n",
+      "\tfloat64 rh(level, lat, lon) ;\n",
+      "\t\trh:standard_name = relative_humidity ;\n",
+      "\t\trh:units = % ;\n",
+      "\t\trh:um_stash_source = m01s30i113 ;\n",
+      "\t\trh:source = Data from Met Office Unified Model ;\n",
+      "\t\trh:um_version = 11.6 ;\n",
+      "\t\trh:grid_mapping = latitude_longitude ;\n",
+      "\tfloat64 skt(lat, lon) ;\n",
+      "\t\tskt:standard_name = surface_temperature ;\n",
+      "\t\tskt:units = K ;\n",
+      "\t\tskt:um_stash_source = m01s00i024 ;\n",
+      "\t\tskt:source = Data from Met Office Unified Model ;\n",
+      "\t\tskt:um_version = 11.6 ;\n",
+      "\t\tskt:grid_mapping = latitude_longitude ;\n",
+      "\tfloat64 sunlit(lat, lon) ;\n",
+      "\t\tsunlit:um_stash_source = m01s02i330 ;\n",
+      "\t\tsunlit:source = Data from Met Office Unified Model ;\n",
+      "\t\tsunlit:um_version = 11.6 ;\n",
+      "\t\tsunlit:grid_mapping = latitude_longitude ;\n",
+      "\tfloat64 T_abs(level, lat, lon) ;\n",
+      "\t\tT_abs:standard_name = air_temperature ;\n",
+      "\t\tT_abs:units = K ;\n",
+      "\t\tT_abs:um_stash_source = m01s30i111 ;\n",
+      "\t\tT_abs:source = Data from Met Office Unified Model ;\n",
+      "\t\tT_abs:um_version = 11.6 ;\n",
+      "\t\tT_abs:grid_mapping = latitude_longitude ;\n",
+      "\tfloat64 tca(level, lat, lon) ;\n",
+      "\t\ttca:units = 1 ;\n",
+      "\t\ttca:grid_mapping = latitude_longitude ;\n",
+      "\tfloat64 u_wind(lat, lon) ;\n",
+      "\t\tu_wind:um_stash_source = m01s30i001 ;\n",
+      "\t\tu_wind:source = Data from Met Office Unified Model ;\n",
+      "\t\tu_wind:um_version = 11.6 ;\n",
+      "\t\tu_wind:grid_mapping = latitude_longitude ;\n",
+      "\tfloat64 level_height_1_bnds(bnds) ;\n",
+      "\tfloat64 sigma_1_bnds(bnds) ;\n",
+      "\tfloat64 v_wind(lat, lon) ;\n",
+      "\t\tv_wind:um_stash_source = m01s30i002 ;\n",
+      "\t\tv_wind:source = Data from Met Office Unified Model ;\n",
+      "\t\tv_wind:um_version = 11.6 ;\n",
+      "\t\tv_wind:grid_mapping = latitude_longitude ;\n",
+      "\tfloat64 emsfc_lw() ;\n",
+      "\t\temsfc_lw:long_name = Surface emissivity at 10.5 micron (fraction) ;\n",
+      "\t\temsfc_lw:units = 1 ;\n",
+      "\tfloat32 lat(lat) ;\n",
+      "\t\tlat:units = degrees ;\n",
+      "\t\tlat:long_name = lat ;\n",
+      "\tfloat32 lon(lon) ;\n",
+      "\t\tlon:units = degrees ;\n",
+      "\t\tlon:long_name = lon ;\n",
+      "\tfloat64 forecast_period() ;\n",
+      "\t\tforecast_period:units = hours ;\n",
+      "\t\tforecast_period:standard_name = forecast_period ;\n",
+      "\tobject forecast_reference_time() ;\n",
+      "\t\tforecast_reference_time:standard_name = forecast_reference_time ;\n",
+      "\tobject time() ;\n",
+      "\t\ttime:standard_name = time ;\n",
+      "\tint32 level(level) ;\n",
+      "\t\tlevel:axis = Z ;\n",
+      "\t\tlevel:units = 1 ;\n",
+      "\t\tlevel:long_name = level ;\n",
+      "\t\tlevel:positive = up ;\n",
+      "\tfloat64 level_height(level) ;\n",
+      "\t\tlevel_height:bounds = level_height_bnds ;\n",
+      "\t\tlevel_height:units = m ;\n",
+      "\t\tlevel_height:long_name = level_height ;\n",
+      "\t\tlevel_height:positive = up ;\n",
+      "\t\tlevel_height:standard_name = atmosphere_hybrid_height_coordinate ;\n",
+      "\t\tlevel_height:axis = Z ;\n",
+      "\t\tlevel_height:formula_terms = a: level_height b: sigma orog: surface_altitude ;\n",
+      "\tfloat64 sigma(level) ;\n",
+      "\t\tsigma:bounds = sigma_bnds ;\n",
+      "\t\tsigma:units = 1 ;\n",
+      "\t\tsigma:long_name = sigma ;\n",
+      "\tfloat64 surface_altitude(lat, lon) ;\n",
+      "\t\tsurface_altitude:units = m ;\n",
+      "\t\tsurface_altitude:standard_name = surface_altitude ;\n",
+      "\t\tsurface_altitude:um_stash_source = m01s00i033 ;\n",
+      "\t\tsurface_altitude:source = Data from Met Office Unified Model ;\n",
+      "\t\tsurface_altitude:um_version = 11.6 ;\n",
+      "\tfloat64 level_height_0(level) ;\n",
+      "\t\tlevel_height_0:bounds = level_height_0_bnds ;\n",
+      "\t\tlevel_height_0:units = m ;\n",
+      "\t\tlevel_height_0:long_name = level_height ;\n",
+      "\t\tlevel_height_0:positive = up ;\n",
+      "\t\tlevel_height_0:standard_name = atmosphere_hybrid_height_coordinate ;\n",
+      "\t\tlevel_height_0:axis = Z ;\n",
+      "\t\tlevel_height_0:formula_terms = a: level_height_0 b: sigma_0 orog: surface_altitude ;\n",
+      "\tfloat64 sigma_0(level) ;\n",
+      "\t\tsigma_0:bounds = sigma_0_bnds ;\n",
+      "\t\tsigma_0:units = 1 ;\n",
+      "\t\tsigma_0:long_name = sigma ;\n",
+      "\tint32 hydro(hydro) ;\n",
+      "\t\thydro:units = 1 ;\n",
+      "\t\thydro:long_name = hydrometeor_index ;\n",
+      "\tint32 level_0() ;\n",
+      "\t\tlevel_0:units = 1 ;\n",
+      "\t\tlevel_0:long_name = level ;\n",
+      "\t\tlevel_0:positive = up ;\n",
+      "\tfloat64 level_height_1() ;\n",
+      "\t\tlevel_height_1:bounds = level_height_1_bnds ;\n",
+      "\t\tlevel_height_1:units = m ;\n",
+      "\t\tlevel_height_1:long_name = level_height ;\n",
+      "\t\tlevel_height_1:positive = up ;\n",
+      "\t\tlevel_height_1:standard_name = atmosphere_hybrid_height_coordinate ;\n",
+      "\t\tlevel_height_1:axis = Z ;\n",
+      "\t\tlevel_height_1:formula_terms = a: level_height_1 b: sigma_1 orog: surface_altitude ;\n",
+      "\tfloat64 sigma_1() ;\n",
+      "\t\tsigma_1:bounds = sigma_1_bnds ;\n",
+      "\t\tsigma_1:units = 1 ;\n",
+      "\t\tsigma_1:long_name = sigma ;\n",
+      "\n",
+      "// global attributes:\n",
+      "\t:Conventions = CF-1.5 ;\n",
+      "}"
+     ]
+    }
+   ],
+   "source": [
+    "ds.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3bab77fc-adea-4019-aa7f-2f644f1c0fe1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Re = ds[\"Reff\"].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "f59cc93c-1af3-4d7a-8975-916deb11111e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(9, 54, 36, 48)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(Re.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "79232327-c8ef-47b1-9187-2e25040becd6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2555.67200062,  2579.45250599,  2612.77552208,  2655.67127004,\n",
+       "        2708.17875217,  2770.34556388,  2842.2278931 ,  2923.89076484,\n",
+       "        3015.40755162,  3116.86064765,  3228.34110315,  3349.9476308 ,\n",
+       "        3481.78939363,  3623.98259556,  3776.65271298,  3939.93387565,\n",
+       "        4113.96950232,  4298.91040074,  4494.91666141,  4702.15767454,\n",
+       "        4920.81019898,  5151.06028593,  5393.10329876,  5647.1419457 ,\n",
+       "        5913.38757075,  6192.06284309,  6483.39507758,  6787.62359741,\n",
+       "        7104.9943558 ,  7435.76263219,  7780.19305938,  8138.55753557,\n",
+       "        8511.13730095,  8898.22368594,  9300.11745156,  9717.11858314,\n",
+       "       10149.55686776, 10597.74965445, 11062.05278729, 11542.81645459,\n",
+       "       12040.41520099, 12555.24096946, 13087.7113953 , 13638.27861956,\n",
+       "       14207.42265373, 14795.69285374, 15403.65395338, 16031.99387819,\n",
+       "       16681.44646124, 17352.87798226, 18046.2905    , 18756.7035    ,\n",
+       "       19483.887     , 20228.776     ])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zlev=ds[\"height\"].values\n",
+    "zlev[:,1,1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a9b7fe64-2571-40d8-b91e-81d0850cfe25",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b8f57645-805a-421b-97db-73a413e636f2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/driver/data/.gitignore b/driver/data/.gitignore
new file mode 100644
index 0000000000..496ee2ca6a
--- /dev/null
+++ b/driver/data/.gitignore
@@ -0,0 +1 @@
+.DS_Store
\ No newline at end of file
diff --git a/driver/run/cosp2_MIROCtest.txt b/driver/run/cosp2_MIROCtest.txt
new file mode 100644
index 0000000000..e0add22ca8
--- /dev/null
+++ b/driver/run/cosp2_MIROCtest.txt
@@ -0,0 +1,109 @@
+! (c) British Crown Copyright 2008, the Met Office.
+! All rights reserved.
+!
+! Redistribution and use in source and binary forms, with or without modification, are permitted
+! provided that the following conditions are met:
+!
+!     * Redistributions of source code must retain the above copyright notice, this list
+!       of conditions and the following disclaimer.
+!     * Redistributions in binary form must reproduce the above copyright notice, this list
+!       of conditions and the following disclaimer in the documentation and/or other materials
+!       provided with the distribution.
+!     * Neither the name of the Met Office nor the names of its contributors may be used
+!       to endorse or promote products derived from this software without specific prior written
+!       permission.
+!
+! THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
+! IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
+! FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+! CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+! DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+! DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER
+! IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT
+! OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+! Namelist that sets up the main COSP options
+&COSP_INPUT
+  NPOINTS=32768,! Number of gridpoints 153,27840,7081,6912
+  NPOINTS_IT=4096,! Max number of gridpoints to be processed in one iteration
+  NCOLUMNS=140,  ! Number of subcolumns
+  NLEVELS=40,   ! Number of model levels
+  USE_VGRID=.true., ! Use fixed vertical grid for outputs? (if .true. then you need to define number of levels with Nlr)
+  NLVGRID=40,       ! Number of levels in statistical outputs (only used if USE_VGRID=.true.)
+  CSAT_VGRID=.true., ! CloudSat vertical grid? (if .true. then the CloudSat standard grid is used for the outputs.
+                     !  USE_VGRID needs also be .true.)
+  DINPUT='./',  ! Directory where the input files are located. Useful when processing multiple files.
+                ! Leave blank ('') if you are using the full path in FINPUT.
+  FINPUT='../data/MIROC_inputs/ctrl_MIROCinput.nc', ! List input NetCDF files
+  FOUTPUT='../data/MIROC_outputs/ctrl_MIROC-COSP2.nc',
+  !----------------------------------------------------------------------------------
+  !--------------- Inputs related to radar simulations
+  !----------------------------------------------------------------------------------
+  cloudsat_RADAR_FREQ=94.0, ! CloudSat radar frequency (GHz)
+  SURFACE_RADAR=0, ! surface=1, spaceborne=0
+  cloudsat_use_gas_abs=1,   ! include gaseous absorption? yes=1,no=0
+  cloudsat_do_ray=0,        ! calculate/output Rayleigh refl=1, not=0
+  cloudsat_k2=-1,           ! |K|^2, -1=use frequency dependent default
+  use_precipitation_fluxes=.true.,  ! True if precipitation fluxes are input to the algorithm
+  cloudsat_micro_scheme='MMF_v3_single_moment', !'MMF_v3.5_two_moment'
+  !----------------------------------------------------------------------------------
+  !---------------- Inputs related to lidar simulations
+  !----------------------------------------------------------------------------------
+  lidar_ice_type=0,    ! Ice particle shape in lidar calculations (0=ice-spheres ; 1=ice-non-spherical)
+  OVERLAP=3,           !  overlap assumption used by scops: 1=max, 2=rand, 3=max/rand
+  !----------------------------------------------------------------------------------
+  !---------------- Inputs related to ISCCP simulator
+  !----------------------------------------------------------------------------------
+  ISCCP_TOPHEIGHT=1,  !  1 = adjust top height using both a computed
+                       !  infrared brightness temperature and the visible
+                       !  optical depth to adjust cloud top pressure. Note
+                       !  that this calculation is most appropriate to compare
+                       !  to ISCCP data during sunlit hours.
+                      !  2 = do not adjust top height, that is cloud top
+                       !  pressure is the actual cloud top pressure
+                       !  in the model
+                      !  3 = adjust top height using only the computed
+                       !  infrared brightness temperature. Note that this
+                       !  calculation is most appropriate to compare to ISCCP
+                       !  IR only algortihm (i.e. you can compare to nighttime
+                       !  ISCCP data with this option)
+  ISCCP_TOPHEIGHT_DIRECTION=2,   ! direction for finding atmosphere pressure level
+                                 ! with interpolated temperature equal to the radiance
+                                 ! determined cloud-top temperature
+                                 ! 1 = find the *lowest* altitude (highest pressure) level
+                                 ! with interpolated temperature equal to the radiance
+                                 ! determined cloud-top temperature
+                                 ! 2 = find the *highest* altitude (lowest pressure) level
+                                 ! with interpolated temperature equal to the radiance
+                                 ! determined cloud-top temperature. This is the
+                                 ! default value since V4.0 of the ISCCP simulator.
+                                 ! ONLY APPLICABLE IF top_height EQUALS 1 or 3
+  !----------------------------------------------------------------------------------
+  !-------------- Swathing inputs
+  !----------------------------------------------------------------------------------
+  N_SWATHS_ISCCP=0,
+  SWATH_LOCALTIMES_ISCCP=5.25,18.0,
+  SWATH_WIDTHS_ISCCP=2000,2000,
+  N_SWATHS_MISR=0,
+  SWATH_LOCALTIMES_MISR=6.5,18.25,
+  SWATH_WIDTHS_MISR=2000,2000,
+  N_SWATHS_MODIS=0,
+  SWATH_LOCALTIMES_MODIS=5.75,13.5,
+  SWATH_WIDTHS_MODIS=2000,2000,
+  N_SWATHS_CSCAL=0,
+  SWATH_LOCALTIMES_CSCAL=6.75,19.25,
+  SWATH_WIDTHS_CSCAL=2000,2000,
+  N_SWATHS_PARASOL=0,
+  SWATH_LOCALTIMES_PARASOL=6.75,19,
+  SWATH_WIDTHS_PARASOL=2000,2000,
+  N_SWATHS_ATLID=0,
+  SWATH_LOCALTIMES_ATLID=7.25,19.5,
+  SWATH_WIDTHS_ATLID=2000,2000,
+  !----------------------------------------------------------------------------------
+  !-------------- RTTOV inputs
+  !----------------------------------------------------------------------------------
+!  rttov_Ninstruments=1,
+!  rttov_instrument_namelists='instrument_nls/cosp2_rttov_inst1.txt',
+  rttov_verbose=.false.
+/
diff --git a/driver/run/cosp2_output_nl.txt b/driver/run/cosp2_output_nl.txt
index 4c61520a2f..a88fe3eebf 100755
--- a/driver/run/cosp2_output_nl.txt
+++ b/driver/run/cosp2_output_nl.txt
@@ -147,4 +147,6 @@
   !- CloudSat+MODIS joint diagnostics
   Lwr_occfreq=.true.,
   Lcfodd=.true.
+  ! Doppler capability
+  Ldplrw=.true.,
 /
diff --git a/driver/src/cosp2_io.f90 b/driver/src/cosp2_io.f90
index 2948dc2629..996a2c58cb 100644
--- a/driver/src/cosp2_io.f90
+++ b/driver/src/cosp2_io.f90
@@ -10,6 +10,8 @@ module mod_cosp_io
        calipso_binCenters, grLidar532_binCenters, atlid_binCenters,                   &
        CFODD_NDBZE,  CFODD_HISTDBZE, CFODD_HISTDBZEcenters,                           &
        CFODD_NICOD,  CFODD_HISTICOD, CFODD_HISTICODcenters
+  USE MOD_COSP_CONFIG, ONLY: Nlvtemp, NlvdBZe, Nlvdplr, Nlvspwd, &  ! added by DPLRW
+       lvtemp_grid, lvdBZe_grid, lvdplr_grid, lvspwd_grid
   implicit none
 
 contains
@@ -42,7 +44,7 @@ subroutine write_cosp2_output(Npoints, Ncolumns, Nlevels, Ninst_rttov, lev, lon,
     ! ---------------------------------------------------------------------------------------
     ! Create output file.
     ! ---------------------------------------------------------------------------------------
-    status = nf90_create(path=trim(outFileName),cmode = nf90_clobber,ncid=fileID)
+    status = nf90_create(path=trim(outFileName),cmode=IOR(NF90_NETCDF4,NF90_CLOBBER),ncid=fileID)
     if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
 
     ! ---------------------------------------------------------------------------------------
@@ -84,12 +86,25 @@ subroutine write_cosp2_output(Npoints, Ncolumns, Nlevels, Ninst_rttov, lev, lon,
     if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
     status = nf90_def_dim(fileID,"CFODD_NICOD",CFODD_NICOD,dimID(18))
     if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+    ! added by DPLRW
+    status = nf90_def_dim(fileID,"Nlvtemp",Nlvtemp,dimID(19))
+    if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+    status = nf90_def_dim(fileID,"NlvdBZe",NlvdBZe,dimID(20))
+    if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+    status = nf90_def_dim(fileID,"Nlvdplr",Nlvdplr,dimID(21))
+    if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+    status = nf90_def_dim(fileID,"Nlvspwd",Nlvspwd,dimID(22))
+    if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+    status = nf90_def_dim(fileID,"regimeID",3,dimID(23))
+    if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+    status = nf90_def_dim(fileID,"nrmlzdID",3,dimID(24))
+    if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
     
     ! Define instrument channel indices for multiple RTTOV instruments
     if (allocated(cospOUT%rttov_outputs)) then
         do i=1,Ninst_rttov
             write (i_str,fmt) i ! converting integer to string i_str using a 'internal file'
-            status = nf90_def_dim(fileID,"RTTOV_CHAN_INST"//trim(i_str),cospOUT % rttov_outputs(i) % nchan_out,dimID(20+i)) ! Start at 100 for RTTOV output channel dimensions
+            status = nf90_def_dim(fileID,"RTTOV_CHAN_INST"//trim(i_str),cospOUT % rttov_outputs(i) % nchan_out,dimID(24+i)) ! Start at 25 for RTTOV output channel dimensions
             if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
         end do
     end if
@@ -873,7 +888,7 @@ subroutine write_cosp2_output(Npoints, Ncolumns, Nlevels, Ninst_rttov, lev, lon,
        status = nf90_put_att(fileID,varID(87),"standard_name", "solar_zenith_angle")
        if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
     endif
-    
+
     ! Cloudsat simulator output
     if (associated(cospOUT%cloudsat_Ze_tot)) then
        status = nf90_def_var(fileID,"dbze94",nf90_float, (/dimID(1),dimID(2),dimID(3)/),varID(22))
@@ -1065,7 +1080,7 @@ subroutine write_cosp2_output(Npoints, Ncolumns, Nlevels, Ninst_rttov, lev, lon,
        status = nf90_put_att(fileID,varID(32),"standard_name", "cloud_area_fraction_in_atmosphere_layer")
        if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))         
     endif
-    
+
     ! MISR simulator output
     if (associated(cospOUT%misr_fq)) then
        status = nf90_def_var(fileID,"clMISR",nf90_float, (/dimID(1),dimID(5),dimID(8)/),varID(33))
@@ -1439,7 +1454,259 @@ subroutine write_cosp2_output(Npoints, Ncolumns, Nlevels, Ninst_rttov, lev, lon,
        if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
        status = nf90_put_att(fileID,varID(147),"standard_name", "modis_in-cloud_optical_depth")
        if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
-    endif 
+    endif
+
+    ! added by DPLRW
+    if (associated(cospOUT%dplrw_Z)) then
+       ! axes
+       status = nf90_def_var(fileID,"lvtemp_grid",nf90_float,(/dimID(19)/),varID(150))
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(150),"long_name","Temperature axis for DPLRW CFED")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(150),"units",        "degree C")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_def_var(fileID,"lvdBZe_grid",nf90_float,(/dimID(20)/),varID(151))
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(151),"long_name","dBZe axis for DPLRW CFED")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(151),"units",        "dBZe")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_def_var(fileID,"lvdplr_grid",nf90_float,(/dimID(21)/),varID(152))
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(152),"long_name","Doppler velocity axis for DPLRW CFED")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(152),"units",        "m/s")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_def_var(fileID,"lvspwd_grid",nf90_float,(/dimID(22)/),varID(153))
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(153),"long_name","spectrum width axis for DPLRW CFED")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(153),"units",        "m/s")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_def_var(fileID,"regimeID",nf90_INT,(/dimID(23)/),varID(154))
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(154),"long_name","regime ID: 0=ALL, 1=LS, 2=CU")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(154),"units",        "None")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_def_var(fileID,"nrmlzdID",nf90_INT,(/dimID(24)/),varID(155))
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(155),"long_name","normalized ID: 1=Ze, 2=mass, 3=number")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(155),"units",        "None")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+
+       ! variables
+       status = nf90_def_var(fileID,"dplrw_Z",nf90_float,(/dimID(1),dimID(21),dimID(4),dimID(23)/),varID(156))
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(156),"long_name","Number of samples onto histogram, Doppler velocity/Altitude")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(156),"units",        "#")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_def_var(fileID,"spwid_Z",nf90_float,(/dimID(1),dimID(22),dimID(4),dimID(23)/),varID(157))
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(157),"long_name","Number of samples onto histogram, Spectrum width/Altitude")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(157),"units",        "#")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_def_var(fileID,"Zef94_Z",nf90_float,(/dimID(1),dimID(20),dimID(4),dimID(23)/),varID(158))
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(158),"long_name","Number of samples onto histogram, Radar reflectivity/Altitude")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(158),"units",        "#")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+
+       status = nf90_def_var(fileID,"dplrw_T",nf90_float,(/dimID(1),dimID(21),dimID(19),dimID(23)/),varID(159))
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(159),"long_name","Number of samples onto histogram, Doppler velocity/Temperature")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(159),"units",        "#")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_def_var(fileID,"spwid_T",nf90_float,(/dimID(1),dimID(22),dimID(19),dimID(23)/),varID(160))
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(160),"long_name","Number of samples onto histogram, Spectrum width/Temperature")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(160),"units",        "#")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_def_var(fileID,"Zef94_T",nf90_float,(/dimID(1),dimID(20),dimID(19),dimID(23)/),varID(161))
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(161),"long_name","Number of samples onto histogram, Radar reflectivity/Temperature")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(161),"units",        "#")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+
+       status = nf90_def_var(fileID,"ZefVd_2",nf90_float,(/dimID(1),dimID(21),dimID(20),dimID(23)/),varID(162))
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(162),"long_name","Number of samples onto histogram, Radar reflectivity/Doppler velocity")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(162),"units",        "#")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       
+       status = nf90_def_var(fileID,"gwcum",nf90_float,(/dimID(1),dimID(3)/),varID(163))
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(163),"long_name","in-cloud cumulus upward velocity")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(163),"units",        "m/s")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+
+       !~~~
+       status = nf90_def_var(fileID,"vfall_Z",nf90_float,(/dimID(1),dimID(21),dimID(4),dimID(23),dimID(24)/),varID(164))
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(164),"long_name","Number of samples onto histogram, terminal velocity/Altitude")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(164),"units",        "#")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_def_var(fileID,"gridw_Z",nf90_float,(/dimID(1),dimID(21),dimID(4),dimID(23)/),varID(165))
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(165),"long_name","Number of samples onto histogram, grid w velocity/Altitude")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(165),"units",        "#")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+
+       status = nf90_def_var(fileID,"vfall_T",nf90_float,(/dimID(1),dimID(21),dimID(19),dimID(23),dimID(24)/),varID(166))
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(166),"long_name","Number of samples onto histogram, terminal velocity/Temperature")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(166),"units",        "#")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_def_var(fileID,"gridw_T",nf90_float,(/dimID(1),dimID(21),dimID(19),dimID(23)/),varID(167))
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(167),"long_name","Number of samples onto histogram, grid w velocity/Temperature")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(167),"units",        "#")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+
+       status = nf90_def_var(fileID,"ZefVf_2",nf90_float,(/dimID(1),dimID(21),dimID(20),dimID(23),dimID(24)/),varID(168))
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(168),"long_name","Number of samples onto histogram, Radar reflectivity/terminal velocity")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_att(fileID,varID(168),"units",        "#")
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       
+    end if
+    
+    
+    ! ---------------------------------------------------------------------------------------
+    ! RTTOV - JKS
+    ! ---------------------------------------------------------------------------------------
+        
+    ! Define instrument channel indices for multiple RTTOV instruments
+    ii = 169 ! RTTOV variable indices start at 170
+    if (allocated(cospOUT%rttov_outputs)) then
+        do i=1,Ninst_rttov
+            write (i_str,fmt) i ! converting integer to string i_str using a 'internal file'
+            if (associated(cospOUT%rttov_outputs(i)%channel_indices)) then
+               ii = ii + 1
+               status = nf90_def_var(fileID,"RTTOV_CHAN_INST"//trim(i_str),nf90_float, (/dimID(24+i)/),varID(ii))
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+               status = nf90_put_att(fileID,varID(ii),"long_name","RTTOV Channel Indices")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+               status = nf90_put_att(fileID,varID(ii),"units",        "1")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))      
+               status = nf90_put_att(fileID,varID(ii),"standard_name", "rttov_ichan")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+            endif           
+            if (associated(cospOUT%rttov_outputs(i)%bt_total)) then
+               ii = ii + 1
+               status = nf90_def_var(fileID,"rttov_bt_total_inst"//trim(i_str),nf90_float, (/dimID(1),dimID(24+i)/),varID(ii))
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+               status = nf90_put_att(fileID,varID(ii),"long_name","RTTOV All-sky Brightness Temperature")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+               status = nf90_put_att(fileID,varID(ii),"units",        "Degrees Kelvin")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))      
+               status = nf90_put_att(fileID,varID(ii),"standard_name", "rttov_allsky_bt")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+            endif
+            if (associated(cospOUT%rttov_outputs(i)%bt_clear)) then
+               ii = ii + 1
+               status = nf90_def_var(fileID,"rttov_bt_clear_inst"//trim(i_str),nf90_float, (/dimID(1),dimID(24+i)/),varID(ii))
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+               status = nf90_put_att(fileID,varID(ii),"long_name","RTTOV Clear-sky Brightness Temperature")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+               status = nf90_put_att(fileID,varID(ii),"units",        "Degrees Kelvin")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))      
+               status = nf90_put_att(fileID,varID(ii),"standard_name", "rttov_clearsky_bt")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+            endif
+            if (associated(cospOUT%rttov_outputs(i)%rad_total)) then
+               ii = ii + 1
+               status = nf90_def_var(fileID,"rttov_rad_total_inst"//trim(i_str),nf90_float, (/dimID(1),dimID(24+i)/),varID(ii))
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+               status = nf90_put_att(fileID,varID(ii),"long_name","RTTOV All-sky Radiance")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+               status = nf90_put_att(fileID,varID(ii),"units",        "mW/cm-1/sr/m2")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))      
+               status = nf90_put_att(fileID,varID(ii),"standard_name", "rttov_allsky_rad")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+            endif    
+            if (associated(cospOUT%rttov_outputs(i)%rad_clear)) then
+               ii = ii + 1
+               status = nf90_def_var(fileID,"rttov_rad_clear_inst"//trim(i_str),nf90_float, (/dimID(1),dimID(24+i)/),varID(ii))
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+               status = nf90_put_att(fileID,varID(ii),"long_name","RTTOV Clear-sky Radiance")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+               status = nf90_put_att(fileID,varID(ii),"units",        "mW/cm-1/sr/m2")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))      
+               status = nf90_put_att(fileID,varID(ii),"standard_name", "rttov_clearsky_rad")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+            endif    
+            if (associated(cospOUT%rttov_outputs(i)%rad_cloudy)) then
+               ii = ii + 1
+               status = nf90_def_var(fileID,"rttov_rad_cloudy_inst"//trim(i_str),nf90_float, (/dimID(1),dimID(24+i)/),varID(ii))
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+               status = nf90_put_att(fileID,varID(ii),"long_name","RTTOV Cloudy-sky Radiance")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+               status = nf90_put_att(fileID,varID(ii),"units",        "mW/cm-1/sr/m2")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))      
+               status = nf90_put_att(fileID,varID(ii),"standard_name", "rttov_cloudysky_rad")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+            endif    
+            if (associated(cospOUT%rttov_outputs(i)%refl_total)) then
+               ii = ii + 1
+               status = nf90_def_var(fileID,"rttov_refl_total_inst"//trim(i_str),nf90_float, (/dimID(1),dimID(24+i)/),varID(ii))
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+               status = nf90_put_att(fileID,varID(ii),"long_name","RTTOV All-sky Reflectance")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+               status = nf90_put_att(fileID,varID(ii),"units",        "unitless")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))      
+               status = nf90_put_att(fileID,varID(ii),"standard_name", "bleh")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+            endif
+            if (associated(cospOUT%rttov_outputs(i)%refl_clear)) then
+               ii = ii + 1
+               status = nf90_def_var(fileID,"rttov_refl_clear_inst"//trim(i_str),nf90_float, (/dimID(1),dimID(24+i)/),varID(ii))
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+               status = nf90_put_att(fileID,varID(ii),"long_name","RTTOV Clear-sky Reflectance")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+               status = nf90_put_att(fileID,varID(ii),"units",        "unitless")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))      
+               status = nf90_put_att(fileID,varID(ii),"standard_name", "rttov_allsky_refl")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+            endif    
+            if (associated(cospOUT%rttov_outputs(i)%bt_total_pc)) then
+               ii = ii + 1
+               status = nf90_def_var(fileID,"rttov_bt_clear_pc_inst"//trim(i_str),nf90_float, (/dimID(1),dimID(24+i)/),varID(ii))
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+               status = nf90_put_att(fileID,varID(ii),"long_name","PC-RTTOV Clear-sky Brightness Temperature")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+               status = nf90_put_att(fileID,varID(ii),"units",        "Degrees Kelvin")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))      
+               status = nf90_put_att(fileID,varID(ii),"standard_name", "pcrttov_clearsky_bt")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+            endif    
+            if (associated(cospOUT%rttov_outputs(i)%rad_total_pc)) then
+               ii = ii + 1
+               status = nf90_def_var(fileID,"rttov_rad_clear_pc_inst"//trim(i_str),nf90_float, (/dimID(1),dimID(24+i)/),varID(ii))
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+               status = nf90_put_att(fileID,varID(ii),"long_name","PC-RTTOV Clear-sky Radiance")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+               status = nf90_put_att(fileID,varID(ii),"units",        "mW/cm-1/sr/m2")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))      
+               status = nf90_put_att(fileID,varID(ii),"standard_name", "pcrttov_clearsky_rad")
+               if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+            endif
+        end do
+    end if
     
     
     ! ---------------------------------------------------------------------------------------
@@ -1601,6 +1868,19 @@ subroutine write_cosp2_output(Npoints, Ncolumns, Nlevels, Ninst_rttov, lev, lon,
     if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
     status = nf90_put_var(fileID,varID(83),loc)
     if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+    ! added by DPLRW
+    status = nf90_put_var(fileID,varID(150),lvtemp_grid)
+    if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+    status = nf90_put_var(fileID,varID(151),lvdBZe_grid)
+    if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+    status = nf90_put_var(fileID,varID(152),lvdplr_grid)
+    if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+    status = nf90_put_var(fileID,varID(153),lvspwd_grid)
+    if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+    status = nf90_put_var(fileID,varID(154),(/0,1,2/) )
+    if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+    status = nf90_put_var(fileID,varID(155),(/1,2,3/) )
+    if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
     
     ! CALIPSO simulator output
     if (associated(cospOUT%calipso_betaperp_tot)) then
@@ -2044,9 +2324,45 @@ subroutine write_cosp2_output(Npoints, Ncolumns, Nlevels, Ninst_rttov, lev, lon,
        status = nf90_put_var(fileID,varID(147),CFODD_HISTICODcenters)
        if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
     endif
-    
+
+    ! added by DPLRW
+    if (associated(cospOUT%dplrw_Z))then
+       status = nf90_put_var(fileID,varID(156),cospOUT%dplrw_Z)
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_var(fileID,varID(157),cospOUT%spwid_Z)
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_var(fileID,varID(158),cospOUT%Zef94_Z)
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+
+       status = nf90_put_var(fileID,varID(159),cospOUT%dplrw_T)
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_var(fileID,varID(160),cospOUT%spwid_T)
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_var(fileID,varID(161),cospOUT%Zef94_T)
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+
+       status = nf90_put_var(fileID,varID(162),cospOUT%ZefVd_2)
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+
+       status = nf90_put_var(fileID,varID(163),cospOUT%gcumw)
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+
+       status = nf90_put_var(fileID,varID(164),cospOUT%vfall_Z)
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_var(fileID,varID(165),cospOUT%gridw_Z)
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+
+       status = nf90_put_var(fileID,varID(166),cospOUT%vfall_T)
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+       status = nf90_put_var(fileID,varID(167),cospOUT%gridw_T)
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+
+       status = nf90_put_var(fileID,varID(168),cospOUT%ZefVf_2)
+       if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+    end if
+
     ! Define instrument channel indices for multiple RTTOV instruments
-    ii = 165 ! RTTOV variable indices start at 166
+    ii = 169 ! RTTOV variable indices start at 170
     if (allocated(cospOUT%rttov_outputs)) then
         do i=1,Ninst_rttov
             write (i_str,fmt) i ! converting integer to string i_str using a 'internal file'
@@ -2100,15 +2416,15 @@ subroutine write_cosp2_output(Npoints, Ncolumns, Nlevels, Ninst_rttov, lev, lon,
                status = nf90_put_var(fileID,varID(ii),cospOUT%rttov_outputs(i)%rad_total_pc)
                if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
             endif            
-        end do  
-    end if
+         end do
+     end if
     
-    ! Close file
-    status = nf90_close(fileID)
-    if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
-
-  end subroutine write_cosp2_output
-
+     ! Close file
+     status = nf90_close(fileID)
+     if (status .ne. nf90_NoERR) print*,trim(nf90_strerror(status))
+     
+   end subroutine write_cosp2_output
+   
 
 
 
@@ -2151,8 +2467,10 @@ SUBROUTINE NC_READ_INPUT_FILE(fname,Npnts,Nl,Nhydro,lon,lat,p,ph,z,zh,T,qv,rh,tc
                                 mr_lsliq,mr_lsice,mr_ccliq,mr_ccice,fl_lsrain,fl_lssnow, &
                                 fl_lsgrpl,fl_ccrain,fl_ccsnow,Reff,dtau_s,dtau_c,dem_s,  &
                                 dem_c,skt,psfc,landmask,mr_ozone,u_wind,v_wind,sunlit,   &
-                                emsfc_lw,mode,Nlon,Nlat,surfelev,year,month,day,         &
-                                hour,minute,seconds)
+                                gwvel, gcumf, & ! added by DPLRW
+                                mr_lsrain, mr_lssnow, & ! adjust to prognostic precipitation
+                                emsfc_lw,mode,Nlon,Nlat,surfelev,                        &
+                                year,month,day,hour,minute,seconds)
      
     ! Arguments
     character(len=512),intent(in) :: fname ! File name
@@ -2164,6 +2482,8 @@ SUBROUTINE NC_READ_INPUT_FILE(fname,Npnts,Nl,Nhydro,lon,lat,p,ph,z,zh,T,qv,rh,tc
     real(wp),dimension(Npnts,Nl,Nhydro),intent(out) :: Reff
     real(wp),dimension(Npnts),intent(out) :: skt,psfc,landmask,u_wind,v_wind,sunlit,surfelev, &
                                              year,month,day,hour,minute,seconds
+    real(wp),dimension(Npnts,Nl),intent(out) :: gwvel,gcumf ! added by DPLRW
+    real(wp),dimension(Npnts,Nl),intent(out) :: mr_lsrain,mr_lssnow ! adjust to prognostic precipitation
     real(wp),intent(out) :: emsfc_lw
     integer,intent(out) :: mode,Nlon,Nlat
     
@@ -2420,6 +2740,18 @@ SUBROUTINE NC_READ_INPUT_FILE(fname,Npnts,Nl,Nhydro,lon,lat,p,ph,z,zh,T,qv,rh,tc
           else
              call map_ll_to_point(Na,Nb,Npoints,x3=x3,y2=fl_lssnow)
           endif
+       case ('mr_lsrain')
+          if (Lpoint) then
+             mr_lsrain(1:Npoints,:) = x2(1:Npoints,1:Nlevels)
+          else
+             call map_ll_to_point(Na,Nb,Npoints,x3=x3,y2=mr_lsrain)
+          endif
+       case ('mr_lssnow')
+          if (Lpoint) then
+             mr_lssnow(1:Npoints,:) = x2(1:Npoints,1:Nlevels)
+          else
+             call map_ll_to_point(Na,Nb,Npoints,x3=x3,y2=mr_lssnow)
+          endif
        case ('fl_lsgrpl')
           if (Lpoint) then
              fl_lsgrpl(1:Npoints,:) = x2(1:Npoints,1:Nlevels)
@@ -2483,7 +2815,7 @@ SUBROUTINE NC_READ_INPUT_FILE(fname,Npnts,Nl,Nhydro,lon,lat,p,ph,z,zh,T,qv,rh,tc
        case ('orography') 
           if (Lpoint) then
              surfelev(1:Npoints) = x1(1:Npoints) 
-          else     
+          else
              call map_ll_to_point(Na,Nb,Npoints,x2=x2,y1=surfelev)
           endif
        case ('landmask')
@@ -2516,6 +2848,20 @@ SUBROUTINE NC_READ_INPUT_FILE(fname,Npnts,Nl,Nhydro,lon,lat,p,ph,z,zh,T,qv,rh,tc
           else
              call map_ll_to_point(Na,Nb,Npoints,x2=x2,y1=sunlit)
           endif
+
+       case ('gwvel') ! added by DPLRW
+          if (Lpoint) then
+             gwvel(1:Npoints,:) = x2(1:Npoints,1:Nlevels)
+          else
+             call map_ll_to_point(Na,Nb,Npoints,x3=x3,y2=gwvel)
+          endif
+       case ('gcumf')
+          if (Lpoint) then
+             gcumf(1:Npoints,:) = x2(1:Npoints,1:Nlevels)
+          else
+             call map_ll_to_point(Na,Nb,Npoints,x3=x3,y2=gcumf)
+          endif
+
        case ('year')
           if (Lpoint) then
              year(1:Npoints) = x1(1:Npoints)
@@ -2552,6 +2898,7 @@ SUBROUTINE NC_READ_INPUT_FILE(fname,Npnts,Nl,Nhydro,lon,lat,p,ph,z,zh,T,qv,rh,tc
           else
              call map_ll_to_point(Na,Nb,Npoints,x2=x2,y1=seconds)
           endif            
+
        end select
        ! Free memory
        if (vrank == 1) deallocate(x1)
diff --git a/driver/src/cosp2_test.F90 b/driver/src/cosp2_test.F90
index 47ead0012f..7b6d14df27 100755
--- a/driver/src/cosp2_test.F90
+++ b/driver/src/cosp2_test.F90
@@ -1,4 +1,4 @@
-! %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+! %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%A%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 ! Copyright (c) 2016, Regents of the University of Colorado
 ! All rights reserved.
 !
@@ -64,11 +64,12 @@ program cosp2_test
   USE mod_scops,           ONLY: scops
   USE mod_prec_scops,      ONLY: prec_scops
   USE MOD_COSP_UTILS,      ONLY: cosp_precip_mxratio
+  use MOD_COSP_RTTOV_UTIL, only: rttov_cfg, rttov_output
   use cosp_optics,         ONLY: cosp_simulator_optics,lidar_optics,modis_optics,         &
                                  modis_optics_partition
   use mod_cosp_stats,      ONLY: COSP_CHANGE_VERTICAL_GRID,cosp_optical_inputs,           &
                                  cosp_column_inputs,radar_cfg,cosp_cleanUp
-  use MOD_COSP_RTTOV_UTIL, only: rttov_cfg
+  use mod_cosp_config,     ONLY: Nlvdplr, Nlvspwd, NlvdBZe, Nlvtemp  ! added by DPLRW
   
   implicit none
 
@@ -126,6 +127,16 @@ program cosp2_test
        frac_out,  & ! Subcolumn cloud cover (0/1)
        Reff         ! Subcolumn effective radius
 
+  ! additional inputs for DPLRW
+  real(wp),dimension(:,:),allocatable,target :: &
+       gwvel,     & ! Model grid vertical velocity (m/s)
+       gcumf        ! Model cumulus mass flux (kg/m^2/s)
+
+  ! adjust to prognostic precipitation
+  real(wp),dimension(:,:),allocatable,target :: &
+       mr_lsrain,     & ! Mass mixing ratio for stratiform cloud rain (kg/kg)
+       mr_lssnow        ! Mass mixing ratio for stratiform cloud snow (kg/kg)
+  
   ! Input namelist fields
   integer ::                      & !
        Npoints,                   & ! Number of gridpoints
@@ -149,8 +160,8 @@ program cosp2_test
   logical ::                      & !
        use_vgrid,                 & ! Use fixed vertical grid for outputs?
        csat_vgrid,                & ! CloudSat vertical grid? 
-       use_precipitation_fluxes     ! True if precipitation fluxes are input to the 
-                                    ! algorithm 
+       use_precipitation_fluxes     ! True if precipitation fluxes are input to the flux
+
   character(len=64) :: &
        cloudsat_micro_scheme        ! Microphysical scheme used in cloudsat radar simulator
   character(len=64) :: &
@@ -224,7 +235,9 @@ program cosp2_test
              Lclmodis,Lptradarflag0,Lptradarflag1,Lptradarflag2,Lptradarflag3,           &
              Lptradarflag4,Lptradarflag5,Lptradarflag6,Lptradarflag7,Lptradarflag8,      &
              Lptradarflag9,Lradarpia,                                                    &
-             Lwr_occfreq,Lcfodd
+             Lwr_occfreq, Lcfodd, &
+             Ldplrw ! for DPLRW
+
   namelist/COSP_OUTPUT/Lcfaddbze94,Ldbze94,Latb532,LcfadLidarsr532,Lclcalipso,           &
                        Lclhcalipso,Lcllcalipso,Lclmcalipso,Lcltcalipso,LparasolRefl,     &
                        Lclcalipsoliq,Lclcalipsoice,Lclcalipsoun,Lclcalipsotmp,           &
@@ -250,7 +263,9 @@ program cosp2_test
                        Lptradarflag0,Lptradarflag1,Lptradarflag2,Lptradarflag3,          &
                        Lptradarflag4,Lptradarflag5,Lptradarflag6,Lptradarflag7,          &
                        Lptradarflag8,Lptradarflag9,Lradarpia,                            &
-                       Lwr_occfreq, Lcfodd
+                       Lwr_occfreq, Lcfodd, &
+                       Ldplrw
+
   ! Local variables
   logical :: &
        lsingle     = .true.,  & ! True if using MMF_v3_single_moment CLOUDSAT microphysical scheme (default)
@@ -320,6 +335,7 @@ program cosp2_test
   !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
   call cpu_time(driver_time(1))
+
   !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
   ! Read in namelists
   !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -374,6 +390,8 @@ program cosp2_test
            dtau_s(Npoints,Nlevels),dtau_c(Npoints,Nlevels),dem_s(Npoints,Nlevels),       &
            dem_c(Npoints,Nlevels),skt(Npoints),psfc(Npoints),landmask(Npoints),          &
            mr_ozone(Npoints,Nlevels),u_wind(Npoints),v_wind(Npoints),sunlit(Npoints),    &
+           gwvel(Npoints,Nlevels),gcumf(Npoints,Nlevels), & ! added by DPLRW
+           mr_lsrain(Npoints,Nlevels),mr_lssnow(Npoints,Nlevels),                        &
            frac_out(Npoints,Ncolumns,Nlevels),surfelev(Npoints),year(Npoints),           &
            month(Npoints),day(Npoints),hour(Npoints),minute(Npoints),seconds(Npoints))
 
@@ -389,9 +407,11 @@ program cosp2_test
   call nc_read_input_file(fileIN,Npoints,Nlevels,N_HYDRO,lon,lat,p,ph,zlev,zlev_half,    &
                           T,sh,rh,tca,cca,mr_lsliq,mr_lsice,mr_ccliq,mr_ccice,fl_lsrain, &
                           fl_lssnow,fl_lsgrpl,fl_ccrain,fl_ccsnow,Reff,dtau_s,dtau_c,    &
-                          dem_s,dem_c,skt,psfc,landmask,mr_ozone,u_wind,v_wind,sunlit,        &
-                          emsfc_lw,geomode,Nlon,Nlat,surfelev,year,month,day,hour,       &
-                          minute,seconds)
+                          dem_s,dem_c,skt,psfc,landmask,mr_ozone,u_wind,v_wind,sunlit,   &
+                          gwvel, gcumf, & ! added by DPLRW
+                          mr_lsrain, mr_lssnow, & ! adjust to prognostic precipitation
+                          emsfc_lw,geomode,Nlon,Nlat,surfelev,                           &
+                          year,month,day,hour,minute,seconds)
   call cpu_time(driver_time(2))
 
   !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -432,8 +452,8 @@ program cosp2_test
        Lptradarflag6 .or. Lptradarflag7 .or. Lptradarflag8 .or. Lptradarflag9 .or.       &
        Lradarpia) Lcloudsat = .true.
   if (Lparasolrefl) Lparasol = .true.
-  
   if (rttov_Ninstruments .gt. 0)  Lrttov = .true.
+  if (Ldplrw) Lcloudsat = .true. ! added by DPLRW  
 
   !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
   !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -469,7 +489,7 @@ program cosp2_test
        rttov_Ninstruments, rttov_instrument_namelists_final, rttov_configs,              &
        debug=rttov_verbose)
   call cpu_time(driver_time(3))
-    
+
   !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
   ! Construct output derived type.
   ! *NOTE* The "construct/destroy" subroutines are local to this module and should be
@@ -496,8 +516,7 @@ program cosp2_test
        LcfadDbze94, Ldbze94, Lparasolrefl,                                               &
        Lptradarflag0,Lptradarflag1,Lptradarflag2,Lptradarflag3,Lptradarflag4,            &
        Lptradarflag5,Lptradarflag6,Lptradarflag7,Lptradarflag8,Lptradarflag9,Lradarpia,  &
-       Lwr_occfreq, Lcfodd,                                                              &
-       rttov_Ninstruments,rttov_configs,                                                 &
+       Lwr_occfreq, Lcfodd, rttov_Ninstruments, rttov_configs, Ldplrw,                   &
        Npoints, Ncolumns, Nlevels, Nlvgrid_local, use_vgrid, cospOUT)
 
   !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -510,7 +529,9 @@ program cosp2_test
      nChunks = nPoints/nPoints_it+1
   endif 
   if (nPoints .eq. nPoints_it) nChunks = 1
+  write(*,*) 'nChunks = ',nChunks
   do iChunk=1,nChunks
+     write(*,*) 'Now ... iChunk = ',iChunk
      !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
      ! Determine indices for "chunking" (again, if necessary)
      !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -577,6 +598,10 @@ program cosp2_test
      ! cospstateIN%hgt_matrix_half(:,Nlevels) contains the bottom of the surface layer.
      cospstateIN%hgt_matrix_half(:,1:Nlevels) = zlev_half(start_idx:end_idx,Nlevels:1:-1) ! km
      
+     ! added by DPLRW
+     cospstateIN%gwvel(:,1:Nlevels) = gwvel(start_idx:end_idx,Nlevels:1:-1)
+     cospstateIN%gcumf(:,1:Nlevels) = gcumf(start_idx:end_idx,Nlevels:1:-1)
+
      ! Assign RTTOV values
      ! Keeping these structures since refl and emis could come from model input
    !   cospstateIN%emis_in(:,:) = 1._wp
@@ -657,7 +682,8 @@ program cosp2_test
      ! The weighted Reff is given by: Reff_net = (M_1 + M_2) / (M_1/Reff_1 + M_2/Reff_2)
      cospstateIN%DeffLiq(:,:) = 0._wp ! Initialize for zero everywhere.
      where ((mr_lsliq(start_idx:end_idx,Nlevels:1:-1) .gt. 0._wp) .and. (mr_ccliq(start_idx:end_idx,Nlevels:1:-1) .gt. 0._wp))
-         cospstateIN%DeffLiq(:,:) = 2._wp * 1.0e6 * (mr_lsliq(start_idx:end_idx,Nlevels:1:-1) + mr_ccliq(start_idx:end_idx,Nlevels:1:-1)) / (mr_lsliq(start_idx:end_idx,Nlevels:1:-1) / Reff(start_idx:end_idx,Nlevels:1:-1,I_LSCLIQ) + mr_ccliq(start_idx:end_idx,Nlevels:1:-1) / Reff(start_idx:end_idx,Nlevels:1:-1,I_CVCLIQ))          
+         cospstateIN%DeffLiq(:,:) = 2._wp * 1.0e6 * (mr_lsliq(start_idx:end_idx,Nlevels:1:-1) + mr_ccliq(start_idx:end_idx,Nlevels:1:-1)) / &
+              & (mr_lsliq(start_idx:end_idx,Nlevels:1:-1) / Reff(start_idx:end_idx,Nlevels:1:-1,I_LSCLIQ) + mr_ccliq(start_idx:end_idx,Nlevels:1:-1) / Reff(start_idx:end_idx,Nlevels:1:-1,I_CVCLIQ))
      elsewhere (mr_lsliq(start_idx:end_idx,Nlevels:1:-1) .gt. 0._wp)
          cospstateIN%DeffLiq(:,:) = 2._wp * 1.0e6 * Reff(start_idx:end_idx,Nlevels:1:-1,I_LSCLIQ)
      elsewhere (mr_ccliq(start_idx:end_idx,Nlevels:1:-1) .gt. 0._wp)
@@ -666,7 +692,8 @@ program cosp2_test
      
      cospstateIN%DeffIce(:,:) = 0._wp ! Initialize for zero everywhere.
      where ((mr_lsice(start_idx:end_idx,Nlevels:1:-1) .gt. 0._wp) .and. (mr_ccice(start_idx:end_idx,Nlevels:1:-1) .gt. 0._wp))
-         cospstateIN%DeffIce(:,:) = 2._wp * 1.0e6 * (mr_lsice(start_idx:end_idx,Nlevels:1:-1) + mr_ccice(start_idx:end_idx,Nlevels:1:-1)) / (mr_lsice(start_idx:end_idx,Nlevels:1:-1) / Reff(start_idx:end_idx,Nlevels:1:-1,I_LSCICE) + mr_ccice(start_idx:end_idx,Nlevels:1:-1) / Reff(start_idx:end_idx,Nlevels:1:-1,I_CVCICE))          
+         cospstateIN%DeffIce(:,:) = 2._wp * 1.0e6 * (mr_lsice(start_idx:end_idx,Nlevels:1:-1) + mr_ccice(start_idx:end_idx,Nlevels:1:-1)) / &
+              & (mr_lsice(start_idx:end_idx,Nlevels:1:-1) / Reff(start_idx:end_idx,Nlevels:1:-1,I_LSCICE) + mr_ccice(start_idx:end_idx,Nlevels:1:-1) / Reff(start_idx:end_idx,Nlevels:1:-1,I_CVCICE))
      elsewhere (mr_lsice(start_idx:end_idx,Nlevels:1:-1) .gt. 0._wp)
          cospstateIN%DeffIce(:,:) = 2._wp * 1.0e6 * Reff(start_idx:end_idx,Nlevels:1:-1,I_LSCICE)
      elsewhere (mr_ccice(start_idx:end_idx,Nlevels:1:-1) .gt. 0._wp)
@@ -686,13 +713,15 @@ program cosp2_test
      ! Generate subcolumns and compute optical inputs.
      !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
      call subsample_and_optics(nPtsPerIt,nLevels,nColumns,N_HYDRO,overlap,                     &
-          use_vgrid,use_precipitation_fluxes,lidar_ice_type,sd,                                &
+          use_vgrid,use_precipitation_fluxes,lidar_ice_type,sd, &
           tca(start_idx:end_idx,Nlevels:1:-1),cca(start_idx:end_idx,Nlevels:1:-1),             &
           fl_lsrain(start_idx:end_idx,Nlevels:1:-1),fl_lssnow(start_idx:end_idx,Nlevels:1:-1), &
           fl_lsgrpl(start_idx:end_idx,Nlevels:1:-1),fl_ccrain(start_idx:end_idx,Nlevels:1:-1), &
           fl_ccsnow(start_idx:end_idx,Nlevels:1:-1),mr_lsliq(start_idx:end_idx,Nlevels:1:-1),  &
           mr_lsice(start_idx:end_idx,Nlevels:1:-1),mr_ccliq(start_idx:end_idx,Nlevels:1:-1),   &
-          mr_ccice(start_idx:end_idx,Nlevels:1:-1),Reff(start_idx:end_idx,Nlevels:1:-1,:),     &
+          mr_ccice(start_idx:end_idx,Nlevels:1:-1), &
+          mr_lsrain(start_idx:end_idx,Nlevels:1:-1),mr_lssnow(start_idx:end_idx,Nlevels:1:-1), &
+          Reff(start_idx:end_idx,Nlevels:1:-1,:), &
           dtau_c(start_idx:end_idx,nLevels:1:-1),dtau_s(start_idx:end_idx,nLevels:1:-1),       &
           dem_c(start_idx:end_idx,nLevels:1:-1),dem_s(start_idx:end_idx,nLevels:1:-1),         &
           cospstateIN,cospIN)
@@ -708,6 +737,7 @@ program cosp2_test
      end do
      
      call cpu_time(driver_time(7))
+
   end do
 
   print*,'Time to read in data:     ',driver_time(2)-driver_time(1)
@@ -747,14 +777,16 @@ program cosp2_test
   ! SUBROUTINE subsample_and_optics
   !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%  
   subroutine subsample_and_optics(nPoints, nLevels, nColumns, nHydro, overlap, use_vgrid,   &
-       use_precipitation_fluxes, lidar_ice_type, sd, tca, cca, fl_lsrainIN, fl_lssnowIN,    &
+       use_precipitation_fluxes, lidar_ice_type, sd, &
+       tca, cca, fl_lsrainIN, fl_lssnowIN,    &
        fl_lsgrplIN, fl_ccrainIN, fl_ccsnowIN, mr_lsliq, mr_lsice, mr_ccliq, mr_ccice,       &
+       mr_lsrain, mr_lssnow, & ! adjust to prognostic precipitation
        reffIN, dtau_c, dtau_s, dem_c, dem_s, cospstateIN, cospIN)
     ! Inputs
     integer,intent(in) :: nPoints, nLevels, nColumns, nHydro, overlap, lidar_ice_type
     real(wp),intent(in),dimension(nPoints,nLevels) :: tca,cca,mr_lsliq,mr_lsice,mr_ccliq,   &
          mr_ccice,dtau_c,dtau_s,dem_c,dem_s,fl_lsrainIN,fl_lssnowIN,fl_lsgrplIN,fl_ccrainIN,&
-         fl_ccsnowIN
+         fl_ccsnowIN, mr_lsrain, mr_lssnow
     real(wp),intent(in),dimension(nPoints,nLevels,nHydro) :: reffIN
     logical,intent(in) :: use_vgrid ! .false.: outputs on model levels
                                     ! .true.:  outputs on evenly-spaced vertical levels.
@@ -782,6 +814,7 @@ subroutine subsample_and_optics(nPoints, nLevels, nColumns, nHydro, overlap, use
          column_frac_out, column_prec_out, fl_lsrain, fl_lssnow, fl_lsgrpl, fl_ccrain, fl_ccsnow
     real(wp),dimension(nPoints,nColumns,Nlvgrid_local) :: tempOut
     logical :: cmpGases=.true.
+    logical :: prog_flag=.true. ! adjust to prognostic precipitation
 
     if (Ncolumns .gt. 1) then
        !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -807,8 +840,8 @@ subroutine subsample_and_optics(nPoints, nLevels, nColumns, nHydro, overlap, use
           cv_p_rate(:,1:nLevels) = fl_ccrainIN + fl_ccsnowIN
        else
           ls_p_rate(:,1:nLevels) = 0 ! mixing_ratio(rain) + mixing_ratio(snow) + mixing_ratio (groupel)
-          cv_p_rate(:,1:nLevels) = 0 ! mixing_ratio(rain) + mixing_ratio(snow)
-       endif
+          cv_p_rate(:,1:nLevels) = 0 ! mixing_ratio(rain) + mixing_ratio(snow) + mixing_ratio (groupel)
+       end if
        
        ! Call PREC_SCOPS
        allocate(frac_prec(nPoints,nColumns,nLevels))
@@ -856,6 +889,10 @@ subroutine subsample_and_optics(nPoints, nLevels, nColumns, nHydro, overlap, use
        mr_hydro(:,:,:,:) = 0._wp
        Reff(:,:,:,:)     = 0._wp
        Np(:,:,:,:)       = 0._wp
+
+       ! check prognostic precipitaion
+       if ( any(mr_lsrain .lt. 0._wp) .or. any(mr_lssnow .lt. 0._wp) ) prog_flag = .false.
+       
        do k=1,nColumns
           ! Subcolumn cloud fraction
           column_frac_out = cospIN%frac_out(:,k,:)
@@ -881,8 +918,8 @@ subroutine subsample_and_optics(nPoints, nLevels, nColumns, nHydro, overlap, use
           where ((column_prec_out == 1) .or. (column_prec_out == 3) )
              Reff(:,k,:,I_LSRAIN) = ReffIN(:,:,I_LSRAIN)
              Reff(:,k,:,I_LSSNOW) = ReffIN(:,:,I_LSSNOW)
-             Reff(:,k,:,I_LSGRPL) = ReffIN(:,:,I_LSGRPL)
-             ! CONV precipitation   
+             Reff(:,k,:,I_LSGRPL) = ReffIN(:,:,I_LSGRPL)             
+          ! CONV precipitation
           elsewhere ((column_prec_out == 2) .or. (column_prec_out == 3))
              Reff(:,k,:,I_CVRAIN) = ReffIN(:,:,I_CVRAIN)
              Reff(:,k,:,I_CVSNOW) = ReffIN(:,:,I_CVSNOW)
@@ -899,6 +936,7 @@ subroutine subsample_and_optics(nPoints, nLevels, nColumns, nHydro, overlap, use
        fl_lsgrpl(:,:) = 0._wp
        fl_ccrain(:,:) = 0._wp
        fl_ccsnow(:,:) = 0._wp
+
        do k=1,nLevels
           do j=1,nPoints
              ! In-cloud mixing ratios.
@@ -913,9 +951,14 @@ subroutine subsample_and_optics(nPoints, nLevels, nColumns, nHydro, overlap, use
              ! Precipitation
              if (use_precipitation_fluxes) then
                 if (prec_ls(j,k) .ne. 0.) then
-                   fl_lsrain(j,k) = fl_lsrainIN(j,k)/prec_ls(j,k)
-                   fl_lssnow(j,k) = fl_lssnowIN(j,k)/prec_ls(j,k)
-                   fl_lsgrpl(j,k) = fl_lsgrplIN(j,k)/prec_ls(j,k)
+                   if (prog_flag) then
+                      mr_hydro(j,:,k,I_LSRAIN) = mr_lsrain(j,k)/prec_ls(j,k)
+                      mr_hydro(j,:,k,I_LSSNOW) = mr_lssnow(j,k)/prec_ls(j,k)
+                   else
+                      fl_lsrain(j,k) = fl_lsrainIN(j,k)/prec_ls(j,k)
+                      fl_lssnow(j,k) = fl_lssnowIN(j,k)/prec_ls(j,k)
+                      fl_lsgrpl(j,k) = fl_lsgrplIN(j,k)/prec_ls(j,k)
+                   end if
                 endif
                 if (prec_cv(j,k) .ne. 0.) then
                    fl_ccrain(j,k) = fl_ccrainIN(j,k)/prec_cv(j,k)
@@ -932,6 +975,13 @@ subroutine subsample_and_optics(nPoints, nLevels, nColumns, nHydro, overlap, use
                    mr_hydro(j,:,k,I_CVSNOW) = mr_hydro(j,:,k,I_CVSNOW)/prec_cv(j,k)
                 endif
              endif
+
+             ! cumulus mass flux, added by DPLRW
+             if (cca(j,k) > 0._wp .and. cca(j,k) <= 1._wp) then
+                cospstateIN%gcumf(j,k) = cospstateIN%gcumf(j,k)/cca(j,k)
+             else
+                cospstateIN%gcumf(j,k) = R_UNDEF
+             end if
           enddo
        enddo
        deallocate(frac_ls,prec_ls,frac_cv,prec_cv)
@@ -941,45 +991,35 @@ subroutine subsample_and_optics(nPoints, nLevels, nColumns, nHydro, overlap, use
        !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
        if (use_precipitation_fluxes) then
           ! LS rain
-          call cosp_precip_mxratio(nPoints, nLevels, nColumns, cospstateIN%pfull,        &
-               cospstateIN%at, frac_prec, 1._wp, n_ax(I_LSRAIN), n_bx(I_LSRAIN),         &
-               alpha_x(I_LSRAIN), c_x(I_LSRAIN),   d_x(I_LSRAIN),   g_x(I_LSRAIN),       &
-               a_x(I_LSRAIN),   b_x(I_LSRAIN),   gamma_1(I_LSRAIN), gamma_2(I_LSRAIN),   &
-               gamma_3(I_LSRAIN), gamma_4(I_LSRAIN), fl_lsrain,                          &
-               mr_hydro(:,:,:,I_LSRAIN), Reff(:,:,:,I_LSRAIN))
+          if (.not. prog_flag) then
+             call cosp_precip_mxratio(Npoints, Nlevels, Ncolumns, &
+                  cospstateIN%pfull, cospstateIN%at, frac_prec, 1._wp, I_LSRAIN, sd, &
+                  fl_lsrain, mr_hydro(:,:,:,I_LSRAIN), Reff(:,:,:,I_LSRAIN) )
+          end if
           ! LS snow
-          call cosp_precip_mxratio(nPoints, nLevels, nColumns, cospstateIN%pfull,        &
-               cospstateIN%at, frac_prec, 1._wp,  n_ax(I_LSSNOW),  n_bx(I_LSSNOW),       &
-               alpha_x(I_LSSNOW), c_x(I_LSSNOW),  d_x(I_LSSNOW),  g_x(I_LSSNOW),         &
-               a_x(I_LSSNOW),   b_x(I_LSSNOW),   gamma_1(I_LSSNOW),  gamma_2(I_LSSNOW),  &
-               gamma_3(I_LSSNOW), gamma_4(I_LSSNOW), fl_lssnow,                          &
-               mr_hydro(:,:,:,I_LSSNOW), Reff(:,:,:,I_LSSNOW))
+          if (.not. prog_flag) then
+             call cosp_precip_mxratio(Npoints, Nlevels, Ncolumns, &
+                  cospstateIN%pfull, cospstateIN%at, frac_prec, 1._wp, I_LSSNOW, sd, &
+                  fl_lssnow, mr_hydro(:,:,:,I_LSSNOW), Reff(:,:,:,I_LSSNOW) )
+          end if
           ! CV rain
-          call cosp_precip_mxratio(nPoints, nLevels, nColumns, cospstateIN%pfull,        &
-               cospstateIN%at, frac_prec, 2._wp, n_ax(I_CVRAIN),  n_bx(I_CVRAIN),        &
-               alpha_x(I_CVRAIN), c_x(I_CVRAIN),   d_x(I_CVRAIN),   g_x(I_CVRAIN),       &
-               a_x(I_CVRAIN),   b_x(I_CVRAIN),   gamma_1(I_CVRAIN), gamma_2(I_CVRAIN),   &
-               gamma_3(I_CVRAIN), gamma_4(I_CVRAIN), fl_ccrain,                          &
-               mr_hydro(:,:,:,I_CVRAIN), Reff(:,:,:,I_CVRAIN))
+          call cosp_precip_mxratio(Npoints, Nlevels, Ncolumns, &
+               cospstateIN%pfull, cospstateIN%at, frac_prec, 2._wp, I_CVRAIN, sd, &
+               fl_lssnow, mr_hydro(:,:,:,I_CVRAIN), Reff(:,:,:,I_CVRAIN) )
           ! CV snow
-          call cosp_precip_mxratio(nPoints, nLevels, nColumns, cospstateIN%pfull,        &
-               cospstateIN%at, frac_prec, 2._wp, n_ax(I_CVSNOW),  n_bx(I_CVSNOW),        &
-               alpha_x(I_CVSNOW),  c_x(I_CVSNOW),   d_x(I_CVSNOW),   g_x(I_CVSNOW),      &
-               a_x(I_CVSNOW),   b_x(I_CVSNOW),   gamma_1(I_CVSNOW), gamma_2(I_CVSNOW),   &
-               gamma_3(I_CVSNOW), gamma_4(I_CVSNOW), fl_ccsnow,                          &
-               mr_hydro(:,:,:,I_CVSNOW), Reff(:,:,:,I_CVSNOW))
+          call cosp_precip_mxratio(Npoints, Nlevels, Ncolumns, &
+               cospstateIN%pfull, cospstateIN%at, frac_prec, 2._wp, I_CVSNOW, sd, &
+               fl_lssnow, mr_hydro(:,:,:,I_CVSNOW), Reff(:,:,:,I_CVSNOW) )
           ! LS groupel.
-          call cosp_precip_mxratio(nPoints, nLevels, nColumns, cospstateIN%pfull,        &
-               cospstateIN%at, frac_prec, 1._wp, n_ax(I_LSGRPL),  n_bx(I_LSGRPL),        &
-               alpha_x(I_LSGRPL), c_x(I_LSGRPL),   d_x(I_LSGRPL),   g_x(I_LSGRPL),       &
-               a_x(I_LSGRPL),   b_x(I_LSGRPL),   gamma_1(I_LSGRPL),  gamma_2(I_LSGRPL),  &
-               gamma_3(I_LSGRPL), gamma_4(I_LSGRPL), fl_lsgrpl,                          &
-               mr_hydro(:,:,:,I_LSGRPL), Reff(:,:,:,I_LSGRPL))
+          call cosp_precip_mxratio(Npoints, Nlevels, Ncolumns, &
+               cospstateIN%pfull, cospstateIN%at, frac_prec, 1._wp, I_LSGRPL, sd, &
+               fl_lssnow, mr_hydro(:,:,:,I_LSGRPL), Reff(:,:,:,I_LSGRPL) )
+
           deallocate(frac_prec)
        endif
 
     else
-       cospIN%frac_out(:,:,:) = 1  
+       cospIN%frac_out(:,:,:) = 1
        allocate(mr_hydro(nPoints,1,nLevels,nHydro),Reff(nPoints,1,nLevels,nHydro),       &
                 Np(nPoints,1,nLevels,nHydro))
        mr_hydro(:,1,:,I_LSCLIQ) = mr_lsliq
@@ -1051,7 +1091,7 @@ subroutine subsample_and_optics(nPoints, nLevels, nColumns, nHydro, overlap, use
              cospIN%g_vol_cloudsat(i,:,j)=g_vol(i,j)
           end do
        end do
-       
+
        ! Loop over all subcolumns
        allocate(fracPrecipIce(nPoints,nColumns,nLevels))
        fracPrecipIce(:,:,:) = 0._wp
@@ -1060,8 +1100,16 @@ subroutine subsample_and_optics(nPoints, nLevels, nColumns, nHydro, overlap, use
                mr_hydro(:,k,:,1:nHydro)*1000._wp, Reff(:,k,:,1:nHydro)*1.e6_wp,&
                Np(:,k,:,1:nHydro), cospstateIN%pfull, cospstateIN%at,          &
                cospstateIN%qv, cospIN%z_vol_cloudsat(1:nPoints,k,:),           &
-               cospIN%kr_vol_cloudsat(1:nPoints,k,:))
-          
+               cospIN%kr_vol_cloudsat(1:nPoints,k,:), &
+               cospIN%vfall(1:nPoints,k,:,1:N_Hydro),                      &
+               cospIN%vfsqu(1:nPoints,k,:,1:N_Hydro),                      &
+               cospIN%zehyd(1:nPoints,k,:,1:N_Hydro),                      &
+               cospIN%krhyd(1:nPoints,k,:,1:N_Hydro), &
+               cospIN%vtrm3(1:nPoints,k,:,1:N_Hydro),                      &
+               cospIN%vtrm0(1:nPoints,k,:,1:N_Hydro),                      &
+               cospIN%mmnt3(1:nPoints,k,:,1:N_Hydro),                      &
+               cospIN%mmnt0(1:nPoints,k,:,1:N_Hydro) )
+
           ! At each model level, what fraction of the precipitation is frozen?
           where(mr_hydro(:,k,:,I_LSRAIN) .gt. 0 .or. mr_hydro(:,k,:,I_LSSNOW) .gt. 0 .or. &
                 mr_hydro(:,k,:,I_CVRAIN) .gt. 0 .or. mr_hydro(:,k,:,I_CVSNOW) .gt. 0 .or. &
@@ -1204,6 +1252,17 @@ subroutine construct_cospIN(npoints,ncolumns,nlevels,ninst_rttov,y,emis_grey)
                 y%kr_vol_cloudsat(npoints, ncolumns,nlevels),&
                 y%g_vol_cloudsat(npoints,  ncolumns,nlevels),&
                 y%fracPrecipIce(npoints,   ncolumns))
+
+       ! added by DPLRW
+       allocate(y%vfall(npoints, ncolumns, nlevels, N_HYDRO), &
+                y%vfsqu(npoints, ncolumns, nlevels, N_HYDRO), &
+                y%zehyd(npoints, ncolumns, nlevels, N_HYDRO), &
+                y%krhyd(npoints, ncolumns, nlevels, N_HYDRO) )
+       allocate(y%vtrm3(npoints, ncolumns, nlevels, N_HYDRO), &
+                y%vtrm0(npoints, ncolumns, nlevels, N_HYDRO), &
+                y%mmnt3(npoints, ncolumns, nlevels, N_HYDRO), &
+                y%mmnt0(npoints, ncolumns, nlevels, N_HYDRO) )
+
     endif
     if (Lmodis) then
        allocate(y%fracLiq(npoints,        ncolumns,nlevels),&
@@ -1238,6 +1297,8 @@ subroutine construct_cospstateIN(npoints,nlevels,y)
              y%tca(nPoints,nLevels),y%hgt_matrix_half(nPoints,nlevels),                  &
              y%rttov_date(nPoints,3),y%rttov_time(nPoints,3),y%sza(nPoints))
 
+    ! added by DPLRW
+    allocate(y%gwvel(npoints,nlevels),y%gcumf(npoints,nlevels))
 
   end subroutine construct_cospstateIN
 
@@ -1278,7 +1339,7 @@ subroutine construct_cosp_outputs(Lpctisccp,Lclisccp,&
                                     Lptradarflag3,Lptradarflag4,Lptradarflag5,           &
                                     Lptradarflag6,Lptradarflag7,Lptradarflag8,           &
                                     Lptradarflag9,Lradarpia,Lwr_occfreq,Lcfodd,          &
-                                    Ninst_rttov,rttov_configs,                           &
+                                    Ninst_rttov,rttov_configs, Ldplrw,                   &
                                     Npoints,Ncolumns,Nlevels,Nlvgrid,use_vgrid,x)
      ! Inputs
      logical,intent(in) :: &
@@ -1389,6 +1450,7 @@ subroutine construct_cosp_outputs(Lpctisccp,Lclisccp,&
          Lradarpia,        & ! CLOUDSAT 
          Lwr_occfreq,      & ! CloudSat+MODIS joint diagnostics
          Lcfodd,           & ! CloudSat+MODIS joint diagnostics
+         Ldplrw,           & ! Doppler capability of radar (CloudSat)
          use_vgrid
          
      integer,intent(in) :: &
@@ -1608,6 +1670,29 @@ subroutine construct_cosp_outputs(Lpctisccp,Lclisccp,&
         x % Ninst_rttov = 0
     end if
 
+    ! Doppler capability of radar (CloudSat)
+    if (Ldplrw) then
+       allocate(x%dplrw_Z(Npoints, Nlvdplr, Nlvgrid, 0:2))
+       allocate(x%spwid_Z(Npoints, Nlvspwd, Nlvgrid, 0:2))
+       allocate(x%Zef94_Z(Npoints, NlvdBZe, Nlvgrid, 0:2))
+       
+       allocate(x%dplrw_T(Npoints, Nlvdplr, Nlvtemp, 0:2))
+       allocate(x%spwid_T(Npoints, Nlvspwd, Nlvtemp, 0:2))
+       allocate(x%Zef94_T(Npoints, NlvdBZe, Nlvtemp, 0:2))
+
+       allocate(x%ZefVd_2(Npoints, Nlvdplr, NlvdBZe, 0:2))
+       
+       allocate(x%gcumw(Npoints, Nlevels))
+
+       allocate(x%vfall_Z(Npoints, Nlvdplr, Nlvgrid, 0:2, 3))
+       allocate(x%gridw_Z(Npoints, Nlvdplr, Nlvgrid, 0:2))
+       allocate(x%vfall_T(Npoints, Nlvdplr, Nlvtemp, 0:2, 3))
+       allocate(x%gridw_T(Npoints, Nlvdplr, Nlvtemp, 0:2))
+
+       allocate(x%ZefVf_2(Npoints, Nlvdplr, NlvdBZe, 0:2, 3))
+       
+    end if
+
   end subroutine construct_cosp_outputs
   
   !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -1644,7 +1729,28 @@ subroutine destroy_cospIN(y)
     if (allocated(y%tau_mol_atlid))       deallocate(y%tau_mol_atlid) 
     if (allocated(y%tautot_atlid))        deallocate(y%tautot_atlid)
     if (allocated(y%fracPrecipIce))       deallocate(y%fracPrecipIce)
+
+    !if (allocated(y%rcfg_cloudsat%N_scale_flag))       deallocate(y%rcfg_cloudsat%N_scale_flag)
+    !if (allocated(y%rcfg_cloudsat%Z_scale_flag))       deallocate(y%rcfg_cloudsat%Z_scale_flag)
+    !if (allocated(y%rcfg_cloudsat%Z_scale_added_flag)) deallocate(y%rcfg_cloudsat%Z_scale_added_flag)
+    !if (allocated(y%rcfg_cloudsat%Ze_scaled))          deallocate(y%rcfg_cloudsat%Ze_scaled)
+    !if (allocated(y%rcfg_cloudsat%Zr_scaled))          deallocate(y%rcfg_cloudsat%Zr_scaled)
+    !if (allocated(y%rcfg_cloudsat%kr_scaled))          deallocate(y%rcfg_cloudsat%kr_scaled)
+    !if (allocated(y%rcfg_cloudsat%fc))                 deallocate(y%rcfg_cloudsat%fc)
+    !if (allocated(y%rcfg_cloudsat%rho_eff))            deallocate(y%rcfg_cloudsat%rho_eff)
+    !if (allocated(y%rcfg_cloudsat%base_list))          deallocate(y%rcfg_cloudsat%base_list)
+    !if (allocated(y%rcfg_cloudsat%step_list))          deallocate(y%rcfg_cloudsat%step_list)
+    if (allocated(y%vfall))               deallocate(y%vfall)
+    if (allocated(y%vfsqu))               deallocate(y%vfsqu)
+    if (allocated(y%zehyd))               deallocate(y%zehyd)
+    if (allocated(y%krhyd))               deallocate(y%krhyd)
+    if (allocated(y%vtrm3))               deallocate(y%vtrm3)
+    if (allocated(y%vtrm0))               deallocate(y%vtrm0)
+    if (allocated(y%mmnt3))               deallocate(y%mmnt3)
+    if (allocated(y%mmnt0))               deallocate(y%mmnt0)
+    
     if (associated(y%cfg_rttov))          nullify(y%cfg_rttov)
+
   end subroutine destroy_cospIN
   !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
   ! SUBROUTINE destroy_cospstateIN     
@@ -1685,6 +1791,9 @@ subroutine destroy_cospstateIN(y)
     if (allocated(y%fl_rain))         deallocate(y%fl_rain)
     if (allocated(y%fl_snow))         deallocate(y%fl_snow)
     if (allocated(y%tca))             deallocate(y%tca)
+
+    if (allocated(y%gwvel))           deallocate(y%gwvel)
+    if (allocated(y%gcumf))           deallocate(y%gcumf)
     
   end subroutine destroy_cospstateIN
   
@@ -2004,7 +2113,54 @@ subroutine destroy_cosp_outputs(y)
         deallocate(y%wr_occfreq_ntotal)
         nullify(y%wr_occfreq_ntotal)
      endif
+
+     ! added by DPLRW
+     if (associated(y%dplrw_Z)) then
+        deallocate(y%dplrw_Z) ; nullify(y%dplrw_Z)
+     end if
+     if (associated(y%spwid_Z)) then
+        deallocate(y%spwid_Z) ; nullify(y%spwid_Z)
+     end if
+     if (associated(y%Zef94_Z)) then
+        deallocate(y%Zef94_Z) ; nullify(y%Zef94_Z)
+     end if
+
+     if (associated(y%dplrw_T)) then
+        deallocate(y%dplrw_T) ; nullify(y%dplrw_T)
+     end if
+     if (associated(y%spwid_T)) then
+        deallocate(y%spwid_T) ; nullify(y%spwid_T)
+     end if
+     if (associated(y%Zef94_T)) then
+        deallocate(y%Zef94_T) ; nullify(y%Zef94_T)
+     end if
+
+     if (associated(y%ZefVd_2)) then
+        deallocate(y%ZefVd_2) ; nullify(y%ZefVd_2)
+     end if
+
+     if (associated(y%gcumw)) then
+        deallocate(y%gcumw) ; nullify(y%gcumw)
+     end if
      
+     if (associated(y%vfall_Z)) then
+        deallocate(y%vfall_Z) ; nullify(y%vfall_Z)
+     end if
+     if (associated(y%gridw_Z)) then
+        deallocate(y%gridw_Z) ; nullify(y%gridw_Z)
+     end if
+
+     if (associated(y%vfall_T)) then
+        deallocate(y%vfall_T) ; nullify(y%vfall_T)
+     end if
+     if (associated(y%gridw_T)) then
+        deallocate(y%gridw_T) ; nullify(y%gridw_T)
+     end if
+
+     if (associated(y%ZefVf_2)) then
+        deallocate(y%ZefVf_2) ; nullify(y%ZefVf_2)
+     end if
+
      ! RTTOV multi-instrument
      if (allocated(y%rttov_outputs)) then
          do i=1,y % Ninst_rttov ! Iterate over each instrument
@@ -2073,4 +2229,3 @@ subroutine rttov_cleanup(y)
   end subroutine rttov_cleanup
 
  end program cosp2_test
-
diff --git a/src/cosp.F90 b/src/cosp.F90
index 02ed2a6337..c36cb6b15e 100755
--- a/src/cosp.F90
+++ b/src/cosp.F90
@@ -70,6 +70,7 @@ MODULE MOD_COSP
   USE MOD_COSP_CLOUDSAT_INTERFACE,   ONLY: cosp_cloudsat_init,    cloudsat_IN, &
                                            COSP_ASSIGN_cloudsatIN,COSP_ASSIGN_cloudsatIN_clean
   USE quickbeam,                     ONLY: quickbeam_subcolumn,   quickbeam_column
+  USE quickbeam,                     ONLY: quickbeam_dplrw  ! for DPLRW
   USE MOD_ICARUS,                    ONLY: icarus_subcolumn,      icarus_column
   USE MOD_MISR_SIMULATOR,            ONLY: misr_subcolumn,        misr_column
   USE MOD_LIDAR_SIMULATOR,           ONLY: lidar_subcolumn,       lidar_column
@@ -171,7 +172,7 @@ MODULE MOD_COSP
           isccp_fq  => null()             ! The fraction of the model grid box covered by each of
                                           ! the 49 ISCCP D level cloud types. (%)
 
-     ! MISR outptus
+     ! MISR outputs
      real(wp),dimension(:,:,:),pointer ::   & !
           misr_fq => null()          ! Fraction of the model grid box covered by each of the MISR
                            ! cloud types
@@ -181,7 +182,7 @@ MODULE MOD_COSP
           misr_meanztop => null(), & ! Mean MISR cloud top height
           misr_cldarea => null()     ! Mean MISR cloud cover area
 
-     ! MODIS outptus
+     ! MODIS outputs
      real(wp),pointer,dimension(:) ::      & !
           modis_Cloud_Fraction_Total_Mean => null(),       & ! L3 MODIS retrieved cloud fraction (total)
           modis_Cloud_Fraction_Water_Mean => null(),       & ! L3 MODIS retrieved cloud fraction (liq)
@@ -214,11 +215,24 @@ MODULE MOD_COSP
           cfodd_ntotal => null()       ! # of CFODD (Npoints,CFODD_NDBZE,CFODD_NICOD,CFODD_NCLASS)
      real(wp),dimension(:,:),    pointer :: &
           wr_occfreq_ntotal => null()  ! # of nonprecip/drizzle/precip (Npoints,WR_NREGIME)
+
+     ! RTTOV outputs
      integer                    :: &
          Ninst_rttov
      type(rttov_output),dimension(:),allocatable :: &
          rttov_outputs
 
+     ! Doppler capability of radar (CloudSat/QuickBeam)
+     real(wp),dimension(:,:,:,:),pointer :: &
+          dplrw_Z => null(), spwid_Z => null(), Zef94_Z => null(), &
+          dplrw_T => null(), spwid_T => null(), Zef94_T => null(), &
+          ZefVd_2 => null(), &
+          gridw_Z => null(), gridw_T => null()
+     real(wp),dimension(:,:,:,:,:),pointer :: &
+          vfall_Z => null(), vfall_T => null(), ZefVf_2 => null()
+     real(wp),dimension(:,:),pointer :: &
+          gcumw => null()
+
   end type cosp_outputs
 
 CONTAINS
@@ -276,7 +290,8 @@ function COSP_SIMULATOR(cospIN,cospgridIN,cospOUT,start_idx,stop_idx,debug)
          Lcloudsat_tcc,        & !
          Lcloudsat_tcc2,       & !         
          Llidar_only_freq_cloud, & ! On/Off switch from joint Calipso/Cloudsat product
-         Lcloudsat_modis_wr      ! On/Off switch from joint CloudSat/MODIS warm rain product
+         Lcloudsat_modis_wr,   & ! On/Off switch from joint CloudSat/MODIS warm rain product
+         Ldplrw                  ! On/Off switch from Doppler capability of CLOUDSAT simulator
     logical :: &
          ok_lidar_cfad    = .false.,         &
          ok_lidar_cfad_grLidar532 = .false., & 
@@ -603,6 +618,12 @@ function COSP_SIMULATOR(cospIN,cospgridIN,cospOUT,start_idx,stop_idx,debug)
        Lcloudsat_modis_wr  = .true. ! WR: warm rain product
     endif
 
+    ! for DPLRW
+    if (associated(cospOUT%dplrw_Z)) then
+       Ldplrw = .true.
+       Lcloudsat_subcolumn = .true.
+    end if
+
     !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
     ! 2b) Error Checking
     !     Enforce bounds on input fields. If input field is out-of-bounds, report error
@@ -1493,7 +1514,7 @@ function COSP_SIMULATOR(cospIN,cospgridIN,cospOUT,start_idx,stop_idx,debug)
        if (cospIN % cospswathsIN(3) % N_inst_swaths .gt. 0) then ! Trigger use of swathed arrays
           if (cloudsatIN%Npoints .gt. 0) then
              allocate(temp_cloudsat_cfad_ze(cloudsatIN%Npoints,cloudsat_DBZE_BINS,Nlvgrid),    &
-                      temp_cloudsat_precip_cover(cloudsatIN%Npoints,cloudsat_DBZE_BINS),       &
+                      temp_cloudsat_precip_cover(cloudsatIN%Npoints,nCloudsatPrecipClass),       &
                       temp_cloudsat_pia(cloudsatIN%Npoints))
              call quickbeam_column(cloudsatIN%Npoints, cloudsatIN%Ncolumns, cloudsatIN%Nlevels,            &
                   Nlvgrid, cloudsat_DBZE_BINS, 'cloudsat', cloudsatDBZe, cloudsatZe_non,                   &
@@ -1543,6 +1564,27 @@ function COSP_SIMULATOR(cospIN,cospgridIN,cospOUT,start_idx,stop_idx,debug)
           deallocate(out1D_3)
           nullify(cospOUT%cloudsat_precip_cover)
        endif
+
+       ! for DPLRW
+       if (Ldplrw) then
+          call quickbeam_dplrw(cospIN%Npoints, cospIN%Ncolumns, cospIN%Nlevels, &
+               cospIN%rcfg_cloudsat, &
+               cospgridIN%hgt_matrix, cospgridIN%hgt_matrix_half, cospgridIN%surfelev, &
+               cospgridIN%at, cospgridIN%pfull, &
+               cospgridIN%gwvel, cospgridIN%gcumf, &
+               cospIN%vfall, cospIN%vfsqu, cospIN%zehyd, &
+               cospIN%g_vol_cloudsat, cospIN%krhyd, &
+               cospIN%vtrm3, cospIN%vtrm0, cospIN%mmnt3, cospIN%mmnt0, &
+               cospOUT%gcumw(ij:ik,:), &
+               cospOUT%dplrw_Z(ij:ik,:,:,:), cospOUT%spwid_Z(ij:ik,:,:,:), &
+               cospOUT%Zef94_Z(ij:ik,:,:,:), &
+               cospOUT%dplrw_T(ij:ik,:,:,:), cospOUT%spwid_T(ij:ik,:,:,:), &
+               cospOUT%Zef94_T(ij:ik,:,:,:), &
+               cospOUT%ZefVd_2(ij:ik,:,:,:), &
+               cospOUT%vfall_Z(ij:ik,:,:,:,:), cospOUT%gridw_Z(ij:ik,:,:,:), &
+               cospOUT%vfall_T(ij:ik,:,:,:,:), cospOUT%gridw_T(ij:ik,:,:,:), &
+               cospOUT%ZefVf_2(ij:ik,:,:,:,:) )
+       end if
     endif
 
     ! MODIS
diff --git a/src/cosp_config.F90 b/src/cosp_config.F90
index d100e1f7da..79a57f9b5e 100755
--- a/src/cosp_config.F90
+++ b/src/cosp_config.F90
@@ -358,6 +358,35 @@ MODULE MOD_COSP_CONFIG
     integer, parameter :: &
          cloudsat_preclvl = 39
 
+    ! for DPLRW simulator
+    real(wp),parameter :: &
+         trbl_LS = 0.5, &
+         trbl_CU = 1.0
+    real(wp),parameter :: &
+         lvtemp_MIN = -80., &
+         lvtemp_MAX =  30., &
+         lvtemp_WID =   2., &
+         lvdBZe_MIN = -40., &
+         lvdBZe_MAX =  30., &
+         lvdBZe_WID =   2., &
+         lvdplr_MIN = -6.0, &
+         lvdplr_MAX =  6.0, &
+         lvdplr_WID =  0.2, &
+         lvspwd_MIN =  0.0, &
+         lvspwd_MAX =  5.0, &
+         lvspwd_WID =  0.1
+    integer,parameter :: &
+         Nlvtemp = nint( (lvtemp_MAX-lvtemp_MIN)/lvtemp_WID ), &
+         NlvdBZe = nint( (lvdBZe_MAX-lvdBZe_MIN)/lvdBZe_WID ), &
+         Nlvdplr = nint( (lvdplr_MAX-lvdplr_MIN)/lvdplr_WID ), &
+         Nlvspwd = nint( (lvspwd_MAX-lvspwd_MIN)/lvspwd_WID )
+
+    real(wp),parameter :: &
+         lvtemp_grid(Nlvtemp) = (/( lvtemp_MIN+lvtemp_WID*(i-0.5), i=1,Nlvtemp )/), &
+         lvdBZe_grid(NlvdBZe) = (/( lvdBZe_MIN+lvdBZe_WID*(i-0.5), i=1,NlvdBZe )/), &
+         lvdplr_grid(Nlvdplr) = (/( lvdplr_MIN+lvdplr_WID*(i-0.5), i=1,Nlvdplr )/), &
+         lvspwd_grid(Nlvspwd) = (/( lvspwd_MIN+lvspwd_WID*(i-0.5), i=1,Nlvspwd )/)
+   
     ! ####################################################################################
     ! CLOUDSAT and MODIS joint product information (2018.11.22)
     ! ####################################################################################
diff --git a/src/cosp_stats.F90 b/src/cosp_stats.F90
index 4619bca2ba..fae50d89ab 100755
--- a/src/cosp_stats.F90
+++ b/src/cosp_stats.F90
@@ -130,6 +130,12 @@ MODULE MOD_COSP_STATS
           refl_in,             & ! Surface reflectance (point,channel)    (1)          
           fl_rain,             & ! Precipitation (rain) flux              (kg/m2/s)
           fl_snow                ! Precipitation (snow) flux              (kg/m2/s)
+
+     ! added by DPLRW
+     real(wp),allocatable,dimension(:,:) :: &
+          gwvel,               & ! grid verical velocity                  (m/s)
+          gcumf                  ! grid cumulus mass flux                 (kg/m^2/s)
+
   end type cosp_column_inputs
 
 
@@ -166,6 +172,9 @@ MODULE MOD_COSP_STATS
      real(wp),dimension(maxhclass,nd,nRe_types)     :: fc, rho_eff
      real(wp),dimension(Re_MAX_BIN)                 :: base_list,step_list
 
+     ! added by DPLRW
+     real(wp),dimension(maxhclass,mt_ntt,nRe_types) :: vf_scaled,vq_scaled,v3_scaled,v0_scaled,m3_scaled,m0_scaled
+
   end type radar_cfg
 
   ! ######################################################################################
@@ -203,6 +212,9 @@ MODULE MOD_COSP_STATS
           z_vol_cloudsat,      & ! Effective reflectivity factor (mm^6/m^3)
           kr_vol_cloudsat,     & ! Attenuation coefficient hydro (dB/km) 
           g_vol_cloudsat         ! Attenuation coefficient gases (dB/km)
+     real(wp),allocatable,dimension(:,:,:,:) :: &
+          vfall, vfsqu, zehyd, krhyd, &    ! for DPLRW, each hydrometeor
+          vtrm3, vtrm0, mmnt3, mmnt0
      real(wp),allocatable,dimension(:,:) :: &
           beta_mol_calipso,    & ! Lidar molecular backscatter coefficient (calipso @ 532nm)
           beta_mol_grLidar532, & ! Lidar molecular backscatter coefficient (ground-lidar @ 532nm) 
diff --git a/src/simulator/cosp_cloudsat_interface.F90 b/src/simulator/cosp_cloudsat_interface.F90
index ff61417104..c4384c4cb0 100644
--- a/src/simulator/cosp_cloudsat_interface.F90
+++ b/src/simulator/cosp_cloudsat_interface.F90
@@ -32,7 +32,7 @@
 ! %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 MODULE MOD_COSP_CLOUDSAT_INTERFACE
   USE COSP_KINDS,      ONLY: wp
-  USE quickbeam,       ONLY: quickbeam_init,Re_MAX_BIN,Re_BIN_LENGTH, &                                                                                                                                                       
+  USE quickbeam,       ONLY: quickbeam_init,Re_MAX_BIN,Re_BIN_LENGTH, &
                              maxhclass, nRe_types, nd, mt_ntt
   use mod_cosp_stats,  ONLY: radar_cfg,compute_orbitmasks,cosp_optical_inputs,cosp_column_inputs
   IMPLICIT NONE
@@ -49,7 +49,7 @@ MODULE MOD_COSP_CLOUDSAT_INTERFACE
      temp_z_vol_cloudsat,   &
      temp_kr_vol_cloudsat,  &
      temp_g_vol_cloudsat
-
+  
   !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
   ! TYPE cloudsat_IN
   !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
diff --git a/src/simulator/quickbeam/quickbeam.F90 b/src/simulator/quickbeam/quickbeam.F90
index 06d78b9a82..50a67e0829 100644
--- a/src/simulator/quickbeam/quickbeam.F90
+++ b/src/simulator/quickbeam/quickbeam.F90
@@ -60,6 +60,7 @@ module quickbeam
                                   maxhclass,nRe_types,nd,mt_ntt,Re_BIN_LENGTH,Re_MAX_BIN,   &
                                   dmin,dmax,radar_cfg
   implicit none
+  
 contains
   ! ######################################################################################
   ! SUBROUTINE quickbeam_subcolumn
@@ -504,6 +505,371 @@ subroutine cloudsat_precipOccurence(Npoints, Ncolumns, llm, Nhydro, Ze_out, Ze_n
 
   end subroutine cloudsat_precipOccurence
   
+  ! ##############################################################################################
+  ! ##############################################################################################
+  subroutine quickbeam_dplrw(Npoints, Ncolumns, Nlevels, rcfg, &
+                             hgt_matrix, hgt_matrix_half, surfelev, &
+                             at, pfull, &
+                             gwvel, gcumf, &
+                             vfall_in, vfsqu_in, zehyd_in, &
+                             g_vol, krhyd_in, &
+                             vtrm3_in, vtrm0_in, mmnt3_in, mmnt0_in, &
+                             gcumw, &
+                             dplrw_Z, spwid_Z, Zef94_Z, &
+                             dplrw_T, spwid_T, Zef94_T, &
+                             ZefVd_2, &
+                             vfall_Z, gridw_Z, &
+                             vfall_T, gridw_T, ZefVf_2 )
+    USE MOD_COSP_CONFIG,      ONLY: Nlvgrid, N_HYDRO, &
+                                    trbl_LS, trbl_CU, &
+                                    Nlvtemp, lvtemp_WID, lvtemp_MAX, lvtemp_MIN, &
+                                    NlvdBZe, lvdBZe_WID, lvdBZe_MAX, lvdBZe_MIN, &
+                                    Nlvdplr, lvdplr_WID, lvdplr_MAX, lvdplr_MIN, &
+                                    Nlvspwd, lvspwd_WID, lvspwd_MAX, lvspwd_MIN
+
+    integer,intent(in) :: Npoints, Ncolumns, Nlevels
+    type(radar_cfg),intent(in) :: rcfg
+    real(wp),intent(in),dimension(npoints,nlevels)   :: hgt_matrix, hgt_matrix_half, at, pfull
+    real(wp),intent(in),dimension(npoints) :: surfelev
+    real(wp),intent(in),dimension(npoints,nlevels) :: gwvel,gcumf
+    logical,dimension(npoints,ncolumns,nlevels,N_HYDRO) :: flags
+    real(wp),intent(in),dimension(npoints,ncolumns,nlevels,N_HYDRO) :: vfall_in, vfsqu_in, zehyd_in
+    real(wp),dimension(npoints,ncolumns,nlevels,N_HYDRO) :: vfall, vfsqu, zehyd
+    real(wp),intent(in),dimension(npoints,ncolumns,nlevels,N_HYDRO) :: vtrm3_in,vtrm0_in,mmnt3_in,mmnt0_in
+    real(wp),dimension(npoints,ncolumns,nlevels,N_HYDRO) :: vtrm3,vtrm0,mmnt3,mmnt0
+    !===
+    real(wp),intent(out),dimension(npoints,nlevels) :: gcumw
+    !===
+    real(wp),dimension(npoints,nlevels+1) :: hgt_bnd
+    real(wp),intent(in),dimension(npoints,nlevels) :: g_vol
+    real(wp),dimension(npoints,nlevels) :: att_gas
+    real(wp),intent(in),dimension(npoints,ncolumns,nlevels,N_HYDRO) :: krhyd_in
+    real(wp),dimension(npoints,ncolumns,nlevels,N_HYDRO) :: krhyd
+    real(wp),dimension(npoints,ncolumns,nlevels,0:2) :: att_hyd
+    real(wp) :: krLS, krCU
+    integer :: sta,end,dif
+    !===
+    real(wp),intent(out),dimension(npoints,Nlvdplr,Nlvgrid,0:2) :: dplrw_Z
+    real(wp),intent(out),dimension(npoints,Nlvspwd,Nlvgrid,0:2) :: spwid_Z
+    real(wp),intent(out),dimension(npoints,NlvdBZe,Nlvgrid,0:2) :: Zef94_Z
+    real(wp),intent(out),dimension(npoints,Nlvdplr,Nlvtemp,0:2) :: dplrw_T
+    real(wp),intent(out),dimension(npoints,Nlvspwd,Nlvtemp,0:2) :: spwid_T
+    real(wp),intent(out),dimension(npoints,NlvdBZe,Nlvtemp,0:2) :: Zef94_T
+    real(wp),intent(out),dimension(npoints,Nlvdplr,NlvdBZe,0:2) :: ZefVd_2
+    !===
+    real(wp),intent(out),dimension(npoints,Nlvdplr,Nlvgrid,0:2,3) :: vfall_Z
+    real(wp),intent(out),dimension(npoints,Nlvdplr,Nlvgrid,0:2)   :: gridw_Z
+    real(wp),intent(out),dimension(npoints,Nlvdplr,Nlvtemp,0:2,3) :: vfall_T
+    real(wp),intent(out),dimension(npoints,Nlvdplr,Nlvtemp,0:2)   :: gridw_T
+    real(wp),intent(out),dimension(npoints,Nlvdplr,NlvdBZe,0:2,3) :: ZefVf_2
+    !===
+    integer :: dbin, sbin, zbin, tbin, hbin, vbin, wbin
+    integer :: i,j,k,n,is,js,id,tp
+    real(wp) :: zesum(0:2),vdmn(0:2),swmn(0:2)
+    real(wp) :: vfmn(0:2,3),gwmn(0:2),m0sum(0:2),m3sum(0:2)
+    real(wp) :: intp,es,qs,Tvs
+    real(wp),dimension(Npoints,Nlvdplr,Nlevels,0:2) :: workD
+    real(wp),dimension(Npoints,Nlvspwd,Nlevels,0:2) :: workS
+    real(wp),dimension(Npoints,NlvdBZe,Nlevels,0:2) :: workZ
+    real(wp),dimension(Npoints,Nlvdplr,Nlevels,0:2,3) :: workV
+    real(wp),dimension(Npoints,Nlvdplr,Nlevels,0:2) :: workW
+
+    integer,parameter :: &
+         I_LSCLIQ = 1, & ! Large-scale (stratiform) liquid 
+         I_LSCICE = 2, & ! Large-scale (stratiform) ice
+         I_LSRAIN = 3, & ! Large-scale (stratiform) rain
+         I_LSSNOW = 4, & ! Large-scale (stratiform) snow
+         I_CVCLIQ = 5, & ! Convective liquid
+         I_CVCICE = 6, & ! Convective ice
+         I_CVRAIN = 7, & ! Convective rain
+         I_CVSNOW = 8, & ! Convective snow
+         I_LSGRPL = 9    ! Large-scale (stratiform) groupel
+    integer,parameter :: &
+         I_ALC = 0, &    ! all (stratiform + convective) clouds
+         I_LSC = 1, &    ! stratiform clouds
+         I_CVC = 2       ! convective clouds
+
+    ! @@@@@ convert: convective mass flux -> cumulus vertical velocity
+    do k=1,nlevels
+       do i=1,npoints
+          if (at(i,k) < 0._wp .or. pfull(i,k) < 0._wp) then
+             gcumw(i,k) = 0._wp
+             flags(i,:,k,:) = .false.
+          else
+             es = 611.*exp( (2.5e+6 + 3.4e+5*(1-sign(1._wp,at(i,k)-273.15))/2._wp)/461. * (1./273.15 - 1./at(i,k)) )
+             qs = (es/pfull(i,k)) * (287./461.)
+             
+             Tvs = at(i,k)*( (1+qs/(287./461.))/(1+qs) )
+             gcumw(i,k) = gcumf(i,k) * (287.*Tvs/pfull(i,k))
+             
+          end if
+       end do
+    end do
+
+    ! @@@@@ flag check @@@@@
+    flags = .true.
+    do j=1,ncolumns
+       where (gwvel < R_UNDEF * 0.1_wp)
+          flags(:,j,:,I_LSCLIQ) = .false.
+          flags(:,j,:,I_LSCICE) = .false.
+          flags(:,j,:,I_LSRAIN) = .false.
+          flags(:,j,:,I_LSSNOW) = .false.
+          flags(:,j,:,I_LSGRPL) = .false.
+       end where
+
+       where (gcumf < R_UNDEF * 0.1_wp)
+          flags(:,j,:,I_CVCLIQ) = .false.
+          flags(:,j,:,I_CVCICE) = .false.
+          flags(:,j,:,I_CVRAIN) = .false.
+          flags(:,j,:,I_CVSNOW) = .false.
+       end where
+    end do
+    where (flags)
+       vfall = vfall_in ; vfsqu = vfsqu_in ; zehyd = zehyd_in ; krhyd = krhyd_in
+       vtrm3 = vtrm3_in ; vtrm0 = vtrm0_in ; mmnt3 = mmnt3_in ; mmnt0 = mmnt0_in
+    elsewhere
+       vfall = 0._wp    ; vfsqu = 0._wp    ; zehyd = 0._wp    ; krhyd = 0._wp
+       vtrm3 = 0._wp    ; vtrm0 = 0._wp    ; mmnt3 = 0._wp    ; mmnt0 = 0._wp
+    end where
+
+    hgt_bnd(:,1:Nlevels) = hgt_matrix_half
+    hgt_bnd(:,Nlevels+1) = surfelev
+
+    ! @@@@@ attenuation: gas, LShyd, and CUhyd
+    if (rcfg%radar_at_layer_one) then
+       dif = 1  ; sta = 1 ; end = nlevels
+    else
+       dif = -1 ; sta = nlevels ; end = 1
+    end if
+
+    att_gas = 0._wp
+    do k=sta,end,dif
+       do i=1,npoints
+          att_gas(i,k) = att_gas(i,k) + &
+               g_vol(i,k) * (hgt_bnd(i,k+1)-hgt_bnd(i,k))/1000._wp
+       end do
+    end do
+
+    att_hyd = 0._wp
+    do k=sta,end,dif
+       do j=1,ncolumns
+          do i=1,npoints
+             krLS = krhyd(i,j,k,I_LSCLIQ)+krhyd(i,j,k,I_LSCICE)+&
+                     krhyd(i,j,k,I_LSRAIN)+krhyd(i,j,k,I_LSSNOW)+krhyd(i,j,k,I_LSGRPL)
+             att_hyd(i,j,k,I_LSC) = att_hyd(i,j,k,I_LSC) + &
+                  krLS * (hgt_bnd(i,k+1)-hgt_bnd(i,k))/1000._wp
+
+             krCU = krhyd(i,j,k,I_CVCLIQ)+krhyd(i,j,k,I_CVCICE)+&
+                     krhyd(i,j,k,I_CVRAIN)+krhyd(i,j,k,I_CVSNOW)
+             att_hyd(i,j,k,I_CVC) = att_hyd(i,j,k,I_CVC) + &
+                  krCU * (hgt_bnd(i,k+1)-hgt_bnd(i,k))/1000._wp
+
+             att_hyd(i,j,k,I_ALC) = att_hyd(i,j,k,I_ALC) +  &
+                  (krLS+krCU) * (hgt_bnd(i,k+1)-hgt_bnd(i,k))/1000._wp
+          end do
+       end do
+    end do
+
+    ! @@@@@ Initialiation
+    dplrw_Z = 0._wp ; spwid_Z = 0._wp ; Zef94_Z = 0._wp ; vfall_Z = 0._wp ; gridw_Z = 0._wp
+    dplrw_T = 0._wp ; spwid_T = 0._wp ; Zef94_T = 0._wp ; vfall_T = 0._wp ; gridw_T = 0._wp
+    ZefVd_2 = 0._wp ; ZefVd_2 = 0._wp
+    workD = 0._wp ; workS = 0._wp ; workZ = 0._wp ; workV = 0._wp ; workW = 0._wp
+
+    ! @@@@@ statistics
+          do i=1,npoints
+       do j=1,ncolumns
+    do k=1,nlevels
+             tbin = floor( ( (at(i,k)-273.15) - lvtemp_MIN)/lvtemp_WID ) + 1
+             hbin = k
+             if (tbin < 1 .or. tbin > Nlvtemp) cycle
+
+             ! radar parameters: for LS and CU
+             zesum = 0._wp ; vdmn = 0._wp ; swmn = 0._wp
+             do tp=1,N_HYDRO
+                if (tp==I_LSCLIQ .or. tp==I_LSCICE .or. tp==I_LSRAIN .or. tp==I_LSSNOW .or. tp==I_LSGRPL) then
+                   zesum(I_LSC) = zesum(I_LSC) + zehyd(i,j,k,tp)
+                   vdmn(I_LSC)  = vdmn(I_LSC)  + vfall(i,j,k,tp)*zehyd(i,j,k,tp)
+                   swmn(I_LSC)  = swmn(I_LSC)  + vfsqu(i,j,k,tp)*zehyd(i,j,k,tp)
+                else if (tp==I_CVCLIQ .or. tp==I_CVCICE .or. tp==I_CVRAIN .or. tp==I_CVSNOW) then
+                   zesum(I_CVC) = zesum(I_CVC) + zehyd(i,j,k,tp)
+                   vdmn(I_CVC)  = vdmn(I_CVC)  + vfall(i,j,k,tp)*zehyd(i,j,k,tp)
+                   swmn(I_CVC)  = swmn(I_CVC)  + vfsqu(i,j,k,tp)*zehyd(i,j,k,tp)
+                end if
+             end do
+
+             zesum(I_ALC) = zesum(I_LSC) + zesum(I_CVC)
+             if (zesum(I_LSC) <= 0 .and. zesum(I_CVC) <= 0.) cycle
+             
+             vdmn(I_ALC) = ( vdmn(I_LSC)+gwvel(i,k)*zesum(I_LSC) + vdmn(I_CVC)+gcumw(i,k)*zesum(I_CVC) ) / (zesum(I_LSC)+zesum(I_CVC))
+             swmn(I_ALC) = ( swmn(I_LSC) + 2*gwvel(i,k)*vdmn(I_LSC) + gwvel(i,k)**2*zesum(I_LSC) + &
+                             swmn(I_CVC) + 2*gcumw(i,k)*vdmn(I_CVC) + gcumw(i,k)**2*zesum(I_CVC) ) / (zesum(I_LSC)+zesum(I_CVC))
+             swmn(I_ALC) = swmn(I_ALC) - vdmn(I_ALC)**2
+             swmn(I_ALC) = swmn(I_ALC) + &
+                           ( trbl_LS**2*zesum(I_LSC) + trbl_CU**2*zesum(I_CVC) ) / (zesum(I_LSC)+zesum(I_CVC))
+             swmn(I_ALC) = sqrt( swmn(I_ALC) )
+
+             swmn(I_LSC) = sqrt(trbl_LS**2 + swmn(I_LSC)/zesum(I_LSC)-vdmn(I_LSC)**2/zesum(I_LSC)**2)
+             vdmn(I_LSC) = vdmn(I_LSC)/zesum(I_LSC) + gwvel(i,k)
+
+             swmn(I_CVC) = sqrt(trbl_CU**2 + swmn(I_CVC)/zesum(I_CVC)-vdmn(I_CVC)**2/zesum(I_CVC)**2)
+             vdmn(I_CVC) = vdmn(I_CVC)/zesum(I_CVC) + gcumw(i,k)
+
+             ! ~~~
+             vfmn = 0._wp ; gwmn = 0._wp ; m3sum = 0._wp ; m0sum = 0._wp
+             do tp=1,N_HYDRO
+                if (tp==I_LSCLIQ .or. tp==I_LSCICE .or. tp==I_LSRAIN .or. tp==I_LSSNOW .or. tp==I_LSGRPL) then
+                   vfmn(I_LSC,1) = vfmn(I_LSC,1) + vfall(i,j,k,tp)*zehyd(i,j,k,tp)
+                   vfmn(I_LSC,2) = vfmn(I_LSC,2) + vtrm3(i,j,k,tp)*mmnt3(i,j,k,tp)
+                   vfmn(I_LSC,3) = vfmn(I_LSC,3) + vtrm0(i,j,k,tp)*mmnt0(i,j,k,tp)
+
+                   m3sum(I_LSC)  = m3sum(I_LSC)  + mmnt3(i,j,k,tp)
+                   m0sum(I_LSC)  = m0sum(I_LSC)  + mmnt0(i,j,k,tp)
+
+                   gwmn(I_ALC)   = gwmn(I_ALC)   + gwvel(i,k)*zehyd(i,j,k,tp)
+                else if (tp==I_CVCLIQ .or. tp==I_CVCICE .or. tp==I_CVRAIN .or. tp==I_CVSNOW) then
+                   vfmn(I_CVC,1) = vfmn(I_CVC,1) + vfall(i,j,k,tp)*zehyd(i,j,k,tp)
+                   vfmn(I_CVC,2) = vfmn(I_CVC,2) + vtrm3(i,j,k,tp)*mmnt3(i,j,k,tp)
+                   vfmn(I_CVC,3) = vfmn(I_CVC,3) + vtrm0(i,j,k,tp)*mmnt0(i,j,k,tp)
+
+                   m3sum(I_CVC)  = m3sum(I_CVC)  + mmnt3(i,j,k,tp)
+                   m0sum(I_CVC)  = m0sum(I_CVC)  + mmnt0(i,j,k,tp)
+
+                   gwmn(I_ALC)   = gwmn(I_ALC)   + gcumw(i,k)*zehyd(i,j,k,tp)
+                end if
+
+                vfmn(I_ALC,1) = vfmn(I_ALC,1) + vfall(i,j,k,tp)*zehyd(i,j,k,tp)
+                vfmn(I_ALC,2) = vfmn(I_ALC,2) + vtrm3(i,j,k,tp)*mmnt3(i,j,k,tp)
+                vfmn(I_ALC,3) = vfmn(I_ALC,3) + vtrm0(i,j,k,tp)*mmnt0(i,j,k,tp)
+
+                m3sum(I_ALC)  = m3sum(I_ALC)  + mmnt3(i,j,k,tp)
+                m0sum(I_ALC)  = m0sum(I_ALC)  + mmnt0(i,j,k,tp)
+             end do
+             do tp=0,2
+                vfmn(tp,1) = vfmn(tp,1)/zesum(tp)
+                vfmn(tp,2) = vfmn(tp,2)/m3sum(tp)
+                vfmn(tp,3) = vfmn(tp,3)/m0sum(tp)
+             end do
+             gwmn(I_ALC) = gwmn(I_ALC)/zesum(I_ALC)
+             gwmn(I_LSC) = gwvel(i,k)
+             gwmn(I_CVC) = gcumw(i,k)
+
+
+             do tp=0,2
+                if (zesum(tp) <= 0._wp) cycle
+
+                !if (tp > 0) write(*,*) 'test 6: ',tp,vfmn(tp,1),zesum(tp)
+                !if (tp > 0) write(*,*) 'test 3: ',tp,vfmn(tp,2),m3sum(tp)
+                !if (tp > 0) write(*,*) 'test 0: ',tp,vfmn(tp,3),m0sum(tp)
+
+                !if (tp==1) write(*,'(a,3(x,I5))')    'test grd: ',i,j,k
+                !if (tp==1) write(*,'(a,8(x,ES10.3))') 'test v6 : ',vfall(i,j,k,1:8)
+                !if (tp==1) write(*,'(a,8(x,ES10.3))') 'test v3 : ',vtrm3(i,j,k,1:8)
+                !if (tp==1) write(*,'(a,8(x,ES10.3))') 'test v0 : ',vtrm0(i,j,k,1:8)
+
+                zbin = floor( ((10*log10(zesum(tp))-att_gas(i,k)-att_hyd(i,j,k,I_ALC)) - lvdBZe_MIN)/lvdBZe_WID ) + 1
+                if (zbin < 1      ) cycle
+                if (zbin > NlvdBZe) cycle
+
+                dbin = floor( (vdmn(tp) - lvdplr_MIN)/lvdplr_WID ) + 1
+                if (dbin < 1      ) dbin = 1
+                if (dbin > Nlvdplr) dbin = Nlvdplr
+
+                sbin = floor( (swmn(tp) - lvspwd_MIN)/lvspwd_WID ) + 1
+                if (sbin < 1      ) sbin = 1
+                if (sbin > Nlvspwd) sbin = Nlvspwd
+
+                workZ(i,zbin,hbin,tp) = workZ(i,zbin,hbin,tp) + 1._wp
+                workD(i,dbin,hbin,tp) = workD(i,dbin,hbin,tp) + 1._wp
+                workS(i,sbin,hbin,tp) = workS(i,sbin,hbin,tp) + 1._wp
+
+                Zef94_T(i,zbin,tbin,tp) = Zef94_T(i,zbin,tbin,tp) + 1._wp
+                dplrw_T(i,dbin,tbin,tp) = dplrw_T(i,dbin,tbin,tp) + 1._wp
+                spwid_T(i,sbin,tbin,tp) = spwid_T(i,sbin,tbin,tp) + 1._wp
+
+                ZefVd_2(i,dbin,zbin,tp) = ZefVd_2(i,dbin,zbin,tp) + 1._wp
+
+                ! ~~~
+                do id=1,3
+                   vbin = floor( (vfmn(tp,id) - lvdplr_MIN)/lvdplr_WID ) + 1
+                   if (vbin < 1      ) vbin = 1
+                   if (vbin > Nlvdplr) vbin = Nlvdplr
+                   workV(i,vbin,hbin,tp,id)   = workV(i,vbin,hbin,tp,id)   + 1._wp
+                   vfall_T(i,vbin,tbin,tp,id) = vfall_T(i,vbin,tbin,tp,id) + 1._wp
+                   ZefVf_2(i,vbin,zbin,tp,id) = ZefVf_2(i,vbin,zbin,tp,id) + 1._wp
+                end do
+
+                wbin = floor( (gwmn(tp) - lvdplr_MIN)/lvdplr_WID ) + 1
+                if (wbin < 1      ) wbin = 1
+                if (wbin > Nlvdplr) wbin = Nlvdplr
+                workW(i,wbin,hbin,tp)   = workW(i,wbin,hbin,tp)   + 1._wp
+                gridw_T(i,wbin,tbin,tp) = gridw_T(i,wbin,tbin,tp) + 1._wp
+                
+             end do
+             
+          end do
+       end do
+    end do
+
+    ! @@@@@ vertical grid conversion
+    if (use_vgrid) then
+       do tp=0,2
+          call cosp_change_vertical_grid(npoints,Nlvdplr,Nlevels, &
+               hgt_matrix(:,nlevels:1:-1),hgt_matrix_half(:,nlevels:1:-1),workD(:,1:Nlvdplr,nlevels:1:-1,tp), &
+               Nlvgrid,vgrid_zl(Nlvgrid:1:-1),vgrid_zu(Nlvgrid:1:-1),dplrw_Z(:,1:Nlvdplr,1:Nlvgrid,tp))
+
+          call cosp_change_vertical_grid(npoints,Nlvspwd,Nlevels, &
+               hgt_matrix(:,nlevels:1:-1),hgt_matrix_half(:,nlevels:1:-1),workS(:,1:Nlvspwd,nlevels:1:-1,tp), &
+               Nlvgrid,vgrid_zl(Nlvgrid:1:-1),vgrid_zu(Nlvgrid:1:-1),spwid_Z(:,1:Nlvspwd,1:Nlvgrid,tp))
+
+          call cosp_change_vertical_grid(npoints,NlvdBZe,Nlevels, &
+               hgt_matrix(:,nlevels:1:-1),hgt_matrix_half(:,nlevels:1:-1),workZ(:,1:NlvdBZe,nlevels:1:-1,tp), &
+               Nlvgrid,vgrid_zl(Nlvgrid:1:-1),vgrid_zu(Nlvgrid:1:-1),Zef94_Z(:,1:NlvdBZe,1:Nlvgrid,tp))
+
+          ! ~~~
+          do id=1,3
+             call cosp_change_vertical_grid(npoints,Nlvdplr,Nlevels, &
+                  hgt_matrix(:,nlevels:1:-1),hgt_matrix_half(:,nlevels:1:-1),workV(:,1:Nlvdplr,nlevels:1:-1,tp,id), &
+                  Nlvgrid,vgrid_zl(Nlvgrid:1:-1),vgrid_zu(Nlvgrid:1:-1),vfall_Z(:,1:Nlvdplr,1:Nlvgrid,tp,id))
+          end do
+
+          call cosp_change_vertical_grid(npoints,Nlvdplr,Nlevels, &
+               hgt_matrix(:,nlevels:1:-1),hgt_matrix_half(:,nlevels:1:-1),workW(:,1:Nlvdplr,nlevels:1:-1,tp), &
+               Nlvgrid,vgrid_zl(Nlvgrid:1:-1),vgrid_zu(Nlvgrid:1:-1),gridw_Z(:,1:Nlvdplr,1:Nlvgrid,tp))
+
+       end do
+       where (dplrw_Z < 0._wp)
+          dplrw_Z = 0._wp
+       end where
+       where (spwid_Z < 0._wp)
+          spwid_Z = 0._wp
+       end where
+       where (Zef94_Z < 0._wp)
+          Zef94_Z = 0._wp
+       end where
+       where (vfall_Z < 0._wp)
+          vfall_Z = 0._wp
+       end where
+       where (gridw_Z < 0._wp)
+          gridw_Z = 0._wp
+       end where
+
+    else
+       do tp=0,2
+          dplrw_Z(:,1:Nlvdplr,1:Nlvgrid,tp) = workD(:,1:Nlvdplr,Nlevels:1:-1,tp)
+          spwid_Z(:,1:Nlvspwd,1:Nlvgrid,tp) = workS(:,1:Nlvspwd,Nlevels:1:-1,tp)
+          Zef94_Z(:,1:NlvdBZe,1:Nlvgrid,tp) = workZ(:,1:NlvdBZe,Nlevels:1:-1,tp)
+          
+          do id=1,3
+             vfall_Z(:,1:Nlvdplr,1:Nlvgrid,tp,id) = workV(:,1:Nlvdplr,Nlevels:1:-1,tp,id)
+          end do
+          gridw_Z(:,1:Nlvdplr,1:Nlvgrid,tp) = workW(:,1:Nlvdplr,Nlevels:1:-1,tp)
+
+       end do
+    end if
+    
+  end subroutine quickbeam_dplrw
+
   ! ##############################################################################################
   ! ##############################################################################################
   subroutine load_scale_LUTs(rcfg)
diff --git a/subsample_and_optics_example/optics/cosp_utils.F90 b/subsample_and_optics_example/optics/cosp_utils.F90
old mode 100644
new mode 100755
index 98c26cd5be..09b9461ef8
--- a/subsample_and_optics_example/optics/cosp_utils.F90
+++ b/subsample_and_optics_example/optics/cosp_utils.F90
@@ -34,56 +34,113 @@
 MODULE MOD_COSP_UTILS
   USE COSP_KINDS, ONLY: wp
   USE MOD_COSP_CONFIG
+  USE mod_quickbeam_optics, only: size_distribution
+  use mod_cosp_error, only: errorMessage
+  use cosp_math_constants, only: pi
   IMPLICIT NONE
 
 CONTAINS
 !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 !------------------- SUBROUTINE COSP_PRECIP_MXRATIO --------------
 !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-SUBROUTINE COSP_PRECIP_MXRATIO(Npoints,Nlevels,Ncolumns,p,T,prec_frac,prec_type, &
-                          n_ax,n_bx,alpha_x,c_x,d_x,g_x,a_x,b_x,gamma1,gamma2,gamma3,gamma4, &
-                          flux,mxratio,reff)
-
+  SUBROUTINE COSP_PRECIP_MXRATIO(Npoints,Nlevels,Ncolumns, &
+                                 p,T,prec_frac,prec_type, index, sd, &
+                                 flux, mxratio, reff)
     ! Input arguments, (IN)
-    integer,intent(in) :: Npoints,Nlevels,Ncolumns
+    integer,intent(in) :: Npoints,Nlevels,Ncolumns,index
     real(wp),intent(in),dimension(Npoints,Nlevels) :: p,T,flux
     real(wp),intent(in),dimension(Npoints,Ncolumns,Nlevels) :: prec_frac
-    real(wp),intent(in) :: n_ax,n_bx,alpha_x,c_x,d_x,g_x,a_x,b_x,gamma1,gamma2,gamma3,gamma4,prec_type
+    real(wp),intent(in) :: prec_type
+    type(size_distribution),intent(in) :: sd
     ! Input arguments, (OUT)
     real(wp),intent(out),dimension(Npoints,Ncolumns,Nlevels) :: mxratio
     real(wp),intent(inout),dimension(Npoints,Ncolumns,Nlevels) :: reff
     ! Local variables
-    integer :: i,j,k
+    integer  :: i,j,k
     real(wp) :: sigma,one_over_xip1,xi,rho0,rho,lambda_x,gamma_4_3_2,delta
+    real(wp) :: apm,bpm,mu,D0,Dm  ! modified gamma DSD
+    real(wp) :: lambda,N0         ! exponential DSD
+    real(wp) :: avf,bvf,vscs_fct  ! fall velocity params
     
-    mxratio = 0.0
+    !!! single-moment bulk microphysics is assumed !!!
+
+    if (sd%apm(index) > 0) then
+       apm = sd%apm(index)
+       bpm = sd%bpm(index)
+    else if (sd%rho(index) > 0) then
+       apm = sd%rho(index)*pi/6._wp
+       bpm = 3._wp
+    else
+       call errorMessage('!!! unexpected sd, at COSP_PRECIP_MXRATIO !!!')
+    end if
+
+    ! fall velocity params
+    select case(sd%ftype(index))
+    case(1)
+       ! vf = a * D**b
+       avf = sd%f1(index)
+       bvf = sd%f2(index)
+    case(2)
+       ! PL08 formulation
+       call errorMessage('!!! not implemented yet, PL08 formulation, at COSP_PRECIP_MXRATIO')
+    end select
+
+    ! size distribution params
+    select case(sd%dtype(index))
+    case(1)
+       Dm = sd%p2(index)
+       mu = sd%p3(index)
+       if (Dm < 0 .or. mu < 0) then
+          call errorMessage('!!! mod.gamma DSD requires mean D and mu, at COSP_PRECIP_MXRATIO')
+       end if
+    case(2)
+       N0 = sd%p1(index)
+       if (N0 < 0) then
+          call errorMessage('!!! exp. DSD requires N0, at COSP_PRECIP_MXRATIO')
+       end if
+    case default
+       call errorMessage('!!! not implemented yet, sd%dtype=3,4,5, at COSP_PRECIP_MXRATIO')
+    end select
 
-    if (n_ax >= 0.0) then ! N_ax is used to control which hydrometeors need to be computed
-        xi      = d_x/(alpha_x + b_x - n_bx + 1._wp)
-        rho0    = 1.29_wp
-        sigma   = (gamma2/(gamma1*c_x))*(n_ax*a_x*gamma2)**xi
-        one_over_xip1 = 1._wp/(xi + 1._wp)
-        gamma_4_3_2 = 0.5_wp*gamma4/gamma3
-        delta = (alpha_x + b_x + d_x - n_bx + 1._wp)
-        
-        do k=1,Nlevels
-            do j=1,Ncolumns
-                do i=1,Npoints
-                    if ((prec_frac(i,j,k)==prec_type).or.(prec_frac(i,j,k)==3.)) then
-                        rho = p(i,k)/(287.05_wp*T(i,k))
-                        mxratio(i,j,k)=(flux(i,k)*((rho/rho0)**g_x)*sigma)**one_over_xip1
-                        mxratio(i,j,k)=mxratio(i,j,k)/rho
-                        ! Compute effective radius
-                        if ((reff(i,j,k) <= 0._wp).and.(flux(i,k) /= 0._wp)) then
-                           lambda_x = (a_x*c_x*((rho0/rho)**g_x)*n_ax*gamma1/flux(i,k))**(1._wp/delta)
-                           reff(i,j,k) = gamma_4_3_2/lambda_x
-                        endif
-                    endif
-                enddo
-            enddo
-        enddo
-    endif
-END SUBROUTINE COSP_PRECIP_MXRATIO
+    do k=1,Nlevels
+       do j=1,Ncolumns
+          do i=1,Npoints
+             if ((prec_frac(i,j,k)/=prec_type).and.(prec_frac(i,j,k)/=3.)) cycle
+             rho0 = 1.29_wp
+             rho  = p(i,k)/(287.05_wp*T(i,k))
+             vscs_fct = merge(sqrt(rho0/rho),1._wp,sd%fvscs(index)==1)
 
+             select case(sd%dtype(index))
+             case(1) ! modified gamma
+                if (flux(i,k) > 1.D-20) then
+                   D0 = Dm*gamma(mu)/gamma(mu+1._wp) * 1.D-6  ! um -> m
+                   mxratio(i,j,k) = flux(i,k)/vscs_fct/avf/D0**bvf * gamma(mu+bpm)/gamma(mu+bvf+bpm) / rho
+                   if (reff(i,j,k) < 1.D-20) then
+                      reff(i,j,k) = D0/2._wp*gamma(mu+3._wp)/gamma(mu+2._wp)
+                   end if
+                else
+                   mxratio(i,j,k) = 0._wp ; reff(i,j,k) = 0._wp
+                end if
+
+             case(2) ! exponential
+                if (flux(i,k) > 1.D-20) then
+                   lambda = (vscs_fct*apm*avf*N0*gamma(1._wp+bpm+bvf)/flux(i,k))**(1._wp/(1._wp+bpm+bvf))
+                   mxratio(i,j,k) = flux(i,k)/vscs_fct/avf*lambda**bvf*gamma(1._wp+bpm)/gamma(1._wp+bpm+bvf)
+                   if (reff(i,j,k) < 1.D-20) then
+                      reff(i,j,k) = 0.5_wp/lambda*gamma(4._wp)/gamma(3._wp)
+                   end if
+                else
+                   mxratio(i,j,k) = 0._wp ; reff(i,j,k) = 0._wp
+                end if
+
+             case default
+                call errorMessage('!!! not implemented yet, sd%dtype=3,4,5, at COSP_PRECIP_MXRATIO')
+             end select
+             
+          end do
+       end do
+    end do
+    
+  END SUBROUTINE COSP_PRECIP_MXRATIO
 
 END MODULE MOD_COSP_UTILS
diff --git a/subsample_and_optics_example/optics/quickbeam_optics/quickbeam_optics.F90 b/subsample_and_optics_example/optics/quickbeam_optics/quickbeam_optics.F90
index dd39c73b3a..7d63754468 100644
--- a/subsample_and_optics_example/optics/quickbeam_optics/quickbeam_optics.F90
+++ b/subsample_and_optics_example/optics/quickbeam_optics/quickbeam_optics.F90
@@ -40,14 +40,18 @@ module mod_quickbeam_optics
   use quickbeam,           ONLY: dmin,dmax,Re_BIN_LENGTH,  &
                                  Re_MAX_BIN,nRe_types,nd,maxhclass
   use mod_cosp_config,     ONLY: N_HYDRO
-  use mod_cosp_error,      ONLY: errorMessage
   use mod_cosp_stats,      ONLY: radar_cfg
+  use mod_cosp_error,      ONLY: errorMessage
   implicit none
 
   ! Derived type for particle size distribution
   TYPE size_distribution
      real(wp),dimension(maxhclass) :: p1,p2,p3,dmin,dmax,apm,bpm,rho
      integer, dimension(maxhclass) :: dtype,phase
+
+     ! for DPLRW
+     integer, dimension(N_HYDRO) :: ftype,fvscs
+     real(wp),dimension(N_HYDRO) :: f1,f2,f3
   END TYPE size_distribution
   
   ! Parameters
@@ -81,7 +85,9 @@ end subroutine quickbeam_optics_init
   ! SUBROUTINE QUICKBEAM_OPTICS
   ! ######################################################################################
   subroutine quickbeam_optics(sd, rcfg, nprof, ngate, undef, hm_matrix, re_matrix,       &
-       Np_matrix, p_matrix, t_matrix, sh_matrix,z_vol,kr_vol)
+       Np_matrix, p_matrix, t_matrix, sh_matrix,z_vol,kr_vol, &
+       vfall, vfsqu, zehyd, krhyd, &
+       vtrm3, vtrm0, mmnt3, mmnt0 )
     
     ! INPUTS
     type(size_distribution),intent(inout) :: &
@@ -108,6 +114,11 @@ subroutine quickbeam_optics(sd, rcfg, nprof, ngate, undef, hm_matrix, re_matrix,
          z_vol,         & ! Effective reflectivity factor (mm^6/m^3)
          kr_vol           ! Attenuation coefficient hydro (dB/km)
 
+    ! OUTPUTS for DPLRW
+    real(wp),intent(out), dimension(nprof, ngate, N_HYDRO) :: &
+         vfall, vfsqu, zehyd, krhyd, &
+         vtrm3, vtrm0, mmnt3, mmnt0
+
     ! INTERNAL VARIABLES   
     integer :: &
          phase, ns,tp,j,k,pr,itt,iRe_type,n 
@@ -116,7 +127,12 @@ subroutine quickbeam_optics(sd, rcfg, nprof, ngate, undef, hm_matrix, re_matrix,
     real(wp) :: &
          t_kelvin,Re_internal
     real(wp) :: &
-         rho_a,kr,ze,zr,scale_factor,Re,Np,base,step 
+         rho_a,kr,ze,zr,scale_factor,Re,Np,base,step
+
+    ! INTERNAL VARIABLES for DPLRW
+    real(wp) :: vf,vq
+    real(wp) :: vt3,vt0,D3int,D0int
+    real(wp),dimension(:),allocatable :: fall_speed
 
     real(wp),dimension(:),allocatable :: &
          Deq,     & ! Discrete drop sizes (um)
@@ -139,6 +155,14 @@ subroutine quickbeam_optics(sd, rcfg, nprof, ngate, undef, hm_matrix, re_matrix,
     z_vol    = 0._wp
     z_ray    = 0._wp
     kr_vol   = 0._wp
+    vfall    = 0._wp
+    vfsqu    = 0._wp
+    zehyd    = 0._wp
+    krhyd    = 0._wp
+    vtrm3    = 0._wp
+    vtrm0    = 0._wp
+    mmnt3    = 0._wp
+    mmnt0    = 0._wp
 
     do k=1,ngate       ! Loop over each profile (nprof)
        do pr=1,nprof
@@ -280,8 +304,14 @@ subroutine quickbeam_optics(sd, rcfg, nprof, ngate, undef, hm_matrix, re_matrix,
                    ! xxa are unused (Mie scattering and extinction efficiencies)
                    xxa(1:ns) = -9.9_wp
                    rhoi = rcfg%rho_eff(tp,1:ns,iRe_type)
+
+                   allocate(fall_speed(ns))
+                   call fall_velocity(ns,Deq*1e-6,sd,p_matrix(pr,k),t_matrix(pr,k),tp,&
+                        & fall_speed)
                    call zeff(rcfg%freq,Deq,Ni,ns,rcfg%k2,t_kelvin,phase,rcfg%do_ray, &
-                        ze,zr,kr,xxa,xxa,rhoi)
+                        ze,zr,kr,xxa,xxa,rhoi, &
+                        fall_speed, vf, vq, &
+                        vt3, vt0, D3int, D0int)
 
                    ! Test compares total number concentration with sum of discrete samples 
                    ! The second test, below, compares ab initio and "scaled" computations 
@@ -302,6 +332,7 @@ subroutine quickbeam_optics(sd, rcfg, nprof, ngate, undef, hm_matrix, re_matrix,
 
                    ! Clean up space
                    deallocate(Di,Ni,rhoi,xxa,Deq)
+                   deallocate(fall_speed)
 
                    ! LUT test code
                    ! This segment of code compares full calculation to scaling result
@@ -319,12 +350,30 @@ subroutine quickbeam_optics(sd, rcfg, nprof, ngate, undef, hm_matrix, re_matrix,
                    zr = rcfg%Zr_scaled(tp,itt,iRe_type) * scale_factor
                    ze = rcfg%Ze_scaled(tp,itt,iRe_type) * scale_factor
                    kr = rcfg%kr_scaled(tp,itt,iRe_type) * scale_factor
+                   
+                   vf = rcfg%vf_scaled(tp,itt,iRe_type) * scale_factor
+                   vq = rcfg%vq_scaled(tp,itt,iRe_type) * scale_factor
+
+                   vt3 = rcfg%v3_scaled(tp,itt,iRe_type) * scale_factor
+                   vt0 = rcfg%v0_scaled(tp,itt,iRe_type) * scale_factor
+                   D3int = rcfg%m3_scaled(tp,itt,iRe_type) * scale_factor
+                   D0int = rcfg%m0_scaled(tp,itt,iRe_type) * scale_factor
                 endif  ! end z_scaling
                 
                 kr_vol(pr,k) = kr_vol(pr,k) + kr
                 z_vol(pr,k)  = z_vol(pr,k)  + ze
                 z_ray(pr,k)  = z_ray(pr,k)  + zr
+
+                vfall(pr,k,tp) = vf/ze
+                vfsqu(pr,k,tp) = vq/ze
+                zehyd(pr,k,tp) = ze
+                krhyd(pr,k,tp) = kr
                 
+                vtrm3(pr,k,tp) = vt3/D3int
+                vtrm0(pr,k,tp) = vt0/D0int
+                mmnt3(pr,k,tp) = D3int
+                mmnt0(pr,k,tp) = D0int
+
                 ! Construct Ze_scaled, Zr_scaled, and kr_scaled ... if we can
                 if ( .not. rcfg%Z_scale_flag(tp,itt,iRe_type) ) then
                    if (iRe_type>1) then
@@ -334,6 +383,14 @@ subroutine quickbeam_optics(sd, rcfg, nprof, ngate, undef, hm_matrix, re_matrix,
                       rcfg%kr_scaled(tp,itt,iRe_type) = kr/ scale_factor
                       rcfg%Z_scale_flag(tp,itt,iRe_type) = .true.
                       rcfg%Z_scale_added_flag(tp,itt,iRe_type)=.true.
+
+                      rcfg%vf_scaled(tp,itt,iRe_type) = vf/ scale_factor
+                      rcfg%vq_scaled(tp,itt,iRe_type) = vq/ scale_factor
+
+                      rcfg%v3_scaled(tp,itt,iRe_type) = vt3/ scale_factor
+                      rcfg%v0_scaled(tp,itt,iRe_type) = vt0/ scale_factor
+                      rcfg%m3_scaled(tp,itt,iRe_type) = D3int/ scale_factor
+                      rcfg%m0_scaled(tp,itt,iRe_type) = D0int/ scale_factor
                    endif
                 endif
              enddo   ! end loop of tp (hydrometeor type)
@@ -862,7 +919,8 @@ subroutine dsd(Q,Re,Np,D,N,nsizes,dtype,rho_a,tk,dmin,dmax,apm,bpm,rho_c,p1,p2,p
   end subroutine dsd
   ! ##############################################################################################
   ! ##############################################################################################
-  subroutine zeff(freq,D,N,nsizes,k2,tt,ice,xr,z_eff,z_ray,kr,qe,qs,rho_e)
+  subroutine zeff(freq,D,N,nsizes,k2,tt,ice,xr,z_eff,z_ray,kr,qe,qs,rho_e, &
+       fall_speed, vf, vq, vt3, vt0, D3int, D0int)
     ! ##############################################################################################
     ! Purpose:
     !   Simulates radar return of a volume given DSD of spheres
@@ -907,13 +965,19 @@ subroutine zeff(freq,D,N,nsizes,k2,tt,ice,xr,z_eff,z_ray,kr,qe,qs,rho_e)
          freq,  & ! Radar frequency (GHz)
          tt       ! Hydrometeor temperature (K)
     real(wp), intent(inout) :: &
-         k2            ! |K|^2, -1=use frequency dependent default 
+         k2            ! |K|^2, -1=use frequency dependent default
+    real(wp), intent(in), dimension(nsizes) :: &
+         fall_speed    ! droplet fall speed
     
     ! Outputs
     real(wp), intent(out) :: &
          z_eff,      & ! Unattenuated effective reflectivity factor (mm^6/m^3)
          z_ray,      & ! Reflectivity factor, Rayleigh only (mm^6/m^3)
          kr            ! Attenuation coefficient (db km^-1)
+    real(wp), intent(out) :: &
+         vf, vq        ! Ze weighted 
+    real(wp), intent(out) :: &
+         vt0, vt3, D3int, D0int ! for terminal velocity
     
     ! Internal Variables
     integer                     :: correct_for_rho ! Correct for density flag
@@ -940,6 +1004,7 @@ subroutine zeff(freq,D,N,nsizes,k2,tt,ice,xr,z_eff,z_ray,kr,qe,qs,rho_e)
          conv_f  = 0.299792458    ! Conversion for radar frequency (to m)
     complex(wp),dimension(nsizes) ::&
          m0             ! Complex index of refraction
+    real(wp) :: fall_sum ! temporal array for DPLRW
     
     ! Initialize
     z0_ray = 0._wp
@@ -1023,7 +1088,65 @@ subroutine zeff(freq,D,N,nsizes,k2,tt,ice,xr,z_eff,z_ray,kr,qe,qs,rho_e)
     cr = 10./log(10.)
     ! DS2014 STOP
     kr = k_sum*0.25_wp*pi*(1000._wp*cr)
+
+    ! <---------- vf_sum --------->
+    fall_sum = 0._wp
+    if (size(D0) == 1) then
+       fall_sum = fall_speed(1)*qbsca(1)*(n(1)*1E6)*D0(1)*D0(1)
+    else
+       xtemp = fall_speed*qbsca*N0*D0*D0
+       call avint(xtemp,D0,nsizes,D0(1),D0(size(D0,1)),fall_sum)
+    end if
+    const   = ((wl*wl*wl*wl)/(pi*pi*pi*pi*pi))*(1._wp/k2)
+    vf = fall_sum*const*0.25_wp*pi*1E18
+
+    fall_sum = 0._wp
+    if (size(D0) == 1) then
+       fall_sum = fall_speed(1)*fall_speed(1)*qbsca(1)*(n(1)*1E6)*D0(1)*D0(1)
+    else
+       xtemp = fall_speed*fall_speed*qbsca*N0*D0*D0
+       call avint(xtemp,D0,nsizes,D0(1),D0(size(D0,1)),fall_sum)
+    end if
+    const   = ((wl*wl*wl*wl)/(pi*pi*pi*pi*pi))*(1._wp/k2)
+    vq = fall_sum*const*0.25_wp*pi*1E18
     
+    ! <---------- vf_sum for terminal velocity --------->
+    fall_sum = 0._wp
+    if (size(D0) == 1) then
+       fall_sum = fall_speed(1)*(n(1)*1E6)*D0(1)*D0(1)*D0(1)
+    else
+       xtemp = fall_speed*N0*D0*D0*D0
+       call avint(xtemp,D0,nsizes,D0(1),D0(size(D0,1)),fall_sum)
+    end if
+    vt3 = fall_sum
+
+    fall_sum = 0._wp
+    if (size(D0) == 1) then
+       fall_sum = (n(1)*1E6)*D0(1)*D0(1)*D0(1)
+    else
+       xtemp = N0*D0*D0*D0
+       call avint(xtemp,D0,nsizes,D0(1),D0(size(D0,1)),fall_sum)
+    end if
+    D3int = fall_sum
+
+    fall_sum = 0._wp
+    if (size(D0) == 1) then
+       fall_sum = fall_speed(1)*(n(1)*1E6)
+    else
+       xtemp = fall_speed*N0
+       call avint(xtemp,D0,nsizes,D0(1),D0(size(D0,1)),fall_sum)
+    end if
+    vt0 = fall_sum
+
+    fall_sum = 0._wp
+    if (size(D0) == 1) then
+       fall_sum = (n(1)*1E6)
+    else
+       xtemp = N0
+       call avint(xtemp,D0,nsizes,D0(1),D0(size(D0,1)),fall_sum)
+    end if
+    D0int = fall_sum
+
     ! z_ray = sum[D^6*N(D)*deltaD]
     if (xr == 1) then
        z0_ray = 0._wp
@@ -1323,18 +1446,25 @@ subroutine hydro_class_init(lsingle,ldouble,sd)
    ! SINGLE MOMENT PARAMETERS
    integer,parameter,dimension(N_HYDRO) :: &
                     ! LSL  LSI  LSR  LSS  CVL  CVI  CVR  CVS  LSG    
-       HCLASS1_TYPE  = (/5,   1,   2,   2,   5,   1,   2,   2,   2/), & ! 
+!       HCLASS1_TYPE  = (/5,   1,   2,   2,   5,   1,   2,   2,   2/), & ! 
+       HCLASS1_TYPE  = (/1,   1,   1,   1,   1,   1,   2,   2,   1/), & ! 
        HCLASS1_PHASE = (/0,   1,   0,   1,   0,   1,   0,   1,   1/)    ! 
    real(wp),parameter,dimension(N_HYDRO) ::&
                       ! LSL   LSI    LSR    LSS    CVL   CVI    CVR    CVS    LSG    
        HCLASS1_DMIN = (/ -1.,  -1.,   -1.,   -1.,   -1.,  -1.,   -1.,   -1.,   -1.  /),  &
        HCLASS1_DMAX = (/ -1.,  -1.,   -1.,   -1.,   -1.,  -1.,   -1.,   -1.,   -1.  /),  &
-       HCLASS1_APM  = (/524., 110.8, 524.,   -1.,  524., 110.8, 524.,   -1.,   -1.  /),  &
-       HCLASS1_BPM  = (/  3.,   2.91,  3.,   -1.,    3.,   2.91,  3.,   -1.,   -1.  /),  &
-       HCLASS1_RHO  = (/ -1.,  -1.,   -1.,  100.,   -1.,  -1.,   -1.,  100.,  400.  /),  &
-       HCLASS1_P1   = (/ -1.,  -1.,    8.e6,  3.e6, -1.,  -1.,    8.e6,  3.e6,  4.e6/),  & 
-       HCLASS1_P2   = (/  6.,  40.,   -1.,   -1.,    6.,  40.,   -1.,   -1.,   -1.   /), & 
-       HCLASS1_P3   = (/  0.3,  2.,   -1.,   -1.,    0.3,  2.,   -1.,   -1.,   -1.   /)
+!       HCLASS1_APM  = (/524., 110.8, 524.,   -1.,  524., 110.8, 524.,   -1.,   -1.  /),  &
+!       HCLASS1_BPM  = (/  3.,   2.91,  3.,   -1.,    3.,   2.91,  3.,   -1.,   -1.  /),  &
+!       HCLASS1_RHO  = (/ -1.,  -1.,   -1.,  100.,   -1.,  -1.,   -1.,  100.,  400.  /),  &
+       HCLASS1_APM  = (/524.,  -1.,  524.,   -1.,  524.,  -1.,  524.,   -1.,   -1.  /),  &
+       HCLASS1_BPM  = (/  3.,  -1.,    3.,   -1.,    3.,  -1.,    3.,   -1.,   -1.  /),  &
+       HCLASS1_RHO  = (/ -1., 500.,   -1.,  250.,   -1., 500.,   -1.,  250.,  500.  /),  &
+!       HCLASS1_P1   = (/ -1.,  -1.,    8.e6,  3.e6, -1.,  -1.,    8.e6,  3.e6,  4.e6/),  & 
+!       HCLASS1_P2   = (/  6.,  40.,   -1.,   -1.,    6.,  40.,   -1.,   -1.,   -1.   /), & 
+!       HCLASS1_P3   = (/  0.3,  2.,   -1.,   -1.,    0.3,  2.,   -1.,   -1.,   -1.   /)
+       HCLASS1_P1   = (/ -1.,  -1.,   -1.,   -1.,   -1.,  -1.,    8.e6,  3.e6, -1.  /),  & 
+       HCLASS1_P2   = (/  6.,  40.,   20.,   40.,    6.,  40.,   -1.,   -1.,   60.  /),  & 
+       HCLASS1_P3   = (/  1.,   1.,    5.,    1.,    1.,   5.,   -1.,   -1.,    1.  /)
 
     ! TWO MOMENT PARAMETERS
     integer,parameter,dimension(N_HYDRO) :: &
@@ -1352,6 +1482,18 @@ subroutine hydro_class_init(lsingle,ldouble,sd)
        HCLASS2_P1   = (/ -1,     -1,     -1,     -1,    -1,    -1,   -1,     -1,   -1/), &
        HCLASS2_P2   = (/ -1,     -1,     -1,     -1,    -1,    -1,   -1,     -1,   -1/), &
        HCLASS2_P3   = (/ -2,      1,      1,      1,    -2,     1,    1,      1,    1/) 
+
+    integer,parameter,dimension(N_HYDRO) :: &
+         ! type1 -> p1*D^p2 ; type2 -> Posselt and Lohmann (2008) Eq.11
+         !         LSL  LSI  LSR  LSS  CVL  CVI  CVR  CVS  LSG
+         ftype = (/   2,   1,   2,   1,   2,   1,   1,   1,   1 /),&
+         fvscs = (/   0,   0,   0,   0,   0,   0,   1,   1,   0 /)
+
+    real(wp),parameter,dimension(N_HYDRO) :: &
+         !          LSL       LSI       LSR       LSS       CVL       CVI       CVR       CVS       LSG  
+         f1   = (/  9.65   ,  1.107  ,  9.65   ,  3.321  ,  9.65   ,  1.107  ,  8.42E+2,  4.84   , 19.3  /),&
+         f2   = (/ 10.43   ,  0.22   , 10.43   ,  0.22   , 10.43   ,  0.22   ,  0.8    ,  0.25   ,  0.37 /),&
+         f3   = (/  6.00E+2,  0.00   ,  6.00E+2,  0.00   ,  6.00E+2,  0.00   ,  0.00   ,  0.00   ,  0.00 /)
     
     if (lsingle) then    
        sd%dtype(1:N_HYDRO) = HCLASS1_TYPE(1:N_HYDRO)
@@ -1376,6 +1518,60 @@ subroutine hydro_class_init(lsingle,ldouble,sd)
        sd%p1(1:N_HYDRO)    = HCLASS2_P1(1:N_HYDRO)
        sd%p2(1:N_HYDRO)    = HCLASS2_P2(1:N_HYDRO)
        sd%p3(1:N_HYDRO)    = HCLASS2_P3(1:N_HYDRO)
-    endif    
-  end subroutine hydro_class_init    
+    endif
+    
+    sd%ftype = ftype
+    sd%fvscs = fvscs
+    sd%f1    = f1
+    sd%f2    = f2
+    sd%f3    = f3
+    
+  end subroutine hydro_class_init
+
+  subroutine fall_velocity(nsizes,D0,sd,p_matrix,t_matrix,tp,fall)
+    implicit none
+    integer,intent(in)   :: &
+         nsizes,tp
+    real(wp),intent(in), dimension(nsizes) :: &
+         D0              ! [m], equivalent volume spheres                                                
+    type(size_distribution),intent(in) :: &
+         sd
+    real(wp),intent(in) :: &
+         p_matrix,t_matrix
+    real(wp),intent(out),dimension(nsizes) :: &
+         fall            ! positive = ascending, [m/s]                                                   
+
+    ! internal work                                                                                      
+    real(wp) :: rho,rho0
+    integer,parameter  :: &  ! same index as in subsample_and_optics                                     
+         LSCLIQ = 1,      &
+         LSCICE = 2,      &
+         LSRAIN = 3,      &
+         LSSNOW = 4,      &
+         CVCLIQ = 5,      &
+         CVCICE = 6,      &
+         CVRAIN = 7,      &
+         CVSNOW = 8,      &
+         LSGRPL = 9
+    real(wp),dimension(N_HYDRO) :: vscs_fct
+
+    rho0 = 101300/273.15/287
+    rho  = p_matrix/t_matrix/287
+    vscs_fct  = merge(sqrt(rho0/rho),1._wp,sd%fvscs==1)
+
+    if (sd%ftype(tp) == 1) then
+       ! p1 * D^p2                                                                                       
+       fall = -1 * vscs_fct(tp) * &
+            ( sd%f1(tp) * D0**sd%f2(tp) + sd%f3(tp) )
+    else if (sd%ftype(tp) == 2) then
+       ! Posselt and Lohmann (2008), Eq.11                                                               
+       fall = -1 * vscs_fct(tp) * &
+            ( sd%f1(tp) - sd%f2(tp)*exp(-1*sd%f3(tp)*D0) + (sd%f2(tp)-sd%f1(tp))*exp(-5*sd%f3(tp)*D0) )
+    else
+       write(*,*) 'WARNING: size_distribution, undefined ftype; STOP'
+       stop
+    end if
+
+  end subroutine fall_velocity
+  
 end module mod_quickbeam_optics