add scaling to LMR data

.gitignore

@@ -45,3 +45,4 @@ docs/_images
 docs/.doctrees
 oggm/version.py
 oggm/ignore
+ignore/

docs/whats-new.rst

@@ -51,6 +51,11 @@ Enhancements
   ``workflow.merge_gridded_data``. If no grid is provided, the default is to
   merge all grids of the provided gdirs (:pull:`1779`).
   By `Patrick Schmitt <https://github.com/pat-schmitt>`_
+- `min_area_h` is now a default diagnostic variable output: this is
+  glacier area trying to avoid spikes due to interannual variability
+  in snowfall (:pull:`1791`).
+  By `Fabien Maussion <https://github.com/fmaussion>`_ and
+  `Patrick Schmitt <https://github.com/pat-schmitt>`_.
 - Flowlines shapefiles output now have more attributes and are easier to
   use (:pull:`1786`).
   By `Fabien Maussion <https://github.com/fmaussion>`_
@@ -62,6 +67,7 @@ Enhancements
   ``flux_divergence`` (:pull:`1792`).
   By `Patrick Schmitt <https://github.com/pat-schmitt>`_
 
+
 Bug fixes
 ~~~~~~~~~
 
@@ -72,6 +78,7 @@ Bug fixes
   By `Dan Goldberg <https://github.com/dngoldberg>`_ and
   `Fabien Maussion <https://github.com/fmaussion>`_.
 
+
 v1.6.2 (August 25, 2024)
 ------------------------
 

oggm/cli/prepro_levels.py

@@ -12,6 +12,7 @@
 import time
 import logging
 import json
+import importlib
 import pandas as pd
 import numpy as np
 import geopandas as gpd
@@ -86,6 +87,8 @@ def run_prepro_levels(rgi_version=None, rgi_reg=None, border=None,
                       add_millan_thickness=False, add_millan_velocity=False,
                       add_hugonnet_dhdt=False, add_bedmachine=False,
                       add_glathida=False,
+                      custom_climate_task=None,
+                      custom_climate_task_kwargs=None,
                       start_level=None, start_base_url=None, max_level=5,
                       logging_level='WORKFLOW',
                       dynamic_spinup=False, err_dmdtda_scaling_factor=0.2,
@@ -169,6 +172,12 @@ def run_prepro_levels(rgi_version=None, rgi_reg=None, border=None,
     add_glathida : bool
         adds (reprojects) the glathida thickness data to the glacier
         directories. Data points are stored as csv files.
+    custom_climate_task : str
+        optional import path to a custom climate task in the form
+        "module_path:function_name". If provided, it will be called instead of
+        the default process_climate_data.
+    custom_climate_task_kwargs : dict
+        optional kwargs passed to the custom climate task when it is executed.
     start_level : int
         the pre-processed level to start from (default is to start from
         scratch). If set, you'll need to indicate start_base_url as well.
@@ -629,7 +638,23 @@ def _time_log():
         utils.mkdir(sum_dir)
 
         # Climate
-        workflow.execute_entity_task(tasks.process_climate_data, gdirs)
+        climate_kwargs = custom_climate_task_kwargs or {}
+        if custom_climate_task:
+            try:
+                mod_path, func_name = custom_climate_task.rsplit(':', 1)
+            except ValueError:
+                raise InvalidParamsError('custom_climate_task must be of the form "module:function"')
+            try:
+                mod = importlib.import_module(mod_path)
+            except ModuleNotFoundError as err:
+                raise InvalidParamsError(f'Cannot import module {mod_path}') from err
+            try:
+                custom_task_func = getattr(mod, func_name)
+            except AttributeError as err:
+                raise InvalidParamsError(f'Module {mod_path} has no attribute {func_name}') from err
+            workflow.execute_entity_task(custom_task_func, gdirs, **climate_kwargs)
+        else:
+            workflow.execute_entity_task(tasks.process_climate_data, gdirs)
 
         # Small optim to avoid concurrency
         utils.get_geodetic_mb_dataframe()
@@ -946,6 +971,11 @@ def parse_args(args):
                         help='adds (reprojects) the glathida point thickness '
                              'observations to the glacier directories. '
                              'The data points are stored as csv.')
+    parser.add_argument('--custom-climate-task', type=str, default=None,
+                        help='Custom climate task import path in the form module:function. '
+                            'If provided, it replaces the default process_climate_data.')
+    parser.add_argument('--custom-climate-task-kwargs', type=json.loads, default=None,
+                        help='JSON dict of kwargs passed to the custom climate task.')
     parser.add_argument('--demo', nargs='?', const=True, default=False,
                         help='if you want to run the prepro for the '
                              'list of demo glaciers.')
@@ -1036,6 +1066,8 @@ def parse_args(args):
                 add_hugonnet_dhdt=args.add_hugonnet_dhdt,
                 add_bedmachine=args.add_bedmachine,
                 add_glathida=args.add_glathida,
+                custom_climate_task=args.custom_climate_task,
+                custom_climate_task_kwargs=args.custom_climate_task_kwargs,
                 dynamic_spinup=dynamic_spinup,
                 err_dmdtda_scaling_factor=args.err_dmdtda_scaling_factor,
                 dynamic_spinup_start_year=args.dynamic_spinup_start_year,

oggm/core/dynamic_spinup.py

@@ -138,7 +138,7 @@ def run_dynamic_spinup(gdir, init_model_filesuffix=None, init_model_yr=None,
         the forward model run. Therefore you could see quite fast changes
         (spikes) in the time-evolution (especially visible in length and area).
         If you set this value to 0 the filtering can be switched off.
-        Default is cfg.PARAMS['dynamic_spinup_min_ice_thick'].
+        Default is cfg.PARAMS['min_ice_thick_for_area'].
     first_guess_t_spinup : float
         The initial guess for the temperature bias for the spinup
         MassBalanceModel in °C.
@@ -231,7 +231,7 @@ def run_dynamic_spinup(gdir, init_model_filesuffix=None, init_model_yr=None,
     yr_min = gdir.get_climate_info()['baseline_yr_0']
 
     if min_ice_thickness is None:
-        min_ice_thickness = cfg.PARAMS['dynamic_spinup_min_ice_thick']
+        min_ice_thickness = cfg.PARAMS['min_ice_thick_for_area']
 
     # check provided maximum start year here, and change min_spinup_period
     if spinup_start_yr_max is not None:
@@ -443,7 +443,7 @@ def run_model_with_spinup_to_target_year(t_spinup):
                 geom_path=geom_path,
                 diag_path=diag_path,
                 fl_diag_path=fl_diag_path,
-                dynamic_spinup_min_ice_thick=min_ice_thickness,
+                min_ice_thick_for_area=min_ice_thickness,
                 fixed_geometry_spinup_yr=fixed_geometry_spinup_yr,
                 store_monthly_step=store_monthly_step,
             )
@@ -453,9 +453,9 @@ def run_model_with_spinup_to_target_year(t_spinup):
             if delete_area_min_h:
                 ovars.remove('area_min_h')
 
-            if type(ds) == tuple:
+            if isinstance(ds, tuple):
                 ds = ds[0]
-            model_area_km2 = ds.area_m2_min_h.loc[target_yr].values * 1e-6
+            model_area_km2 = ds.area_min_h_m2.loc[target_yr].values * 1e-6
             model_volume_km3 = ds.volume_m3.loc[target_yr].values * 1e-9
         else:
             # only run to rgi date and extract values
@@ -1047,7 +1047,7 @@ def dynamic_melt_f_run_with_dynamic_spinup(
         the forward model run. Therefore you could see quite fast changes
         (spikes) in the time-evolution (especially visible in length and area).
         If you set this value to 0 the filtering can be switched off.
-        Default is cfg.PARAMS['dynamic_spinup_min_ice_thick'].
+        Default is cfg.PARAMS['min_ice_thick_for_area'].
     first_guess_t_spinup : float
         The initial guess for the temperature bias for the spinup
         MassBalanceModel in °C.
@@ -1332,7 +1332,7 @@ def dynamic_melt_f_run_with_dynamic_spinup_fallback(
         the forward model run. Therefore you could see quite fast changes
         (spikes) in the time-evolution (especially visible in length and area).
         If you set this value to 0 the filtering can be switched off.
-        Default is cfg.PARAMS['dynamic_spinup_min_ice_thick'].
+        Default is cfg.PARAMS['min_ice_thick_for_area'].
     first_guess_t_spinup : float
         The initial guess for the temperature bias for the spinup
         MassBalanceModel in °C.

oggm/core/flowline.py

@@ -926,7 +926,7 @@ def run_until_and_store(self, y1,
                             store_monthly_step=None,
                             stop_criterion=None,
                             fixed_geometry_spinup_yr=None,
-                            dynamic_spinup_min_ice_thick=None,
+                            min_ice_thick_for_area=None,
                             ):
         """Runs the model and returns intermediate steps in xarray datasets.
 
@@ -978,13 +978,13 @@ def run_until_and_store(self, y1,
             starting from the chosen year. The only output affected are the
             glacier wide diagnostic files - all other outputs are set
             to constants during "spinup"
-        dynamic_spinup_min_ice_thick : float or None
+        min_ice_thick_for_area : float or None
             if set to a float, additional variables are saved which are useful
             in combination with the dynamic spinup. In particular only grid
             points with a minimum ice thickness are considered for the total
             area or the total volume. This is useful to smooth out yearly
             fluctuations when matching to observations. The names of this new
-            variables include the suffix _min_h (e.g. 'area_m2_min_h')
+            variables include the suffix _min_h (e.g. 'area_min_h_m2')
 
         Returns
         -------
@@ -1106,16 +1106,16 @@ def run_until_and_store(self, y1,
             diag_ds['area_m2'].attrs['description'] = 'Total glacier area'
             diag_ds['area_m2'].attrs['unit'] = 'm 2'
 
-        if dynamic_spinup_min_ice_thick is None:
-            dynamic_spinup_min_ice_thick = cfg.PARAMS['dynamic_spinup_min_ice_thick']
+        if min_ice_thick_for_area is None:
+            min_ice_thick_for_area = cfg.PARAMS['min_ice_thick_for_area']
 
         if 'area_min_h' in ovars:
-            # filled with a value if dynamic_spinup_min_ice_thick is not None
-            diag_ds['area_m2_min_h'] = ('time', np.zeros(nm) * np.nan)
-            diag_ds['area_m2_min_h'].attrs['description'] = \
-                f'Total glacier area of gridpoints with a minimum ice' \
-                f'thickness of {dynamic_spinup_min_ice_thick} m'
-            diag_ds['area_m2_min_h'].attrs['unit'] = 'm 2'
+            # filled with a value if min_ice_thick_for_area is not None
+            diag_ds['area_min_h_m2'] = ('time', np.zeros(nm) * np.nan)
+            diag_ds['area_min_h_m2'].attrs['description'] = \
+                f'Total glacier area of gridpoints with a minimum ice ' \
+                f'thickness of {min_ice_thick_for_area} m'
+            diag_ds['area_min_h_m2'].attrs['unit'] = 'm 2'
 
         if 'length' in ovars:
             diag_ds['length_m'] = ('time', np.zeros(nm) * np.nan)
@@ -1381,8 +1381,8 @@ def run_until_and_store(self, y1,
             if 'volume_bwl' in ovars:
                 diag_ds['volume_bwl_m3'].data[i] = self.volume_bwl_m3
             if 'area_min_h' in ovars:
-                diag_ds['area_m2_min_h'].data[i] = np.sum([np.sum(
-                    fl.bin_area_m2[fl.thick > dynamic_spinup_min_ice_thick])
+                diag_ds['area_min_h_m2'].data[i] = np.sum([np.sum(
+                    fl.bin_area_m2[fl.thick > min_ice_thick_for_area])
                     for fl in self.fls])
             # Terminus thick is a bit more logic
             ti = None

oggm/core/massbalance.py

@@ -1630,10 +1630,14 @@ def mb_calibration_from_geodetic_mb(gdir, *,
     temp_bias = 0
     if cfg.PARAMS['use_temp_bias_from_file']:
         climinfo = gdir.get_climate_info()
-        if 'w5e5' not in climinfo['baseline_climate_source'].lower():
-            raise InvalidWorkflowError('use_temp_bias_from_file currently '
-                                       'only available for W5E5 data.')
-        bias_df = get_temp_bias_dataframe()
+        # if 'w5e5' not in climinfo['baseline_climate_source'].lower():
+        #     raise InvalidWorkflowError('use_temp_bias_from_file currently '
+        #                                'only available for W5E5 data.')
+        if 'w5e5' in climinfo['baseline_climate_source']:
+            bias_df = get_temp_bias_dataframe()
+        elif 'lmr_mira' in climinfo['baseline_climate_source']:
+            bias_df = get_temp_bias_dataframe(dataset='lmr_mira')
+
         ref_lon = climinfo['baseline_climate_ref_pix_lon']
         ref_lat = climinfo['baseline_climate_ref_pix_lat']
         # Take nearest

oggm/params.cfg

@@ -339,7 +339,7 @@ error_when_glacier_reaches_boundaries = True
 # some options to the user.
 # This option sets an arbitrary limit on how thick (m) a glacier should be
 # to be defined as "glacier" (https://github.com/OGGM/oggm/issues/914)
-min_ice_thick_for_length = 0
+min_ice_thick_for_length = 2
 # How to calculate the length of a glacier?
 # - 'naive' (the default) computes the length by summing the number of
 #   grid points with an ice thickness above min_ice_thick_for_length
@@ -361,8 +361,9 @@ use_inversion_params_for_run = True
 # Defines the minimum ice thickness which is used during the dynamic spinup to
 # match area or volume. Only grid points with a larger thickness are considered
 # to the total area. This is needed to filter out area changes due to climate
-# variability around the rgi year (spikes).
-dynamic_spinup_min_ice_thick = 2.
+# variability (spikes). The resulting diagnostic variable is stored in
+# in 'area_min_h_m2' - the recommended area variable for most applications.
+min_ice_thick_for_area = 2.
 
 ### Tidewater glaciers options
 
@@ -430,7 +431,7 @@ store_model_geometry = False
 #   melt_residual_off_glacier, melt_residual_on_glacier
 #   model_mb, residual_mb, snow_bucket,
 # You need to keep all variables in one line unfortunately
-store_diagnostic_variables =  volume, volume_bsl, volume_bwl, area, length, calving, calving_rate, off_area, on_area, melt_off_glacier, melt_on_glacier, liq_prcp_off_glacier, liq_prcp_on_glacier, snowfall_off_glacier, snowfall_on_glacier
+store_diagnostic_variables =  volume, volume_bsl, volume_bwl, area, area_min_h, length, calving, calving_rate, off_area, on_area, melt_off_glacier, melt_on_glacier, liq_prcp_off_glacier, liq_prcp_on_glacier, snowfall_off_glacier, snowfall_on_glacier
 
 # Whether to store the model flowline diagnostic files during operational runs
 # This can be useful for advanced diagnostics along the flowlines but is