Added functionality for importing daily ERA5 data

pygem/bin/run/run_calibration.py

@@ -581,9 +581,9 @@ def run(list_packed_vars):
     gcm_elev = gcm.importGCMfxnearestneighbor_xarray(
         gcm.elev_fn, gcm.elev_vn, main_glac_rgi
     )
-    # Lapse rate [degC m-1]
+    # Lapse rate [degC m-1] (always monthly)
     gcm_lr, gcm_dates = gcm.importGCMvarnearestneighbor_xarray(
-        gcm.lr_fn, gcm.lr_vn, main_glac_rgi, dates_table
+        gcm.lr_fn, gcm.lr_vn, main_glac_rgi, dates_table, upscale_var_timestep=True
     )
 
     # ===== LOOP THROUGH GLACIERS TO RUN CALIBRATION =====

pygem/class_climate.py

@@ -111,7 +111,8 @@ def __init__(self, name=str(), sim_climate_scenario=str(), realization=None):
                     + pygem_prms['climate']['paths']['cesm2_fp_fx_ending']
                 )
                 # Extra information
-                self.timestep = pygem_prms['time']['timestep']
+                # self.timestep = pygem_prms['time']['timestep']
+                self.timestep = 'monthly'  # future scenario is always monthly timestep
                 self.rgi_lat_colname = pygem_prms['rgi']['rgi_lat_colname']
                 self.rgi_lon_colname = pygem_prms['rgi']['rgi_lon_colname']
                 self.sim_climate_scenario = sim_climate_scenario
@@ -164,7 +165,8 @@ def __init__(self, name=str(), sim_climate_scenario=str(), realization=None):
                     + pygem_prms['climate']['paths']['gfdl_fp_fx_ending']
                 )
                 # Extra information
-                self.timestep = pygem_prms['time']['timestep']
+                # self.timestep = pygem_prms['time']['timestep']
+                self.timestep = 'monthly'  # future scenario is always monthly timestep
                 self.rgi_lat_colname = pygem_prms['rgi']['rgi_lat_colname']
                 self.rgi_lon_colname = pygem_prms['rgi']['rgi_lon_colname']
                 self.sim_climate_scenario = sim_climate_scenario
@@ -190,12 +192,18 @@ def __init__(self, name=str(), sim_climate_scenario=str(), realization=None):
                 self.elev_fn = pygem_prms['climate']['paths']['era5_elev_fn']
                 self.lr_fn = pygem_prms['climate']['paths']['era5_lr_fn']
                 # Variable filepaths
-                self.var_fp = (
-                    pygem_prms['root'] + pygem_prms['climate']['paths']['era5_relpath']
-                )
-                self.fx_fp = (
-                    pygem_prms['root'] + pygem_prms['climate']['paths']['era5_relpath']
-                )
+                if pygem_prms['climate']['paths']['era5_fullpath']:
+                    self.var_fp = ''
+                    self.fx_fp = ''
+                else:
+                    self.var_fp = (
+                        pygem_prms['root']
+                        + pygem_prms['climate']['paths']['era5_relpath']
+                    )
+                    self.fx_fp = (
+                        pygem_prms['root']
+                        + pygem_prms['climate']['paths']['era5_relpath']
+                    )
                 # Extra information
                 self.timestep = pygem_prms['time']['timestep']
                 self.rgi_lat_colname = pygem_prms['rgi']['rgi_lat_colname']
@@ -295,7 +303,8 @@ def __init__(self, name=str(), sim_climate_scenario=str(), realization=None):
                         + '/'
                     )
                 # Extra information
-                self.timestep = pygem_prms['time']['timestep']
+                # self.timestep = pygem_prms['time']['timestep']
+                self.timestep = 'monthly'  # future scenario is always monthly timestep
                 self.rgi_lat_colname = pygem_prms['rgi']['rgi_lat_colname']
                 self.rgi_lon_colname = pygem_prms['rgi']['rgi_lon_colname']
                 self.sim_climate_scenario = sim_climate_scenario
@@ -341,7 +350,8 @@ def __init__(self, name=str(), sim_climate_scenario=str(), realization=None):
                     + '/'
                 )
                 # Extra information
-                self.timestep = pygem_prms['time']['timestep']
+                # self.timestep = pygem_prms['time']['timestep']
+                self.timestep = 'monthly'  # future scenario is always monthly timestep
                 self.rgi_lat_colname = pygem_prms['rgi']['rgi_lat_colname']
                 self.rgi_lon_colname = pygem_prms['rgi']['rgi_lon_colname']
                 self.sim_climate_scenario = sim_climate_scenario
@@ -443,6 +453,7 @@ def importGCMvarnearestneighbor_xarray(
         main_glac_rgi,
         dates_table,
         realizations=['r1i1p1f1', 'r4i1p1f1'],
+        upscale_var_timestep=False,
     ):
         """
         Import time series of variables and extract nearest neighbor.
@@ -472,13 +483,98 @@ def importGCMvarnearestneighbor_xarray(
             timestep, i.e., be from the beginning/middle/end of month)
         """
         # Import netcdf file
-        if not os.path.exists(self.var_fp + filename):
-            if os.path.exists(self.var_fp + filename.replace('r1i1p1f1', 'r4i1p1f1')):
-                filename = filename.replace('r1i1p1f1', 'r4i1p1f1')
-            if os.path.exists(self.var_fp + filename.replace('_native', '')):
-                filename = filename.replace('_native', '')
+        if self.timestep == 'monthly':
+            if not os.path.exists(self.var_fp + filename):
+                if os.path.exists(
+                    self.var_fp + filename.replace('r1i1p1f1', 'r4i1p1f1')
+                ):
+                    filename = filename.replace('r1i1p1f1', 'r4i1p1f1')
+                if os.path.exists(self.var_fp + filename.replace('_native', '')):
+                    filename = filename.replace('_native', '')
+
+            data = xr.open_dataset(self.var_fp + filename)
+        elif self.timestep == 'daily':
+            year_start = pd.Timestamp(dates_table['date'].values[0]).year
+            year_end = pd.Timestamp(dates_table['date'].values[-1]).year
+
+            lats = main_glac_rgi[self.rgi_lat_colname].values
+            lons = main_glac_rgi[self.rgi_lon_colname].values
+            min_lat, max_lat = np.floor(lats.min() * 4) / 4, np.ceil(lats.max() * 4) / 4
+            min_lon, max_lon = np.floor(lons.min() * 4) / 4, np.ceil(lons.max() * 4) / 4
+            if 'YYYY' in filename:
+                datasets = []
+                for yr in range(year_start, year_end + 1):
+                    data_yr = xr.open_dataset(
+                        self.var_fp + filename.replace('YYYY', str(yr))
+                    )
+                    if 'valid_time' in data_yr.coords or 'valid_time' in data_yr.dims:
+                        data_yr = data_yr.rename({'valid_time': self.time_vn})
+
+                    # convert longitude from -180—180 to 0—360
+                    if data_yr.longitude.min() < 0:
+                        data_yr = data_yr.assign_coords(
+                            longitude=(data_yr.longitude % 360)
+                        )
+
+                    # subset for desired lats and lons
+                    data_yr = data_yr.sel(
+                        latitude=slice(max_lat, min_lat),
+                        longitude=slice(min_lon, max_lon),
+                    )
+
+                    # append data to list
+                    datasets.append(data_yr)
+
+                # combine along the time dimension
+                data = xr.concat(datasets, dim=self.time_vn)
+
+            else:
+                data = xr.open_dataset(self.var_fp + filename)
+                data = data.sel(
+                    latitude=slice(max_lat, min_lat), longitude=slice(min_lon, max_lon)
+                )
+
+            # mask out leap days
+            if pygem_prms['time']['option_leapyear'] == 0 and not upscale_var_timestep:
+                time_index = pd.to_datetime(data[self.time_vn].values)
+                mask = ~((time_index.month == 2) & (time_index.day == 29))
+                data = data.isel({self.time_vn: mask})
+
+            # Upscale timestep to match data (e.g., monthly lapserate for daily data)
+            if upscale_var_timestep:
+                # convert time to datetime
+                time_monthly = pd.to_datetime(data[self.time_vn].values)
+                var_monthly = data[vn]  # xarray DataArray with dims (time, lat, lon)
+
+                # create empty DataArray for daily data
+                daily_times = dates_table['date'].values
+                daily_data = xr.DataArray(
+                    np.zeros(
+                        (len(daily_times), len(data.latitude), len(data.longitude))
+                    ),
+                    dims=(self.time_vn, 'latitude', 'longitude'),
+                    coords={
+                        self.time_vn: daily_times,
+                        'latitude': data.latitude,
+                        'longitude': data.longitude,
+                    },
+                    name=vn,
+                )
+
+                # loop through months and fill daily slots
+                for i, t in enumerate(time_monthly):
+                    # find all days in this month
+                    idx = np.where(
+                        (dates_table['year'] == t.year)
+                        & (dates_table['month'] == t.month)
+                    )[0]
+
+                    # assign monthly values to these daily indices
+                    daily_data[idx, :, :] = var_monthly.isel(time=i).values
+
+                # convert to Dataset with data variable vn
+                data = daily_data.to_dataset(name=vn)
 
-        data = xr.open_dataset(self.var_fp + filename)
         glac_variable_series = np.zeros((main_glac_rgi.shape[0], dates_table.shape[0]))
 
         # Check GCM provides required years of data
@@ -525,19 +621,22 @@ def importGCMvarnearestneighbor_xarray(
                     pd.Series(data[self.time_vn]).apply(
                         lambda x: x.strftime('%Y-%m-%d')
                     )
-                    == dates_table['date'].apply(lambda x: x.strftime('%Y-%m-%d'))[0]
+                    == dates_table['date']
+                    .apply(lambda x: x.strftime('%Y-%m-%d'))
+                    .iloc[0]
                 )
             )[0][0]
             end_idx = (
                 np.where(
                     pd.Series(data[self.time_vn]).apply(
                         lambda x: x.strftime('%Y-%m-%d')
                     )
-                    == dates_table['date'].apply(lambda x: x.strftime('%Y-%m-%d'))[
-                        dates_table.shape[0] - 1
-                    ]
+                    == dates_table['date']
+                    .apply(lambda x: x.strftime('%Y-%m-%d'))
+                    .iloc[-1]
                 )
             )[0][0]
+
         # Extract the time series
         time_series = pd.Series(data[self.time_vn][start_idx : end_idx + 1])
         # Find Nearest Neighbor
@@ -577,6 +676,7 @@ def importGCMvarnearestneighbor_xarray(
             # Find unique latitude/longitudes
             latlon_nearidx = list(zip(lat_nearidx, lon_nearidx))
             latlon_nearidx_unique = list(set(latlon_nearidx))
+
             # Create dictionary of time series for each unique latitude/longitude
             glac_variable_dict = {}
             for latlon in latlon_nearidx_unique:
@@ -634,4 +734,5 @@ def importGCMvarnearestneighbor_xarray(
                     )
         elif vn != self.lr_vn:
             print('Check units of air temperature or precipitation')
+
         return glac_variable_series, time_series

pygem/pygem_modelsetup.py

@@ -100,8 +100,9 @@ def datesmodelrun(
         dates_table['month'] = dates_table['date'].dt.month
         dates_table['day'] = dates_table['date'].dt.day
         dates_table['daysinmonth'] = dates_table['date'].dt.daysinmonth
+        dates_table['timestep'] = np.arange(len(dates_table['date']))
         # Set date as index
-        dates_table.set_index('date', inplace=True)
+        dates_table.set_index('timestep', inplace=True)
         # Remove leap year days if user selected this with option_leapyear
         if pygem_prms['time']['option_leapyear'] == 0:
             # First, change 'daysinmonth' number
@@ -119,9 +120,8 @@ def datesmodelrun(
     # Water year for northern hemisphere using USGS definition (October 1 - September 30th),
     # e.g., water year for 2000 is from October 1, 1999 - September 30, 2000
     dates_table['wateryear'] = dates_table['year']
-    for step in range(dates_table.shape[0]):
-        if dates_table.loc[step, 'month'] >= 10:
-            dates_table.loc[step, 'wateryear'] = dates_table.loc[step, 'year'] + 1
+    dates_table.loc[dates_table['month'] >= 10, 'wateryear'] = dates_table['year'] + 1
+
     # Add column for seasons
     # create a season dictionary to assist groupby functions
     seasondict = {}

pygem/setup/config.py

@@ -157,6 +157,7 @@ def _validate_config(self, config):
         'climate.sim_wateryear': str,
         'climate.constantarea_years': int,
         'climate.paths': dict,
+        'climate.paths.era5_fullpath': bool,
         'climate.paths.era5_relpath': str,
         'climate.paths.era5_temp_fn': str,
         'climate.paths.era5_tempstd_fn': str,

pygem/setup/config.yaml

@@ -66,13 +66,15 @@ climate:
   # ===== CLIMATE DATA FILEPATHS AND FILENAMES =====
   paths:
     # ERA5 (default reference climate data)
+    era5_fullpath: False # bool. 'True' ignores 'root' and 'era5_relpath' for ERA5 data and assumes below filenames are absolute
     era5_relpath: /climate_data/ERA5/
     era5_temp_fn: ERA5_temp_monthly.nc
     era5_tempstd_fn: ERA5_tempstd_monthly.nc
     era5_prec_fn: ERA5_totalprecip_monthly.nc
     era5_elev_fn: ERA5_geopotential.nc
     era5_pressureleveltemp_fn: ERA5_pressureleveltemp_monthly.nc
     era5_lr_fn: ERA5_lapserates_monthly.nc
+
     # CMIP5 (GCM data)
     cmip5_relpath: /climate_data/cmip5/
     cmip5_fp_var_ending: _r1i1p1_monNG/
@@ -324,7 +326,7 @@ time:
   wateryear_month_start: 10                       # water year starting month
   winter_month_start: 10                          # first month of winter (for HMA winter is October 1 - April 30)
   summer_month_start: 5                           # first month of summer (for HMA summer is May 1 - Sept 30)
-  timestep: monthly                               # time step ('monthly' only option at present)
+  timestep: monthly                                 # time step ('monthly' or 'daily')
 
 # ===== MODEL CONSTANTS =====
 constants: