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Add mask to edge of domain in smoke preprocessing #556

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merged 2 commits into from
Dec 2, 2024
Merged

Add mask to edge of domain in smoke preprocessing #556

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3fa2ed7
Add mask to edge of domain in smoke preprocessing
Nov 26, 2024
6094f8c
Merge branch 'main' into mask_smoke_preproc
MatthewPyle-NOAA Dec 2, 2024
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34 changes: 30 additions & 4 deletions ush/interp_tools.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,7 @@ def date_range(current_day):
print(f'Searching for interpolated RAVE for {current_day}')

fcst_datetime = dt.datetime.strptime(current_day, "%Y%m%d%H")

start_datetime = fcst_datetime - dt.timedelta(days=1, hours=1)

fcst_dates = pd.date_range(start=start_datetime, periods=24, freq='H').strftime("%Y%m%d%H")
Expand Down Expand Up @@ -167,6 +168,30 @@ def generate_regrider(rave_avail_hours, srcfield, tgtfield, weightfile, inp_file

return(regridder, use_dummy_emiss)


#Masks the edges of the data array to remove artifacts due to interpolation method on the top and bottom boundaries.
def mask_edges(data, mask_width=1):
"""
param data: numpy array, the data to mask
param mask_width: int, the width of the mask at each edge
return: numpy array, the masked data
"""
original_shape = data.shape
if mask_width < 1:
return data # No masking if mask_width is less than 1

# Mask top and bottom rows
data[:mask_width, :] = np.nan
data[-mask_width:, :] = np.nan

# Mask left and right columns
data[:, :mask_width] = np.nan
data[:, -mask_width:] = np.nan
assert data.shape == original_shape, "Data shape altered during masking."

return(data)


#process RAVE available for interpolation
def interpolate_rave(RAVE, rave_avail, rave_avail_hours, use_dummy_emiss, vars_emis, regridder,
srcgrid, tgtgrid, rave_to_intp, intp_dir, src_latt, tgt_latt, tgt_lont, cols, rows):
Expand Down Expand Up @@ -203,16 +228,17 @@ def interpolate_rave(RAVE, rave_avail, rave_avail_hours, use_dummy_emiss, vars_e
src_QA = xr.where(ds_togrid['FRE'] > 1000, src_rate, 0.0)
srcfield.data[...] = src_QA[0, :, :]
tgtfield = regridder(srcfield, tgtfield)
masked_tgt_data = mask_edges(tgtfield.data, mask_width=1)

if svar == 'FRP_MEAN':
Store_by_Level(fout, 'frp_avg_hr', 'Mean Fire Radiative Power', 'MW', '3D', '0.f', '1.f')
tgt_rate = tgtfield.data
tgt_rate = masked_tgt_data
fout.variables['frp_avg_hr'][0, :, :] = tgt_rate
print('=============after regridding===========' + svar)
print(np.sum(tgt_rate))
print(np.nansum(tgt_rate))
elif svar == 'FRE':
Store_by_Level(fout, 'FRE', 'FRE', 'MJ', '3D', '0.f', '1.f')
tgt_rate = tgtfield.data
tgt_rate = masked_tgt_data
fout.variables['FRE'][0, :, :] = tgt_rate
except (ValueError, KeyError) as e:
print(f"Error processing variable {svar} in {rave_file_path}: {e}")
Expand All @@ -221,4 +247,4 @@ def interpolate_rave(RAVE, rave_avail, rave_avail_hours, use_dummy_emiss, vars_e
except (OSError, IOError, RuntimeError, FileNotFoundError, TypeError, IndexError, MemoryError) as e:
print(f"Error reading NetCDF file {rave_file_path}: {e}")
else:
print(f"File not found or dummy emissions required: {rave_file_path}")
print(f"File not found or dummy emissions required: {rave_file_path}")