Adds smoke mask update for dev-sci branch (#563)

ush/interp_tools.py

@@ -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")
@@ -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):
@@ -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}")
@@ -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}")