support different input: ERA5, HRES, GFS

data_util.py

@@ -11,6 +11,7 @@ sfc_names = ['t2m', 'u10', 'v10', 'msl', 'tp']
 levels = [50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000]
 degree = 0.25
 
+
 def weighted_rmse(out, tgt):
     wlat = np.cos(np.deg2rad(tgt.lat))
     wlat /= wlat.mean()
@@ -35,20 +36,19 @@ def split_variable(ds, name):
 def save_like(output, input, step, save_dir="", input_type="hres", freq=6, split=False):
     if save_dir:
         os.makedirs(save_dir, exist_ok=True)
-        dtime = (step+1) * freq
+        step = (step+1) * freq
         init_time = pd.to_datetime(input.time.values[-1])
 
         if input_type.upper() == "HRES":
-            dtime = (step+2) * freq
+            step = (step+2) * freq
             init_time = pd.to_datetime(input.time.values[0])
 
         ds = xr.DataArray(
-            output[None, None],
-            dims=['member', 'time', 'dtime', 'level', 'lat', 'lon'],
+            output[None],
+            dims=['time', 'step', 'level', 'lat', 'lon'],
             coords=dict(
-                member=['FuXi'],
                 time=[init_time],
-                dtime=[dtime],
+                step=[step],
                 level=input.level,
                 lat=input.lat.values,
                 lon=input.lon.values,
@@ -70,8 +70,39 @@ def save_like(output, input, step, save_dir="", input_type="hres", freq=6, split
                 new_ds.append(v)
             ds = xr.merge(new_ds, compat="no_conflicts")
 
-        save_name = os.path.join(save_dir, f'{dtime:03d}.nc')
-        print(f'Save to {save_name} ...')
+        save_name = os.path.join(save_dir, f'{step:03d}.nc')
+        # print(f'Save to {save_name} ...')
+        ds.to_netcdf(save_name)
+
+
+def make_era5_input(init_time, data_dir, save_dir):   
+    ds = []
+    init_time =  pd.to_datetime(init_time)
+    hist_time = init_time - pd.Timedelta(hours=6)
+    print(f"init_time: {init_time}")
+    level = []
+
+    for name in pl_names + sfc_names:
+        data_name = os.path.join(data_dir, name, f'{init_time.year}')
+        v = xr.open_zarr(data_name)
+        v = v.sel(time=[hist_time, init_time])
+        v = v.rename({name: 'data'})
+        v.attrs = {}                
+        ds.append(v)
+
+        if name in pl_names:
+            level.extend([f'{name.lower()}{l}' for l in levels])
+
+        if name in sfc_names:
+            level.append(name.lower())
+        
+    ds = xr.concat(ds, 'level')
+    ds = ds.assign_coords(level=level)  
+
+    os.makedirs(save_dir, exist_ok=True)
+    save_name = os.path.join(save_dir, init_time.strftime("input.%Y%m%d.t%H.nc"))
+    print(f"save to {save_name} ...")
+    ds = ds.astype(np.float32)
     ds.to_netcdf(save_name)    
 
 
@@ -185,7 +216,8 @@ def make_gfs_input(init_time, data_dir, save_dir):
             return
 
         if v.shape[-2:] != (721, 1440):
-            v = v.interp(lat=lat, lon=lon, kwargs={"fill_value": "extrapolate"})
+            v = v.interp(lat=lat, lon=lon, kwargs={
+                         "fill_value": "extrapolate"})
             if np.isnan(v).sum() > 0:
                 print(f"{src_name} has nan value")
                 return
@@ -219,7 +251,8 @@ def make_gfs_input(init_time, data_dir, save_dir):
 
     input = xr.concat(input, "level")  # T
     input = input.rename({"latitude": "lat", "longitude": "lon"})
-    times = [pd.to_datetime(str(t), format='%Y%m%d%H') for t in input.time.values]
+    times = [pd.to_datetime(str(t), format='%Y%m%d%H')
+             for t in input.time.values]
     input = input.assign_coords(level=level)
     input = input.assign_coords(time=times)
 
@@ -229,7 +262,6 @@ def make_gfs_input(init_time, data_dir, save_dir):
     input.to_netcdf(save_name)
 
 
-
 def visualize(save_name, vars=[], titles=[], vmin=None, vmax=None):
     import cartopy.crs as ccrs
     import matplotlib.pyplot as plt
@@ -277,4 +309,14 @@ def test_visualize():
     visualize('tp.jpg', [tp], ['tp'], vmin=0, vmax=20)
 
 
-# test_make_input()
+def test_rmse(output_name, target_name):
+    output = xr.open_dataarray(output_name)
+    output = output.isel(time=0).sel(step=120)
+    target = xr.open_dataarray(target_name)
+
+    for level in ["z500", "t850", "t2m", "u10", "v10", "msl", "tp"]:
+        out = output.sel(level=level)
+        tgt = target.sel(level=level)
+        rmse = weighted_rmse(out, tgt).load()
+        print(f"{level.upper()} 120h rmse: {rmse:.3f}")
+