def collect_WRF(location, minyear, maxyear):
""" Load uncorrected WRF run data. """
wrf_ds = xr.open_dataset('_Data/Bannister/Bannister_WRF_raw.nc')
if type(location) == str:
loc_ds = ls.select_basin(wrf_ds, location)
else:
lat, lon = location
loc_ds = wrf_ds.interp(
coords={"lon": lon, "lat": lat}, method="nearest")
tim_ds = loc_ds.sel(time=slice(minyear, maxyear))
ds = tim_ds.assign_attrs(plot_legend="WRF")
return ds
def collect_ERA5(location, minyear, maxyear):
""" Downloads data from ERA5 for a given location"""
era5_ds = download_data(location, xarray=True)
if type(location) == str:
loc_ds = ls.select_basin(era5_ds, location)
else:
lat, lon = location
loc_ds = era5_ds.interp(coords={
"lon": lon,
"lat": lat
},
method="nearest")
tim_ds = loc_ds.sel(time=slice(minyear, maxyear))
ds = tim_ds.assign_attrs(plot_legend="ERA5") # in mm/day
return ds
def collect_GPM(location, minyear, maxyear):
""" Load GPM data """
gpm_ds = xr.open_dataset("_Data/GPM/gpm_pr_unc_2000-2010.nc")
if type(location) == str:
loc_ds = ls.select_basin(gpm_ds, location)
else:
lat, lon = location
loc_ds = gpm_ds.interp(coords={
"lon": lon,
"lat": lat
},
method="nearest")
tim_ds = loc_ds.sel(time=slice(minyear, maxyear))
ds = tim_ds.assign_attrs(plot_legend="TRMM") # in mm/day
return ds
def collect_CRU(location, minyear, maxyear):
""" Downloads interpolated data from CRU model"""
cru_ds = xr.open_dataset("_Data/CRU/interpolated_cru_1901-2019.nc")
if type(location) == str:
loc_ds = ls.select_basin(cru_ds, location)
else:
lat, lon = location
loc_ds = cru_ds.interp(coords={
"lon": lon,
"lat": lat
},
method="nearest")
tim_ds = loc_ds.sel(time=slice(minyear, maxyear))
ds = tim_ds.assign_attrs(plot_legend="CRU") # in mm/month
return ds
def collect_APHRO(location, minyear, maxyear):
""" Downloads data from APHRODITE model"""
aphro_ds = xr.open_dataset("_Data/APHRODITE/aphrodite_indus_1951_2016.nc")
if type(location) == str:
loc_ds = ls.select_basin(aphro_ds, location)
else:
lat, lon = location
loc_ds = aphro_ds.interp(coords={
"lon": lon,
"lat": lat
},
method="nearest")
tim_ds = loc_ds.sel(time=slice(minyear, maxyear))
ds = tim_ds.assign_attrs(plot_legend="APHRODITE") # in mm/day
return ds