CARRA tutorial to download and plot data, part III

Overview

Here, we do a comparison of CARRA with ERA5. The data was downloaded in script I.

To perform the comparison, we interpolate the ERA5 data onto the CARRA grid with help of the CDOs.

Define the files needed for the interpolation.

The CDO command uses the grid description from the CARRA file to interpolate the ERA5 data. The corresponding files are defined here.

DATADIR = '/ec/res4/scratch/smos/CARRA'
fCARRA = f'{DATADIR}/Raw_data/T2m_an_202306.grb'
fERA5 = f'{DATADIR}/ERA5/era5_t2m_202306.nc'
fERA5_CARRA_grid = f'{DATADIR}/ERA5/era5_t2m_202306_CARRA_grid.nc'

Interpolation to CARRA grid with CDO

After importing the CDOs into python it is one line to interpolate between the grids. Note that CDO can handle both GRIB format and NetCDF data. For instance, CARRA is downloaded in GRIB whereas we downloaded ERA5 as NetCDF.

More information regarding the CDOs (Climate data operators) can be found at https://code.mpimet.mpg.de/projects/cdo

from cdo import *
cdo   = Cdo()

cdo.remapbil(fCARRA, input=fERA5, output=fERA5_CARRA_grid)

'/ec/res4/scratch/smos/CARRA/ERA5/era5_t2m_202306_CARRA_grid.nc'

Open the datasets (similar to script II)

import xarray as xr

# Open Datasets
CARRA = xr.open_dataset(fCARRA)
ERA5  = xr.open_dataset(fERA5_CARRA_grid)

# Compute monthly mean
print("Compute the mean")
CARRA_mean = CARRA.mean(dim="time", keep_attrs=True)
ERA5_mean = ERA5.mean(dim="time", keep_attrs=True)
print("Done.")

# Change longitudes from 0-360 to -180 to 180, needed for the plotting
CARRA_mean = CARRA_mean.assign_coords(longitude=(((CARRA_mean.longitude + 180) % 360) - 180))
ERA5_mean = ERA5_mean.assign_coords(longitude=CARRA_mean.longitude)

# Change unit from K to C and add the unit to the attributes
CARRA_mean_C = CARRA_mean - 273.15
CARRA_mean_C = CARRA_mean_C.assign_attrs(CARRA_mean.attrs)
CARRA_mean_C.attrs['units'] = 'deg C'
ERA5_mean_C = ERA5_mean - 273.15
ERA5_mean_C = ERA5_mean_C.assign_attrs(ERA5_mean.attrs)
ERA5_mean_C.attrs['units'] = 'deg C'

Ignoring index file '/ec/res4/scratch/smos/CARRA/Raw_data/T2m_an_202306.grb.923a8.idx' older than GRIB file

Compute the mean
Done.

Plotting of ERA5

import matplotlib.pyplot as plt
import cartopy.crs as ccrs

print("Start plotting maps")
print("Print ERA5")
# create the figure panel and the map using the Cartopy Lambert conformal projection
fig, ax = plt.subplots(1, 1, figsize = (16, 8), subplot_kw={'projection': ccrs.LambertConformal(central_latitude=70.0, central_longitude=-40.0)})

# Plot the data
im = plt.pcolormesh(ERA5_mean_C.longitude, ERA5_mean_C.latitude, ERA5_mean_C.t2m, transform = ccrs.PlateCarree(), cmap='RdBu_r', vmin=-15, vmax=15)

# Set the figure title
ax.set_title('Near-surface air temperature for June 2023 based on ERA5', fontsize=16)
ax.coastlines(color='black')
ax.gridlines(draw_labels=True, linewidth=1, color='gray', alpha=0.5, linestyle='--') 

# Specify the colourbar
cbar = plt.colorbar(im,fraction=0.05, pad=0.04)
cbar.set_label('temperature')

# Save the figure
fig.savefig(f'{DATADIR}/Figures/ERA5_202306_map.png')

Start plotting maps
Print ERA5

/perm/smos/conda/envs/dataviz/lib/python3.9/site-packages/cartopy/mpl/geoaxes.py:1781: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which case, please supply explicit cell edges to pcolormesh.
  result = super().pcolormesh(*args, **kwargs)

Plot the difference between CARRA and ERA5

Note that the differences are computed in the plotting command itself.

print("Print CARRA - ERA5")
# create the figure panel and the map using the Cartopy Lambert conformal projection
fig, ax = plt.subplots(1, 1, figsize = (16, 8), subplot_kw={'projection': ccrs.LambertConformal(central_latitude=70.0, central_longitude=-40.0)})

# Plot the data, the differences are computed on the fly.
im = plt.pcolormesh(CARRA_mean_C.longitude, CARRA_mean_C.latitude, ERA5_mean_C.t2m - CARRA_mean_C.t2m,
                    transform = ccrs.PlateCarree(), cmap='RdBu_r', vmin=-10, vmax=10)

# Set the figure title
ax.set_title('Near-surface air temperature June 2023, difference ERA5 - CARRA', fontsize=16)
ax.coastlines(color='black')
ax.gridlines(draw_labels=True, linewidth=1, color='gray', alpha=0.5, linestyle='--') 

# Specify the colourbar
cbar = plt.colorbar(im,fraction=0.05, pad=0.04)
cbar.set_label('temperature')

# Save the figure
fig.savefig(f'{DATADIR}/Figures/ERA5-CARRA_map.png')

Print CARRA - ERA5

/perm/smos/conda/envs/dataviz/lib/python3.9/site-packages/cartopy/mpl/geoaxes.py:1781: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which case, please supply explicit cell edges to pcolormesh.
  result = super().pcolormesh(*args, **kwargs)