CARRA tutorial to download and plot data, part IV

Overview

Here, plotting time series for a single grid cell.

Loading the data

Same procedure as in the earlier scripts.

import xarray as xr

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

CARRA_ds = xr.open_dataset(fCARRA)
CARRA_da = CARRA_ds['t2m']
ERA5_ds  = xr.open_dataset(fERA5)
#ERA5_ds = ERA5_ds.assign_coords(longitude=CARRA_ds.longitude)
ERA5_da = ERA5_ds['t2m']

# Change unit from K to C and add the unit to the attributes
ERA5 = ERA5_da - 273.15
ERA5 = ERA5.assign_attrs(ERA5_da.attrs)
ERA5.attrs['units'] = 'deg C'
CARRA = CARRA_da - 273.15
CARRA = CARRA.assign_attrs(CARRA_da.attrs)
CARRA.attrs['units'] = 'deg C'

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

Libraries for plotting and visualising data

import matplotlib.pyplot as plt
import pandas as pd

Choose a point, here in the middle of Greenland.

print(CARRA.latitude[775,430].data)
print(CARRA.longitude[775,430].data)
p_CARRA = CARRA[:,775,430]
p_ERA5  =  ERA5[:,775,430]

75.00409928068338
314.9650694326491

Create pandas.DataFrame for plotting.

Pandas has functionalities which are beneficial here when plotting the time series. For instance, the metadata from the time axes is used automatically to describe the x-axis.

CARRA_df = p_CARRA.to_dataframe()
CARRA_df = CARRA_df.rename(columns={'t2m':'CARRA'})
ERA5_df  = p_ERA5.to_dataframe()
ERA5_df  = ERA5_df.rename(columns={'t2m':'ERA5'})

print("Start plotting time series")
plt.rcParams.update({'font.size': 18}) # must set in top
CARRA_df['CARRA'].plot(
    figsize=(14,8),
    fontsize=14,
    legend=['CARRA'],
    xlabel='\nday',
    ylabel='Temperature',
    title='\nNear surface temperature central Greenland in June 2023\n',
    )
ERA5_df['ERA5'].plot(
    legend=['ERA5'],
    )
plt.savefig(f'{DATADIR}/Figures/Central_Greenland_202306.png')

Start plotting time series