Using EOBS + upscaling¶

Here we explore how to best extract areal averaged precipitation and test this for UK precipitation within SEAS5 and EOBS. The code is inspired on Matteo De Felice’s blog – credits to him!

We create a mask for all 241 countries within Regionmask, that has predefined countries from Natural Earth datasets (shapefiles). We use the mask to go from gridded precipitation to country-averaged timeseries. We regrid EOBS to the SEAS5 grid so we can select the same grid cells in calculating the UK average for both datasets. The country outline would not be perfect, but the masks would be the same so the comparison would be fair.

I use the xesmf package for upscaling, a good example can be found in this notebook.

Import packages¶

We need the packages regionmask for masking and xesmf for regridding. I cannot install xesmf into the UNSEEN-open environment without breaking my environment, so in this notebook I use a separate ‘upscale’ environment, as suggested by this issue. I use the packages esmpy=7.1.0 xesmf=0.2.1 regionmask cartopy matplotlib xarray numpy netcdf4.

[2]:

##This is so variables get printed within jupyter
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

[3]:

##import packages
import os
import sys
sys.path.insert(0, os.path.abspath('../../../'))
os.chdir(os.path.abspath('../../../'))

[4]:

import xarray as xr
import numpy as np
import matplotlib.pyplot as plt
import cartopy
import cartopy.crs as ccrs
import matplotlib.ticker as mticker

import regionmask       # Masking
import xesmf as xe      # Regridding

Illustrate the SEAS5 and EOBS UK average¶

And the area-weighted average UK precipitation for SEAS5 and EOBS I plot here. For SEAS5 I plot the range, both min/max and the 2.5/97.5 % percentile of all ensemble members and leadtimes for each year.

[33]:

ax = plt.axes()

Quantiles = (SEAS5_UK_weighted['tprate']
             .quantile([0,2.5/100, 0.5, 97.5/100,1],
                       dim=['number','leadtime']
                      )
            )
ax.plot(Quantiles.time, Quantiles.sel(quantile=0.5),
        color='orange',
        label = 'SEAS5 median')
ax.fill_between(Quantiles.time.values, Quantiles.sel(quantile=0.025), Quantiles.sel(quantile=0.975),
                color='orange',
                alpha=0.2,
                label = '95% / min max')
ax.fill_between(Quantiles.time.values, Quantiles.sel(quantile=0), Quantiles.sel(quantile=1),
                color='orange',
                alpha=0.2)

EOBS_UK_weighted['rr'].plot(ax=ax,
                            x='time',
                            label = 'E-OBS')
plt.legend(loc = 'lower left',
           ncol=2 ) #loc = (0.1, 0) upper left

[33]:

[<matplotlib.lines.Line2D at 0x7f16cc417410>]

[33]:

<matplotlib.collections.PolyCollection at 0x7f16d5497610>

[33]:

<matplotlib.collections.PolyCollection at 0x7f16cc2ad7d0>

[33]:

[<matplotlib.lines.Line2D at 0x7f16cc50cb10>]

[33]:

<matplotlib.legend.Legend at 0x7f16cc3d18d0>

../../_images/Notebooks_2.Preprocess_2.3Upscale_47_5.png

And save the UK weighted average datasets¶

[34]:

SEAS5_UK_weighted.to_netcdf('Data/SEAS5_UK_weighted_masked.nc')
SEAS5_UK_weighted.to_dataframe().to_csv('Data/SEAS5_UK_weighted_masked.csv')
EOBS_UK_weighted.to_netcdf('Data/EOBS_UK_weighted_upscaled.nc') ## save as netcdf
EOBS_UK_weighted.to_dataframe().to_csv('Data/EOBS_UK_weighted_upscaled.csv') ## and save as csv.

[35]:

SEAS5_UK_weighted.close()
EOBS_UK_weighted.close()

Other methods¶

There are many different sources and methods available for extracting areal-averages from shapefiles. Here I have used shapely / masking in xarray. Something that lacks with this method is the weighted extraction from a shapefile, that is more precise on the boundaries. In R, raster:extract can use the percentage of the area that falls within the country for each grid cell to use as weight in averaging. For more information on this method, see the EGU 2018 course. For SEAS5, with its coarse resolution, this might make a difference. However, for it’s speed and reproducibility, we have chosen to stick to xarray.

We have used xarray where you can apply weights yourself to a dataset and then calculate the weighted mean. Sources I have used: * xarray weighted reductions * Matteo’s blog * regionmask package * Arctic weighted average example * area weighted temperature example.

And this pretty awesome colab notebook on seasonal forecasting regrids seasonal forecasts and reanalysis on the same grid before calculating skill scores.

Using EOBS + upscaling¶

Import packages¶

Load SEAS5 and EOBS¶

Masking¶

Upscale¶

Illustrate the SEAS5 and EOBS masks for the UK¶

Extract the spatial average¶

Illustrate the SEAS5 and EOBS UK average¶

And save the UK weighted average datasets¶

Other methods¶