WEMC Tech Blog 4.5: Calculating NUTS2 Regional Averages with Land Sea Mask.

This post serves as a continuation on the techniques described in Tech Blog #4. So please familiarise with those steps beforehand.

NUTS2

As before, load in the NUTS shapefiles, this time selecting NUTS2.

NUTS2 is higher resolution and as a result, there are many more shapefiles. NUTS2 contains polygons at a regional (sub-country) level. In total there are 332 shapes for the Eurostat EU region.

Obtain the latest shapefiles from Eurostat.

Select a particular region with the 4 character NUTS_ID string.

For example, Stuttgart (DE):

region = nuts[nuts['NUTS_ID'] == 'DE11']

 

Land Sea Mask (LSM)

The land-sea mask is a field that contains, for every grid point, the proportion of land in the grid box. The values are between 0 (sea) and 1 (land).

You can obtain the ECMWF LSM NetCDF file here

A quick plot of the LSM gives this visual representation of the mask:

Calculate NUT2 Area Averages, with LSM

Now, following the same steps as described in Tech Blog #4, it is possible to calculate the area averages, with the LSM applied.

14/5/19 Update:

The code has been updated and now uses pre-computed LSM from the NUTS shape files as individual .nc files.

This speeds up the computation process considerably, so the shape files don’t have to be computed in the loop on each iteration. You can download the precomputed shapes here.


# coding: utf-8

# NUTS Averaging V3
# if running on C3S_Energy VM, activate conda before running: source activate c3s_wemc
# luke.sanger@wemcouncil.org

# import packages
import iris
import geopandas as gpd
import pandas as pd
import numpy as np
import iris.pandas
import iris.analysis.cartography
import shapely
import glob, os
import warnings
import time

# IMPORTANT: place this file in directory where ERA5 files are located and define path below:
path = r'your/path/here'
allFiles = [os.path.basename(x) for x in glob.glob(path + r"/*.nc")]

# IMPORTANT: specify one nuts level for processing .nc files (Must be nut0 or nut2)
nuts = 'nut0'
# nuts = 'nut2'

if nuts == 'nut0':
# load nuts0 country regions for processing
    path2 = r'your/path/here/nuts0_masked_nc/'
    nutsFiles = [os.path.basename(x) for x in glob.glob(path2 + r"/*.nc")]
elif nuts == 'nut2':
# load nuts2 sub-country regions for processing
    path2 = r'your/path/here/nuts2_masked_nc/'
    nutsFiles = [os.path.basename(x) for x in glob.glob(path2 + r"/*.nc")]

# get first cube in list to get cosine weights
nutslist2 = iris.load(path2 + nutsFiles[0])
nutcube2 = nutslist2[0]
nutcube2 = nutcube2.intersection(longitude=(-180, 180))
lats2 = nutcube2.coord('latitude').points
lons2 = nutcube2.coord('longitude').points

# get array of latitudes from nutcube
cos_lat = iris.analysis.cartography.cosine_latitude_weights(nutcube2)

# hide pointless iris warning message about lat/lon
warnings.filterwarnings("ignore", category=UserWarning)

# run timer for measuring operation speed
start = time.time()

# literate through nc files for processing
for file_ in allFiles:
    cubelist = iris.load(file_)
    cube = cubelist[0]
    cube = cube.intersection(longitude=(-180, 180))
    df = pd.DataFrame()
    name = os.path.splitext(file_)[0]
    print('loaded ' + name + ' for ' + nuts + ' averaging')

    # iterate through nuts regions 
    for nut in nutsFiles:
        nutslist = iris.load(path2 + nut)
        nutcube = nutslist[0]
        nutcube = nutcube.intersection(longitude=(-180, 180))
        lats = nutcube.coord('latitude').points
        lons = nutcube.coord('longitude').points

        # get NUTSID from filename for column naming
        if nuts == 'nut0':     
            edit = str(nut)
            NUTSID = edit[:-15]
            print('processing ' + NUTSID + ' area, level = ' + nuts)
        elif nuts == 'nut2':
            edit = str(nut)
            NUTSID = edit[:-13]
            print('processing ' + NUTSID + ' area, level = ' + nuts)    
        
        # multiply region by cosine lats
        lsm_cos_lat = nutcube.copy()
        lsm_cos_lat.data *= cos_lat
        
        # apply the lsm_cos_lat to the main cube
        cube_lsm_cos_lat = cube.copy()
        cube_lsm_cos_lat.data *= lsm_cos_lat.data
        
        #sum of lsm_cos_lat 
        lsm_cos_lat_sum = lsm_cos_lat.collapsed(['latitude','longitude'], iris.analysis.SUM, weights=None)

        # sum of mc_lsm_cos_lat
        cube_lsm_cos_lat_sum = cube_lsm_cos_lat.collapsed(['latitude','longitude'], iris.analysis.SUM, weights=None)
        
        # divide sum of cube_lsm_cos_lat by sum of lsm_cos_lat
        end_nuts_lsm_cos_lat_sum = cube_lsm_cos_lat_sum.copy()
        end_nuts_lsm_cos_lat_sum.data /= lsm_cos_lat_sum.data
        
        # save as series, rename colume to NUTSID and concat to dataframe
        dfs = iris.pandas.as_series(end_nuts_lsm_cos_lat_sum, copy=True)
        dfs.rename(columns={1: NUTSID}, inplace=True) 
        df = pd.concat((df, dfs.rename(NUTSID)), axis=1)
    
    # organise dataframe columns alphabetically
    df1 = df.groupby(axis=1, level=0).first() 
  
    # create csv name
    n1 = str(name)
    n2 = n1.rstrip(".nc")
    csv_name = n2[:32] + nuts + n2[36:] + ".csv"

    df1.to_csv(csv_name, mode='a')
    
    print(csv_name +' created')
    
print(nuts + ' processing complete!')
end = time.time()
print('time elapsed in seconds:')
print(end - start)
NUTS2 Area Average

As usual, you can also get the code from my GitHub page.

by Luke Sanger (WEMC Data Engineer, 2019)

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