Met Office UKV high-resolution atmosphere model data - Met Office UKV high-resolution atmosphere model data (Jupyter Notebook) published in the Environmental Data Science book

Context¶

Purpose¶

To load, plot, regrid and extract an urban region from the UKV gridded model data using the Iris package.

Sensor description¶

Met Office UKV model data is fairly high resolution (approximately 1 km horizontal) and available over the whole of the UK for a variety of atmospheric variables at surface and pressure levels. A selection of variables has been made openly available as part of the Met Office contribution to the COVID 19 modelling effort McCaie, 2020.

This notebook uses a single sample data file for 1.5 m temperature included with the notebook.

Highlights¶

Data for the whole UK is loaded and plotted using Iris
Data is regridded to a geographic projection
A region over London is extracted

Contributions¶

Dataset originator/creator¶

Met Office Informatics Lab (creator)
Microsoft (support)
European Regional Development Fund (support)

Dataset authors¶

Met Office

Install and load libraries¶

Source

import os
import iris
import iris.analysis
import iris.plot as iplt
from iris.coords import DimCoord
from iris.coord_systems import GeogCS
from iris.cube import Cube

from iris.fileformats.pp import EARTH_RADIUS

import requests
import urllib.request
import pooch

import numpy as np
import matplotlib.pyplot as plt

import warnings
warnings.filterwarnings(action='ignore')

%matplotlib inline

Set project structure¶

notebook_folder = './notebook'
if not os.path.exists(notebook_folder):
    os.makedirs(notebook_folder)

Retrieve and load a sample data file¶

filepath = 'https://metdatasa.blob.core.windows.net/covid19-response-non-commercial/metoffice_ukv_daily/t1o5m_mean/'
filename = 'ukv_daily_t1o5m_mean_20150801.nc'

response = requests.get(filepath+filename)
if response.status_code == 200:
    urllib.request.urlretrieve(filepath+filename, os.path.join(notebook_folder, filename))
else:
    pooch.retrieve(
        url="doi:10.5281/zenodo.7087009/ukv_daily_t1o5m_mean_20150801.nc",
        known_hash="md5:b71e092ead092f419f12073ddf2d3256",
        path=notebook_folder,
        fname="ukv_daily_t1o5m_mean_20150801.nc"
    )

Downloading data from 'doi:10.5281/zenodo.7087009/ukv_daily_t1o5m_mean_20150801.nc' to file '/home/jovyan/notebook/ukv_daily_t1o5m_mean_20150801.nc'.

air_temp = iris.load_cube(os.path.join(notebook_folder, filename))
air_temp.coord('grid_latitude').guess_bounds()
air_temp.coord('grid_longitude').guess_bounds()

Visualisation¶

Here we use the Iris wrapper to matplotlib pyplot to plot the gridded data with added gridlines and coastline.

Source

plt.figure(figsize=(30, 10))
iplt.pcolormesh(air_temp)
plt.title("UKV Air temperature", fontsize="xx-large")
cbar = plt.colorbar()
cbar.set_label('Temperature (' + str(air_temp.units) + ')')
ax = plt.gca()
ax.coastlines(resolution="50m")
ax.gridlines()

<cartopy.mpl.gridliner.Gridliner at 0x7f4dd4d72d30>

Regridding from Azimuthal equal-area projection to geographic¶

Create a target cube with a lat-lon coord system for regrid¶

It is filled with random data so we can plot it to check it looks correct.

latitude = DimCoord(np.linspace(48.5, 59.5, 1222),
                    standard_name='latitude',
                    coord_system = GeogCS(EARTH_RADIUS),
                    units='degrees')
longitude = DimCoord(np.linspace(-10.5, 2.0, 1389),
                     standard_name='longitude',
                    coord_system = GeogCS(EARTH_RADIUS),                     
                     units='degrees')
global_cube = Cube(np.random.uniform(low=0.0, high=1.0, size=(1222, 1389)),
                   dim_coords_and_dims=[(latitude, 0),
                                        (longitude, 1)])

global_cube.coord('latitude').guess_bounds()
global_cube.coord('longitude').guess_bounds()

Source

plt.figure(figsize=(30, 10))
iplt.pcolormesh(global_cube)
plt.title("Target global cube", fontsize="xx-large")
ax = plt.gca()
ax.coastlines(resolution="50m")
ax.gridlines()

<cartopy.mpl.gridliner.Gridliner at 0x7f4dd4b475e0>

Perform the regridding from source data cube to target cube¶

# Note we need to use extrapolation masking in case regridded source data is actually smaller
# than the target cube extents
global_air_temp = air_temp.regrid(global_cube, iris.analysis.Linear(extrapolation_mode="mask"))

Plot the regridded data to check it is correct¶

Source

plt.figure(figsize=(30, 10))

iplt.pcolormesh(global_air_temp)
plt.title("UKV Air temperature on a global grid", fontsize="xx-large")
cbar = plt.colorbar()
cbar.set_label('Temperature (' + str(global_air_temp.units) + ')')
ax = plt.gca()
ax.coastlines(resolution="50m")
ax.gridlines()

<cartopy.mpl.gridliner.Gridliner at 0x7f4dd4950310>

Extract the London Region¶

Use the Iris Intersection method and supply the region lat-lon bounds¶

min_lon = -0.52
min_lat = 51.3
max_lon = 0.3
max_lat = 51.7

air_temp_london = global_air_temp.intersection(longitude=(min_lon, max_lon), latitude=(min_lat, max_lat))

Plot the results¶

Source

plt.figure(figsize=(20, 5))

iplt.pcolormesh(air_temp_london)
plt.title("UKV Air temperature for london", fontsize="xx-large")
cbar = plt.colorbar()
cbar.set_label('Temperature (' + str(air_temp_london.units) + ')')
ax = plt.gca()
ax.coastlines(resolution="50m")
ax.gridlines()

plt.show()

Save as a new NetCDF file¶

iris.save(air_temp_london, os.path.join(notebook_folder,'ukv_london_sample.nc'))

Summary¶

This notebook has demonstrated the use of the Iris package to easily load, plot and manipulate gridded environmental NetCDF data.

Citing this Notebook¶

Please see CITATION.cff for the full citation information. The citation file can be exported to APA or BibTex formats (learn more here).

Additional information¶

Review: This notebook has been reviewed by one or more members of the Environmental Data Science book community. The open review is available here.

License: The code in this notebook is licensed under the MIT License. The Environmental Data Science book is licensed under the Creative Commons by Attribution 4.0 license. See further details here.

Contact: If you have any suggestion or report an issue with this notebook, feel free to create an issue or send a direct message to environmental.ds.book@gmail.com.

from datetime import date

print('Notebook repository version: v2.0.0')
print(f'Last tested: {date.today()}')

Notebook repository version: v2.0.0
Last tested: 2025-03-25

References¶

McCaie, T. (2020). Met Office and partners offer data and compute platform for COVID-19 researchers (Met Office Informatics Lab, Ed.). https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f