Met Office COVID-19 response dataset

This dataset is created, curated and updated for researchers looking to understand links between COVID-19 and environmental factors.

For more information check out our blog post and the data readme.

We are constantly updating the available data; subscribe to our news group to stay up to date or contact us if you have any requests or questions.

Imports and globals

Import the required modules, set up the default plot size and set some constants

import matplotlib.pyplot as plt 
import datetime

from azure.storage.blob import BlobClient, ContainerClient
from IPython.display import Markdown
from collections import namedtuple

%matplotlib inline
plt.rcParams['figure.figsize'] = (20.0, 10.0)

Set up the blob client with the connection details

account_url  = 'https://metdatasa.blob.core.windows.net/'
container_name = 'covid19-response'

# Create the ContainerClient object which will be used to enumerate blobs
container_client = ContainerClient(account_url=account_url,
                                   container_name=container_name,
                                   credential=None)

List the files under metoffice_global_daily/t1o5m_max

max_blobs = 10
for i_blob,blob in enumerate(container_client.list_blobs(
    name_starts_with='metoffice_global_daily/t1o5m_max')):
    print(f'{blob.name}')
    if i_blob >= max_blobs:
        break

metoffice_global_daily/t1o5m_max/global_daily_t1o5m_max_20200101.nc
metoffice_global_daily/t1o5m_max/global_daily_t1o5m_max_20200102.nc
metoffice_global_daily/t1o5m_max/global_daily_t1o5m_max_20200103.nc
metoffice_global_daily/t1o5m_max/global_daily_t1o5m_max_20200104.nc
metoffice_global_daily/t1o5m_max/global_daily_t1o5m_max_20200105.nc
metoffice_global_daily/t1o5m_max/global_daily_t1o5m_max_20200106.nc
metoffice_global_daily/t1o5m_max/global_daily_t1o5m_max_20200107.nc
metoffice_global_daily/t1o5m_max/global_daily_t1o5m_max_20200108.nc
metoffice_global_daily/t1o5m_max/global_daily_t1o5m_max_20200109.nc
metoffice_global_daily/t1o5m_max/global_daily_t1o5m_max_20200110.nc
metoffice_global_daily/t1o5m_max/global_daily_t1o5m_max_20200111.nc

Get a particular file based on the data required

data_end = (datetime.datetime.now() - datetime.timedelta(days=9)).date()
data_start = datetime.date(2020,1,1)

def url_from_properties(model, param, freq, stat=None, day=None, hour=None):
    
    assert model in ["global","ukv"]
    assert param in ["rain", "sh", "snow", "t1o5m", "pmsl","precip","sw"]
    assert freq in ["daily","hourly"]
    if freq == 'daily':
        assert stat in ['max', 'min', 'mean']
    else:
        assert stat is None  
    assert data_start <= day <= data_end
    
    stat = '_'+stat if stat else ''

    filepath = f'metoffice_{model}_{freq}/{param}{stat}/{model}_{freq}_{param}{stat}_{day:%Y%m%d}.nc'
    return f"{account_url}/{container_name}/{filepath}"

Properties = namedtuple('Properties',["model","param","freq","stat","day"])

files = [
    Properties("global","precip","daily","mean",datetime.date(2020,3,3)),
    Properties("ukv","t1o5m","daily","min",datetime.date(2020,4,1)),
    Properties("ukv","snow","hourly",None,datetime.date(2020,2,2)),
]

for file in files:
    path = url_from_properties(*file)
    print(path.replace(account_url,''))

/covid19-response/metoffice_global_daily/precip_mean/global_daily_precip_mean_20200303.nc
/covid19-response/metoffice_ukv_daily/t1o5m_min/ukv_daily_t1o5m_min_20200401.nc
/covid19-response/metoffice_ukv_hourly/snow/ukv_hourly_snow_20200202.nc

xarray and iris are useful tools for interacting with this sort of data

import xarray as xr
import iris
from io import BytesIO

Stream blob into memory and load dataset in xarray

data_description = Properties("global","precip","daily","mean",datetime.date(2020,1,30))
file_data = BytesIO(BlobClient.from_blob_url(
    url_from_properties(*data_description)).download_blob().readall())
ds = xr.open_dataset(file_data)
ds

array([-89.953125, -89.859375, -89.765625, ...,  89.765625,  89.859375,
        89.953125], dtype=float32)

array([7.031250e-02, 2.109375e-01, 3.515625e-01, ..., 3.596484e+02,
       3.597891e+02, 3.599297e+02], dtype=float32)

array(7200000000000, dtype='timedelta64[ns]')

array('2020-01-30T09:00:00.000000000', dtype='datetime64[ns]')

array('2020-01-30T11:00:00.000000000', dtype='datetime64[ns]')

[4915200 values with dtype=float32]

array(-2147483647, dtype=int32)

array([-1.,  5.])

array(['2020-01-30T00:00:00.000000000', '2020-01-30T18:00:00.000000000'],
      dtype='datetime64[ns]')

array(['2020-01-29T23:00:00.000000000', '2020-01-30T23:00:00.000000000'],
      dtype='datetime64[ns]')

Plot it with iris

import tempfile

ds.precipitation_flux.plot()

tmp = tempfile.NamedTemporaryFile(delete=False)
data_description = Properties("ukv","sw","hourly",None,datetime.date(2020,1,30))
tmp.write(BlobClient.from_blob_url(
    url_from_properties(*data_description)).download_blob().readall())
local_path = tmp.name
tmp.close()

sw = iris.load_cube(local_path)
sw

M01S01I202 (1)	forecast_period	forecast_reference_time	grid_latitude	grid_longitude
Shape	6	4	808	621
Dimension coordinates
forecast_period	x	-	-	-
forecast_reference_time	-	x	-	-
grid_latitude	-	-	x	-
grid_longitude	-	-	-	x
Auxiliary coordinates
time	x	x	-	-
Attributes
Conventions	CF-1.5
STASH	m01s01i202
source	Data from Met Office Unified Model
um_version	11.2

UK Met Office Global Weather Data for COVID-19 Analysis

Lizenz

Informationen zu den Daten

Speicherort

Quicklinks

Datenvolumen, Aufbewahrung und Aktualisierungsfrequenz

Schnellstart

Zeigen und Klicken

Azure-Blobbibliotheken

Download mit AzCopy

Wie die Daten organisiert sind

`metoffice_global_daily/`

`metoffice_global_hourly/`

`metoffice_ukv_daily/`

`metoffice_ukv_hourly/`

`regional_subset_data/`

`shapefiles/`

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Benachrichtigungen

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