Wind Chill Index with Reanalysis Data

Wind Chill Index with Reanalysis Data#

About#

This tutorial will demonstrate how to combine multiple variables of climate data to create an index. The example provided is of the wind speed index, but similar principles can be applied to many other types of single or multi-variable indices.

The data is provided by Copernicus Climate Change Service (C3S) and includes regional climate reanalysis data (UERRA) of wind speed and temperature over Europe from 1961 to 2019.

The tutorial will first show how to download the necessary data from the C3S Climate Data Store (CDS). It will then describe how to apply a commonly used Wind Chill Index formula to the data, and finally plot a map of the wind chill climatology for a particular day over Europe.

The steps shown in this tutorial can be applied to other climate datasets, such as the 5th version of the ECMWF Reanalysis (ERA5), or other data.

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Search, download and view data#

Before we begin we must prepare our environment. This includes installing the Application Programming Interface (API) of the CDS, and importing the various python libraries that we will need.

Install CDS API#

To install the CDS API, run the following command. We use an exclamation mark to pass the command to the shell (not to the Python interpreter).

!pip install cdsapi

Requirement already satisfied: cdsapi in c:\users\cxcs\anaconda3\lib\site-packages (0.5.1)
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Requirement already satisfied: tqdm in c:\users\cxcs\anaconda3\lib\site-packages (from cdsapi) (4.62.3)
Requirement already satisfied: certifi>=2017.4.17 in c:\users\cxcs\anaconda3\lib\site-packages (from requests>=2.5.0->cdsapi) (2021.10.8)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\users\cxcs\anaconda3\lib\site-packages (from requests>=2.5.0->cdsapi) (1.26.7)
Requirement already satisfied: charset-normalizer~=2.0.0 in c:\users\cxcs\anaconda3\lib\site-packages (from requests>=2.5.0->cdsapi) (2.0.4)
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Requirement already satisfied: colorama in c:\users\cxcs\anaconda3\lib\site-packages (from tqdm->cdsapi) (0.4.4)

Import libraries#

We will be working with data in NetCDF format. To best handle this data we will use libraries for working with multidimensional arrays, in particular Xarray. We will also need libraries for plotting and viewing data, in this case we will use Matplotlib and Cartopy.

# CDS API
import cdsapi

# Libraries for working with multidimensional arrays
import numpy as np
import xarray as xr

# Libraries for plotting and visualising data
import matplotlib.path as mpath
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature

# Disable warnings for data download via API
import urllib3 
urllib3.disable_warnings()

Enter your CDS API key#

We will request data from the Climate Data Store (CDS) programmatically with the help of the CDS API. Let us make use of the option to manually set the CDS API credentials. First, you have to define two variables: URL and KEY which build together your CDS API key. The string of characters that make up your KEY include your personal User ID and CDS API key. To obtain these, first register or login to the CDS (http://cds.climate.copernicus.eu), then visit https://cds.climate.copernicus.eu/api-how-to and copy the string of characters listed after “key:”. Replace the ######### below with this string.

URL = 'https://cds.climate.copernicus.eu/api/v2'
KEY = '##################################'

Here we specify a data directory in which we will download our data and all output files that we will generate:

DATADIR = './'

Search for climate data to calculate wind chill index#

The wind chill index we will calculate takes two parameters as input, these are 2m near-surface air temperature, and 10m wind speed. Data for these parameters are available as part of the UERRA regional reanalysis dataset for Europe for the period 1961 to 2019. We will search for this data on the CDS website: http://cds.climate.copernicus.eu. The specific dataset we will use is the UERRA regional reanalysis for Europe on single levels from 1961 to 2019.

Having selected the dataset, we now need to specify what product type, variables, temporal and geographic coverage we are interested in. These can all be selected in the “Download data” tab. In this tab a form appears in which we will select the following parameters to download:

Origin: UERRA-HARMONIE
Variable: 10m wind speed and 2m temperature (these will need to be selected one at a time)
Year: 1989 to 2018
Month: January
Day: 15
Time: 06:00
Format: NetCDF

At the end of the download form, select “Show API request”. This will reveal a block of code, which you can simply copy and paste into a cell of your Jupyter Notebook (see cells below). You will do this twice: once for 10m wind speed and again for 2m temperature.

Download data#

… having copied the API request into the cell below, running this will retrieve and download the data you requested into your local directory. However, before you run the cell below, the terms and conditions of this particular dataset need to have been accepted in the CDS. The option to view and accept these conditions is given at the end of the download form, just above the “Show API request” option.

Note: UERRA data are stored on tapes in MARS, the ECMWF Meteorological Archival and Retrieval System. Accessing data from tapes is generally slower than accessing data directly from disk. The data requests below may take some hours to complete. For a quicker response you can replace the UERRA data request with similar download parameters for lower resolution ERA5 global reanalysis datasets.

c = cdsapi.Client(url=URL, key=KEY)
c.retrieve(
    'reanalysis-uerra-europe-single-levels',
    {
        'origin': 'uerra_harmonie',
        'variable': '10m_wind_speed',
        'year': [
            '1989', '1990', '1991',
            '1992', '1993', '1994',
            '1995', '1996', '1997',
            '1998', '1999', '2000',
            '2001', '2002', '2003',
            '2004', '2005', '2006',
            '2007', '2008', '2009',
            '2010', '2011', '2012',
            '2013', '2014', '2015',
            '2016', '2017', '2018',
        ],
        'month': '01',
        'day': '15',
        'time': '06:00',
        'format': 'netcdf',
    },
    f'{DATADIR}UERRA_ws10m.nc')

c = cdsapi.Client(url=URL, key=KEY)
c.retrieve(
    'reanalysis-uerra-europe-single-levels',
    {
        'origin': 'uerra_harmonie',
        'variable': '2m_temperature',
        'year': [
            '1989', '1990', '1991',
            '1992', '1993', '1994',
            '1995', '1996', '1997',
            '1998', '1999', '2000',
            '2001', '2002', '2003',
            '2004', '2005', '2006',
            '2007', '2008', '2009',
            '2010', '2011', '2012',
            '2013', '2014', '2015',
            '2016', '2017', '2018',
        ],
        'month': '01',
        'day': '15',
        'time': '06:00',
        'format': 'netcdf',
    },
    f'{DATADIR}UERRA_t2m.nc')

Calculate wind chill index#

The wind chill index is a metric in temperature-like units, e.g. Kelvin or °C, and represents the lowering of body temperature due to a combination of low temperature and wind.

Since 2001, Canada, the United States and the United Kingdom have implemented the new wind chill index which is defined with the following formula:

\(\textit{T}_{WC} = 13.12 + 0.6215\textit{T}_{a} - 11.37\upsilon^{0.16} + 0.3965\textit{T}_{a}\upsilon^{0.16}\)

where:

\(\textit{T}_{WC}\) = wind chill index
\(\textit{T}_{a}\) = air temperature in degrees Celsius
\(\upsilon\) = wind speed at 10 m standard anemometer height, in kilometres per hour

We will apply this formula on our data to calculate the wind chill index for a particular period of time over Europe.

To calculate \(\textit{T}_{WC}\) we first have to ensure our data is in the right units. For the wind speed we need to convert from m/s to km/h, and for air temperature we need to convert from Kelvin to degrees Celsius:

# wind speed, convert from m/s to km/h: si10 * 1000 / (60*60)
w = aw * 3600 / 1000
# air temperature, convert from Kelvin to Celsius: t2m - 273.15
t = at - 273.15

Now we can calculate the North American and United Kingdom wind chill index: \(\textit{T}_{WC} = 13.12 + 0.6215\textit{T}_{a} - 11.37\upsilon^{0.16} + 0.3965\textit{T}_{a}\upsilon^{0.16}\)

twc = 13.12 + (0.6215*t) - (11.37*(w**0.16)) + (0.3965*t*(w**0.16))

Let’s calculate the average wind chill for 06:00 on 15 January for the 30 year period from 1989 to 2018:

twc_mean = twc.mean(dim='time')

Before we plot the results, let’s take the same European subset as that used for the C3S Climate Bulletins.

twc_mean_sub = twc_mean.where((twc_mean.latitude < 72) & 
                              (twc_mean.latitude > 34) & 
                              (twc_mean.longitude < 40) & 
                              (twc_mean.longitude > -25), drop=True)

Now let’s plot the average wind chill for this time over Europe:

fig, ax = plt.subplots(1, 1, figsize = (18, 9), 
                       subplot_kw={'projection': ccrs.PlateCarree()})

im = plt.pcolormesh(twc_mean_sub.longitude, twc_mean_sub.latitude, twc_mean_sub, cmap='cool') 

ax.set_title('Wind Chill Index 12:00, 15 Jan, 1989 to 2018', fontsize=16)
ax.gridlines(draw_labels=False, linewidth=1, color='gray', alpha=0.5, linestyle='--') 
ax.coastlines(color='black')
ax.set_extent([-25, 40, 34, 72], crs=ccrs.PlateCarree())

cbar = plt.colorbar(im,fraction=0.04, pad=0.01)
cbar.set_label('Wind Chill Index') 

fig.savefig(f'{DATADIR}UERRA_wind_chill_index_midJan.png')

_images/6c94576f3fad18623c014af3781264a8c3e24119cce80614cbbf53728bc1631c.png

Under which category of the wind chill index do the coldest areas in Europe generally lie at this time (see chart below)?

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RicHard-59, CC BY-SA 3.0 https://creativecommons.org/licenses/by-sa/3.0, via Wikimedia Commons

Exercise: Repeat process with ERA5 data and compare results#

So far you have plotted wind chill using the UERRA regional reanalysis dataset, but how accurate is this plot? One way to assess a dataset is to compare it with an alternative independent one to see what differences there may be. An alternative to UERRA is the ECMWF Reanalysis datasets, ERA5 (currently in it’s 5th version). Repeat the steps above with ERA5 data and compare your results with those obtained using UERRA.

Wind Chill Index with Reanalysis Data

Contents

Wind Chill Index with Reanalysis Data#

About#

Search, download and view data#

Install CDS API#

Import libraries#

Enter your CDS API key#

Search for climate data to calculate wind chill index#

Download data#

Inspect Data#

Calculate wind chill index#

Exercise: Repeat process with ERA5 data and compare results#