Wind Chill Index with Reanalysis Data

Wind Chill Index with Reanalysis Data#

This notebook can be run on free online platforms, such as Binder, Kaggle and Colab, or they can be accessed from GitHub. The links to run this notebook in these environments are provided here, but please note they are not supported by ECMWF.

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Learning Objectives#

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.

Prepare your environment#

Set up the CDSAPI and your credentials#

The code below will ensure that the cdsapi package is installed. If you have not setup your ~/.cdsapirc file with your credenials, you can replace None with your credentials that can be found on the how to api page (you will need to log in to see your credentials).

!pip install -q cdsapi
# If you have already setup your .cdsapirc file you can leave this as None
cdsapi_key = None
cdsapi_url = None

Requirement already satisfied: cdsapi in c:\users\cxcs\anaconda3\lib\site-packages (0.5.1)
Requirement already satisfied: requests>=2.5.0 in c:\users\cxcs\anaconda3\lib\site-packages (from cdsapi) (2.26.0)
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)
Requirement already satisfied: idna<4,>=2.5 in c:\users\cxcs\anaconda3\lib\site-packages (from requests>=2.5.0->cdsapi) (3.2)
Requirement already satisfied: colorama in c:\users\cxcs\anaconda3\lib\site-packages (from tqdm->cdsapi) (0.4.4)

(Install and) 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()

Specify data directory#

# Directory to store data
DATADIR = './data_dir/'
# Create this directory if it doesn't exist
os.makedirs(DATADIR, exist_ok=True)

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#

Learning Objectives#

Prepare your environment#

Set up the CDSAPI and your credentials#

(Install and) Import libraries#

Specify data directory#

Explore Data#

Search for the Data#

Download the Data#

Inspect Data#

Calculate wind chill index#

Exercise: Repeat process with ERA5 data and compare results#