ERA5-Land selected indicators daily aggregates for Africa

Introduction

The ERA5-Land reanalysis from The Copernicus Programme is an incredible source of climate data with global coverage of land areas from 1950 to the present, at 10km spatial resolution. Its original data is at hourly interval, and monthly aggregates are also available at the Copernicus Data Store (CDS).

For some applications like Climate-Sensitive Diseases (CSD) modelling, the hourly interval may be too much detailed, but the monthly aggregation is too coarse.

For this reason, I created daily aggregates from some ERA5-Land indicators for some regions.

Methodology

I developed an R script using the KrigR package (Kusch and Davy 2022). The script downloads a set of indicators, starting on 1950, for a geographical bounding box covering Africa (coordinates -47.11,71.01,34.88,71.38) and aggregates the data from hourly to daily, saving its results as NetCDF files. Each resulting file covers a year’s month and presents data layers for each day of the respective month.

The table bellow contains the time aggregation functions applied to each climate indicator.

Indicator Daily aggregation function
2m temperature mean, max, min
2m dewpoint temperature mean
u component of wind mean
v component of wind mean
surface pressure mean
total precipitation sum

Datasets

Data from 1950 to 2023 is ready to use and available on Zenodo. Data from 1950 to 1969 is being processed and will be made available soon.

Year Zenodo deposit
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
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
2019
2020
2021
2022
2023

Usage statistics

Usage statistics of this and other datasets are available here.

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References

Kusch, Erik, and Richard Davy. 2022. “KrigRa Tool for Downloading and Statistically Downscaling Climate Reanalysis Data.” Environmental Research Letters 17 (2): 024005. https://doi.org/10.1088/1748-9326/ac48b3.