Revealing the spatiotemporal precipitation patterns of ECMWF fifth-generation reanalyses since the mid-20th century in West-Africa

• The reanalyses ERA5 and ERA5-Land show identical precipitation on spatial and temporal scales. • Both reanalyses capture better seasonal and interannual precipitation variability. • Daily and hourly precipitation distributions are poorly captured by both reanalyses. • Selecting the Nearest Pixel (SNP) and Inverse Distance Weighted (IDW) give identical results. • Performance changes significantly under the influence of topography and wind. Reanalysis datasets are a viable alternative to ensure the continuity of hydrological studies and to assess climate variability in regions where the availability and/or quality of observational data is limited. This study revealed the performance of the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses, namely ERA5 (0.25°x0.25°) and ERA5-Land (0.1°x0.1°), over West Africa. They were analyzed on spatial and temporal scales (annual, monthly, daily, and hourly). The reanalysis time series were obtained by Selecting the Nearest Pixel (SNP) closest to the point station or by the Inverse Distance Weighted (IDW) method and compared with the observational data using Pearson correlation (c) and Relative Mean Absolute Error (RMAE). The results showed on average similar performance between ERA5 and ERA5-Land reanalyses, and also between SNP and IDW methods. However, a significant difference can be observed at some stations/areas due to the influence of topography and wind. Both reanalyses performed well on a spatial scale, with a clear distinction between the wet and dry areas. They also performed well on an annual ( c = 0.60 and RMAE=22 %) and monthly ( c = 0.80 and RMAE=40 %) scales. For 713 point stations, ERA5 and ERA5-Land showed negative trends in interannual variability for 82 % and 94 %, respectively. They agreed with the trends derived from observations (negative trends for 96 % of stations). However, the reanalyses performed poorly on both daily ( c = 0.21 and RMAE=128 %) and hourly ( c = 0.06 and RMAE=170 %) scales. Maximum daily precipitation (RX1day) is also less well represented, sometimes with negative correlations. The ECMWF fifth generation reanalyses need to be adjusted to improve their performance in describing precipitation.

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Publication Details

Journal
Environmental Advances
Published
2025-04-20
DOI
https://doi.org/10.1016/j.envadv.2025.100636
Citations
5
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
5.53

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article

Revealing the spatiotemporal precipitation patterns of ECMWF fifth-generation reanalyses since the mid-20th century in West-Africa

Françoise Comandan, René Bodjrènou, Luc Ollivier Sintondji
5 citations
Environmental Advances
Climate variability and models
5.53
article

Revealing the spatiotemporal precipitation patterns of ECMWF fifth-generation reanalyses since the mid-20th century in West-Africa

Françoise Comandan, René Bodjrènou, Luc Ollivier Sintondji
article en
5 citations

Abstract

• The reanalyses ERA5 and ERA5-Land show identical precipitation on spatial and temporal scales. • Both reanalyses capture better seasonal and interannual precipitation variability. • Daily and hourly precipitation distributions are poorly captured by both reanalyses. • Selecting the Nearest Pixel (SNP) and Inverse Distance Weighted (IDW) give identical results. • Performance changes significantly under the influence of topography and wind. Reanalysis datasets are a viable alternative to ensure the continuity of hydrological studies and to assess climate variability in regions where the availability and/or quality of observational data is limited. This study revealed the performance of the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses, namely ERA5 (0.25°x0.25°) and ERA5-Land (0.1°x0.1°), over West Africa. They were analyzed on spatial and temporal scales (annual, monthly, daily, and hourly). The reanalysis time series were obtained by Selecting the Nearest Pixel (SNP) closest to the point station or by the Inverse Distance Weighted (IDW) method and compared with the observational data using Pearson correlation (c) and Relative Mean Absolute Error (RMAE). The results showed on average similar performance between ERA5 and ERA5-Land reanalyses, and also between SNP and IDW methods. However, a significant difference can be observed at some stations/areas due to the influence of topography and wind. Both reanalyses performed well on a spatial scale, with a clear distinction between the wet and dry areas. They also performed well on an annual ( c = 0.60 and RMAE=22 %) and monthly ( c = 0.80 and RMAE=40 %) scales. For 713 point stations, ERA5 and ERA5-Land showed negative trends in interannual variability for 82 % and 94 %, respectively. They agreed with the trends derived from observations (negative trends for 96 % of stations). However, the reanalyses performed poorly on both daily ( c = 0.21 and RMAE=128 %) and hourly ( c = 0.06 and RMAE=170 %) scales. Maximum daily precipitation (RX1day) is also less well represented, sometimes with negative correlations. The ECMWF fifth generation reanalyses need to be adjusted to improve their performance in describing precipitation.

Environmental AdvancesVol. 20
Centre National de la Recherche Scientifique (FR), Université d'Abomey-Calavi (BJ), Institut des Géosciences de l'Environnement (FR), Office National de l'Eau Potable (MA), Institut de Recherche pour le Développement (FR), Université Grenoble Alpes (FR)
Institut de Recherche pour le Développement
Climate action
Openalex Percentile: Top 4%
Climate variability and models
5.53
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