ArticleMeteorological applications
Verification of operational numerical weather prediction model forecasts of precipitation using satellite rainfall estimates over Africa.
Article in Meteorological applications. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Article
- Preconception and Prenatal Environment and Growth Faltering Among Children in Uganda.JAMA network open · 2025Article
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Authors and funding
9 authors.
Funding
Abstract
Rainfall is an important variable to be able to monitor and forecast across Africa, due to its impact on agriculture, food security, climate-related diseases and public health. Numerical weather models (NWMs) are an important component of this work, due to their complete spatial coverage, high resolution and ability to forecast into the future. In this study, the spatio-temporal skill of short-term forecasts of rainfall across Africa from 2016 through 2018 is evaluated. Specifically, the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction-Global Forecast System (NCEP-GFS) forecast models are verified by Rainfall Estimates 2.0 (RFE2) and African Rainfall Climatology Version 2 (ARC2), which are fused products of satellite and in situ observations and are commonly used in analysis of African rainfall. Model rainfall forecasts show good consistency with the satellite rainfall observations in spatial distribution over Africa on the seasonal timescale. Evaluation metrics of daily and weekly forecasts show high spatial and seasonal variations over the African continent, including a strong link to the location of the inter-tropical convergence zone (ITCZ) and topographically enhanced precipitation. The rainfall forecasts at 1 week aggregation time are improved against daily forecasts.
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