Subseasonal Precipitation Prediction for Africa: Forecast Evaluation and Sources of Predictability Article Swipe
Felipe M. de Andrade
,
Matthew Young
,
David MacLeod
,
Linda Hirons
,
Steven J. Woolnough
,
Emily Black
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1175/waf-d-20-0054.1
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1175/waf-d-20-0054.1
This paper evaluates subseasonal precipitation forecasts for Africa using hindcasts from three models (ECMWF, UKMO, and NCEP) participating in the Subseasonal to Seasonal (S2S) prediction project. A variety of verification metrics are employed to assess weekly precipitation forecast quality at lead times of one to four weeks ahead (weeks 1–4) during different seasons. Overall, forecast evaluation indicates more skillful predictions for ECMWF over other models and for East Africa over other regions. Deterministic forecasts show substantial skill reduction in weeks 3–4 linked to lower association and larger underestimation of predicted variance compared to weeks 1–2. Tercile-based probabilistic forecasts reveal similar characteristics for extreme categories and low quality in the near-normal category. Although discrimination is low in weeks 3–4, probabilistic forecasts still have reasonable skill, especially in wet regions during particular rainy seasons. Forecasts are found to be overconfident for all weeks, indicating the need to apply calibration for more reliable predictions. Forecast quality within the ECMWF model is also linked to the strength of climate drivers’ teleconnections, namely, El Niño–Southern Oscillation, Indian Ocean dipole, and the Madden–Julian oscillation. The impact of removing all driver-related precipitation regression patterns from observations and hindcasts shows reduction of forecast quality compared to including all drivers’ signals, with more robust effects in regions where the driver strongly relates to precipitation variability. Calibrating forecasts by adding observed regression patterns to hindcasts provides improved forecast associations particularly linked to the Madden–Julian oscillation. Results from this study can be used to guide decision-makers and forecasters in disseminating valuable forecasting information for different societal activities in Africa.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1175/waf-d-20-0054.1
- OA Status
- hybrid
- Cited By
- 80
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3112528374
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3112528374Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1175/waf-d-20-0054.1Digital Object Identifier
- Title
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Subseasonal Precipitation Prediction for Africa: Forecast Evaluation and Sources of PredictabilityWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-12-15Full publication date if available
- Authors
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Felipe M. de Andrade, Matthew Young, David MacLeod, Linda Hirons, Steven J. Woolnough, Emily BlackList of authors in order
- Landing page
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https://doi.org/10.1175/waf-d-20-0054.1Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://doi.org/10.1175/waf-d-20-0054.1Direct OA link when available
- Concepts
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Predictability, Climatology, Forecast skill, Quantitative precipitation forecast, Precipitation, Teleconnection, Environmental science, Hindcast, Probabilistic logic, Meteorology, Regression, Madden–Julian oscillation, Statistics, Mathematics, Geography, Geology, ConvectionTop concepts (fields/topics) attached by OpenAlex
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80Total citation count in OpenAlex
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2025: 18, 2024: 24, 2023: 17, 2022: 11, 2021: 10Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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