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July 12, 2026ClimateOpen Access

Evaluation and Post-Processing of Precipitation Forecast Skills at Short Lead Times for Hydrological Applications over the Ouémé Basin

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Authors

YBYaovi Aymar BossaUniversité d'Abomey-CalaviJHJean HounkpèBurkina Faso Ministry of the Environment, Green Economy, and Climate Change

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Implication

Randomized trial evaluates precipitation forecasts in the Ouémé basin, suggesting bias correction enhances forecast reliability.

Key Points

  • The central aim is to evaluate and improve precipitation forecasting skills for hydrological applications in the Ouémé basin.
  • Evaluated six numerical weather prediction models from 1985 to 2015.
  • Utilized continuous and event-based verification metrics across six sub-basins.
  • Applied empirical quantile mapping for statistical bias correction.
  • ECMWF and UK Met Office models achieved KGE values of up to 0.5, indicating high skill at short lead times.
  • Bias correction with empirical quantile mapping improved predictions, reaching median Likelihood Ratio values of approximately 6.
  • Raw predictions remained below 10, showing over 50% improvement after bias correction.

Cite This Study

Bossa et al. (2026) studied this question.

synapsesocial.com/papers/6a53315f4f7abc118aded9c2https://doi.org/10.3390/cli14070146
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