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February 2, 2026Earth system science data0 citationsOpen Access

SEEPS4ALL: an open dataset for the verification of daily precipitation forecasts using station climate statistics

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ZBZied Ben-BouallegueAPAna Prieto-NemesioAWAngela Iza Wong

Key Points

  • The central goal is to provide a dataset for verifying daily precipitation forecasts using climate statistics.
  • Introduced SEEPS4ALL dataset for verification purposes.
  • Utilized both deterministic and probabilistic forecast models.
  • Provided verification results for daily precipitation forecasts.
  • Democratized access to climate statistics for forecast verification.
  • Showcased performance of precipitation forecasts using SEEPS score.

Abstract

Abstract. Forecast verification is an essential task when developing a forecasting model. How well does a model perform? How does the forecast performance compare with previous versions or other models? Which aspects of the forecast could be improved? In weather forecasting, these questions apply in particular to precipitation, a key weather parameter with vital societal applications. Scores specifically designed to assess the performance of precipitation forecasts have been developed over the years. One example is the Stable and Equitable Error in Probability Space (SEEPS, Rodwell et al., 2010). The computation of this score is however not straightforward because it requires information about the precipitation climatology at the verification locations. More generally, climate statistics are key to assessing forecasts for extreme precipitation and high-impact events. Here, we introduce SEEPS4ALL, a set of data and tools that democratize the use of climate statistics for verification purposes. In particular, verification results for daily precipitation are showcased with both deterministic and probabilistic forecasts. The data is available through Zenodo at https://doi.org/10.5281/zenodo.18197534 (Ben Bouallègue, 2026) and the verification scripts at https://doi.org/10.5281/zenodo.18392042 (Ben Bouallegue and Prieto Nemesio, 2026).

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Cite This Study

Ben-Bouallegue et al. (2026) studied this question.

synapsesocial.com/papers/6980ff19c1c9540dea811bd3https://doi.org/10.5194/essd-18-713-2026
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