Article Dans Une Revue Tellus A: Dynamic meteorology and oceanography Année : 2025

Self-Adaptive Quantiles for Precipitation Forecasting

Résumé

How much rain can we expect in Toulouse on Wednesday next week? It is impossible to provide a precise and definitive answer to this question due to the limited predictability of the atmosphere. Ideally, a forecast would be probabilistic, for example expressed in the form of a probability of, say, having at least some rain. However, for some forecast users and applications, an answer expressed in millimeter of rain per 24 h would be needed. A so-called point-forecast can be the output of a single deterministic model. But with ensemble forecasts at hand, how to summarize optimally the ensemble information into a single outcome? The ensemble mean or quantile forecasts are commonly used and proved useful in certain circumstances. In this study, we suggest a new type of point-forecasts, the crossing-point quantile, and argue that it could be better suited for precipitation forecasting than existing approaches, at least for some users. More precisely, for a well-calibrated predictive distribution, the crossing-point quantile is the optimal forecast in terms of Peirce skill score (and equivalently in terms of area under the relative operating characteristic curve) for any event of interest. Along a theoretical proof, we present an application to daily precipitation forecasting over France and discuss the necessary conditions for optimality.

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Est identique à 10.20944/preprints202504.1224.v2 Autre Ben Bouallègue, Z., & Taillardat, M. (2025). Self-Adaptive Quantiles for Precipitation Forecasting. MDPI AG. https://doi.org/10.20944/preprints202504.1224.v2

Dates et versions

hal-05114258 , version 1 (16-06-2025)

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Zied Ben Bouallègue, Maxime Taillardat. Self-Adaptive Quantiles for Precipitation Forecasting. Tellus A: Dynamic meteorology and oceanography, 2025, 77 (1), pp.160-172. ⟨10.16993/tellusa.4110⟩. ⟨hal-05114258⟩
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