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arXiv · 2610.09752

SoftSEEPS improves ML-based precipitation forecasting

Jost Arndt·Utku Isil·Noelia Otero·Rodrigo Almeida·Wojciech Samek·Jackie Ma
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Abstract

In this paper we have developed a differentiable approximation of the well-known SEEPS score, which we name SoftSEEPS. This allows the training of a Machine Learning model to forecast precipitation directly. We test SoftSEEPS on the IMERG dataset (0.1 degree resolution) by training a decoder for precipitation on the latent space of a pre-trained low-resolution forecasting model. Combining SoftSEEPS and RMSE in a joint objective is possible with marginal trade-offs in either metric.

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Jost Arndt, Utku Isil, Noelia Otero, Rodrigo Almeida, Wojciech Samek, Jackie Ma. 2026-10-07. SoftSEEPS improves ML-based precipitation forecasting. https://arxiv.org/abs/2610.09752

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