SoftSEEPS improves ML-based precipitation forecasting
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 23 references that are in the map. First posted on arXiv 7 Oct 2026.
Builds on
5 of 23 references.
References · 5
- 2020WeatherBench: A benchmark dataset for data-driven weather forecastingcited by 272
- 2023GenCast: Diffusion-based ensemble forecasting for medium-range weathercited by 169
- 2024FuXi-2.0: Advancing machine learning weather forecasting model for practical applicationscited by 11
- 2024ArchesWeather: An efficient AI weather forecasting model at 1.5deg resolutioncited by 6
- 2025WeatherMesh-3: Fast and accurate operational global weather forecastingcited by 4
Built on by
Not cited yet.