SoftSEEPS relaxes the SEEPS score into a differentiable loss, letting precipitation models optimize dry/light/heavy categories directly, with SEEPS dropping by up to about 0.1 at no resolvable RMSE cost
The authors introduce SoftSEEPS, a sigmoid relaxation of the SEEPS dry/light/heavy categorical score that is differentiable and usable directly as a training loss; training a convolutional decoder on the latent space of a frozen ArchesWeather-M backbone for IMERG daily precipitation, validation discrete SEEPS falls from 0.481 with MSE only to 0.375 with SoftSEEPS only, and to 0.381 under a joint objective while RMSE rises only from 5.316 to 5.334.