Papers for

climate impact analysts

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

SoftSEEPS enhances precipitation forecasts with differentiable scoring

SoftSEEPS improves ML-based precipitation forecasting

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.

Wed 7 OctMachine Learning
The gist
Forecasting rainfall is tricky, and standard ways to measure the accuracy of forecasts are often not easy for computers to use when learning. The authors created SoftSEEPS, a new method that smooths out a popular evaluation score so a machine learning model can train directly with it. They tested this on weather data to improve rain predictions from a simpler forecasting model. Their approach works well when combined with traditional error measures without losing much accuracy.
Open → 2610.09752v1

Graphcast model reveals hidden measures of atmospheric river strength

Mechanistic Interpretability of Atmospheric Rivers in GraphCast

Abstract: While AI weather models now rival operational forecasts, how they represent the atmosphere internally remains an open question: feature attribution reveals which input patterns matter, not what the model computes or how it combines information internally. We train sparse autoencoders (SAEs) on GraphCast to uncover its learned concepts, using atmospheric rivers as our phenomenon of focus. Both standard and Matryoshka SAEs show GraphCast computes atmospheric river intensity, measured by integrated vapor transport (IVT), as a stable internal variable, despite IVT being neither an input nor a target. In contrast to the unstructured concept retrieval of the standard SAE, the Matryoshka SAE orders concepts by importance and exposes their relations. Atmospheric river concepts persist across depth and direct interventions confirm causality. This method offers a way to find internal variables and determine which of them the model actually relies on, which is a prerequisite for asking whether those variables remain meaningful as the phenomenon changes under a warming climate.

Tue 6 OctMachine LearningArtificial Intelligence
The gist
Weather AI models can make great forecasts, but it's unclear how they think about weather internally. The authors trained special tools called sparse autoencoders on the GraphCast model to find what ideas it forms inside, focusing on atmospheric rivers, which are big flows of moisture in the air. They found that GraphCast calculates a measure of river strength called integrated vapor transport as an internal concept, even though it’s not directly told to do so. This method helps explain what the AI really uses to make predictions and could help check if AI understands weather as climates change.
Open → 2610.07583v1