← Back to all news
arXiv Computer Vision By Gurjeet Sangra Singh, Frantzeska Lavda, Alexandros Kalousis

Climate Physics Dynamic Matching

Read the original on arXiv Computer Vision →

The paper introduces Climate Physics Dynamic Matching (ClimPhyDM), a variational, simulation‑free framework that blends an advection‑type physics prior with data‑driven components for weather forecasting. It leverages deep generative models to capture complex dynamical systems while preserving underlying physical structure. On the ERA5 benchmark, ClimPhyDM outperforms existing methods such as ClimODE and GB‑DM, achieving lower error over extended horizons and demonstrating improved temporal stability and resistance to error accumulation, all while training on a single modest 12 GB consumer GPU.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv Machine Learning
Sep 21

Learning to Advect: A Neural Semi-Lagrangian Architecture for Weather Forecasting

arXiv:2601.21151v3 Announce Type: replace Abstract: Machine-learning approaches to weather forecasting often employ a monolithic architecture in which distinct physical mechanisms, such as advection,...

By Carlos A. Pereira, St\'ephane Gaudreault, Valentin Dallerit, Christopher Subich, Shoyon Panday, Siqi Wei, Sasa Zhang, Siddharth Rout, Eldad Haber, Raymond J. Spiteri, David Millard
diffusionbenchmarks
More like this →
arXiv Machine Learning
Jun 18

Benchmarking Physics-Informed Time-Series Models for Operational Global Station Weather Forecasting

arXiv:2406. 14399v4 Announce Type: replace Abstract: The development of Time-Series Forecasting (TSF) models is often constrained by the lack of comprehensive datasets, especially in Global Station Weather Forecasting (GSWF), where existing datasets are small, temporally short, and spatially sparse.

By Tao Han, Zhibin Wen, Zhenghao Chen, Dazhao Du, Song Guo, Lei Bai
llmsbenchmarkssafety
More like this →
arXiv Machine Learning
Jun 30

High-Resolution Climate Projections Using Diffusion-Based Downscaling of a Lightweight Climate Emulator

arXiv:2602. 13416v2 Announce Type: replace Abstract: The proliferation of data-driven models in weather and climate sciences has marked a significant paradigm shift, with advanced models demonstrating exceptional skill in medium-range forecasting.

By Haiwen Guan, Dibyajyoti Chakraborty, Moein Darman, Troy Arcomano, Ashesh Chattopadhyay, Romit Maulik
diffusion
More like this →