arXiv:2606. 04143v1 Announce Type: cross Abstract: Accurate flood forecasting is essential for mitigating disaster risks and protecting communities.
By Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-Beni
arXiv:2606. 06524v1 Announce Type: cross Abstract: Accurate and scalable flood mapping remains challenging due to limited ground observations, heterogeneous terrain conditions, and the difficulty of enforcing hydrodynamic consistency within data-driven models.
By Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-Beni
arXiv:2607. 21080v1 Announce Type: new Abstract: Long-horizon weather forecasting is a fundamental challenge in atmospheric science, for which autoregressive Deep Learning Weather Prediction (DLWP) has emerged as the primary paradigm.
By Yun-Ye Cai, Hsuan-Tien Lin
arXiv:2607. 18877v1 Announce Type: new Abstract: In the context of global warming, extreme events have become more frequent and intense, making their trustworthy detection and forecasting more important than ever.
By Chang Xu, Gencer Sumbul, Hugo Porta, Manon B\'echaz, Sebastian Schemm, Devis Tuia
arXiv:2509. 09195v2 Announce Type: replace Abstract: Current evaluation metrics for deep learning weather models create a "Statistical Similarity Trap", rewarding blurry predictions while missing rare, high-impact events.
By Md Tanveer Hossain Munim
arXiv:2608. 04230v1 Announce Type: new Abstract: Deep learning models for scientific spatio-temporal downscaling often minimize reconstruction error while failing to preserve physically meaningful multi-scale structure.
By Parth Doshi, Priyanka Aravindan, Vaishnav Vaidheeswaran, Md Mahbub Alam, Gabriel Spadon