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
The study presents a spatially aware deep learning framework that retrieves all‑sky tropospheric temperature and humidity profiles from the Meteosat Third Generation Flexible Combined Imager (FCI) without relying on numerical weather prediction background fields. Using a Residual U‑Net trained on 14 months of collocated FCI observations and CERRA reanalysis data, the model achieves temperature biases below 0.4 K and relative humidity standard deviations between 12–20 %, with modest performance degradation under cloud cover. Ablation and feature‑sensitivity analyses confirm that incorporating spatial context across all 16 FCI channels, including visible and near‑infrared bands, improves retrieval accuracy, especially beneath cloud tops.
By Alejandro Salgueiro, Johannes Rausch, Julie Th\'er\`ese Villinger, Angela Meyer
The Meteosat Third Generation (MTG) Flexible Combined Imager (FCI) offers new opportunities for tropospheric temperature and humidity profiling, at higher spatio-temporal resolutions and expanded spec...
arXiv:2609.25505v1 Announce Type: cross
Abstract: Rapid intensification (RI) remains one of the most consequential and difficult aspects of tropical cyclone (TC) forecasting. Although full-physics nu...
By Shijie Xiao, Jonathan Lin, Thomas Ehrmann, Ali Sarhadi
arXiv:2608. 20117v1 Announce Type: new Abstract: The emergence of large-scale Weather and Climate (W&C) datasets offers new opportunities for modeling extreme Earth events (ExEE) and their impacts using deep learning.
By Hugo Porta, Emanuele Dalsasso, Chang Xu, Theo Gnassounou, Devis Tuia
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