arXiv:2609.39833v1 Announce Type: new
Abstract: Extreme rainfall events are increasing in intensity and frequency as climate change accelerates. While kilometer-scale precipitation forecasts are crit...
By Pierre-Louis Lemaire, Luca Schmidt, Wietze Suijker, Alex Hernandez-Garcia, David Rolnick
The study evaluates three conditioning strategies for a denoising diffusion probabilistic model to downscale daily precipitation for the Colorado River Basin. Channel concatenation of upsampled coarse predictors yields the lowest point‑wise CRPS and MSE but tends to over‑smooth high‑intensity events. Cross‑attention conditioning—both with a learned encoder and with the frozen encoder of the pretrained Prithvi‑WxC weather foundation model—provides better distributional realism, improved spectral fidelity, and stronger performance on extreme events, especially when data are limited.
By Victor Nascimento Ribeiro, Jorge Guevara, Jorge Sebastian Moraga, Chris Lucas, Natalie Lord, Andrew Taylor, Edward Lockhart, Will Trojak, Johannes Schmude, Anne Jones
The paper introduces PRISMA, a generative framework that separates precipitation prior training from sensor-specific constraints, allowing flexible composition of heterogeneous satellite observations without retraining the core model. By integrating FY‑4B/AGRI, GPM/GMI, F16‑F18 SSMIS, and GPM/DPR‑Ka data, PRISMA consistently improves precipitation‑estimation accuracy and outperforms IMERG Final in CRPS and RMSE while maintaining positive Brier skill across thresholds. The framework supports rapid, accurate ensemble precipitation estimates, enhancing satellite‑based monitoring for hydrometeorological hazards.
By Yunfan Yang, Haofei Sun, Xiuyu Sun, Wei Han, Xiaoze Xu, Xingtao Song, Jun Li, Zhiqiu Gao, Wei Huang
MAGPIE‑Net is a deep‑learning framework that directly maps multitemporal FY‑4A AGRI infrared and water‑vapor observations to short‑duration heavy‑rainfall warnings for irregular station neighborhoods. By embedding a geographically adaptive, differentiable grid‑to‑station mapping and training with station‑neighborhood event losses, the model outperforms traditional gridded‑precipitation baselines, achieving higher detection rates and longer lead times in 2023 warm‑season tests over China.
By Xiang Lin, Yunying Li, Chengzhi Ye, Zitong Chen, Jing Sun
arXiv:2608.12685v2 Announce Type: replace-cross
Abstract: An ``Attention Residual U-Net'' method is described for probabilistic quantitative precipitation forecasting (PQPF) that predicts the hourly...
By Thomas M Hamill
arXiv:2606. 00281v1 Announce Type: cross Abstract: Generative machine learning is an increasingly important complement to dynamical downscaling for producing high-resolution precipitation projections, with diffusion models currently the leading approach.
By Tom Wetherell