The paper introduces a plug‑and‑play framework called Dynamics‑Precipitation Decoupling that employs a diffusion generator to convert non‑precipitation forecasts from a frozen weather model into observation‑aligned daily precipitation. The generator is conditioned on atmospheric states and satellite‑gauge precipitation estimates, using a multi‑scale recurrent encoder and cross‑attention to capture heterogeneous rainfall distributions and align daily accumulations with evolving atmospheric signals. Experiments on global and regional datasets, including a heavy‑rainfall case study in China, show that the method improves daily precipitation accuracy, event detection, and localized accumulation patterns compared to several baseline weather models and ensemble forecasts.
By Guowen Li, Yang Liu, Mengxuan Chen, Jiashun Cheng, Haoyuan Liang, Shilei Cao, Yujie Wang, Juepeng Zheng, Fang Wang, Yerong Feng, Meng Jin, Hong Cheng, Yutong Lu, Haohuan Fu
The paper presents Laxmi, a retrained version of the AIFS weather model that uses satellite-based precipitation observations instead of ERA5 reanalysis data. Laxmi achieves a 19% improvement in global probabilistic accuracy, reduces drizzle overprediction by 33%, and boosts the 95th percentile Brier skill score by 57%. In a case study of 10 Indian tropical storms, Laxmi accurately forecasted 150 mm event-total precipitation in 7 events, outperforming both the original AIFS and the leading physical model IFS.
By Julian F. Schmitt, Bertrand Delorme, Robert C. King, Yashica Patodia, Tapio Schneider, Aditi Sheshadri, Ravi Jain
The paper introduces a scale‑recursive rectified flow that first generates broad rainfall patterns and then refines local details, allocating sampling steps based on ensemble variability and prediction error across spatial scales. This approach reduces the number of sampling steps while maintaining ensemble diversity, improving probabilistic accuracy and rain detection in satellite‑to‑radar downscaling over the United States. The method outperforms a non‑recursive flow with more steps, achieving better accuracy in less time.
By Shunya Nagashima, Takumi Bannai
Huayu-Global is a cross‑constellation deep‑learning system that fuses data from three geostationary sensor streams across five satellites to produce coherent quasi‑global precipitation fields every thirty minutes. By combining multiple satellite observations, it improves event detection and precipitation‑amount estimation compared to existing real‑time products, achieving skill levels comparable to delayed‑product releases. The system offers a scalable framework that can incorporate future geostationary missions into hydrological and weather‑prediction applications.
By Zijiang Song, Shuang Yang, Yuying Li, Zilong Zhu, Xigang Sun, Ting Liu, Wenye Xiao, Lina Yuan, Min Liu
arXiv:2510. 20769v2 Announce Type: replace-cross Abstract: Accurate medium-range precipitation forecasting is essential for hydrometeorological risk management but remains challenging for both numerical weather prediction (NWP) systems and data-driven models.
By Tianyi Xiong, Haonan Chen, Kelly Mahoney, Jingyin Tang, Tim Smith, Janice Bytheway
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