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
We introduce a stretched-grid artificial intelligence (AI) weather forecasting model with 2-km resolution over the western United States and part of the Northeast Pacific and approximately 31-km resol...
PCSDiff is a diffusion-based framework designed to correct systematic biases and enhance spatial resolution in medium-term (10‑day) precipitation forecasts. It uses a Precipitation Intensity‑aware Multi‑branch Decoder to mitigate dynamic multi‑day errors and a two‑phase conditional diffusion super‑resolution module to restore fine‑scale rainfall patterns. Evaluated over China, PCSDiff reduces RMSE by 16.1% and increases ACC by 13.9% compared to raw ECMWF forecasts, outperforming mainstream deep‑learning baselines and enabling low‑latency rolling forecasts for operational use.
By Yuze Sun, Shiyi Wang, Jiancheng Pan, Die Wang, Andreas F. Prein, Wentao Luo, Linhan Jiang, Jie Wu, Quan Zhang, Xiaomeng Huang
arXiv:2607. 04862v1 Announce Type: new Abstract: Accurate extreme precipitation forecasting is critical for disaster mitigation but remains challenging for numerical weather prediction (NWP) models due to systemic intensity underestimation and spatial displacement.
By Yu Wang, Yong Cao, Kan Dai, Yue Shen, Xiaoqing Zeng, Ruixia Zhao
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
MZ‑Rain is a moisture‑budget‑guided zero‑inflated sLSTM framework designed for station‑level precipitation nowcasting. It decomposes precipitation formation into moisture storage, transport, surface evaporation, and persistence pathways, each modeled by dedicated sLSTM branches, and employs an adaptive Tweedie strategy to handle the dataset’s severe zero inflation. Experiments across varied climates show MZ‑Rain outperforms strong baselines on metrics such as CSI, FAR, MSE, and MAE, especially for heavy precipitation events.
By Yifang Zhang, Shengwu Xiong, Henan Wang, Wenjie Yin, Yuqiang Zhang, Chen Zhou, Hua Chen, Qile Zhao, Pengfei Duan
The paper presents a graph-transformer AI weather model that is fine‑tuned with high‑resolution IMERG precipitation observations, moving beyond the traditional reliance on the ERA5 reanalysis dataset. This approach yields up to a 19% improvement in medium‑range continuous ranked probability scores and a 57% better Brier skill score for extreme rainfall compared to leading operational models, while also excelling in tropical storm and drizzle prediction. The study demonstrates that directly incorporating observation‑based precipitation data into AI training can markedly enhance forecast accuracy, though physics‑based models still outperform for the heaviest events.
By Julian F. Schmitt, Bertrand Delorme, Robert C. King, Yashica Patodia, Tapio Schneider, Aditi Sheshadri, Ravi Jain
arXiv:2607. 17511v1 Announce Type: new Abstract: Large \emph{Time Series Foundation Models} (TSFMs) demonstrate strong zero-shot forecasting capabilities across diverse domains.
By Wentao Gao, Jiuyong Li, Lin Liu, Thuc Duy Le, Jixue Liu, Yanchang Zhao, Yun Chen
The paper introduces Nested‑EAGLE, a 0.25° global weather model with a 6 km refinement over the contiguous United States, designed to merge short‑ and medium‑range forecasts into a single system. It shows lower mean‑squared error for near‑surface and low‑level variables over the U.S. compared to NOAA’s GFS and HRRR, while remaining competitive globally. Although precipitation forecasts are less skillful than HRRR’s deterministic training, Nested‑EAGLE delivers the most accurate storm‑location predictions at longer lead times, with blurred extrema.
By Timothy A. Smith, Mariah Pope, Sergey Frolov, Brett Basarab, Daniel Abdi, Paul Madden, Isidora Jankov
arXiv:2605. 16163v2 Announce Type: replace-cross Abstract: Skillful medium-range precipitation forecasting at kilometer scale remains challenging over complex terrain because precipitation arises from multiscale nonlinear processes that global models cannot explicitly resolve at affordable cost.
By Dan Assouline, Erwan Koch, Federico Amato, Filippo Quarenghi, Daniele Nerini, Thibaut Loiseau, Kyle van de Langemheen, Tom Beucler
Data-driven models now rival numerical weather prediction in the medium range, but extending them to sub-seasonal lead times raises challenges absent at shorter horizons. Errors accumulate over long autoregressive rollouts, systematic biases grow with lead time, and several years of data must be held out for independent verification, even though machine-learning models otherwise benefit from longer training records.
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