Hugging Face Trending Papers
Huayu-Global: Skillful real-time quasi-global precipitation retrieval
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arXiv AI
Oct 23
Huayu-Global: Skillful real-time quasi-global precipitation retrieval
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.
arXiv AI
Sep 16
Composable multi-satellite precipitation estimation for evolving observing systems
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.
arXiv Machine Learning
Oct 12
Diffusion Precipitation Generation with Medium-range Weather Forecasting
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.
arXiv Machine Learning
Jul 8
Integrating GNSS-Derived Zenith Wet Delay into a Weather Foundation Model Improves Precipitation Forecasting
arXiv:2607. 05658v1 Announce Type: cross Abstract: Global Navigation Satellite Systems (GNSS), best known for positioning, also serve weather science, as atmospheric water vapour delays their signals.
arXiv Machine Learning
Sep 25
Improving global precipitation forecasts with an AI weather model trained on satellite observations
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.
arXiv Machine Learning
Aug 19
MAGPIE-Net: Predicting short-duration heavy-rainfall events in station neighborhoods from multitemporal FY-4A AGRI observations
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.