WeatherNext 3: Increasing resolution and performance of global weather models with raw observations

WeatherNext 2: Our most advanced weather forecasting model

The new AI model delivers more efficient, more accurate and higher-resolution global weather predictions.

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.

Introducing WeatherNext 3, our most advanced and accurate global weather AI model

Varda-single-1.0: deterministic data-driven weather forecasting at 1 km resolution over Switzerland's complex topography

We present Varda-single-1.0, a medium-range data-driven weather prediction system built for the Alpine domain. It provides hourly deterministic regional forecasts on a mesh of 1 km resolution and glob...

From Nowcasting to Forecasting: Adapting a Reanalysis-Trained

The paper introduces CloudCast v2, a machine‑learning model that forecasts 12‑hour cloud‑cover from satellite‑derived initial conditions. Trained first on the Copernicus European Regional Reanalysis to learn cloud‑evolution dynamics, it is then adapted to real satellite data using conditional flow matching, a generative technique that conditions noise on observed cloud fields and NWP inputs. CloudCast v2 achieves a 10 % reduction in mean absolute error compared to its predecessor and surpasses it in spatial skill after 3–6 hours, extending useful forecasting beyond the typical 1–3‑hour nowcasting window while preserving satellite‑level spatial detail.

Varda-single-1.0: deterministic data-driven weather forecasting at 1 km resolution over Switzerland's complex topography

Varda‑single‑1.0 is a medium‑range, data‑driven weather prediction system designed for Switzerland’s Alpine region, delivering hourly deterministic forecasts at 1 km resolution and global forecasts at 31 km. It uses two independently trained stretched‑grid Graph Transformer models—an autoregressive forecaster and a temporal downscaler—trained through a curriculum that starts with ERA5 reanalysis, proceeds to a 20‑year regional reanalysis, and ends with fine‑tuning on operational analyses. Over a one‑year verification, Varda‑single matches or surpasses MeteoSwiss’s numerical weather prediction baselines for most key metrics, though it underestimates local wind peaks and produces smoother convective precipitation fields. whyItMatters":"The system demonstrates that high‑resolution machine‑learning models can rival traditional numerical weather prediction in complex terrain, offering a complementary tool for operational forecasting and research."

Benchmarking Physics-Informed Time-Series Models for Operational Global Station Weather Forecasting

arXiv:2406. 14399v4 Announce Type: replace Abstract: The development of Time-Series Forecasting (TSF) models is often constrained by the lack of comprehensive datasets, especially in Global Station Weather Forecasting (GSWF), where existing datasets are small, temporally short, and spatially sparse.

Bridging short- and medium-range weather forecasting with machine learning

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.

West-WRF AI 2-km: High-Resolution Prediction of Integrated Vapor Transport and Precipitation

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...

3DTCR: Vortex-following generative reconstruction improves tropical cyclone intensity prediction from global AI weather forecasts

The paper introduces 3DTCR, a vortex‑following generative framework that reconstructs high‑resolution, three‑dimensional tropical cyclone (TC) structures from coarse global AI forecasts. By training on a 3‑km, TC‑centered dataset generated with the WRF model, 3DTCR learns a coarse‑to‑fine mapping and adapts it to forecast inputs, producing detailed TC fields in minutes on a single GPU. Evaluations on 2024 western North Pacific forecasts show significant reductions in RMSE and mean bias error for maximum 10‑m wind speed compared to both FuXi and ECMWF‑HRES, while capturing key wind gradients and vortex asymmetries.

From Nowcasting to Forecasting: Adapting a Reanalysis-Trained

The paper introduces CloudCast v2, a machine‑learning model that forecasts 12‑hour cloud cover from observation‑based initial conditions. It is first trained on the Copernicus European Regional Reanalysis to capture cloud‑evolution dynamics, then adapted to satellite‑derived cloud fields using conditional flow matching. Compared to its predecessor, CloudCast v2 reduces mean absolute error by 10% over 1–12 h and surpasses it in spatial skill after 3–6 h, demonstrating that observation‑initialized forecasts can extend beyond the typical 1–3‑hour nowcasting window while preserving spatial detail.

AIFS-SUBS: Extending Data-Driven Forecasting to Sub-Seasonal Timescales

arXiv:2607. 05100v1 Announce Type: cross Abstract: 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.