reGRAF: a global MPAS reforecast data set with convection-allowing refinements over the US and Europe

2026-09-30

AI Summary

This study addresses the lack of high-resolution, publicly available reforecast datasets for high-impact weather by developing a global reforecast system based on the MPAS-Atmosphere model. Through adaptive mesh refinement, regional resolution over the United States and Europe is enhanced to 4 km, spanning more than two decades of initial conditions. Methodologically, the system integrates NVIDIA GPU-accelerated parallel computing with optimized sampling strategies tailored for extreme weather events, while employing the Zarr format for efficient data storage. The project ultimately releases a high spatiotemporal resolution dataset comprising 1,836 cases, which is shared globally via the AWS Open Data Program. This initiative provides critical data infrastructure to advance research in extreme weather prediction.

Abstract

NVIDIA and The Weather Company (TWC) have generated a data set of reforecasts from TWC's GRAF (Global high-Resolution Atmospheric Forecasting) model, a version of the National Center for Atmospheric Research (NCAR) Model for Predictions Across Scales (\href{https://ncar.ucar.edu/what-we-offer/models/model-prediction-across-scales-mpas}{\underline{MPAS}}). GRAF is global, but the configuration for this reforecast had a mesh refinement to \textasciitilde4 km over the US, Caribbean Basin, and Europe, and 15 km elsewhere. This model was designed to run much of the computation on graphical processing units (GPUs), with this development assisted by NVIDIA. The 1836 reforecast cases (\textasciitilde5 years) were generated from ECMWF reanalyses (ERA5) for selected initial condition dates spanning more than 20 years, 2004--2024. These dates of the chosen initial conditions were mostly selected based on high-impact weather in the contiguous US (CONUS) and Caribbean. Sampling in this way, the reforecast spanned a wider range of interesting, high-impact weather scenarios than had we performed five contiguous years of once-daily reforecasts. The reforecast still provides many samples in non-precipitating regions with more ordinary weather. GRAF reforecasts were mostly run to $+$27 h lead time, assuming a 3-h spin-up followed by a full diurnal cycle. Data were saved in zarr format on the native model vertical coordinate. Most fields were archived at 15-min intervals, though several precipitation variables were saved at 5-min cadence. Data are made publicly available to all through Amazon Web Services' Open-Data Initiative at https://registry.opendata.aws/graf-reforecast.

Problem

• reforecast dataset
• high-resolution forecasting
• convection-allowing model
• MPAS
• high-impact weather

Innovation

• MPAS
• convection-allowing mesh refinement
• GPU acceleration
• reforecast dataset
• high-impact weather sampling