Evaluation of forecasts by a global data-driven weather model with and without probabilistic post-processing at Norwegian stations

Abstract

During the last 2 years, tremendous progress has been made in global data-driven weather models trained on numerical weather prediction (NWP) reanalysis data. The most recent models trained on the ERA5 reanalysis at 0.25° resolution demonstrate forecast quality on par with ECMWF’s high-resolution model with respect to a wide selection of verification metrics. In this study, one of these models, Pangu-Weather, is compared to several NWP models with and without probabilistic post-processing for 2 m temperature and 10 m wind speed forecasting at 183 Norwegian SYNOP (surface synoptic observation) stations up to + 60 h ahead. The NWP models included are the ECMWF HRES, ECMWF ENS and the HARMONIE-AROME ensemble model MEPS with 2.5 km spatial resolution. Results show that the performances of the global models are on the same level, with Pangu-Weather being slightly better than the ECMWF models for temperature and slightly worse for wind speed. The MEPS model clearly provided the best forecasts for both parameters. The post-processing improved the forecast quality considerably for all models but to a larger extent for the coarse-resolution global models due to stronger systematic deficiencies in these. Apart from this, the main characteristics in the scores were more or less the same with and without post-processing. Our results thus confirm the conclusions from other studies that global data-driven models are promising for operational weather forecasting.