High-Resolution Probabilistic Data-Driven Weather Modeling with a Stretched-Grid

Abstract

We present a probabilistic data-driven weather model providing ensembles of high spatial resolution realizations of 87 variables at arbitrary ensemble size and forecast length. The model uses a global stretched grid, dedicating 2.5 km resolution to our Nordic region of interest and 31 km resolution elsewhere, with 6-hour temporal resolution. Unique ensemble members are generated by a stochastic model architecture, and we train it using a loss function based on the Continuous Ranked Probability Score (CRPS) evaluated in grid-point and spectral space. The spectral loss component is shown to be necessary to create fields that are spatially coherent, which is not the case when training with mean-squared error loss, nor CRPS in grid-point space only. We evaluate the forecasts against observations from surface weather stations and compare them to high-resolution operational numerical weather prediction forecasts from the MetCoOp Ensemble Prediction System (MEPS). The model shows lower CRPS than MEPS for 2 m temperature and mean sea-level pressure, with average improvements of 13\% and 10\%, respectively, while differences for wind speed and precipitation are smaller. For Storm Dave, the model captures the location and structure of strong-wind systems, but underestimates the peak winds.