arXiv subjects

Magnus Sikora Ingstad

Publications and source records attributed to Magnus Sikora Ingstad.

3 recordsLinked to original sources

arXiv2026

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

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.

physics.ao-ph↗

arXiv2026

HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting

Many forecast applications require high frequency temporal resolution, yet most state-of-the-art data-driven weather forecasting systems operate at 6-hourly resolution. Although direct hourly forecasting is possible, it suffers from error accumulation and temporal inconsistency. We introduce HourGlass, a probabilistic data-driven temporal downscaling method that reconstructs the evolution between forecast states. HourGlass is trained using variants of the continuous ranked probability score (CRPS) preserving small-scale spatial variability while encouraging temporal consistency. Unlike existing deterministic temporal downscaling approaches, which tend to produce overly smooth fields, HourGlass generates realistic probabilistic forecasts. Training on forecast trajectories rather than reanalysis or analysis data also avoids the temporal inconsistencies present in datasets used by previous methods. We evaluate HourGlass in two settings: AIFS-HourGlass, applied globally to ECMWF's AIFS-Single and AIFS-ENS forecast systems, and Bris-HourGlass, applied regionally to MET Norway's high-resolution stretched-grid ensemble model, Bris. Verification against observations shows that both models retain the skill of their underlying forecasting systems while producing temporally coherent hourly forecasts with realistic small-scale variability. Case studies demonstrate physically consistent evolution during rapidly developing weather events, including extratropical cyclones and organised convection. Hourly precipitation remains challenging: HourGlass improves the spatial realism of precipitation fields but still underestimates the most intense extremes, a common limitation of data-driven weather forecasting models. These results demonstrate that HourGlass effectively bridges the gap between 6-hourly data-driven forecasts and the hourly products required for operational regional and global forecasting.

physics.ao-ph↗

arXiv2024

Regional data-driven weather modeling with a global stretched-grid

A data-driven model (DDM) suitable for regional weather forecasting applications is presented. The model extends the Artificial Intelligence Forecasting System by introducing a stretched-grid architecture that dedicates higher resolution over a regional area of interest and maintains a lower resolution elsewhere on the globe. The model is based on graph neural networks, which naturally affords arbitrary multi-resolution grid configurations. The model is applied to short-range weather prediction for the Nordics, producing forecasts at 2.5 km spatial and 6 h temporal resolution. The model is pre-trained on 43 years of global ERA5 data at 31 km resolution and is further refined using 3.3 years of 2.5 km resolution operational analyses from the MetCoOp Ensemble Prediction System (MEPS). The performance of the model is evaluated using surface observations from measurement stations across Norway and is compared to short-range weather forecasts from MEPS. The DDM outperforms both the control run and the ensemble mean of MEPS for 2 m temperature. The model also produces competitive precipitation and wind speed forecasts, but is shown to underestimate extreme events.

physics.ao-ph↗