5 citations · 7 across the 5 of their papers we have counts for
5 papers
HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting
Magnus Sikora Ingstad, Mariana C. A. Clare, Olav Ersland +16
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 di…
Pointwise is Pointless? A Multimodal Ablation Study for Precipitation Nowcasting with Graph Neural Networks
Ophélia Miralles, Máté Mile, Christoffer Artturi +2
Sparse point observations are increasingly available for precipitation nowcasting, but it is unclear how much they improve dense radar-field forecasts. We partially address this qu…
High-Resolution Probabilistic Data-Driven Weather Modeling with a Stretched-Grid
Even Marius Nordhagen, Håvard Homleid Haugen, Magnus Sikora Ingstad +9
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…
Regional data-driven weather modeling with a global stretched-grid
Thomas Nils Nipen, Håvard Homleid Haugen, Magnus Sikora Ingstad +11
A data-driven model (DDM) suitable for regional weather forecasting applications is presented. The model extends the Artificial Intelligence Forecasting System by introducing a str…
Evaluation of forecasts by a global data-driven weather model with and without probabilistic post-processing at Norwegian stations
John Bjørnar Bremnes, Thomas N. Nipen, Ivar A. Seierstad
During the last two years, tremendous progress in global data-driven weather models trained on numerical weather prediction (NWP) re-analysis data has been made. The most recent mo…