4 citations · 5 across the 5 of their papers we have counts for
7 papers
Varda-single-1.0: deterministic data-driven weather forecasting at 1 km resolution over Switzerland's complex topography
Alberto Pennino, Francesco Zanetta, Michele Cattaneo +28
We present Varda-single-1.0, a medium-range data-driven weather prediction system built for the Alpine domain. It provides hourly deterministic regional forecasts on a mesh of 1 km…
Efficient Analog-Initialized Latent Transport for Spatially Coherent Probabilistic Downscaling from Global to Kilometer Scales
Ophélia Miralles
Probabilistic downscaling must represent kilometer-scale structure left unresolved by a deterministic regional prediction. We introduce an analog-initialized latent transport metho…
Exploring the role of input data on hail nowcast skill using spatiotemporal neural networks
George Pacey, Ophélia Miralles, Ulrich Hamann +1
Hail can cause large financial losses and poses risks to public safety, making reliable nowcasts essential for timely warnings. Deep-learning approaches have emerged as a strong al…
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…
Observation-guided Interpolation Using Graph Neural Networks for High-Resolution Nowcasting in Switzerland
Ophélia Miralles, Daniele Nerini, Jonas Bhend +2
Recent advances in neural weather forecasting have shown significant potential for accurate short-term forecasts. However, adapting such gridded approaches to smaller, topographica…
Bayesian modeling of insurance claims for hail damage
Ophélia Miralles, Anthony C. Davison, Timo Schmid
Despite its importance for insurance, there is almost no literature on statistical hail damage modeling. Statistical models for hailstorms exist, though they are generally not open…