194 citations · 224 across the 9 of their papers we have counts for
10 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…
SwAIther-Precip: Lead-Time-Aware Bias Correction Enables Kilometer-Scale Downscaling of Global AI Precipitation Forecasts over Switzerland
Dan Assouline, Erwan Koch, Federico Amato +5
Skillful medium-range precipitation forecasting at kilometer scale remains challenging over complex terrain because precipitation arises from multiscale nonlinear processes that gl…
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…
PeakWeather: MeteoSwiss Weather Station Measurements for Spatiotemporal Deep Learning
Daniele Zambon, Michele Cattaneo, Ivan Marisca +3
Accurate weather forecasts are essential for supporting a wide range of activities and decision-making processes, as well as mitigating the impacts of adverse weather events. While…
Improving Predictions of Convective Storm Wind Gusts through Statistical Post-Processing of Neural Weather Models
Antoine Leclerc, Erwan Koch, Monika Feldmann +2
Issuing timely severe weather warnings helps mitigate potentially disastrous consequences. Recent advancements in Neural Weather Models (NWMs) offer a computationally inexpensive a…
Efficient modeling of sub-kilometer surface wind with Gaussian processes and neural networks
Francesco Zanetta, Daniele Nerini, Matteo Buzzi +1
Accurately representing surface weather at the sub-kilometer scale is crucial for optimal decision-making in a wide range of applications. This motivates the use of statistical tec…
Intraday probabilistic forecasts of solar resources with cloud scale-dependent autoregressive advection
Alberto Carpentieri, Seppo Pulkkinen, Daniele Nerini +3
Solar energy supply is usually highly volatile which limits its integration in the power grid. Accurate probabilistic intraday forecasts of solar resources are essential to increas…
Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification
Jussi Leinonen, Ulrich Hamann, Daniele Nerini +2
Diffusion models have been widely adopted in image generation, producing higher-quality and more diverse samples than generative adversarial networks (GANs). We introduce a latent…
Physics-constrained deep learning postprocessing of temperature and humidity
Francesco Zanetta, Daniele Nerini, Tom Beucler +1
Weather forecasting centers currently rely on statistical postprocessing methods to minimize forecast error. This improves skill but can lead to predictions that violate physical p…
Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network
Jussi Leinonen, Daniele Nerini, Alexis Berne
Generative adversarial networks (GANs) have been recently adopted for super-resolution, an application closely related to what is referred to as "downscaling" in the atmospheric sc…