205 citations · 360 across the 4 of their papers we have counts for
5 papers
Assessing the ability of a stretched-grid deep-learning weather prediction model to capture physical balances
Francesco Pasquini, Michiel Baatsen, Bastien François +2
Weather forecasting has traditionally relied on Numerical Weather Prediction (NWP) models, which simulate weather by solving the governing fluid equations. Recently, the emergence…
Statistical post-processing of wind speed forecasts using convolutional neural networks
Simon Veldkamp, Kirien Whan, Sjoerd Dirksen +1
Current statistical post-processing methods for probabilistic weather forecasting are not capable of using full spatial patterns from the numerical weather prediction (NWP) model.…
Statistical Postprocessing for Weather Forecasts -- Review, Challenges and Avenues in a Big Data World
Stéphane Vannitsem, John Bjørnar Bremnes, Jonathan Demaeyer +21
Statistical postprocessing techniques are nowadays key components of the forecasting suites in many National Meteorological Services (NMS), with for most of them, the objective of…
Comparison of statistical post-processing methods for probabilistic NWP forecasts of solar radiation
Kilian Bakker, Kirien Whan, Wouter Knap +1
The increased usage of solar energy places additional importance on forecasts of solar radiation. Solar panel power production is primarily driven by the amount of solar radiation…
Improving precipitation forecasts using extreme quantile regression
Jasper Velthoen, Juan-Juan Cai, Geurt Jongbloed +1
Aiming to estimate extreme precipitation forecast quantiles, we propose a nonparametric regression model that features a constant extreme value index. Using local linear quantile r…