123 citations · 132 across the 24 of their papers we have counts for
7 papers · 1 filter
Evaluating Skill and Stability of ArchesWeather and ArchesWeatherGen under Multi-Decadal Climate Simulations
Renu Singh, Robert Brunstein, Antonia Jost +5
We evaluate the climate simulation capabilities of ArchesWeather and ArchesWeatherGen, two machine learning models originally trained for weather forecasting and evaluated up to a…
STIPP: Space-time in situ postprocessing over the French Alps using proper scoring rules
David Landry, Isabelle Gouttevin, Hugo Merizen +2
We propose Space-time in situ postprocessing (STIPP), a machine learning model that generates spatio-temporally consistent weather forecasts for a network of station locations. Gri…
ArchesClimate: Probabilistic Decadal Ensemble Generation With Flow Matching
Graham Clyne, Guillaume Couairon, Guillaume Gastineau +2
Internal variability is a dominant contributor to the uncertainty of predictions at the interannual to decadal timescale. A typical approach to separating the internal variability…
Generating ensembles of spatially-coherent in-situ forecasts using flow matching
David Landry, Claire Monteleoni, Anastase Charantonis
We propose a machine-learning-based methodology for in-situ weather forecast postprocessing that is both spatially coherent and multivariate. Compared to previous work, our Flow MA…
Machine Learning for the Physics of Climate
Annalisa Bracco, Julien Brajard, Henk A. Dijkstra +3
An exponential growth in computing power, which has brought more sophisticated and higher resolution simulations of the climate system, and an exponential increase in observations…
Leveraging deterministic weather forecasts for in-situ probabilistic temperature predictions via deep learning
David Landry, Anastase Charantonis, Claire Monteleoni
We propose a neural network approach to produce probabilistic weather forecasts from a deterministic numerical weather prediction. Our approach is applied to operational surface te…