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physics.ao-ph2026
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…
physics.ao-ph2025
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…
physics.ao-ph2024
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…