5 papers · 1 filter
On the sensitivity of machine-learned probabilistic weather forecast models to scale-aware scoring rules
Simon Lang, Martin Leutbecher, Sam Hatfield
Probabilistic forecast models can be machine-learned from data using loss functions based on scoring rules such as the Continuous Ranked Probability Score (CRPS). This note summari…
Downscaling weather forecasts from Low- to High-Resolution with Diffusion Models
Joffrey Dumont Le Brazidec, Simon Lang, Martin Leutbecher +9
We introduce a probabilistic diffusion-based method for global atmospheric downscaling implemented within the Anemoi framework. The approach transforms low-resolution ensemble fore…
A multi-scale loss formulation for learning a probabilistic model with proper score optimisation
Simon Lang, Martin Leutbecher, Pedro Maciel
We assess the impact of a multi-scale loss formulation for training probabilistic machine-learned weather forecasting models. The multi-scale loss is tested in AIFS-CRPS, a machine…
Unbiased calculation, evaluation, and calibration of ensemble forecast anomalies
Christopher D. Roberts, Martin Leutbecher
Long-range ensemble forecasts are typically verified as anomalies with respect to a lead-time dependent climatological mean to remove the influence of systematic biases. However, c…
AIFS-CRPS: Ensemble forecasting using a model trained with a loss function based on the Continuous Ranked Probability Score
Simon Lang, Mihai Alexe, Mariana C. A. Clare +15
Over the last three decades, ensemble forecasts have become an integral part of forecasting the weather. They provide users with more complete information than single forecasts as…