75 citations · 99 across the 14 of their papers we have counts for
6 papers · 2 filters
High-Resolution Probabilistic Data-Driven Weather Modeling with a Stretched-Grid
Even Marius Nordhagen, Håvard Homleid Haugen, Magnus Sikora Ingstad +9
We present a probabilistic data-driven weather model providing ensembles of high spatial resolution realizations of 87 variables at arbitrary ensemble size and forecast length. The…
Using data assimilation tools to dissect GraphDOP
Patrick Laloyaux, Mihai Alexe, Eulalie Boucher +5
The Data Assimilation (DA) community has been developing various diagnostics to understand the importance of the observing system in accurately forecasting the weather. They usuall…
Learning Coupled Earth System Dynamics with GraphDOP
Eulalie Boucher, Mihai Alexe, Peter Lean +7
Interactions between different components of the Earth System (e.g. ocean, atmosphere, land and cryosphere) are a crucial driver of global weather patterns. Modern Numerical Weathe…
An update to ECMWF's machine-learned weather forecast model AIFS
Gabriel Moldovan, Ewan Pinnington, Ana Prieto Nemesio +18
We present an update to ECMWF's machine-learned weather forecasting model AIFS Single with several key improvements. The model now incorporates physical consistency constraints thr…
Learning from nature: insights into GraphDOP's representations of the Earth System
Peter Lean, Mihai Alexe, Eulalie Boucher +5
Through a series of experiments, we provide evidence that the GraphDOP model - trained solely on meteorological observations, using no prior knowledge - develops internal represent…
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…