8 papers
Global reanalysis from observations alone with machine learning
Peter Lean, Ewan Pinnington, Patrick Laloyaux +9
Earth system reanalysis datasets are foundational for weather and climate research and provide the gridded training data used by most machine learning weather prediction systems. H…
AIFS-DOP: End-to-End Medium-Range Weather Prediction from Observations Alone with Machine Learning
Ewan Pinnington, Peter Lean, Mihai Alexe +8
We introduce the Artificial Intelligence Forecasting System for Direct Observation Prediction (AIFS-DOP). AIFS-DOP is trained on a 40-year harmonized dataset of gridded observation…
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…