5 citations · 10 across the 10 of their papers we have counts for
10 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…
GraphDOP: Towards skilful data-driven medium-range weather forecasts learnt and initialised directly from observations
Mihai Alexe, Eulalie Boucher, Peter Lean +11
We introduce GraphDOP, a new data-driven, end-to-end forecast system developed at the European Centre for Medium-Range Weather Forecasts (ECMWF) that is trained and initialised exc…
The ecological forecast limit revisited: Potential, actual and relative system predictability
Marieke Wesselkamp, Jakob Albrecht, Ewan Pinnington +3
Ecological forecasts are model-based statements about currently unknown ecosystem states in time or space. For a model forecast to be useful to inform decision makers, model valida…
Advances in Land Surface Model-based Forecasting: A comparative study of LSTM, Gradient Boosting, and Feedforward Neural Network Models as prognostic state emulators
Marieke Wesselkamp, Matthew Chantry, Ewan Pinnington +7
Most useful weather prediction for the public is near the surface. The processes that are most relevant for near-surface weather prediction are also those that are most interactive…
Data driven weather forecasts trained and initialised directly from observations
Anthony McNally, Christian Lessig, Peter Lean +11
Skilful Machine Learned weather forecasts have challenged our approach to numerical weather prediction, demonstrating competitive performance compared to traditional physics-based…