506 citations · 1k across the 10 of their papers we have counts for
11 papers
Deep learning for quality control of surface physiographic fields using satellite Earth observations
Tom Kimpson, Margarita Choulga, Matthew Chantry +4
A purposely built deep learning algorithm for the Verification of Earth-System ParametERisation (VESPER) is used to assess recent upgrades of the global physiographic datasets unde…
A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts
Lucy Harris, Andrew T. T. McRae, Matthew Chantry +2
Despite continuous improvements, precipitation forecasts are still not as accurate and reliable as those of other meteorological variables. A major contributing factor to this is t…
Machine Learning Emulation of Urban Land Surface Processes
David Meyer, Sue Grimmond, Peter Dueben +2
Can we improve the modeling of urban land surface processes with machine learning (ML)? A prior comparison of urban land surface models (ULSMs) found that no single model is 'best'…
A comparison of data-driven approaches to build low-dimensional ocean models
Niraj Agarwal, Dmitri Kondrashov, Peter Dueben +2
We present a comprehensive inter-comparison of linear regression (LR), stochastic, and deep-learning approaches for reduced-order statistical emulation of ocean circulation. The re…
Mixed-precision for Linear Solvers in Global Geophysical Flows
Jan Ackmann, Peter D. Düben, Tim N. Palmer +1
Semi-implicit time-stepping schemes for atmosphere and ocean models require elliptic solvers that work efficiently on modern supercomputers. This paper reports our study of the pot…
Machine Learning Emulation of 3D Cloud Radiative Effects
David Meyer, Robin J. Hogan, Peter D. Dueben +1
The treatment of cloud structure in numerical weather and climate models is often greatly simplified to make them computationally affordable. Here we propose to correct the Europea…