Compressing atmospheric data into its real information content
Nature Computational Science Springer Nature 1:11 (2021) 713-724
More accuracy with less precision
Quarterly Journal of the Royal Meteorological Society Wiley 147:741 (2021) 4358-4370
Building Tangent‐Linear and Adjoint Models for Data Assimilation With Neural Networks
Journal of Advances in Modeling Earth Systems American Geophysical Union (AGU) 13:9 (2021)
Machine learning emulation of gravity wave drag in numerical weather forecasting
Journal of Advances in Modeling Earth Systems American Geophysical Union 13:7 (2021) e2021MS002477
Abstract:
We assess the value of machine learning as an accelerator for the parameterization schemes of operational weather forecasting systems, specifically the parameterization of nonorographic gravity wave drag. Emulators of this scheme can be trained to produce stable and accurate results up to seasonal forecasting timescales. Generally, networks that are more complex produce emulators that are more accurate. By training on an increased complexity version of the existing parameterization scheme, we build emulators that produce more accurate forecasts. For medium range forecasting, we have found evidence that our emulators are more accurate than the version of the parametrization scheme that is used for operational predictions. Using the current operational CPU hardware, our emulators have a similar computational cost to the existing scheme, but are heavily limited by data movement. On GPU hardware, our emulators perform 10 times faster than the existing scheme on a CPU.Your minds on free will
Physics World IOP Publishing 34:2 (2021) 21i-21i