Post-processing using deep learning to create operational, high-resolution, and probabilistic weather forecasts Luke Conibear, Ashley E. Payne, Allison E Reed Harris et al. · preprint · Oct 2023 Operational multi-task neural network post-processes HRRR forecasts with a CRPS loss, improving RMSE by 3–25% and CRPS by 31–45% over HRRR. Post-processing & calibrationTomorrow.ioAmazon (AWS)
A Neural-Network Based MPAS—Shallow Water Model and Its 4D-Var Data Assimilation System Xiaoxu Tian, Luke Conibear, Jeffrey Steward · Atmosphere · Jan 2023 A neural-network shallow-water emulator of MPAS with tangent-linear and adjoint code enables a continuous 4D-Var assimilation system demonstrated on twin experiments. Data assimilationTomorrow.io
Physically regularized machine learning emulators of aerosol activation Sam James Silva, Po‐Lun Ma, Joseph Clinton Hardin et al. · Geoscientific model development · May 2021 Physically regularized ML emulators of aerosol activation outperform many existing parameterizations, enabling hybrid models for Earth system models. Hybrid physics-ML & parameterizationTomorrow.io
