8 papers in the archive.
Chronological review of machine learning in weather and climate modelling, from solvers and parameterization emulation to full ML forecast models, with ethics and future directions.
Reviews seasonal and multi-annual tropical cyclone forecasting, including emerging ML/AI methods, ENSO-neutral predictability and landfall skill gaps relative to user needs.
Sub-seasonal to seasonalNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)NSF NCAR / UCARMet OfficeMétéo-France (incl. CNRM)Colorado State University (incl. CIRA)China Meteorological Administration (CMA)Japan Meteorological Agency (JMA)Bureau of Meteorology (Australia)
Temporal convolutional networks project live fuel moisture content three months ahead across the US, with RMSE 27.5% close to nowcasting accuracy.
XGBoost and random forest models trained on 25 years of Southern Ocean surface observations cut ERA5 shortwave mean bias to near zero and reduce RMSE by about 25%.
Post-processing & calibrationBureau of Meteorology (Australia)
A CNN trained on CMIP5/6 models forecasts eastern Indian Ocean Dipole SST anomalies up to 6 months ahead, matching the ACCESS-S2 dynamical model.
Sub-seasonal to seasonalNanjing University of Information Science & Technology (NUIST)Bureau of Meteorology (Australia)
Facebook Prophet and ML regressions using CMIP5 decadal hindcasts improve monthly rainfall prediction for the Brisbane River catchment.
Post-processing & calibrationBureau of Meteorology (Australia)
Fuzzy-logic ALPHA algorithm fuses satellite, NWP and radar data to detect high ice water content for aviation, with AUC 0.85 against in situ measurements.
Observations & remote sensingNASANSF NCAR / UCARBureau of Meteorology (Australia)
Uses machine learning on Darwin C-band radar data to derive transition functions for a stochastic convective-stratiform cloud model aimed at global climate model parameterization.
Hybrid physics-ML & parameterizationUniversity of ReadingBureau of Meteorology (Australia)
