Improvements to skill (and other characteristics) of quantitative probabilistic rainfall forecasting for weather and hydrological purposes are a high priority at the Australian Bureau of Meteorology (the Bureau), highlighted by a series of major floods in eastern Australia in early 2022. Post-processed ensemble numerical weather prediction (NWP) rainfall guidance is key to increasing forecast automation in routine conditions and providing better guidance for high impact rainfall events.
The Bureau's existing NWP post-processing system has markedly increased weather rainfall forecast skill in recent years. However, to enable further forecast improvements and greater integration of rainfall processing for weather and hydrological purposes, a more general approach with fewer calibration steps was required. To these ends, we have developed 'RainForests': a multi-ensemble rainfall processing system, utilising gradient boosted decision tree (GBDT) ensembles for forecast calibration.
RainForests is inspired by the ECPoint method of Hewson and Pillosu (2021). RainForests, like ECPoint, is a non-parametric and generally non-local method which uses decision trees to create situation-dependent error distributions for each input (analogous to an extension of Bayesian Joint Probabilities) that can be used to calibrate grid-scale rainfall guidance to point-scale.
Key features of RainForests:
Additionally, RainForests uses and contributes to capabilities in the Integrated Model post-PROcessing and VERification (IMPROVER) system being developed in collaboration with the UK Met Office.
Initial RainForests outputs have comparable skill to the Bureau's existing post-processing system. Improvements are planned and will be reported on. It is also planned to provide calibrated rainfall ensemble members, derived from RainForests outputs, to downstream applications. This aims to support production of ensemble multiple-variable indices, hydrological applications and aggregation of rainfall forecasts in space and time.
How to cite: Owen, B., Trotta, B., Liu, J., Gale, T., Schubert, A., and Weymouth, G.: RainForests: A novel Machine Learning approach to calibrating rainfall forecasts, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-367, https://doi.org/10.5194/ems2022-367, 2022.