Classical and Machine Learning EMOS for postprocessing precipitation forecasts over India

Postprocessing of ensemble forecasts with Ensemble Model Output Statistics (EMOS) can reduce systematic errors in the ensemble mean and spread. Classical EMOS finds linear transformations between the original and postprocessed ensemble mean and variance that optimise the Continuous Ranked Probability Score (CRPS). Within the Weather and Climate Science for Service Partnership - India (WCSSP-India) project HEavy Precipitation Forecast Post-processing over India (HEPPI) we have implemented this approach to postprocess daily precipitation forecasts over India for the monsoon seasons 2018-2022 from the NEPS-G forecasting system run at the National Centre for Medium Range Weather Forecasting (NCMRWF) in Noida. In Angus et al. (2024) we have shown that over most of India EMOS improves the CRPS and the prediction of the probability for heavy precipitation, and also reduces over- or underdispersion in the ensemble.

The EMOS approach is not restricted to the classical linear transformations between the original and postprocessed ensemble mean and variance, and to the optimisation of CRPS. It can be made more flexible by using Machine Learning (ML) to find non-linear transformations that optimise the CRPS or other forecast evaluation criteria. Within the WCSSP-India project HEPPI-ML we have explored different versions of ML-EMOS to postprocess NEPS-G daily precipitation forecasts. These include local and non-local Multilayer Perceptrons, and convolutional neural networks. We will present first evaluation results, including a comparison of ML and classical EMOS.

 

Angus, M., M. Widmann, A. Orr, G.C. Leckebusch, R. Ashrit, and A. Mitra, 2024: A comparison of two statistical postprocessing methods for heavy‐precipitation forecasts over India during the summer monsoon. Quarterly Journal of the Royal Meteorological Society, 150(761), 1865-1883.