Performance evaluation and verification of post-processing methods for TIGGE ensemble data using machine learning approaches

Abstract

Ensemble modelling has become a significant technique in the field of machine learning, as it utilises the combined knowledge of multiple base models to improve the accuracy of predictions in different domains. Nevertheless, the effectiveness of ensemble predictions relies on the implementation of post-processing techniques that enhance and optimize the outputs of the ensemble. This study explores the domain of ensemble data post-processing, utilizing a machine learning-focused methodology to thoroughly assess and contrast a variety of post-processing methods. TIGGE Ensemble data from ECMWF and NCEP were used from 2010 to 2020. Research covers machine learning approaches post-processing methods such as BMA, cNLR, HXLR, OLR, logreg, hlogreg, QM were applied. The probabilistic forecasts were validated using the Brier Score (BS), Area Under Curve (AUC) of Receiver Operator Characteristics (ROC) plots and reliability plots. The cNLR and BMA strategies for post-processing performed exceptionally well with BS value of 0.10 and RPS value of 0.11 at all grid points for both methods. The ROC–AUC values for the cNLR and BMA methods were found to be 91.87 and 91.82%, respectively. The results show that improved post-processing techniques can be helpful to predict the flood in advance with accurate precision and warning. HIGHLIGHTS A comprehensive evaluation of TIGGE ensemble precipitation forecast using post-processing methods for the Sabarmati River Basin.; Improve forecast accuracy by cNLR, BMA, logreg, hlogreg, HXLR, and OLR are used.; Brier Score (BS), reliability plots, and AUC and ROC plots are used to rigorously compare post-processing algorithms for model verification.;

Keywords

• ensemble
• model verification
• post-processing
• precipitation
• tigge