A comparison of two statistical postprocessing methods for heavy‐precipitation forecasts over India during the summer monsoon

Michael Angus, Martin Widmann*, Andrew Orr, Raghavendra Ashrit, Gregor C. Leckebusch, Ashis Mitra

*Corresponding author for this work

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Abstract

Accurate ensemble forecasts of heavy precipitation in India are vital for many applications and essential for early warning of damaging flood events, especially during the monsoon season. In this study we investigate to what extent Quantile Mapping (QM) and Ensemble Model Output Statistics (EMOS) statistical postprocessing reduce errors in precipitation ensemble forecasts over India, in particular for heavy precipitation. Both methods are applied to day‐1 forecasts at 12‐km resolution from the 23‐member National Centre for Medium Range Weather Forecasting (NCMRWF) global ensemble prediction system (NEPS‐G). By construction, QM leads to distributions close to the observed ones, while EMOS optimizes the ensemble spread, and it is not a priori clear which is better suited for practical applications. The methods are therefore compared with respect to several key aspects of the forecasts: local distributions, ensemble spread, and skill for forecasting precipitation amounts and the exceedance of heavy‐precipitation thresholds. The evaluation includes rank histograms, Continuous Ranked Probability Skill Scores (CRPSS), Brier Skill Scores (BSS), reliability diagrams, and receiver operating characteristic. EMOS performs best not only with respect to correcting under‐ or overdispersive ensembles, but also in terms of forecast skill for precipitation amounts and heavy precipitation events, with positive CRPSS and BSS in most regions (both up to about 0.4 in some areas), while QM in many regions performs worse than the raw forecast. QM performs best with respect to the overall local precipitation distributions. Which aspects of the forecasts are most relevant depends to some extent on how the forecasts are used. If the main criteria are the correction of under‐ or overdispersion, forecast reliability, match between the forecasted distribution for individual days and observations (CRPSS), and the skill in forecasting heavy‐precipitation events (BSS), then EMOS is the better choice for postprocessing NEPS‐G forecasts for short lead times.
Original languageEnglish
Pages (from-to)1-19
Number of pages19
JournalQuarterly Journal of the Royal Meteorological Society
Early online date6 Mar 2024
DOIs
Publication statusE-pub ahead of print - 6 Mar 2024

Bibliographical note

ACKNOWLEDGEMENTS
Michael Angus, Martin Widmann, Andrew Orr, and Gregor C. Leckebusch were funded under the HEavy Precipitation forecast Post-processing over India (HEPPI) project, which is part of the Weather and Climate Science for Service Partnership (WCSSP) programme, supported by the UK Government Newton Fund. We are grateful to Joanne Robbins from the Met Office for her support of HEPPI. We also thank two anonymous reviewers for their detailed and constructive comments which substantially improved the paper.

Keywords

  • statistical postprocessing
  • heavy precipitation
  • EMOS
  • India
  • quantile mapping
  • weather forecasts
  • summer monsoon

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