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

Angus, Michael; Widmann, Martin; Orr, Andrew ORCID: https://orcid.org/0000-0001-5111-8402; Ashrit, Raghavendra; Leckebusch, Gregor C.; Mitra, Ashis. 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. https://doi.org/10.1002/qj.4677

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Text (Open Access) This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2024 The Authors. Quarterly Journal of the Royal Meteorological Society published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society. Quart J Royal Meteoro Soc - 2024 - Angus - A comparison of two statistical postprocessing methods for heavy‐precipitation.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (10MB) | Preview

Abstract/Summary

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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1002/qj.4677
ISSN:	0035-9009
Additional Keywords:	EMOS, heavy precipitation, India, quantile mapping, statistical postprocessing, summer monsoon, weather forecast
Date made live:	06 Mar 2024 12:05 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/537032

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