A comparison of two statistical postprocessing methods for heavy‐precipitation forecasts over India during the summer monsoon
Michael Angus,
Martin Widmann,
Andrew Orr
et al.
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 Nation… Show more
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