Logo image
Using a deterministic time-lagged ensemble forecast with a probabilistic threshold for improving 6–15day summer precipitation prediction in China
Journal article   Open access   Peer reviewed

Using a deterministic time-lagged ensemble forecast with a probabilistic threshold for improving 6–15day summer precipitation prediction in China

Weihua Jie, Tongwen Wu, Jun Wang, Weijing Li and Thomas Polivka
Atmospheric research, Vol.156, pp.142-159
04/01/2015
DOI: 10.1016/j.atmosres.2015.01.004
url
https://doi.org/10.1016/j.atmosres.2015.01.004View
Published (Version of record) Open Access

Abstract

A Deterministic Time-lagged Ensemble Forecast using a Probabilistic Threshold (DEFPT) method is suggested for improving summer 6–15day categorical precipitation prediction in China from the Beijing Climate Center Atmospheric General Circulation Model version 2.1 (BCC_AGCM2.1). It is based on a time-lagged ensemble system that consists of 13 ensemble members separated sequentially at 6hour intervals lagging the last three days. The DEFPT is not intended to predict the probability of rainfall, but rather to forecast rainfall (yes/no) occurrence for different categories of precipitation at any model grid box. A given categorical precipitation is forecasted to occur at one gridbox only when the ensemble probability for that categorical precipitation exceeds a certain threshold. This method is useful for providing an estimate of whether precipitation events will occur to decision-makers based on probabilistic forecasts during days 6–15. A large number of hindcast experiments for 1996–2005 summers reveal that this threshold can be best (and empirically) set as 5/13 and 4/13 respectively for the 6–15day prediction of 1+ mm (i.e., above 1mm per day) and 5+ mm rainfall events, using the Relative Operating Characteristic (ROC) curve, the Equitable Threat Score (ETS), the Hanssen and Kuipers (HK) score, and frequency bias (BIA) to achieve best prediction performance. With this set of thresholds, the DEFPT shows skill improvement over the corresponding single deterministic forecast using one initial value and the Time-Lagged Average Forecast (LAF) ensemble method. Similar improvements by the DEFPT are also found for the prediction of several other categories of precipitation between 1+ mm and 10+ mm per day. Application of DEFPT to larger ensemble size and BCC_AGCM version 2.2 with a higher horizontal resolution also demonstrates the effectiveness of the DEFPT for 6–15day categorical precipitation forecasts. •We focused on the 6–15day precipitation forecasting in China.•We suggested a new method of precipitation categorical ensemble forecast.•We compared this new method and traditional ensemble mean method.•We examined the effect of ensemble size and model horizontal resolution on the method.
Ensemble forecasting Precipitation 6–15 day forecasts

Details

Metrics

Logo image