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Quan, D., Dai, K., Zhou, J. and Tao, Y.W. (2017) Application and Verification of Extreme Weather Forecast Products of ECMWF Ensemble Prediction System. Meteorological Monthly, 43, 1095-1109.

has been cited by the following article:

• TITLE: Calibration and Quantitative Forecast of Extreme Daily Precipitation Using the Extreme Forecast Index (EFI)

  AUTHORS: Quan Dong

  KEYWORDS: Extreme Forecast Index (EFI), Extreme Precipitation, Quantitative Precipita-tion Forecast (QPF)

  JOURNAL NAME: Journal of Geoscience and Environment Protection, Vol.6 No.2, February 28, 2018

  ABSTRACT: Based on the extreme forecast index (EFI) of ECMWF, the “observed” EFI (OEFI) of observation is defined and the EFI is calibrated. Then the EFI equivalent percentile (EFIEP) and EFI equivalent quantile (EFIEQ) are designed to forecast the daily extreme precipitation quantitatively. The formulation indicates that the EFIEP is correlated not only to the EFI but also to the proportion of no precipitation. This characteristic is prominent as two areas with nearly same EFIs but different proportions of no precipitation. Cases study shows that the EFIEP can forecast reliable percentile of daily precipitation and 100% percentiles are forecasted for over max extreme events. The EFIEQ is a considerable tool for quantitative precipitation forecast (QPF). Compared to the probabilistic forecast of ensemble prediction system (EPS), it is quantitative and synthesizes the advantage of extreme precipitation location forecast of EPS. Using the observations of 2311 stations of China in 2016 to verify the EFIEP and EFIEQ, the results show that the forecast biases are around 1. The threat scores (TS) for 20 years return period events are about 0.21 and 0.07 for 36 and 180 hours lead times respectively. The equivalent threat scores (ETS) are all larger than 0 and nearly equal to the TS. The TS for heavy rainfall are 0.23 and 0.07 for 36 and 180 lead times respectively. The scores are better than those of high resolution deterministic model (HRDet) and show significant forecast skills for quantitative forecast of extreme daily precipitation.