MM5 ensemble mean precipitation forecasts in the Taiwan area for three early summer convective (mei-yu) seasons
Journal
Weather and Forecasting
Journal Volume
19
Journal Issue
4
Pages
735-750
Date Issued
2004
Author(s)
Chien, F.-C.
Abstract
This study presents precipitation verification of individual members and ensemble means in the Taiwan area for a real-time mesoscale ensemble prediction system, during the 2000, 2001, and 2002 early summer convective (mei-yu) seasons. The ensemble system, using the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) as a forecast model, consists of six members, each run with a different combination of moisture physics schemes. Precipitation forecasts within the 15-km domain were verified against the observational data from the 342 rain gauge stations on the island. In general the model that utilized the Grell cumulus parameterization scheme (CPS) and the Reisner I microphysics scheme (the GR model) had the best forecast skill among the six members. This physics combination is, therefore, recommended for MM5 rainfall simulations in the Taiwan area during the mei-yu season. The Kain–Fritsch CPS dominated the rainfall process and generally underforecast rainfall at high rainfall thresholds. The Betts–Miller CPS overforecast rainfall, especially at high thresholds. The equitable threat scores of the ensemble mean were not the highest, but were, in general, above the average among all members. Several other methods were examined for determining an ensemble mean (or weighted mean) rainfall forecast. WT1, which calculated rainfall by giving each member weightings determined by model performance of the member in rainfall forecast of the A period (0–12 h), generally outperformed the ensemble mean and every single member in the B period (12–24 h). This advantage did not extend to the C period (24–36 h), because the relation of model performance between the C and the A periods became weaker. WT2, in which weightings were determined according to the performance of each member in rainfall forecasts of the preceding year, performed slightly worse than WT1 in the B period, while it did better than WT1 in the C period. Another method that utilized the multiple linear regression technique to calculate weightings also showed positive impact on improving the rainfall forecast at medium to heavy precipitation thresholds. Unfortunately, its weightings appeared to be inadequate for another year's rainfall forecasts. The probability matching method helped reduce the bias problem inherent in the ensemble mean.
SDGs
Type
journal article