Analysis of the forecast performance of a rainstorm process based on a convective scale ensemble prediction system

Abstract

In order to understand more comprehensively the ensemble forecast results of rainfall with the convective scale ensemble prediction system and thus to further recommend them to the weather forecasters, this study carried out the analysis of the forecast performance of a rainstorm process with a convective scale ensemble prediction system. The results show that: (1) The forecast difference of each ensemble member increases with precipitation magnitude, and the threat score difference between the best and worst performed ensemble member is more than 0.3. (2) Probability-matched mean forecast performs better than control forecast and ensemble mean forecast for both rainstorm and heavy rainstorm precipitation. Ensemble mean is insensitive to extreme precipitation due to the smoothing effect of ensemble member forecast. Therefore, ensemble mean is not suitable for extreme precipitation forecast. (3) From the minimum forecast to the maximum forecast, with the increase of ensemble percentile, the probability of detection, false alarm rate, and frequency bias gradually increase. The forecast at 70% or 80% ensemble percentile performs the best, and it is better than the ensemble mean and probability-matched mean forecast. (4) For the heavy rainstorm precipitation in the west part of northeastern Chongqing, the long-time ensemble probability forecasts with leading-times up to 60 h all successfully predict certain precipitation probability of rainstorm, and the forecasted precipitation from the corresponding best performed ensemble member is close to the observation.

Keywords

• ensemble forecast
• probability matched mean
• probability forecast
• ensemble percentile