Improvement of an Extreme Heavy Rainfall Simulation Using Nudging Assimilation

From 21 to 22 July 2012, Beijing and its surrounding areas suffered from an extreme precipitation event that was unprecedented relative to the past 61 years, and the event caused 79 deaths and reported direct economic losses of11.64 billion Yuan. However, current models have difficulty to simulate the spatial and temporal distribution characteristics of such events. Therefore, improved simulations of these extreme precipitation processes are needed. In this study, nudging methods, including grid nudging(GN) and spectral nudging(SN), and more accurate surface type data retrieved from remote sensing were used in the Weather Research and Forecasting(WRF) model to simulate this extreme precipitation case. When the default city underlay surface of the WRF model was replaced by a more accurate urban surface(NU), the precipitation intensity could be better simulated, but the peak moment of precipitation seriously lagged. Although the peak precipitation intensity simulated by the GN experiment was weak, the simulated precipitation time was basically consistent with the observations. Using GN in only the outside domain could better simulate precipitation peaks, while using GN in both the inside and outside domains could better simulate the spatial distribution characteristics of precipitation. Additionally, the precipitation from GN could be better simulated than that from SN. Overall, the two nudging methods could contribute to better simulations of this case because the nudging methods could improve the simulations of 500-hPa geopotential height, 850-hPa water vapor transport, and low-level weather systems, which are the key factors in adjusting the spatial and temporal distributions of precipitation. This study is the basis for the investigation of the mechanism and attribution of extreme precipitation processes,and the results are of great significance for promoting understanding of and mitigating disasters caused by extreme precipitation.

Study on Ensemble-Based Forecast of Extremely Heavy Rainfalls in China: Experiments for July 2011 Cases

According to the Anderson-Darling principle, a method for forecast of extremely heavy rainfall （abbre- viated as extreme rainfall/precipitation） was developed based on the ensemble forecast data of the T213 global ensemble prediction system （EPS） of the China Meteorological Administration （CMA）. Using the T213 forecast precipitation data during 2007-2010 and the observed rainfall data in June-August of 2001 2010, characteristics of the cumulative distribution functions （CDFs） of the observed and the T213 EPS forecast precipitation were analyzed. Accordingly, in the light of the continuous differences of the CDFs between model climate and EPS forecasts, a mathematical model of Extreme Precipitation Forecast Index （EPFI） was established and applied to forecast experiments of several extreme rainfall events in China during 17-31 July 2011. The results show that the EPFI has taken advantage of the tail information of the model climatic CDF and provided agreeable forecasts of extreme rainfalls. The EPFI based on the T213 EPS is useful for issuing early warnings of extreme rainfalls 3 7 days in advance. With extension of the forecast lead time, the EPFI becomes less skillful. The results also demonstrate that the rationality of the model climate CDF was of vital importance to the skill of EPFI.