The mesoscale ensemble prediction system based on the Tropical Regional Atmosphere Model for the South China Sea(CMA-TRAMS(EPS))has been pre-operational since April 2020 at South China Regional Meteorological Center(S...The mesoscale ensemble prediction system based on the Tropical Regional Atmosphere Model for the South China Sea(CMA-TRAMS(EPS))has been pre-operational since April 2020 at South China Regional Meteorological Center(SCRMC),which was developed by the Guangzhou Institute of Tropical and Marine Meteorology(GITMM).To better understand the performance of the CMA-TRAMS(EPS)and provide guidance to forecasters,we assess the performance of this system on both deterministic and probabilistic forecasts from April to September 2020 in this study through objective verification.Compared with the control(deterministic)forecasts,the ensemble mean of the CMATRAMS(EPS)shows advantages in most non-precipitation variables.In addition,the threat score indicates that the CMA-TRAMS(EPS)obviously improves light and heavy rainfall forecasts in terms of the probability-matched mean.Compared with the European Center for Medium-range Weather Forecasts operational ensemble prediction system(ECMWF-EPS),the CMA-TRAMS(EPS)improves the probabilistic forecasts of light rainfall in terms of accuracy,reliability and discrimination,and this system also improves the heavy rainfall forecasts in terms of discrimination.Moreover,two typical heavy rainfall cases in south China during the pre-summer rainy season are investigated to visually demonstrate the deterministic and probabilistic forecasts,and the results of these two cases indicate the differences and advantages(deficiencies)of the two ensemble systems.展开更多
On 21 July 2012,an extreme rainfall event that recorded a maximum rainfall amount over 24 hours of 460 mm,occurred in Beijing,China. Most operational models failed to predict such an extreme amount. In this study,a co...On 21 July 2012,an extreme rainfall event that recorded a maximum rainfall amount over 24 hours of 460 mm,occurred in Beijing,China. Most operational models failed to predict such an extreme amount. In this study,a convective-permitting ensemble forecast system(CEFS),at 4-km grid spacing,covering the entire mainland of China,is applied to this extreme rainfall case. CEFS consists of 22 members and uses multiple physics parameterizations. For the event,the predicted maximum is 415 mm d^-1 in the probability-matched ensemble mean. The predicted high-probability heavy rain region is located in southwest Beijing,as was observed. Ensemble-based verification scores are then investigated. For a small verification domain covering Beijing and its surrounding areas,the precipitation rank histogram of CEFS is much flatter than that of a reference global ensemble. CEFS has a lower(higher) Brier score and a higher resolution than the global ensemble for precipitation,indicating more reliable probabilistic forecasting by CEFS. Additionally,forecasts of different ensemble members are compared and discussed. Most of the extreme rainfall comes from convection in the warm sector east of an approaching cold front. A few members of CEFS successfully reproduce such precipitation,and orographic lift of highly moist low-level flows with a significantly southeasterly component is suggested to have played important roles in producing the initial convection. Comparisons between good and bad forecast members indicate a strong sensitivity of the extreme rainfall to the mesoscale environmental conditions,and,to less of an extent,the model physics.展开更多
基金National Key Research and Development Project(2019YFEO110100)National Natural Science Foundation of China(41975136)+5 种基金the Intelligent Gridded Forecasting Team of Guangdong Meteorological Bureau(GRMCTD202004)Guangdong Basic and Applied Basic Research Foundation(2019A1515011118)Science and Technology Planning Project of Guangzhou(202103000030)the Innovation and Development Project of the China Meteorological Administration(CXF2021Z009)the Science and Technology Research Project of Guangdong Meteorological Bureau(GMRC2020M06)the Open Fund of Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction(J202006)。
文摘The mesoscale ensemble prediction system based on the Tropical Regional Atmosphere Model for the South China Sea(CMA-TRAMS(EPS))has been pre-operational since April 2020 at South China Regional Meteorological Center(SCRMC),which was developed by the Guangzhou Institute of Tropical and Marine Meteorology(GITMM).To better understand the performance of the CMA-TRAMS(EPS)and provide guidance to forecasters,we assess the performance of this system on both deterministic and probabilistic forecasts from April to September 2020 in this study through objective verification.Compared with the control(deterministic)forecasts,the ensemble mean of the CMATRAMS(EPS)shows advantages in most non-precipitation variables.In addition,the threat score indicates that the CMA-TRAMS(EPS)obviously improves light and heavy rainfall forecasts in terms of the probability-matched mean.Compared with the European Center for Medium-range Weather Forecasts operational ensemble prediction system(ECMWF-EPS),the CMA-TRAMS(EPS)improves the probabilistic forecasts of light rainfall in terms of accuracy,reliability and discrimination,and this system also improves the heavy rainfall forecasts in terms of discrimination.Moreover,two typical heavy rainfall cases in south China during the pre-summer rainy season are investigated to visually demonstrate the deterministic and probabilistic forecasts,and the results of these two cases indicate the differences and advantages(deficiencies)of the two ensemble systems.
基金supported by the National Fundamental Research (973) Program of China (Grant No. 2013CB430103)the Special Foundation of the China Meteorological Administration (Grant No. GYHY201506006)supported by the National Science Foundation of China (Grant No. 41405100)
文摘On 21 July 2012,an extreme rainfall event that recorded a maximum rainfall amount over 24 hours of 460 mm,occurred in Beijing,China. Most operational models failed to predict such an extreme amount. In this study,a convective-permitting ensemble forecast system(CEFS),at 4-km grid spacing,covering the entire mainland of China,is applied to this extreme rainfall case. CEFS consists of 22 members and uses multiple physics parameterizations. For the event,the predicted maximum is 415 mm d^-1 in the probability-matched ensemble mean. The predicted high-probability heavy rain region is located in southwest Beijing,as was observed. Ensemble-based verification scores are then investigated. For a small verification domain covering Beijing and its surrounding areas,the precipitation rank histogram of CEFS is much flatter than that of a reference global ensemble. CEFS has a lower(higher) Brier score and a higher resolution than the global ensemble for precipitation,indicating more reliable probabilistic forecasting by CEFS. Additionally,forecasts of different ensemble members are compared and discussed. Most of the extreme rainfall comes from convection in the warm sector east of an approaching cold front. A few members of CEFS successfully reproduce such precipitation,and orographic lift of highly moist low-level flows with a significantly southeasterly component is suggested to have played important roles in producing the initial convection. Comparisons between good and bad forecast members indicate a strong sensitivity of the extreme rainfall to the mesoscale environmental conditions,and,to less of an extent,the model physics.