Tropical cyclone(TC) genesis forecasting is essential for daily operational practices during the typhoon season.The updated version of the Tropical Regional Atmosphere Model for the South China Sea(CMA-TRAMS) offers f...Tropical cyclone(TC) genesis forecasting is essential for daily operational practices during the typhoon season.The updated version of the Tropical Regional Atmosphere Model for the South China Sea(CMA-TRAMS) offers forecasters reliable numerical weather prediction(NWP) products with improved configurations and fine resolution. While traditional evaluation of typhoon forecasts has focused on track and intensity, the increasing accuracy of TC genesis forecasts calls for more comprehensive evaluation methods to assess the reliability of these predictions. This study aims to evaluate the effectiveness of the CMA-TRAMS for cyclogenesis forecasts over the western North Pacific and South China Sea. Based on previous research and typhoon observation data over five years, a set of localized, objective criteria has been proposed. The analysis results indicate that the CMA-TRAMS demonstrated superiority in cyclogenesis forecasts, predicting 6 out of 22 TCs with a forecast lead time of up to 144 h. Additionally, over 80% of the total could be predicted 72 h in advance. The model also showed an average TC genesis position error of 218.3 km, comparable to the track errors of operational models according to the annual evaluation. The study also briefly investigated the forecast of Noul(2011). The forecast field of the CMA-TRAMS depicted thermal and dynamical conditions that could trigger typhoon genesis, consistent with the analysis field. The 96-hour forecast field of the CMA-TRAMS displayed a relatively organized threedimensional structure of the typhoon. These results can enhance understanding of the mechanism behind typhoon genesis,fine-tune model configurations and dynamical frameworks, and provide reliable forecasts for forecasters.展开更多
In this paper, the CMA-TRAMS tropical high-resolution system was used to forecast a typical hot weather process in Guangdong, China with different horizontal resolutions and surface coverage. The results of resolution...In this paper, the CMA-TRAMS tropical high-resolution system was used to forecast a typical hot weather process in Guangdong, China with different horizontal resolutions and surface coverage. The results of resolutions of 0.02° and 0.06° were presented with the same surface coverage of the GlobeLand30 V2020, companies with the results of resolution 0.02° with the USGS global surface coverage. The results showed that, on the overall assessment the 2 km model performed better in forecasting 2 m temperature, while the 6 km model was more accurate in predicting 10 m wind speed. In the evaluation of representative stations, the 2 km model performed better in forecasting 2 m temperature and 2 m relative humidity at the coastal stations, and the 2 km model was also better in forecasting 2 m pressure at the representative stations. However, the 6 km model performed better in forecasting 10 m wind speed at the representative stations. Furthermore, the 2 km model, owing to its higher horizontal resolution, presented a more detailed stratification of various meteorological field maps, allowing for a more pronounced simulation of local meteorological element variations. And the use of the surface coverage data of the GlobeLand30 V2020 improved the forecasting of 2 m temperature, and 10 m wind speed compared to the USGS surface coverage data.展开更多
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
Despite marked improvements in tropical cyclone(TC) track ensemble forecasting,forecasters still have difficulty in making quick decisions when facing multiple potential predictions,so it is demanding to develop post-...Despite marked improvements in tropical cyclone(TC) track ensemble forecasting,forecasters still have difficulty in making quick decisions when facing multiple potential predictions,so it is demanding to develop post-processing techniques reducing the uncertainty in TC track forecasts,and one of such techniques is the cluster-based methods.To improve the effect and efficiency of the previous cluster-based methods,this study adopts recombination clustering(RC) by optimizing the use of limited TC variables and constructing better features that can accurately capture the good TC track forecasts from the ensemble prediction system(EPS) of the China Meteorological Administration Tropical Regional Atmosphere Model for the South China Sea(CMA-TRAMS).The RC technique is further optimized by constraining the number of clusters using the absolute track bias between the ensemble mean(EM) and ensemble spread(ES).Finally,the RC-based deterministic and weighted probabilistic forecasts are compared with the TC track forecasts from traditional methods.It is found that(1) for deterministic TC track forecasts,the RC-based TC track forecasts outperform all other methods at 12–72-h lead times;compared with the skillful EM(118.6 km),the improvements introduced by the use of RC reach up to 10.8%(8.1 km),10.2%(13.7 km),and 8.7%(20.5 km) at forecast times of 24,48,and 72 h,respectively.(2) For probabilistic TC track forecasts,RC yields significantly more accurate and discriminative forecasts than traditional equal-weight track forecasts,by increasing the weight of the best cluster,with a decrease of 4.1% in brier score(BS) and an increase of 1.4% in area under the relative operating characteristic curve(AUC).(3) In particular,for cases with recurved tracks,such as typhoons Saudel(2017) and Bavi(2008),RC significantly reduces track errors relative to EM by 56.0%(125.5 km) and 77.7%(192.2 km),respectively.Our results demonstrate that the RC technique not only improves TC track forecasts but also helps to unravel skillful ensemble members,and is likely useful for feature construction in machine learning.展开更多
基金Science and Technology Innovation Project of Guangdong Provincial Water Resources Department (2022-01)Science and Technology Program of Guangdong Province(2022A1515011870)+1 种基金China Meteorological Administration Key Innovation Team of Tropical Meteorology (CMA2023ZD08)Open Research Program of the State Key Laboratory of Severe Weather (2022LASW-B18)。
文摘Tropical cyclone(TC) genesis forecasting is essential for daily operational practices during the typhoon season.The updated version of the Tropical Regional Atmosphere Model for the South China Sea(CMA-TRAMS) offers forecasters reliable numerical weather prediction(NWP) products with improved configurations and fine resolution. While traditional evaluation of typhoon forecasts has focused on track and intensity, the increasing accuracy of TC genesis forecasts calls for more comprehensive evaluation methods to assess the reliability of these predictions. This study aims to evaluate the effectiveness of the CMA-TRAMS for cyclogenesis forecasts over the western North Pacific and South China Sea. Based on previous research and typhoon observation data over five years, a set of localized, objective criteria has been proposed. The analysis results indicate that the CMA-TRAMS demonstrated superiority in cyclogenesis forecasts, predicting 6 out of 22 TCs with a forecast lead time of up to 144 h. Additionally, over 80% of the total could be predicted 72 h in advance. The model also showed an average TC genesis position error of 218.3 km, comparable to the track errors of operational models according to the annual evaluation. The study also briefly investigated the forecast of Noul(2011). The forecast field of the CMA-TRAMS depicted thermal and dynamical conditions that could trigger typhoon genesis, consistent with the analysis field. The 96-hour forecast field of the CMA-TRAMS displayed a relatively organized threedimensional structure of the typhoon. These results can enhance understanding of the mechanism behind typhoon genesis,fine-tune model configurations and dynamical frameworks, and provide reliable forecasts for forecasters.
文摘In this paper, the CMA-TRAMS tropical high-resolution system was used to forecast a typical hot weather process in Guangdong, China with different horizontal resolutions and surface coverage. The results of resolutions of 0.02° and 0.06° were presented with the same surface coverage of the GlobeLand30 V2020, companies with the results of resolution 0.02° with the USGS global surface coverage. The results showed that, on the overall assessment the 2 km model performed better in forecasting 2 m temperature, while the 6 km model was more accurate in predicting 10 m wind speed. In the evaluation of representative stations, the 2 km model performed better in forecasting 2 m temperature and 2 m relative humidity at the coastal stations, and the 2 km model was also better in forecasting 2 m pressure at the representative stations. However, the 6 km model performed better in forecasting 10 m wind speed at the representative stations. Furthermore, the 2 km model, owing to its higher horizontal resolution, presented a more detailed stratification of various meteorological field maps, allowing for a more pronounced simulation of local meteorological element variations. And the use of the surface coverage data of the GlobeLand30 V2020 improved the forecasting of 2 m temperature, and 10 m wind speed compared to the USGS surface coverage data.
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 Natural Science Foundation of China (42375002, 41975136, U2242201, and 42105146)Hunan Provincial Natural Science Foundation of China (2021JC0009)。
文摘Despite marked improvements in tropical cyclone(TC) track ensemble forecasting,forecasters still have difficulty in making quick decisions when facing multiple potential predictions,so it is demanding to develop post-processing techniques reducing the uncertainty in TC track forecasts,and one of such techniques is the cluster-based methods.To improve the effect and efficiency of the previous cluster-based methods,this study adopts recombination clustering(RC) by optimizing the use of limited TC variables and constructing better features that can accurately capture the good TC track forecasts from the ensemble prediction system(EPS) of the China Meteorological Administration Tropical Regional Atmosphere Model for the South China Sea(CMA-TRAMS).The RC technique is further optimized by constraining the number of clusters using the absolute track bias between the ensemble mean(EM) and ensemble spread(ES).Finally,the RC-based deterministic and weighted probabilistic forecasts are compared with the TC track forecasts from traditional methods.It is found that(1) for deterministic TC track forecasts,the RC-based TC track forecasts outperform all other methods at 12–72-h lead times;compared with the skillful EM(118.6 km),the improvements introduced by the use of RC reach up to 10.8%(8.1 km),10.2%(13.7 km),and 8.7%(20.5 km) at forecast times of 24,48,and 72 h,respectively.(2) For probabilistic TC track forecasts,RC yields significantly more accurate and discriminative forecasts than traditional equal-weight track forecasts,by increasing the weight of the best cluster,with a decrease of 4.1% in brier score(BS) and an increase of 1.4% in area under the relative operating characteristic curve(AUC).(3) In particular,for cases with recurved tracks,such as typhoons Saudel(2017) and Bavi(2008),RC significantly reduces track errors relative to EM by 56.0%(125.5 km) and 77.7%(192.2 km),respectively.Our results demonstrate that the RC technique not only improves TC track forecasts but also helps to unravel skillful ensemble members,and is likely useful for feature construction in machine learning.
