This study quantitatively analyzes the effects of cloud seeding on precipitation and seasonal variations over the Boryeong Dam region,which has the lowest dam storage in South Korea,based on a one-year numerical simul...This study quantitatively analyzes the effects of cloud seeding on precipitation and seasonal variations over the Boryeong Dam region,which has the lowest dam storage in South Korea,based on a one-year numerical simulation for2021.The Morrison microphysics scheme in the WRF(Weather Research and Forecasting)model was modified to estimate differences in precipitation between simulations with seeding materials(Ag I and Ca Cl2;SEED)and without them(UNSD).The effect of cloud seeding on increasing precipitation or artificial rainfall(AR)between the two simulations was highest in August(average:0.21 mm;31%of the SEED-simulated monthly mean)and lowest in January(average:0.003 mm;30%).This large AR may be attributable to a combination of abundant moisture from the summer monsoon climate and enhanced cloud droplet growth resulting from cloud seeding.In the analysis of seasonal representative cases,cloud seeding demonstrated more pronounced effects in spring and summer,with mean 180-min accumulated AR values of 0.46 and 0.43 mm,respectively,within the study area.In the spring,where an actual flight experiment was conducted,the simulated mean180-min accumulated AR(1.41 mm)in the flight experiment area was close to the observed value(1.61 mm)for the same area.Additionally,cloud seeding promoted the hygroscopic growth of water vapor,thereby reducing the cloud water mixing ratio and increasing the rain water mixing ratio.Seasonal cross-sectional analysis further highlighted the impact of cloud seeding on changes in these two mixing ratios,with the most pronounced effects observed in spring and summer.展开更多
In this study,a tropical cyclogenesis detection system,Tropical Cyclone Analysis&Forecast(TCAF),was evaluated with an operational numerical model of the Korea Meteorological Administration(KMA).The tracking perfor...In this study,a tropical cyclogenesis detection system,Tropical Cyclone Analysis&Forecast(TCAF),was evaluated with an operational numerical model of the Korea Meteorological Administration(KMA).The tracking performance was compared with the result with the ECMWF model input field(TCAF-ECMWF).In order to improve the performance,different tracking time at an interval of 6 hours were investigated.The lowest false alarm rate and the highest hit rate(correct detection)were achieved at 06 hour after the initial tracking time.The tracking performance was also tested on two typhoons in 2013,LEEPI(1304)and DANAS(1324).The results showed that the TCAF-ECMWF detected tropical depressions 72 hours before the formation of the typhoon DANAS,which is a 12-hour earlier detection compared with the current performance with the use of KMA’s numerical weather prediction(NWP)model data.So,it is expected that TC genesis detection could be improved by determining an optimal tracking time and by using more accurate NWP model data.展开更多
基金funded by the Korea Meteorological Administration Research and Development Program“Research on Weather Modification and Cloud Physics”(Grant No.KMA2018-00224)supported by Korea Institute of Marine Science&Technology Promotion(KIMST)funded by the Ministry of Oceans and Fisheries,Korea(RS-202502217872)supported by an NRF grant funded by the Korean government(MSIT)(Grant No.NRF2023R1A2C1002367)。
文摘This study quantitatively analyzes the effects of cloud seeding on precipitation and seasonal variations over the Boryeong Dam region,which has the lowest dam storage in South Korea,based on a one-year numerical simulation for2021.The Morrison microphysics scheme in the WRF(Weather Research and Forecasting)model was modified to estimate differences in precipitation between simulations with seeding materials(Ag I and Ca Cl2;SEED)and without them(UNSD).The effect of cloud seeding on increasing precipitation or artificial rainfall(AR)between the two simulations was highest in August(average:0.21 mm;31%of the SEED-simulated monthly mean)and lowest in January(average:0.003 mm;30%).This large AR may be attributable to a combination of abundant moisture from the summer monsoon climate and enhanced cloud droplet growth resulting from cloud seeding.In the analysis of seasonal representative cases,cloud seeding demonstrated more pronounced effects in spring and summer,with mean 180-min accumulated AR values of 0.46 and 0.43 mm,respectively,within the study area.In the spring,where an actual flight experiment was conducted,the simulated mean180-min accumulated AR(1.41 mm)in the flight experiment area was close to the observed value(1.61 mm)for the same area.Additionally,cloud seeding promoted the hygroscopic growth of water vapor,thereby reducing the cloud water mixing ratio and increasing the rain water mixing ratio.Seasonal cross-sectional analysis further highlighted the impact of cloud seeding on changes in these two mixing ratios,with the most pronounced effects observed in spring and summer.
基金supported by the project“Management of National Typhoon Center”and“Development and ap-plication of technology for weather forecast”funded by KMA.
文摘In this study,a tropical cyclogenesis detection system,Tropical Cyclone Analysis&Forecast(TCAF),was evaluated with an operational numerical model of the Korea Meteorological Administration(KMA).The tracking performance was compared with the result with the ECMWF model input field(TCAF-ECMWF).In order to improve the performance,different tracking time at an interval of 6 hours were investigated.The lowest false alarm rate and the highest hit rate(correct detection)were achieved at 06 hour after the initial tracking time.The tracking performance was also tested on two typhoons in 2013,LEEPI(1304)and DANAS(1324).The results showed that the TCAF-ECMWF detected tropical depressions 72 hours before the formation of the typhoon DANAS,which is a 12-hour earlier detection compared with the current performance with the use of KMA’s numerical weather prediction(NWP)model data.So,it is expected that TC genesis detection could be improved by determining an optimal tracking time and by using more accurate NWP model data.
