The capabilities of cloud-resolving numerical models, observational instruments and cloud seeding have improved greatly over recent years in China. The subject of this review focuses on the main progresses made in Chi...The capabilities of cloud-resolving numerical models, observational instruments and cloud seeding have improved greatly over recent years in China. The subject of this review focuses on the main progresses made in China in the areas of cloud modeling, field observations, aerosol–cloud interactions, the effects of urbanization on cloud and precipitation, and weather modification.Well-equipped aircraft and ground-based advanced Doppler and polarized radars have been rapidly applied in cloudseeding operations. The combined use of modern techniques such as the Global Positioning System, remote sensing, and Geographical Information Systems has greatly decreased the blindness and uncertainties in weather-modification activities.Weather-modification models based on state-of-the-art cloud-resolving models are operationally run at the National Weather Modification Centre in China for guiding weather-modification programs.Despite important progress having been made, many critical issues or challenges remain to be solved, or require stronger scientific evidence and support, such as the chain of physical events involved in the effects induced by cloud seeding. Current important progresses in measurements and seeding techniques provide the opportunity and possibility to reduce these deficiencies. Long-term scientific projects aimed at reducing these key uncertainties are extremely urgent and important for weather-modification activities in China.展开更多
To improve the accuracy of short-term （0-12 h） forecasts of severe weather in southern China, a real-time storm-scale forecasting system, the Hourly Assimilation and Prediction System （HAPS）, has been implemented ...To improve the accuracy of short-term （0-12 h） forecasts of severe weather in southern China, a real-time storm-scale forecasting system, the Hourly Assimilation and Prediction System （HAPS）, has been implemented in Shenzhen, China. The forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting （WRF-ARW） model and the Advanced Regional Prediction System （ARPS） three-dimensional variational data assimilation （3DVAR） pack- age. It is capable of assimilating radar reflectivity and radial velocity data from multiple Doppler radars as well as surface automatic weather station （AWS） data. Experiments are designed to evaluate the impacts of data assimilation on quantitative precipitation forecasting （QPF） by studying a heavy rainfall event in southern China. The forecasts from these experiments are verified against radar, surface, and precipitation observations. Comparison of echo structure and accumulated precipitation suggests that radar data assimilation is useful in improving the short-term forecast by capturing the location and orientation of the band of accumulated rainfall. The assimilation of radar data improves the short-term precipitation forecast skill by up to 9 hours by producing more convection. The slight but generally positive impact that surface AWS data has on the forecast of near-surface variables can last up to 6-9 hours. The assimilation of AWS observations alone has some benefit for improving the Fractions Skill Score （FSS） and bias scores; when radar data are assimilated, the additional AWS data may increase the degree of rainfall overprediction.展开更多
A new method for driving a One-Dimensional Stratiform Cold （1DSC） cloud model with Weather Research and Fore casting （WRF） model outputs was developed by conducting numerical experiments for a typical large-scale ...A new method for driving a One-Dimensional Stratiform Cold （1DSC） cloud model with Weather Research and Fore casting （WRF） model outputs was developed by conducting numerical experiments for a typical large-scale stratiform rainfall event that took place on 4-5 July 2004 in Changchun, China. Sensitivity test results suggested that, with hydrometeor pro files extracted from the WRF outputs as the initial input, and with continuous updating of soundings and vertical velocities （including downdraft） derived from the WRF model, the new WRF-driven 1DSC modeling system （WRF-1DSC） was able to successfully reproduce both the generation and dissipation processes of the precipitation event. The simulated rainfall intensity showed a time-lag behind that observed, which could have been caused by simulation errors of soundings, vertical velocities and hydrometeor profiles in the WRF output. Taking into consideration the simulated and observed movement path of the precipitation system, a nearby grid point was found to possess more accurate environmental fields in terms of their similarity to those observed in Changchun Station. Using profiles from this nearby grid point, WRF-1DSC was able to repro duce a realistic precipitation pattern. This study demonstrates that 1D cloud-seeding models do indeed have the potential to predict realistic precipitation patterns when properly driven by accurate atmospheric profiles derived from a regional short range forecasting system, This opens a novel and important approach to developing an ensemble-based rain enhancement prediction and operation system under a probabilistic framework concept.展开更多
基金jointly sponsored by the Chinese Natural Science Foundation (Grant Nos. 41005072 and 40575003)the Key Science and Technology Supporting Project of the Ministry of Science and Technology of China (Grant Nos. 2006BAC12B03 and GYHY200806001)the Third Tibetan Plateau Scientific Experiment: Observations for Boundary Layer and Troposphere (GYHY201406001)
文摘The capabilities of cloud-resolving numerical models, observational instruments and cloud seeding have improved greatly over recent years in China. The subject of this review focuses on the main progresses made in China in the areas of cloud modeling, field observations, aerosol–cloud interactions, the effects of urbanization on cloud and precipitation, and weather modification.Well-equipped aircraft and ground-based advanced Doppler and polarized radars have been rapidly applied in cloudseeding operations. The combined use of modern techniques such as the Global Positioning System, remote sensing, and Geographical Information Systems has greatly decreased the blindness and uncertainties in weather-modification activities.Weather-modification models based on state-of-the-art cloud-resolving models are operationally run at the National Weather Modification Centre in China for guiding weather-modification programs.Despite important progress having been made, many critical issues or challenges remain to be solved, or require stronger scientific evidence and support, such as the chain of physical events involved in the effects induced by cloud seeding. Current important progresses in measurements and seeding techniques provide the opportunity and possibility to reduce these deficiencies. Long-term scientific projects aimed at reducing these key uncertainties are extremely urgent and important for weather-modification activities in China.
基金supported by a grant to CAPS from Shenzhen Meteorological Bureau (SZMB) and Shenzhen Key Laboratory of Severe Weather in South ChinaSupport was jointly provided by the National Basic Research Program of China (973 Program, Grant No. 2013CB430105)+1 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA05100300)the National Natural Science Foundation of China (Grant No. 41105095)
文摘To improve the accuracy of short-term （0-12 h） forecasts of severe weather in southern China, a real-time storm-scale forecasting system, the Hourly Assimilation and Prediction System （HAPS）, has been implemented in Shenzhen, China. The forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting （WRF-ARW） model and the Advanced Regional Prediction System （ARPS） three-dimensional variational data assimilation （3DVAR） pack- age. It is capable of assimilating radar reflectivity and radial velocity data from multiple Doppler radars as well as surface automatic weather station （AWS） data. Experiments are designed to evaluate the impacts of data assimilation on quantitative precipitation forecasting （QPF） by studying a heavy rainfall event in southern China. The forecasts from these experiments are verified against radar, surface, and precipitation observations. Comparison of echo structure and accumulated precipitation suggests that radar data assimilation is useful in improving the short-term forecast by capturing the location and orientation of the band of accumulated rainfall. The assimilation of radar data improves the short-term precipitation forecast skill by up to 9 hours by producing more convection. The slight but generally positive impact that surface AWS data has on the forecast of near-surface variables can last up to 6-9 hours. The assimilation of AWS observations alone has some benefit for improving the Fractions Skill Score （FSS） and bias scores; when radar data are assimilated, the additional AWS data may increase the degree of rainfall overprediction.
基金jointly supported by the Main Direction Program of Knowledge Innovation of the Chinese Academy of Sciences(Grant No.KZCX2EW203)the National Key Basic Research Program of China(Grant No.2013CB430105)the National Department of Public Benefit Research Foundation(Grant No.GYHY201006031)
文摘A new method for driving a One-Dimensional Stratiform Cold （1DSC） cloud model with Weather Research and Fore casting （WRF） model outputs was developed by conducting numerical experiments for a typical large-scale stratiform rainfall event that took place on 4-5 July 2004 in Changchun, China. Sensitivity test results suggested that, with hydrometeor pro files extracted from the WRF outputs as the initial input, and with continuous updating of soundings and vertical velocities （including downdraft） derived from the WRF model, the new WRF-driven 1DSC modeling system （WRF-1DSC） was able to successfully reproduce both the generation and dissipation processes of the precipitation event. The simulated rainfall intensity showed a time-lag behind that observed, which could have been caused by simulation errors of soundings, vertical velocities and hydrometeor profiles in the WRF output. Taking into consideration the simulated and observed movement path of the precipitation system, a nearby grid point was found to possess more accurate environmental fields in terms of their similarity to those observed in Changchun Station. Using profiles from this nearby grid point, WRF-1DSC was able to repro duce a realistic precipitation pattern. This study demonstrates that 1D cloud-seeding models do indeed have the potential to predict realistic precipitation patterns when properly driven by accurate atmospheric profiles derived from a regional short range forecasting system, This opens a novel and important approach to developing an ensemble-based rain enhancement prediction and operation system under a probabilistic framework concept.
