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
Characterized by sudden changes in strength,complex influencing factors,and significant impacts,the wind speed in the circum-Bohai Sea area is relatively challenging to forecast.On the western side of Bohai Bay,as the...Characterized by sudden changes in strength,complex influencing factors,and significant impacts,the wind speed in the circum-Bohai Sea area is relatively challenging to forecast.On the western side of Bohai Bay,as the economic center of the circum-Bohai Sea,Tianjin exhibits a high demand for accurate wind forecasting.In this study,three machine learning algorithms were employed and compared as post-processing methods to correct wind speed forecasts by the Weather Research and Forecast(WRF)model for Tianjin.The results showed that the random forest(RF)achieved better performance in improving the forecasts because it substantially reduced the model bias at a lower computing cost,while the support vector machine(SVM)performed slightly worse(especially for stronger winds),but it required an approximately 15 times longer computing time.The back propagation(BP)neural network produced an average forecast significantly closer to the observed forecast but insufficiently reduced the RMSE.In regard to wind speed frequency forecasting,the RF method commendably corrected the forecasts of the frequency of moderate(force 3)wind speeds,while the BP method showed a desirable capability for correcting the forecasts of stronger(force>6)winds.In addition,the 10-m u and v components of wind(u_(10)and v_(10)),2-m relative humidity(RH_(2))and temperature(T_(2)),925-hPa u(u925),sea level pressure(SLP),and 500-hPa temperature(T_(500))were identified as the main factors leading to bias in wind speed forecasting by the WRF model in Tianjin,indicating the importance of local dynamical/thermodynamic processes in regulating the wind speed.This study demonstrates that the combination of numerical models and machine learning techniques has important implications for refined local wind forecasting.展开更多
海上风电场群基地的规划设计需要准确、科学地评估风场间的尾流效应。以我国江苏省某300MW海上风电场为研究对象,采用耦合风电场参数化模型的中尺度天气研究与预报(weather research and forecasting,WRF)模式对海上风电场尾流效应影响...海上风电场群基地的规划设计需要准确、科学地评估风场间的尾流效应。以我国江苏省某300MW海上风电场为研究对象,采用耦合风电场参数化模型的中尺度天气研究与预报(weather research and forecasting,WRF)模式对海上风电场尾流效应影响进行模拟研究。结果表明:WRF模式的计算结果与测风数据吻合较好,准确度满足海上风电前期风资源评估要求;进一步与海上风电场运行数据进行对比,分析了耦合风电场参数化模型的WRF模式计算结果偏高的原因;受上游风电场尾流的影响,下游风电场中心处的风速下降19.3%、尾流长度由14km增加至45km,且总功率下降13.7%。若将海上风电场内额定功率为4.2MW的风力机替换为20MW,一定程度上可减轻上游风场的尾流影响。展开更多
为了使台风路径的数值预报更加精确,本文对人造(Bogus)涡旋构建方案进行了改进,形成了一种新的Bogus方案。该方案直接采用台风外围的风圈观测信息,并成功地植入到WRF(weather research and forecasting)模式中。本文利用该方案,选取2011...为了使台风路径的数值预报更加精确,本文对人造(Bogus)涡旋构建方案进行了改进,形成了一种新的Bogus方案。该方案直接采用台风外围的风圈观测信息,并成功地植入到WRF(weather research and forecasting)模式中。本文利用该方案,选取2011年9号台风“梅花”这一典型案例展开讨论,结果表明:1)新构造的台风切向风廓线更加真实地反映了台风风场的实际情况;2)新的Bogus方案对台风中心位置的预报,更有利于对台风路径的预报;3)台风内核风场强度,对其非对称结构起到关键的作用,直接影响对台风路径的预报。展开更多
Urbanization-related precipitation and surface runoff changes have been widely investigated,but few studies have directly quantified these changes and their link to urbanization in the hydrological cycle.A two-way dyn...Urbanization-related precipitation and surface runoff changes have been widely investigated,but few studies have directly quantified these changes and their link to urbanization in the hydrological cycle.A two-way dynamically coupled atmospheric–hydrological modeling system,Weather Research and Forecasting(WRF)-Hydro,has been applied in this study to perform the quantification.The offline WRF-Hydro was first calibrated and validated for several flooding events against gauge observed streamflow data,with the Nash–Sutcliffe efficiency reaching 0.9.Compared to the WRF model,WRF-Hydro resolves more detailed rainfall pattern features and reproduces the gauge rainfall with a correlation coefficient of 0.8.Then,the impact of urbanization on hydrometeorological processes was investigated with coupled WRF-Hydro sensitivity simulations over the Qinhuai River basin of China during 2 June–31 July 2015.The results indicate that urbanization enhances regional precipitation,resulting in an indirect increase in surface runoff,overland flow,and streamflow by 16.7,93.5,and 111.2 mm,respectively;however,the impervious area results in higher surface runoff,overland flow,and streamflow.Moreover,changes in main hydrometeorological processes further impact the atmospheric–terrestrial water budget,resulting in a decrease in terrestrial water storage and an increase(a decrease)in precipitable water storage in the middle(lower)parts of the lower troposphere.These changes are likely associated with the warmer urban environment than rural areas.Increased water vapor and strengthened convective conditions in the middle part of the lower troposphere due to urban warming are advantageous to the formation of precipitation in urban areas,which in turn increases surface runoff,thereby facilitating the water cycle and altering the atmospheric–terrestrial water budget.展开更多
Microwave radiometer(MWR) demonstrates exceptional efficacy in monitoring the atmospheric temperature and humidity profiles.A typical inversion algorithm for MWR involves the use of radiosonde measurements as the trai...Microwave radiometer(MWR) demonstrates exceptional efficacy in monitoring the atmospheric temperature and humidity profiles.A typical inversion algorithm for MWR involves the use of radiosonde measurements as the training dataset.However,this is challenging due to limitations in the temporal and spatial resolution of available sounding data,which often results in a lack of coincident data with MWR deployment locations.Our study proposes an alternative approach to overcome these limitations by harnessing the Weather Research and Forecasting(WRF) model's renowned simulation capabilities,which offer high temporal and spatial resolution.By using WRF simulations that collocate with the MWR deployment location as a substitute for radiosonde measurements or reanalysis data,our study effectively mitigates the limitations associated with mismatching of MWR measurements and the sites,which enables reliable MWR retrieval in diverse geographical settings.Different machine learning(ML) algorithms including extreme gradient boosting(XGBoost),random forest(RF),light gradient boosting machine(LightGBM),extra trees(ET),and backpropagation neural network(BPNN) are tested by using WRF simulations,among which BPNN appears as the most superior,achieving an accuracy with a root-mean-square error(RMSE) of 2.05 K for temperature,0.67 g m~(-3) for water vapor density(WVD),and 13.98% for relative humidity(RH).Comparisons of temperature,RH,and WVD retrievals between our algorithm and the sounding-trained(RAD) algorithm indicate that our algorithm remarkably outperforms the latter.This study verifies the feasibility of utilizing WRF simulations for developing MWR inversion algorithms,thus opening up new possibilities for MWR deployment and airborne observations in global locations.展开更多
2009年4月9—12日黄海海域发生了一次受高压系统影响的海雾过程。利用卫星观测与探空数据、WRF模式(Weather Research and Forecasting Model)对此次海雾过程及相伴的大气波导进行了观测分析与数值模拟。海雾与波导发展可分为3个阶段:(1...2009年4月9—12日黄海海域发生了一次受高压系统影响的海雾过程。利用卫星观测与探空数据、WRF模式(Weather Research and Forecasting Model)对此次海雾过程及相伴的大气波导进行了观测分析与数值模拟。海雾与波导发展可分为3个阶段:(1)大气波导先于海雾存在于黄海海面;受高压下沉影响,黄海上空存在逆温层和较强的湿度梯度,表现为较强的贴海表面波导和非贴海表面波导。(2)海雾始于高压西部,并随高压系统逐渐东移减弱,向黄海北部扩展;辐射冷却虽然使雾顶附近逆温增强,但海雾的机械湍流使其顶部湿度梯度减小,雾顶附近对应弱悬空波导或波导消失。(3)高压系统影响使干空气下沉到雾区导致黄海海雾消散;雾顶附近逆温仍存在,同时湿度梯度增大,黄海上空逐渐变为非贴海表面波导。本研究结果表明:高压系统不仅极易为波导的发生提供有利条件,而且有利于海雾的生成,在海雾演变过程中主要是雾顶水汽梯度的变化导致了波导类型及强度的变化。展开更多
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.
基金Supported by the Open Project of Tianjin Key Laboratory of Oceanic Meteorology(2020TKLOMYB05)National Natural Science Foundation of China(42275191).
文摘Characterized by sudden changes in strength,complex influencing factors,and significant impacts,the wind speed in the circum-Bohai Sea area is relatively challenging to forecast.On the western side of Bohai Bay,as the economic center of the circum-Bohai Sea,Tianjin exhibits a high demand for accurate wind forecasting.In this study,three machine learning algorithms were employed and compared as post-processing methods to correct wind speed forecasts by the Weather Research and Forecast(WRF)model for Tianjin.The results showed that the random forest(RF)achieved better performance in improving the forecasts because it substantially reduced the model bias at a lower computing cost,while the support vector machine(SVM)performed slightly worse(especially for stronger winds),but it required an approximately 15 times longer computing time.The back propagation(BP)neural network produced an average forecast significantly closer to the observed forecast but insufficiently reduced the RMSE.In regard to wind speed frequency forecasting,the RF method commendably corrected the forecasts of the frequency of moderate(force 3)wind speeds,while the BP method showed a desirable capability for correcting the forecasts of stronger(force>6)winds.In addition,the 10-m u and v components of wind(u_(10)and v_(10)),2-m relative humidity(RH_(2))and temperature(T_(2)),925-hPa u(u925),sea level pressure(SLP),and 500-hPa temperature(T_(500))were identified as the main factors leading to bias in wind speed forecasting by the WRF model in Tianjin,indicating the importance of local dynamical/thermodynamic processes in regulating the wind speed.This study demonstrates that the combination of numerical models and machine learning techniques has important implications for refined local wind forecasting.
文摘海上风电场群基地的规划设计需要准确、科学地评估风场间的尾流效应。以我国江苏省某300MW海上风电场为研究对象,采用耦合风电场参数化模型的中尺度天气研究与预报(weather research and forecasting,WRF)模式对海上风电场尾流效应影响进行模拟研究。结果表明:WRF模式的计算结果与测风数据吻合较好,准确度满足海上风电前期风资源评估要求;进一步与海上风电场运行数据进行对比,分析了耦合风电场参数化模型的WRF模式计算结果偏高的原因;受上游风电场尾流的影响,下游风电场中心处的风速下降19.3%、尾流长度由14km增加至45km,且总功率下降13.7%。若将海上风电场内额定功率为4.2MW的风力机替换为20MW,一定程度上可减轻上游风场的尾流影响。
文摘为了使台风路径的数值预报更加精确,本文对人造(Bogus)涡旋构建方案进行了改进,形成了一种新的Bogus方案。该方案直接采用台风外围的风圈观测信息,并成功地植入到WRF(weather research and forecasting)模式中。本文利用该方案,选取2011年9号台风“梅花”这一典型案例展开讨论,结果表明:1)新构造的台风切向风廓线更加真实地反映了台风风场的实际情况;2)新的Bogus方案对台风中心位置的预报,更有利于对台风路径的预报;3)台风内核风场强度,对其非对称结构起到关键的作用,直接影响对台风路径的预报。
基金the National Key Project of China(No.GJXM92579)the Aero⁃nautic Science Foundation of China(No.2018ZA53014)the Shenyang Key Laboratory of Aircraft Icing and Ice Protection.
基金Supported by the National Natural Science Foundation of China(42205193 and 42330608)Open Fundation of China Meteorological Administration Hydro-Meteorology Key Laboratory(23SWQXM001)Young Beijing Scholars Program(2018-007)。
文摘Urbanization-related precipitation and surface runoff changes have been widely investigated,but few studies have directly quantified these changes and their link to urbanization in the hydrological cycle.A two-way dynamically coupled atmospheric–hydrological modeling system,Weather Research and Forecasting(WRF)-Hydro,has been applied in this study to perform the quantification.The offline WRF-Hydro was first calibrated and validated for several flooding events against gauge observed streamflow data,with the Nash–Sutcliffe efficiency reaching 0.9.Compared to the WRF model,WRF-Hydro resolves more detailed rainfall pattern features and reproduces the gauge rainfall with a correlation coefficient of 0.8.Then,the impact of urbanization on hydrometeorological processes was investigated with coupled WRF-Hydro sensitivity simulations over the Qinhuai River basin of China during 2 June–31 July 2015.The results indicate that urbanization enhances regional precipitation,resulting in an indirect increase in surface runoff,overland flow,and streamflow by 16.7,93.5,and 111.2 mm,respectively;however,the impervious area results in higher surface runoff,overland flow,and streamflow.Moreover,changes in main hydrometeorological processes further impact the atmospheric–terrestrial water budget,resulting in a decrease in terrestrial water storage and an increase(a decrease)in precipitable water storage in the middle(lower)parts of the lower troposphere.These changes are likely associated with the warmer urban environment than rural areas.Increased water vapor and strengthened convective conditions in the middle part of the lower troposphere due to urban warming are advantageous to the formation of precipitation in urban areas,which in turn increases surface runoff,thereby facilitating the water cycle and altering the atmospheric–terrestrial water budget.
基金Supported by the National Natural Science Foundation of China (42175144)。
文摘Microwave radiometer(MWR) demonstrates exceptional efficacy in monitoring the atmospheric temperature and humidity profiles.A typical inversion algorithm for MWR involves the use of radiosonde measurements as the training dataset.However,this is challenging due to limitations in the temporal and spatial resolution of available sounding data,which often results in a lack of coincident data with MWR deployment locations.Our study proposes an alternative approach to overcome these limitations by harnessing the Weather Research and Forecasting(WRF) model's renowned simulation capabilities,which offer high temporal and spatial resolution.By using WRF simulations that collocate with the MWR deployment location as a substitute for radiosonde measurements or reanalysis data,our study effectively mitigates the limitations associated with mismatching of MWR measurements and the sites,which enables reliable MWR retrieval in diverse geographical settings.Different machine learning(ML) algorithms including extreme gradient boosting(XGBoost),random forest(RF),light gradient boosting machine(LightGBM),extra trees(ET),and backpropagation neural network(BPNN) are tested by using WRF simulations,among which BPNN appears as the most superior,achieving an accuracy with a root-mean-square error(RMSE) of 2.05 K for temperature,0.67 g m~(-3) for water vapor density(WVD),and 13.98% for relative humidity(RH).Comparisons of temperature,RH,and WVD retrievals between our algorithm and the sounding-trained(RAD) algorithm indicate that our algorithm remarkably outperforms the latter.This study verifies the feasibility of utilizing WRF simulations for developing MWR inversion algorithms,thus opening up new possibilities for MWR deployment and airborne observations in global locations.
文摘2009年4月9—12日黄海海域发生了一次受高压系统影响的海雾过程。利用卫星观测与探空数据、WRF模式(Weather Research and Forecasting Model)对此次海雾过程及相伴的大气波导进行了观测分析与数值模拟。海雾与波导发展可分为3个阶段:(1)大气波导先于海雾存在于黄海海面;受高压下沉影响,黄海上空存在逆温层和较强的湿度梯度,表现为较强的贴海表面波导和非贴海表面波导。(2)海雾始于高压西部,并随高压系统逐渐东移减弱,向黄海北部扩展;辐射冷却虽然使雾顶附近逆温增强,但海雾的机械湍流使其顶部湿度梯度减小,雾顶附近对应弱悬空波导或波导消失。(3)高压系统影响使干空气下沉到雾区导致黄海海雾消散;雾顶附近逆温仍存在,同时湿度梯度增大,黄海上空逐渐变为非贴海表面波导。本研究结果表明:高压系统不仅极易为波导的发生提供有利条件,而且有利于海雾的生成,在海雾演变过程中主要是雾顶水汽梯度的变化导致了波导类型及强度的变化。
