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
本文将全球预报系统(GFS,Global forecast system)分析数据和预报数据作为训练集和测试集,利用BP(Back propagation)神经网络后报风场,将BP后报结果松弛逼近到天气研究和预报(WRF,Weather research and forecasting)模式的后报阶段,改善...本文将全球预报系统(GFS,Global forecast system)分析数据和预报数据作为训练集和测试集,利用BP(Back propagation)神经网络后报风场,将BP后报结果松弛逼近到天气研究和预报(WRF,Weather research and forecasting)模式的后报阶段,改善WRF模式对强降水的预报效果。以2018年5月22日青岛地区强降水为例,利用青岛地区7个气象站的观测数据和雷达回波图检验优化方法对强降水的后报效果。结果表明,松弛逼近BP后报风场后,降水强度有了明显改善,相比于不松弛逼近任何数据的WRF模式,松弛逼近BP后报风场的WRF模式24 h降水量误差减少了8.62 mm,但后报降水量仍弱于实际降水量。展开更多
The WRF-lake vertically one-dimensional(1D)water temperature model,as a submodule of the Weather Research and Forecasting(WRF)system,is being widely used to investigate water-atmosphere interactions.But previous appli...The WRF-lake vertically one-dimensional(1D)water temperature model,as a submodule of the Weather Research and Forecasting(WRF)system,is being widely used to investigate water-atmosphere interactions.But previous applications revealed that it cannot accurately simulate the water temperature in a deep riverine reservoir during a large flow rate period,and whether it can produce sufficiently accurate heat flux through the water surface of deep riverine reservoirs remains uncertain.In this study,the WRF-lake model was improved for applications in large,deep riverine reservoirs by parametric scheme optimization,and the accuracy of heat flux calculation was evaluated compared with the results of a better physically based model,the Delft3D-Flow,which was previously applied to different kinds of reservoirs successfully.The results show:(1)The latest version of WRF-lake can describe the surface water temperature to some extent but performs poorly in the large flow period.We revised WRF-lake by modifying the vertical thermal diffusivity,and then,the water temperature simulation in the large flow period was improved significantly.(2)The latest version of WRF-lake overestimates the reservoir-atmosphere heat exchange throughout the year,mainly because of underestimating the downward energy transfer in the reservoir,resulting in more heat remaining at the surface and returning to the atmosphere.The modification of vertical thermal diffusivity can improve the surface heat flux calculation significantly.(3)The longitudinal temperature variation and the temperature difference between inflow and outflow,which cannot be considered in the 1D WRF-lake,can also affect the water surface heat flux.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.
文摘本文将全球预报系统(GFS,Global forecast system)分析数据和预报数据作为训练集和测试集,利用BP(Back propagation)神经网络后报风场,将BP后报结果松弛逼近到天气研究和预报(WRF,Weather research and forecasting)模式的后报阶段,改善WRF模式对强降水的预报效果。以2018年5月22日青岛地区强降水为例,利用青岛地区7个气象站的观测数据和雷达回波图检验优化方法对强降水的后报效果。结果表明,松弛逼近BP后报风场后,降水强度有了明显改善,相比于不松弛逼近任何数据的WRF模式,松弛逼近BP后报风场的WRF模式24 h降水量误差减少了8.62 mm,但后报降水量仍弱于实际降水量。
基金the financial support from the National Key R&D Program of China(Grant No.2018YFE0196000)the National Natural Science Foundation of China(Grant No.52179069)。
文摘The WRF-lake vertically one-dimensional(1D)water temperature model,as a submodule of the Weather Research and Forecasting(WRF)system,is being widely used to investigate water-atmosphere interactions.But previous applications revealed that it cannot accurately simulate the water temperature in a deep riverine reservoir during a large flow rate period,and whether it can produce sufficiently accurate heat flux through the water surface of deep riverine reservoirs remains uncertain.In this study,the WRF-lake model was improved for applications in large,deep riverine reservoirs by parametric scheme optimization,and the accuracy of heat flux calculation was evaluated compared with the results of a better physically based model,the Delft3D-Flow,which was previously applied to different kinds of reservoirs successfully.The results show:(1)The latest version of WRF-lake can describe the surface water temperature to some extent but performs poorly in the large flow period.We revised WRF-lake by modifying the vertical thermal diffusivity,and then,the water temperature simulation in the large flow period was improved significantly.(2)The latest version of WRF-lake overestimates the reservoir-atmosphere heat exchange throughout the year,mainly because of underestimating the downward energy transfer in the reservoir,resulting in more heat remaining at the surface and returning to the atmosphere.The modification of vertical thermal diffusivity can improve the surface heat flux calculation significantly.(3)The longitudinal temperature variation and the temperature difference between inflow and outflow,which cannot be considered in the 1D WRF-lake,can also affect the water surface heat flux.
