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
Global WRF (GWRF) is an extension of the mesoscale Weather Research and Forecasting (WRF) model that was developed for global weather research and forecasting applications. GWRF is being expanded to simulate atmospher...Global WRF (GWRF) is an extension of the mesoscale Weather Research and Forecasting (WRF) model that was developed for global weather research and forecasting applications. GWRF is being expanded to simulate atmospheric chemistry and its interactions with meteorology on a global scale. In this work, the ability of GWRF to reproduce major boundary layer meteorological variables that affect the fate and transport of air pollutants is assessed using observations from surface networks and satellites. The model evaluation shows an overall good performance in simulating global shortwave and longwave radiation, temperature, and specific humidity, despite large biases at high latitudes and over-Arctic and Antarctic areas. Larger biases exist in wind speed and precipitation predictions. These results are generally consistent with the performance of most current general circulation models where accuracies are often limited by a coarse grid resolution and inadequacies in sub-filter-scale parameterizations and errors in the specification of external forcings. The sensitivity simulations show that a coarse grid resolution leads to worse predictions of surface temperature and precipitation. The combinations of schemes that include the Dudhia shortwave radiation scheme or the Purdue Lin microphysics module, or the Grell-Devenyi cumulus parameterization lead to a worse performance for predictions of downward shortwave radiation flux, temperature, and specific humidity, as compared with those with respective alternative schemes. The physical option with the Purdue Lin microphysics module leads to a worse performance for precipitation predictions. The projected climate in 2050 indicates a warmer and drier climate, which may have important impacts on the fate and lifetime of air pollutants.展开更多
This paper evaluates the skills of physical Parameterization schemes in simulating extreme rainfall events over Dar es Salaam Region, Tanzania using the Weather Research and Forecasting (WRF) model. The model skill is...This paper evaluates the skills of physical Parameterization schemes in simulating extreme rainfall events over Dar es Salaam Region, Tanzania using the Weather Research and Forecasting (WRF) model. The model skill is determined during the 21 December 2011 flooding event. Ten sensitivity experiments have been conducted using Cumulus, Convective and Planetary boundary layer schemes to find the best combination and optimize the WRF model for the study area for heavy rainfall events. Model simulation results were verified against observed data using standard statistical tests. The model simulations show encouraging and better statistical results with the combination of Kain-Fritsch cumulus parameterization scheme, Lin microphysics scheme and Asymmetric Convection Model 2 (ACM2) planetary boundary scheme than any other combinations of physical parameterization schemes over Dar es Salaam region.展开更多
为给通过地形复杂,缺少气象资料的西南艰险山区的铁路研究风吹雪易发性提供方法,以位于该区域的四川省康定市为例,采用中尺度数值天气预报模式(The Weather Research and Forecasting Mode,WRF)对该区域气象要素的时空分布进行模拟.基于...为给通过地形复杂,缺少气象资料的西南艰险山区的铁路研究风吹雪易发性提供方法,以位于该区域的四川省康定市为例,采用中尺度数值天气预报模式(The Weather Research and Forecasting Mode,WRF)对该区域气象要素的时空分布进行模拟.基于WRF模式中各种参数的特点,设计4种参数方案进行计算,采用双层网格嵌套达到降尺度模拟,为了高精度解析大气边界层过程,在竖直方向、近地面1.5km高度内加密为15层,提取康定站计算结果与观测结果进行比较.结果表明,WRF模式的计算结果符合康定市气候特征,气象要素的相关系数均高于0.5;风吹雪发生概率从高到低的区域依次为康定市东部和南部边缘的贡嘎山区,内部的大雪山段,以及位于101.7°E~102.0°E位置处的铁路线路,概率分别为19%、14%和12%,其他区域的概率低于4%.展开更多
WRF(weather research and forecasting)模式中参数化方案的选择与近地面风场的仿真模拟结果关系密切。为解决新疆北部不同地形地区风场模拟准确性的问题,采用WRF中尺度气象模式,探究4类参数化方案(边界层、微物理、陆面过程、近地面层...WRF(weather research and forecasting)模式中参数化方案的选择与近地面风场的仿真模拟结果关系密切。为解决新疆北部不同地形地区风场模拟准确性的问题,采用WRF中尺度气象模式,探究4类参数化方案(边界层、微物理、陆面过程、近地面层)以及次网格地形方案对新疆北部不同地形地区风场模拟结果的影响。结果表明:每组试验均能模拟出风速的变化趋势;陆面过程RUC(rapid update cycle)方案和微物理Lin(Purdue Lin)方案对平原地区模拟结果较好,陆面过程Noah方案和微物理WSM6(WRF single moment 6 class)方案对山区地形模拟结果较好,且对于平原和山谷地形,次网格地形方案对模拟地区均能起到较好的修正作用。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Global WRF (GWRF) is an extension of the mesoscale Weather Research and Forecasting (WRF) model that was developed for global weather research and forecasting applications. GWRF is being expanded to simulate atmospheric chemistry and its interactions with meteorology on a global scale. In this work, the ability of GWRF to reproduce major boundary layer meteorological variables that affect the fate and transport of air pollutants is assessed using observations from surface networks and satellites. The model evaluation shows an overall good performance in simulating global shortwave and longwave radiation, temperature, and specific humidity, despite large biases at high latitudes and over-Arctic and Antarctic areas. Larger biases exist in wind speed and precipitation predictions. These results are generally consistent with the performance of most current general circulation models where accuracies are often limited by a coarse grid resolution and inadequacies in sub-filter-scale parameterizations and errors in the specification of external forcings. The sensitivity simulations show that a coarse grid resolution leads to worse predictions of surface temperature and precipitation. The combinations of schemes that include the Dudhia shortwave radiation scheme or the Purdue Lin microphysics module, or the Grell-Devenyi cumulus parameterization lead to a worse performance for predictions of downward shortwave radiation flux, temperature, and specific humidity, as compared with those with respective alternative schemes. The physical option with the Purdue Lin microphysics module leads to a worse performance for precipitation predictions. The projected climate in 2050 indicates a warmer and drier climate, which may have important impacts on the fate and lifetime of air pollutants.
文摘This paper evaluates the skills of physical Parameterization schemes in simulating extreme rainfall events over Dar es Salaam Region, Tanzania using the Weather Research and Forecasting (WRF) model. The model skill is determined during the 21 December 2011 flooding event. Ten sensitivity experiments have been conducted using Cumulus, Convective and Planetary boundary layer schemes to find the best combination and optimize the WRF model for the study area for heavy rainfall events. Model simulation results were verified against observed data using standard statistical tests. The model simulations show encouraging and better statistical results with the combination of Kain-Fritsch cumulus parameterization scheme, Lin microphysics scheme and Asymmetric Convection Model 2 (ACM2) planetary boundary scheme than any other combinations of physical parameterization schemes over Dar es Salaam region.
文摘为给通过地形复杂,缺少气象资料的西南艰险山区的铁路研究风吹雪易发性提供方法,以位于该区域的四川省康定市为例,采用中尺度数值天气预报模式(The Weather Research and Forecasting Mode,WRF)对该区域气象要素的时空分布进行模拟.基于WRF模式中各种参数的特点,设计4种参数方案进行计算,采用双层网格嵌套达到降尺度模拟,为了高精度解析大气边界层过程,在竖直方向、近地面1.5km高度内加密为15层,提取康定站计算结果与观测结果进行比较.结果表明,WRF模式的计算结果符合康定市气候特征,气象要素的相关系数均高于0.5;风吹雪发生概率从高到低的区域依次为康定市东部和南部边缘的贡嘎山区,内部的大雪山段,以及位于101.7°E~102.0°E位置处的铁路线路,概率分别为19%、14%和12%,其他区域的概率低于4%.
文摘WRF(weather research and forecasting)模式中参数化方案的选择与近地面风场的仿真模拟结果关系密切。为解决新疆北部不同地形地区风场模拟准确性的问题,采用WRF中尺度气象模式,探究4类参数化方案(边界层、微物理、陆面过程、近地面层)以及次网格地形方案对新疆北部不同地形地区风场模拟结果的影响。结果表明:每组试验均能模拟出风速的变化趋势;陆面过程RUC(rapid update cycle)方案和微物理Lin(Purdue Lin)方案对平原地区模拟结果较好,陆面过程Noah方案和微物理WSM6(WRF single moment 6 class)方案对山区地形模拟结果较好,且对于平原和山谷地形,次网格地形方案对模拟地区均能起到较好的修正作用。
