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
黄河源区是黄河流域主要的产流区和水源涵养区,研究和探索该区域陆面水文过程对理解陆面过程及水文循环特征,揭示陆面—水文耦合过程具有重要的科学意义。本研究基于2009~2018年中国区域高时空分辨率地面气象要素驱动数据(China Meteoro...黄河源区是黄河流域主要的产流区和水源涵养区,研究和探索该区域陆面水文过程对理解陆面过程及水文循环特征,揭示陆面—水文耦合过程具有重要的科学意义。本研究基于2009~2018年中国区域高时空分辨率地面气象要素驱动数据(China Meteorological Forcing Dataset,简称CMFD)、全球高分辨率降水数据集(Climate Prediction Center Morphing Technique,简称CMORPH)、热带降雨测量卫星(Tropical Rainfall Measuring Mission,简称TRMM)及全球陆地数据同化系统(Global Land Data Assimilation System,简称GLDAS)降水,评估了四类降水产品在黄河源区的降水精度,在此基础上,利用最优降水数据驱动独立运行的天气研究预报及水文耦合模型系统(Weather Research and Forecasting Model Hydrological modeling system,简称WRF-Hydro),探究该模式在黄河源区径流模拟的适用性。结果表明:四类降水产品均能够反映出降水的分布特征,但在量值及细节捕捉上存在显著差异。CMFD在不同时空尺度上都能很好地捕捉到降水的演变特征,其与日观测降水的相关系数达到0.99,均方根误差仅为0.25 mm。在表征降水能力方面,四类降水产品总体表现为CMFD>CMORPH>TRMM>GLDAS,CMFD的平均探测成功率(Critical Success Index,简称CSI)在0.93以上。经参数率定后的WRF-Hydro模式在黄河源区月径流模拟方面表现较好,率定期纳什系数(Nash-Sutcliffe efficiency coefficient,简称NSE)均在0.92以上,而验证期丰水年模拟结果明显好于枯水年(NSE=0.15),这与降水和径流的非线性程度有关。本研究方案和结果为亚寒带半干旱气候区大尺度流域水文模拟及径流预测提供了一定的参考价值。展开更多
WRF(Weather Research and Forecasting)等物理驱动预测方法被证实能够获取有效的风速预测结果。本研究面向WRF预测方法对初始条件的敏感性,构建多目标集成模型MOEnWRF(Multi-Objective Ensemble WRF),优化WRF的预测精度。该方法主要包...WRF(Weather Research and Forecasting)等物理驱动预测方法被证实能够获取有效的风速预测结果。本研究面向WRF预测方法对初始条件的敏感性,构建多目标集成模型MOEnWRF(Multi-Objective Ensemble WRF),优化WRF的预测精度。该方法主要包括两个步骤,首先构建描述预测精度与稳定性的评价指标体系,采用多目标灰狼优化算法(MOGWO,Multi-Objective Grey Wolf Optimizer)实现对模型精度以及稳定性的同时优化,获得集成权重的帕累托解集。然后,采用组合距离评估法(CODAS,COmbinative Distance-based ASsessment)评估帕累托解集的有效性,从解集中选择出最优解,并应用于集成WRF模型。经过4个站点的实际验证,得到所提方法能够有效提升WRF的预测精度,并优于单目标优化集成方法。展开更多
Seasonal forecasting of the Indian summer monsoon by dynamically downscaling the CFSv2 output using a high resolution WRF model over the hindcast period of 1982-2008 has been performed in this study. The April start e...Seasonal forecasting of the Indian summer monsoon by dynamically downscaling the CFSv2 output using a high resolution WRF model over the hindcast period of 1982-2008 has been performed in this study. The April start ensemble mean of the CFSv2 has been used to provide the initial and lateral boundary conditions for driving the WRF. The WRF model is integrated from 1st May through 1st October for each monsoon season. The analysis suggests that the WRF exhibits potential skill in improving the rainfall skill as well as the seasonal pattern and minimizes the meteorological errors as compared to the parent CFSv2 model. The rainfall pattern is simulated quite closer to the observation (IMD) in the WRF model over CFSv2 especially over the significant rainfall regions of India such as the Western Ghats and the central India. Probability distributions of the rainfall show that the rainfall is improved with the WRF. However, the WRF simulates copious amounts of rainfall over the eastern coast of India. Surface and upper air meteorological parameters show that the WRF model improves the simulation of the lower level and upper-level winds, MSLP, CAPE and PBL height. The specific humidity profiles show substantial improvement along the vertical column of the atmosphere which can be directly related to the net precipitable water. The CFSv2 underestimates the specific humidity along the vertical which is corrected by the WRF model. Over the Bay of Bengal, the WRF model overestimates the CAPE and specific humidity which may be attributed to the copious amount of rainfall along the eastern coast of India. Residual heating profiles also show that the WRF improves the thermodynamics of the atmosphere over 700 hPa and 400 hPa levels which helps in improving the rainfall simulation. Improvement in the land surface fluxes is also witnessed in the WRF model.展开更多
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%.
文摘黄河源区是黄河流域主要的产流区和水源涵养区,研究和探索该区域陆面水文过程对理解陆面过程及水文循环特征,揭示陆面—水文耦合过程具有重要的科学意义。本研究基于2009~2018年中国区域高时空分辨率地面气象要素驱动数据(China Meteorological Forcing Dataset,简称CMFD)、全球高分辨率降水数据集(Climate Prediction Center Morphing Technique,简称CMORPH)、热带降雨测量卫星(Tropical Rainfall Measuring Mission,简称TRMM)及全球陆地数据同化系统(Global Land Data Assimilation System,简称GLDAS)降水,评估了四类降水产品在黄河源区的降水精度,在此基础上,利用最优降水数据驱动独立运行的天气研究预报及水文耦合模型系统(Weather Research and Forecasting Model Hydrological modeling system,简称WRF-Hydro),探究该模式在黄河源区径流模拟的适用性。结果表明:四类降水产品均能够反映出降水的分布特征,但在量值及细节捕捉上存在显著差异。CMFD在不同时空尺度上都能很好地捕捉到降水的演变特征,其与日观测降水的相关系数达到0.99,均方根误差仅为0.25 mm。在表征降水能力方面,四类降水产品总体表现为CMFD>CMORPH>TRMM>GLDAS,CMFD的平均探测成功率(Critical Success Index,简称CSI)在0.93以上。经参数率定后的WRF-Hydro模式在黄河源区月径流模拟方面表现较好,率定期纳什系数(Nash-Sutcliffe efficiency coefficient,简称NSE)均在0.92以上,而验证期丰水年模拟结果明显好于枯水年(NSE=0.15),这与降水和径流的非线性程度有关。本研究方案和结果为亚寒带半干旱气候区大尺度流域水文模拟及径流预测提供了一定的参考价值。
文摘WRF(Weather Research and Forecasting)等物理驱动预测方法被证实能够获取有效的风速预测结果。本研究面向WRF预测方法对初始条件的敏感性,构建多目标集成模型MOEnWRF(Multi-Objective Ensemble WRF),优化WRF的预测精度。该方法主要包括两个步骤,首先构建描述预测精度与稳定性的评价指标体系,采用多目标灰狼优化算法(MOGWO,Multi-Objective Grey Wolf Optimizer)实现对模型精度以及稳定性的同时优化,获得集成权重的帕累托解集。然后,采用组合距离评估法(CODAS,COmbinative Distance-based ASsessment)评估帕累托解集的有效性,从解集中选择出最优解,并应用于集成WRF模型。经过4个站点的实际验证,得到所提方法能够有效提升WRF的预测精度,并优于单目标优化集成方法。
文摘Seasonal forecasting of the Indian summer monsoon by dynamically downscaling the CFSv2 output using a high resolution WRF model over the hindcast period of 1982-2008 has been performed in this study. The April start ensemble mean of the CFSv2 has been used to provide the initial and lateral boundary conditions for driving the WRF. The WRF model is integrated from 1st May through 1st October for each monsoon season. The analysis suggests that the WRF exhibits potential skill in improving the rainfall skill as well as the seasonal pattern and minimizes the meteorological errors as compared to the parent CFSv2 model. The rainfall pattern is simulated quite closer to the observation (IMD) in the WRF model over CFSv2 especially over the significant rainfall regions of India such as the Western Ghats and the central India. Probability distributions of the rainfall show that the rainfall is improved with the WRF. However, the WRF simulates copious amounts of rainfall over the eastern coast of India. Surface and upper air meteorological parameters show that the WRF model improves the simulation of the lower level and upper-level winds, MSLP, CAPE and PBL height. The specific humidity profiles show substantial improvement along the vertical column of the atmosphere which can be directly related to the net precipitable water. The CFSv2 underestimates the specific humidity along the vertical which is corrected by the WRF model. Over the Bay of Bengal, the WRF model overestimates the CAPE and specific humidity which may be attributed to the copious amount of rainfall along the eastern coast of India. Residual heating profiles also show that the WRF improves the thermodynamics of the atmosphere over 700 hPa and 400 hPa levels which helps in improving the rainfall simulation. Improvement in the land surface fluxes is also witnessed in the WRF model.
