Accurate meteorological predictions in the Arctic are important in response to the rapid climate change and insufficient meteorological observations in the Arctic.In this study,we adopted a high-resolution Weather Res...Accurate meteorological predictions in the Arctic are important in response to the rapid climate change and insufficient meteorological observations in the Arctic.In this study,we adopted a high-resolution Weather Research and Forecasting(WRF)model to simulate the meteorology at two Arctic stations(Barrow and Summit)in April 2019.Simulation results were also evaluated by using surface measurements and statistical parameters.In addition,weather charts during the studied time period were also used to assess the model performance.The results demonstrate that the WRF model is able to accurately capture the meteorological parameters for the two Arctic stations and the weather systems such as cyclones and anticyclones in the Arctic.Moreover,we found the model performance in predicting the surface pressure the best while the performance in predicting the wind the worst among these meteorological predictions.However,the wind predictions at these Arctic stations were found to be more accurate than those at urban stations in mid-latitude regions,due to the differences in land features and anthropogentic heat sources between these regions.In addition,a comparison of the simulation results showed that the prediction of meteorological conditions at Summit is superior to that at Barrow.Possible reasons for the deviations in temperature predictions between these two Arctic stations are uncertainties in the treatments of the sea ice and the cloud in the model.With respect to the wind,the deviations may source from the overestimation of the wind over the sea and at coastal stations.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
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
随着城市化、工业化的快速发展,空气污染已经成为了公众最关注的问题之一。为了提高空气质量预报的准确度,以多尺度空气质量模型(Community Multi-Scale Air Quality,CMAQ)为工具,结合中尺度WRF(Weather Research and Forecast Model)...随着城市化、工业化的快速发展,空气污染已经成为了公众最关注的问题之一。为了提高空气质量预报的准确度,以多尺度空气质量模型(Community Multi-Scale Air Quality,CMAQ)为工具,结合中尺度WRF(Weather Research and Forecast Model)气象预报数据、气象观测数据、污染物浓度观测数据,基于极端随机树方法建立了WRF-CMAQ-MOS(Weather Research and Forecast Model-Community Multi-Scale Air Quality-Model Output Statistics)统计修正模型。结果表明,结合WRF气象预报的CMAQ-MOS方法明显修正了由于模型非客观性产生的模式预报偏差,提高了预报效果。使用线性回归方法不能获得较好的优化效果,选取极端随机树方法和梯度提升回归树方法对模型进行改进和比较,发现极端随机树方法对结合WRF气象要素的CMAQ-MOS模型有较大的提升。针对徐州地区空气质量预报,进一步使用基于极端随机树方法的WRF-CMAQ-MOS模型对2016年1、2、3月的空气质量指数(AQI)及PM_(2.5)、PM_(10)、NO_2、SO_2、O_3、CO六种污染物优化试验进行验证,发现优化效果最为明显的两种污染物分别是NO_2及O_3,2016年1、2、3月整体相关系数NO_2由0.35升至0.63,O_3由0.39升至0.79,均方根误差NO_2由0.0346减至0.0243 mg/m^3,O_3由0.0447减至0.0367 mg/m^3。文中发展的WRFCMAQ-MOS统计修正模型可以有效提升预报精度,在空气质量预报中具有很好的应用前景。展开更多
基金funded by the National Key Research and Development Program of China(Grant no.2022YFC3701204)the 2023 Outstanding Young Backbone Teacher of Jiangsu“Qinglan”Project(Grant no.R2023Q02)the National Natural Science Foundation of China(Grant no.41705103).
文摘Accurate meteorological predictions in the Arctic are important in response to the rapid climate change and insufficient meteorological observations in the Arctic.In this study,we adopted a high-resolution Weather Research and Forecasting(WRF)model to simulate the meteorology at two Arctic stations(Barrow and Summit)in April 2019.Simulation results were also evaluated by using surface measurements and statistical parameters.In addition,weather charts during the studied time period were also used to assess the model performance.The results demonstrate that the WRF model is able to accurately capture the meteorological parameters for the two Arctic stations and the weather systems such as cyclones and anticyclones in the Arctic.Moreover,we found the model performance in predicting the surface pressure the best while the performance in predicting the wind the worst among these meteorological predictions.However,the wind predictions at these Arctic stations were found to be more accurate than those at urban stations in mid-latitude regions,due to the differences in land features and anthropogentic heat sources between these regions.In addition,a comparison of the simulation results showed that the prediction of meteorological conditions at Summit is superior to that at Barrow.Possible reasons for the deviations in temperature predictions between these two Arctic stations are uncertainties in the treatments of the sea ice and the cloud in the model.With respect to the wind,the deviations may source from the overestimation of the wind over the sea and at coastal stations.
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.
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。
文摘随着城市化、工业化的快速发展,空气污染已经成为了公众最关注的问题之一。为了提高空气质量预报的准确度,以多尺度空气质量模型(Community Multi-Scale Air Quality,CMAQ)为工具,结合中尺度WRF(Weather Research and Forecast Model)气象预报数据、气象观测数据、污染物浓度观测数据,基于极端随机树方法建立了WRF-CMAQ-MOS(Weather Research and Forecast Model-Community Multi-Scale Air Quality-Model Output Statistics)统计修正模型。结果表明,结合WRF气象预报的CMAQ-MOS方法明显修正了由于模型非客观性产生的模式预报偏差,提高了预报效果。使用线性回归方法不能获得较好的优化效果,选取极端随机树方法和梯度提升回归树方法对模型进行改进和比较,发现极端随机树方法对结合WRF气象要素的CMAQ-MOS模型有较大的提升。针对徐州地区空气质量预报,进一步使用基于极端随机树方法的WRF-CMAQ-MOS模型对2016年1、2、3月的空气质量指数(AQI)及PM_(2.5)、PM_(10)、NO_2、SO_2、O_3、CO六种污染物优化试验进行验证,发现优化效果最为明显的两种污染物分别是NO_2及O_3,2016年1、2、3月整体相关系数NO_2由0.35升至0.63,O_3由0.39升至0.79,均方根误差NO_2由0.0346减至0.0243 mg/m^3,O_3由0.0447减至0.0367 mg/m^3。文中发展的WRFCMAQ-MOS统计修正模型可以有效提升预报精度,在空气质量预报中具有很好的应用前景。
