Soil moisture plays an important role in land-atmosphere interactions. It is an important geophysical parameter in research on climate, hydrology, agriculture, and forestry. Soil moisture has important climatic effect...Soil moisture plays an important role in land-atmosphere interactions. It is an important geophysical parameter in research on climate, hydrology, agriculture, and forestry. Soil moisture has important climatic effects by influencing ground evapotranspi ration, runoff, surface reflectivity, surface emissivity, surface sensible heat and latent heat flux. At the global scale, the extent of its influence on the atmosphere is second only to that of sea surface temperature. At the terrestrial scale, its influence is even greater than that of sea surface temperatures. This paper presents a China Land Soil Moisture Data Assimilation System (CLSMDAS) based on EnKF and land process models, and results of the application of this system in the China Land Soil Moisture Data Assimilation tests. CLSMDAS is comprised of the following components: 1) A land process mo del—Community Land Model Version 3.0 (CLM3.0)—developed by the US National Center for Atmospheric Research (NCAR); 2) Precipitation of atmospheric forcing data and surface-incident solar radiation data come from hourly outputs of the FY2 geostationary meteorological satellite; 3) EnKF (Ensemble Kalman Filter) land data assimilation method; and 4) Observa tion data including satellite-inverted soil moisture outputs of the AMSR-E satellite and soil moisture observation data. Results of soil moisture assimilation tests from June to September 2006 were analyzed with CLSMDAS. Both simulation and assimila tion results of the land model reflected reasonably the temporal-spatial distribution of soil moisture. The assimilated soil mois ture distribution matches very well with severe summer droughts in Chongqing and Sichuan Province in August 2006, the worst since the foundation of the People’s Republic of China in 1949. It also matches drought regions that occurred in eastern Hubei and southern Guangxi in September.展开更多
In this paper, both state variables and parameters of one-dimensional open channel model are estimated using a framework of the Ensemble Kalman Filter （EnKF）. Compared with observation, the predicted accuracy of wat...In this paper, both state variables and parameters of one-dimensional open channel model are estimated using a framework of the Ensemble Kalman Filter （EnKF）. Compared with observation, the predicted accuracy of water level and discharge are impro- ved while the parameters of the model are identified simultaneously. With the principles of the EnKF, a state-space description of the Saint-Venant equation is constructed by perturbing the measurements with Gaussian error distribution. At the same time, the rough- ness, one of the key parameters in one-dimensional open channel, is also considered as a state variable to identify its value dynamica- lly. The updated state variables and the parameters are then used as the initial values of the next time step to continue the assimilation process. The usefulness and the capability of the dual EnKF are demonstrated in the lower Yellow River during the water-sediment regulation in 2009. In the optimization process, the errors between the prediction and the observation are analyzed, and the rationale of inverse roughness is discussed. It is believed that （1） the flexible approach of the dual EnKF can improve the accuracy of predi- cting water level and discharge, （2） it provides a probabilistic way to identify the model error which is feasible to implement but hard to handle in other filter systems, and （3） it is practicable for river engineering and management.展开更多
The Ensemble Kalman Filter (EnKF) is well known and widely used in land data assimilation for its high precision and simple operation. The land surface models used as the forecast operator in a land data assimilation ...The Ensemble Kalman Filter (EnKF) is well known and widely used in land data assimilation for its high precision and simple operation. The land surface models used as the forecast operator in a land data assimilation system are usually designed to consider the model subgrid-heterogeneity and soil water thawing and freezing. To neglect their effects could lead to some errors in soil moisture assimilation. The dual EnKF method is employed in soil moisture data assimilation to build a soil moisture data as- similation framework based on the NCAR Community Land Model version 2.0 (CLM 2.0) in considera- tion of the effects of the model subgrid-heterogeneity and soil water thawing and freezing: Liquid volumetric soil moisture content in a given fraction is assimilated through the state filter process, while solid volumetric soil moisture content in the same fraction and solid/liquid volumetric soil moisture in the other fractions are optimized by the parameter filter. Preliminary experiments show that this dual EnKF-based assimilation framework can assimilate soil moisture more effectively and precisely than the usual EnKF-based assimilation framework without considering the model subgrid-scale heteroge- neity and soil water thawing and freezing. With the improvement of soil moisture simulation, the soil temperature-simulated precision can be also improved to some extent.展开更多
This study examines the effectiveness of an ensemble Kalman filter based on the weather research and forecasting model to assimilate Doppler-radar radial-velocity observations for convection-permitting prediction of c...This study examines the effectiveness of an ensemble Kalman filter based on the weather research and forecasting model to assimilate Doppler-radar radial-velocity observations for convection-permitting prediction of convection evolution in a high-impact heavy-rainfall event over coastal areas of South China during the pre-summer rainy season. An ensemble of 40 deterministic forecast experiments(40 DADF) with data assimilation(DA) is conducted, in which the DA starts at the same time but lasts for different time spans(up to 2 h) and with different time intervals of 6, 12, 24, and 30 min. The reference experiment is conducted without DA(NODA).To show more clearly the impact of radar DA on mesoscale convective system(MCS)forecasts, two sets of 60-member ensemble experiments(NODA EF and exp37 EF) are performed using the same 60-member perturbed-ensemble initial fields but with the radar DA being conducted every 6 min in the exp37 EF experiments from 0200 to0400 BST. It is found that the DA experiments generally improve the convection prediction. The 40 DADF experiments can forecast a heavy-rain-producing MCS over land and an MCS over the ocean with high probability, despite slight displacement errors. The exp37 EF improves the probability forecast of inland and offshore MCSs more than does NODA EF. Compared with the experiments using the longer DA time intervals, assimilating the radial-velocity observations at 6-min intervals tends to produce better forecasts. The experiment with the longest DA time span and shortest time interval shows the best performance.However, a shorter DA time interval(e.g., 12 min) or a longer DA time span does not always help. The experiment with the shortest DA time interval and maximum DA window shows the best performance, as it corrects errors in the simulated convection evolution over both the inland and offshore areas. An improved representation of the initial state leads to dynamic and thermodynamic conditions that are more conducive to earlier initiation of the inland MCS and longer 展开更多
基于集合卡尔曼滤波(ensemble Kalman filter,EnKF)方法和分布式水文模型SWAT(soil and water assessment tool),构建了一个土壤水分状态与参数同时更新的土壤湿度数据同化方案,通过遥感观测土壤湿度数据同化的仿真试验,研究土壤湿度数...基于集合卡尔曼滤波(ensemble Kalman filter,EnKF)方法和分布式水文模型SWAT(soil and water assessment tool),构建了一个土壤水分状态与参数同时更新的土壤湿度数据同化方案,通过遥感观测土壤湿度数据同化的仿真试验,研究土壤湿度数据同化在优化土壤水分参数、改进模型产汇流过程模拟方面的效果及潜力。结果表明:通过表层(0~5 cm)土壤湿度数据同化可实现土壤持水能力参数的准确估计;当给定的参数更新平滑因子在合理范围时,基于EnKF方法的参数优化效果具有较好的稳定性;表层土壤湿度数据同化对SWAT模型产汇流过程模拟有一定改进,但受降雨误差的影响,其对流域出口径流过程改进效果有限,表明基于遥感土壤湿度数据同化改进流域水文过程模拟还有赖于降雨输入精度及可靠性的提高。展开更多
集合卡尔曼滤波(the Ensemble Kalman Filter,简称EnKF)中将预报集合的统计协方差作为预报误差协方差,但该估计可能严重偏离真实的预报误差协方差,影响同化精度。基于极大似然估计理论,发展了一种优化预报误差协方差矩阵的实时膨胀方法,...集合卡尔曼滤波(the Ensemble Kalman Filter,简称EnKF)中将预报集合的统计协方差作为预报误差协方差,但该估计可能严重偏离真实的预报误差协方差,影响同化精度。基于极大似然估计理论,发展了一种优化预报误差协方差矩阵的实时膨胀方法,即MLE(the Maximum Likelihood Estimation)方法。利用蒙古国基准站Delgertsgot(简称DGS站)观测资料,基于EnKF方法和MLE方法,在通用陆面模式(the Common Land Model,简称CoLM)中同化了地表温度和10 cm土壤温度观测资料,建立了土壤温度同化系统。结果表明:MLE方法对地表温度和各层土壤温度(尤其深层土壤温度)的估计比EnKF方法准确。考虑到浅层和深层土壤温度的差别,在实施MLE方法时对浅层和深层土壤温度采用了不同的膨胀因子。对比膨胀因子为单一标量时的结果,多因子膨胀能缓解深层土壤温度的不合理膨胀,改善同化效果。展开更多
With the combination of three land surface models （LSMs） and the ensemble Kalman filter （EnKF）, a multimodel EnKF is proposed in which the multimodel background superensemble error covariance matrix is estimated b...With the combination of three land surface models （LSMs） and the ensemble Kalman filter （EnKF）, a multimodel EnKF is proposed in which the multimodel background superensemble error covariance matrix is estimated by two different algorithms： the Simple Model Average （SMA） and the Weighted Average Method （WAM）. The two algorithms are tested and compared in terms of their abilities to retrieve the true soil moisture profile by respectively assimilating both synthetically-generated and actual near-surface soil moisture measurements. The results from the synthetic experiment show that the performances of the SMA and WAM algorithms were quite different. The SMA algorithm did not help to improve the estimates of soil moisture at the deep layers, although its performance was not the worst when compared with the results from the single-model EnKF. On the contrary, the results from the WAM algorithm were better than those from any single-model EnKF. The tested results from assimilating the field measurements show that the performance of the two multimodel EnKF algorithms was very stable compared with the single-model EnKF. Although comparisons could only be made at three shallow layers, on average, the performance of the WAM algorithm was still slightly better than that of the SMA algorithm. As a result, the WAM algorithm should be adopted to approximate the multimodel background superensemble error covariance and hence used to estimate soil moisture states at the relatively deep layers.展开更多
利用基于中尺度数值模式WRF(Weather Research and Forecast)的集合卡尔曼滤波(EnKF,Ensemble Kalman Filter)同化系统直接同化广东地区雷达反射率资料,对2017年台风“天鸽”(1713,Hato)近海发展以及降水预报效果进行数值模拟分析研究...利用基于中尺度数值模式WRF(Weather Research and Forecast)的集合卡尔曼滤波(EnKF,Ensemble Kalman Filter)同化系统直接同化广东地区雷达反射率资料,对2017年台风“天鸽”(1713,Hato)近海发展以及降水预报效果进行数值模拟分析研究。结果显示,直接同化雷达反射率资料后,台风的回波强度和范围有了明显改善,可更好地调整水汽场、水凝物和温度场。当台风风场和水汽场调整后,进入台风主体部分的水汽量显著增加,使得台风强度增强,台风中心最低海平面气压降低,与实况更接近。同化雷达反射率资料后,6 h和24 h降水强度和落区预报效果有显著改善,尤其是能提高大暴雨和特大暴雨量级的TS评分,此外地面2 m温度和2 m相对湿度的预报效果也有改进。展开更多
基金supported by National High Technology Research and Development Program of China (Grant Nos. 2007AA12Z144, 2009AA12Z129)Chinese COPES Project (Grant Nos. GYHY200706005, GYHY200806014)China Meteorological Administration New Technology Promotion Project (Grant No. CMATG2008Z04)
文摘Soil moisture plays an important role in land-atmosphere interactions. It is an important geophysical parameter in research on climate, hydrology, agriculture, and forestry. Soil moisture has important climatic effects by influencing ground evapotranspi ration, runoff, surface reflectivity, surface emissivity, surface sensible heat and latent heat flux. At the global scale, the extent of its influence on the atmosphere is second only to that of sea surface temperature. At the terrestrial scale, its influence is even greater than that of sea surface temperatures. This paper presents a China Land Soil Moisture Data Assimilation System (CLSMDAS) based on EnKF and land process models, and results of the application of this system in the China Land Soil Moisture Data Assimilation tests. CLSMDAS is comprised of the following components: 1) A land process mo del—Community Land Model Version 3.0 (CLM3.0)—developed by the US National Center for Atmospheric Research (NCAR); 2) Precipitation of atmospheric forcing data and surface-incident solar radiation data come from hourly outputs of the FY2 geostationary meteorological satellite; 3) EnKF (Ensemble Kalman Filter) land data assimilation method; and 4) Observa tion data including satellite-inverted soil moisture outputs of the AMSR-E satellite and soil moisture observation data. Results of soil moisture assimilation tests from June to September 2006 were analyzed with CLSMDAS. Both simulation and assimila tion results of the land model reflected reasonably the temporal-spatial distribution of soil moisture. The assimilated soil mois ture distribution matches very well with severe summer droughts in Chongqing and Sichuan Province in August 2006, the worst since the foundation of the People’s Republic of China in 1949. It also matches drought regions that occurred in eastern Hubei and southern Guangxi in September.
基金Project supported by the National Basic Research and Development Program of China (973 Program, Grant No.2011CB403306)the Ministry of Water Resources’ Special Funds for Scientific Research on Public Causes (Grant No.200901023)the Central Scientific Institutes Foundation for Public Service (Grant No. HKY-JBYW-2012-5)
文摘In this paper, both state variables and parameters of one-dimensional open channel model are estimated using a framework of the Ensemble Kalman Filter （EnKF）. Compared with observation, the predicted accuracy of water level and discharge are impro- ved while the parameters of the model are identified simultaneously. With the principles of the EnKF, a state-space description of the Saint-Venant equation is constructed by perturbing the measurements with Gaussian error distribution. At the same time, the rough- ness, one of the key parameters in one-dimensional open channel, is also considered as a state variable to identify its value dynamica- lly. The updated state variables and the parameters are then used as the initial values of the next time step to continue the assimilation process. The usefulness and the capability of the dual EnKF are demonstrated in the lower Yellow River during the water-sediment regulation in 2009. In the optimization process, the errors between the prediction and the observation are analyzed, and the rationale of inverse roughness is discussed. It is believed that （1） the flexible approach of the dual EnKF can improve the accuracy of predi- cting water level and discharge, （2） it provides a probabilistic way to identify the model error which is feasible to implement but hard to handle in other filter systems, and （3） it is practicable for river engineering and management.
基金the National Natural Science Foundation of China (Grant No. 40705035)the Knowledge Innovation Project of Chinese Academy of Sciences (Grant Nos. KZCX2-YW-217 and KZCX2-YW-126-2)the National Basic Research Program of China (Grant No.2005CB321704)
文摘The Ensemble Kalman Filter (EnKF) is well known and widely used in land data assimilation for its high precision and simple operation. The land surface models used as the forecast operator in a land data assimilation system are usually designed to consider the model subgrid-heterogeneity and soil water thawing and freezing. To neglect their effects could lead to some errors in soil moisture assimilation. The dual EnKF method is employed in soil moisture data assimilation to build a soil moisture data as- similation framework based on the NCAR Community Land Model version 2.0 (CLM 2.0) in considera- tion of the effects of the model subgrid-heterogeneity and soil water thawing and freezing: Liquid volumetric soil moisture content in a given fraction is assimilated through the state filter process, while solid volumetric soil moisture content in the same fraction and solid/liquid volumetric soil moisture in the other fractions are optimized by the parameter filter. Preliminary experiments show that this dual EnKF-based assimilation framework can assimilate soil moisture more effectively and precisely than the usual EnKF-based assimilation framework without considering the model subgrid-scale heteroge- neity and soil water thawing and freezing. With the improvement of soil moisture simulation, the soil temperature-simulated precision can be also improved to some extent.
基金supported by the National Natural Science Foundation of China(Grant Nos.41405050,91437104&41461164006)the Public Welfare Scientific Research Projects in Meteorology(Grant No.GYHY201406013)the National Basic Research Program of China(Grant No.2014CB441402)
文摘This study examines the effectiveness of an ensemble Kalman filter based on the weather research and forecasting model to assimilate Doppler-radar radial-velocity observations for convection-permitting prediction of convection evolution in a high-impact heavy-rainfall event over coastal areas of South China during the pre-summer rainy season. An ensemble of 40 deterministic forecast experiments(40 DADF) with data assimilation(DA) is conducted, in which the DA starts at the same time but lasts for different time spans(up to 2 h) and with different time intervals of 6, 12, 24, and 30 min. The reference experiment is conducted without DA(NODA).To show more clearly the impact of radar DA on mesoscale convective system(MCS)forecasts, two sets of 60-member ensemble experiments(NODA EF and exp37 EF) are performed using the same 60-member perturbed-ensemble initial fields but with the radar DA being conducted every 6 min in the exp37 EF experiments from 0200 to0400 BST. It is found that the DA experiments generally improve the convection prediction. The 40 DADF experiments can forecast a heavy-rain-producing MCS over land and an MCS over the ocean with high probability, despite slight displacement errors. The exp37 EF improves the probability forecast of inland and offshore MCSs more than does NODA EF. Compared with the experiments using the longer DA time intervals, assimilating the radial-velocity observations at 6-min intervals tends to produce better forecasts. The experiment with the longest DA time span and shortest time interval shows the best performance.However, a shorter DA time interval(e.g., 12 min) or a longer DA time span does not always help. The experiment with the shortest DA time interval and maximum DA window shows the best performance, as it corrects errors in the simulated convection evolution over both the inland and offshore areas. An improved representation of the initial state leads to dynamic and thermodynamic conditions that are more conducive to earlier initiation of the inland MCS and longer
文摘基于集合卡尔曼滤波(ensemble Kalman filter,EnKF)方法和分布式水文模型SWAT(soil and water assessment tool),构建了一个土壤水分状态与参数同时更新的土壤湿度数据同化方案,通过遥感观测土壤湿度数据同化的仿真试验,研究土壤湿度数据同化在优化土壤水分参数、改进模型产汇流过程模拟方面的效果及潜力。结果表明:通过表层(0~5 cm)土壤湿度数据同化可实现土壤持水能力参数的准确估计;当给定的参数更新平滑因子在合理范围时,基于EnKF方法的参数优化效果具有较好的稳定性;表层土壤湿度数据同化对SWAT模型产汇流过程模拟有一定改进,但受降雨误差的影响,其对流域出口径流过程改进效果有限,表明基于遥感土壤湿度数据同化改进流域水文过程模拟还有赖于降雨输入精度及可靠性的提高。
文摘集合卡尔曼滤波(the Ensemble Kalman Filter,简称EnKF)中将预报集合的统计协方差作为预报误差协方差,但该估计可能严重偏离真实的预报误差协方差,影响同化精度。基于极大似然估计理论,发展了一种优化预报误差协方差矩阵的实时膨胀方法,即MLE(the Maximum Likelihood Estimation)方法。利用蒙古国基准站Delgertsgot(简称DGS站)观测资料,基于EnKF方法和MLE方法,在通用陆面模式(the Common Land Model,简称CoLM)中同化了地表温度和10 cm土壤温度观测资料,建立了土壤温度同化系统。结果表明:MLE方法对地表温度和各层土壤温度(尤其深层土壤温度)的估计比EnKF方法准确。考虑到浅层和深层土壤温度的差别,在实施MLE方法时对浅层和深层土壤温度采用了不同的膨胀因子。对比膨胀因子为单一标量时的结果,多因子膨胀能缓解深层土壤温度的不合理膨胀,改善同化效果。
基金supported by the National Natural Science Foundation of China (Grant Nos 40775065 and 41075074)the National Special Fund for Meteorology (Grant No GYHY200806029)
文摘With the combination of three land surface models （LSMs） and the ensemble Kalman filter （EnKF）, a multimodel EnKF is proposed in which the multimodel background superensemble error covariance matrix is estimated by two different algorithms： the Simple Model Average （SMA） and the Weighted Average Method （WAM）. The two algorithms are tested and compared in terms of their abilities to retrieve the true soil moisture profile by respectively assimilating both synthetically-generated and actual near-surface soil moisture measurements. The results from the synthetic experiment show that the performances of the SMA and WAM algorithms were quite different. The SMA algorithm did not help to improve the estimates of soil moisture at the deep layers, although its performance was not the worst when compared with the results from the single-model EnKF. On the contrary, the results from the WAM algorithm were better than those from any single-model EnKF. The tested results from assimilating the field measurements show that the performance of the two multimodel EnKF algorithms was very stable compared with the single-model EnKF. Although comparisons could only be made at three shallow layers, on average, the performance of the WAM algorithm was still slightly better than that of the SMA algorithm. As a result, the WAM algorithm should be adopted to approximate the multimodel background superensemble error covariance and hence used to estimate soil moisture states at the relatively deep layers.
文摘利用基于中尺度数值模式WRF(Weather Research and Forecast)的集合卡尔曼滤波(EnKF,Ensemble Kalman Filter)同化系统直接同化广东地区雷达反射率资料,对2017年台风“天鸽”(1713,Hato)近海发展以及降水预报效果进行数值模拟分析研究。结果显示,直接同化雷达反射率资料后,台风的回波强度和范围有了明显改善,可更好地调整水汽场、水凝物和温度场。当台风风场和水汽场调整后,进入台风主体部分的水汽量显著增加,使得台风强度增强,台风中心最低海平面气压降低,与实况更接近。同化雷达反射率资料后,6 h和24 h降水强度和落区预报效果有显著改善,尤其是能提高大暴雨和特大暴雨量级的TS评分,此外地面2 m温度和2 m相对湿度的预报效果也有改进。
