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
Capabilities to assimilate Geostationary Operational Environmental Satellite “R-series ”(GOES-R) Geostationary Lightning Mapper(GLM) flash extent density(FED) data within the operational Gridpoint Statistical Interp...Capabilities to assimilate Geostationary Operational Environmental Satellite “R-series ”(GOES-R) Geostationary Lightning Mapper(GLM) flash extent density(FED) data within the operational Gridpoint Statistical Interpolation ensemble Kalman filter(GSI-EnKF) framework were previously developed and tested with a mesoscale convective system(MCS) case. In this study, such capabilities are further developed to assimilate GOES GLM FED data within the GSI ensemble-variational(EnVar) hybrid data assimilation(DA) framework. The results of assimilating the GLM FED data using 3DVar, and pure En3DVar(PEn3DVar, using 100% ensemble covariance and no static covariance) are compared with those of EnKF/DfEnKF for a supercell storm case. The focus of this study is to validate the correctness and evaluate the performance of the new implementation rather than comparing the performance of FED DA among different DA schemes. Only the results of 3DVar and pEn3DVar are examined and compared with EnKF/DfEnKF. Assimilation of a single FED observation shows that the magnitude and horizontal extent of the analysis increments from PEn3DVar are generally larger than from EnKF, which is mainly caused by using different localization strategies in EnFK/DfEnKF and PEn3DVar as well as the integration limits of the graupel mass in the observation operator. Overall, the forecast performance of PEn3DVar is comparable to EnKF/DfEnKF, suggesting correct implementation.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
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
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。
基金supported by NOAA JTTI award via Grant #NA21OAR4590165, NOAA GOESR Program funding via Grant #NA16OAR4320115provided by NOAA/Office of Oceanic and Atmospheric Research under NOAA-University of Oklahoma Cooperative Agreement #NA11OAR4320072, U.S. Department of Commercesupported by the National Oceanic and Atmospheric Administration (NOAA) of the U.S. Department of Commerce via Grant #NA18NWS4680063。
文摘Capabilities to assimilate Geostationary Operational Environmental Satellite “R-series ”(GOES-R) Geostationary Lightning Mapper(GLM) flash extent density(FED) data within the operational Gridpoint Statistical Interpolation ensemble Kalman filter(GSI-EnKF) framework were previously developed and tested with a mesoscale convective system(MCS) case. In this study, such capabilities are further developed to assimilate GOES GLM FED data within the GSI ensemble-variational(EnVar) hybrid data assimilation(DA) framework. The results of assimilating the GLM FED data using 3DVar, and pure En3DVar(PEn3DVar, using 100% ensemble covariance and no static covariance) are compared with those of EnKF/DfEnKF for a supercell storm case. The focus of this study is to validate the correctness and evaluate the performance of the new implementation rather than comparing the performance of FED DA among different DA schemes. Only the results of 3DVar and pEn3DVar are examined and compared with EnKF/DfEnKF. Assimilation of a single FED observation shows that the magnitude and horizontal extent of the analysis increments from PEn3DVar are generally larger than from EnKF, which is mainly caused by using different localization strategies in EnFK/DfEnKF and PEn3DVar as well as the integration limits of the graupel mass in the observation operator. Overall, the forecast performance of PEn3DVar is comparable to EnKF/DfEnKF, suggesting correct implementation.
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。
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。
