CAJ | 학술논문

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 Delgerts-got（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。
집합잡이만려파（the Ensemble Kalman Filter，간칭 EnKF）중장예보집합적통계협방차작위예보오차협방차，단해고계가능엄중편리진실적예보오차협방차，영향동화정도。기우겁대사연고계이론，발전료일충우화예보오차협방차구진적실시팽창방법，즉 MLE（the Maximum Likelihood Estimation）방법。이용몽고국기준참 Delgerts-got（간칭 DGS 참）관측자료，기우 EnKF 방법화 MLE 방법，재통용륙면모식（the Common Land Model，간칭CoLM）중동화료지표온도화10 cm 토양온도관측자료，건립료토양온도동화계통。결과표명：MLE 방법대지표온도화각층토양온도（우기심층토양온도）적고계비 EnKF 방법준학。고필도천층화심층토양온도적차별，재실시MLE 방법시대천층화심층토양온도채용료불동적팽창인자。대비팽창인자위단일표량시적결과，다인자팽창능완해심층토양온도적불합리팽창，개선동화효과。
In the ensemble Kalman filter (EnKF),the forecast error covariance matrix is estimated as the sam-pling covariance matrix of the forecast ensemble.However,previous studies suggest that the sampling er-ror resulting from finite-size ensembles may make such estimations far from the true forecast error covari-ance,and finally degrade the performance of EnKF.A common way to address this problem is covariance inflation with a time-constant inflation factor.A time-dependent infiation approach on forecast error covar-iance matrix (i.e.,MLE method)is developed based on the maximum likelihood estimation theory,so as to improve estimates of forecast error covariances.At Delgertsgot (DGS)Station in the Mongolian Plateau reference site,point observations of ground temperature and soil temperature at the depth of 10 cm are as-similated into the Common Land Model (CoLM)with a frequency of every 12 hours,using two assimila-tion algorithms (EnKF method and MLE method),in order to test the effectivity of MLE in practical as-similation.In this way,a soil temperature assimilation system is constructed on the point scale. <br> Results indicate that MLE method performs better than EnKF method for ground temperature and soil temperatures at most depths (especially for soil temperatures at deeper depths).Moreover,considering differences between soil temperatures at shallower depths and those at deeper depths,different inflation factors are adopted to them when implementing MLE method.Compared to results of MLE using a single scalar inflation factor,it shows that multiple-factor inflation is able to alleviate the unreasonable inflation of soil temperatures at deeper depths and therefore get better assimilation results.In addition,the leaf area index (LAI)in the CoLM is updated dynamically by MODIS LAI products,and results derived using MO-DIS LAI are compared to those derived using LAI computed by experiential formula,so as to study the effect of the LAI accuracy on simulated and assimilated soil temperatures.It shows that using MODIS LAI can get better simulation of soil temperature at depths of 0 cm and 3 cm,as well as more accurate assimila-tion of soil temperature at most depths. <br> The inflation factor is set to be variable in time,but constant in space.However,variables such as soil temperature and soil moisture behave quite differently at shallow surfaces and deep depths,and obser-vations may be unevenly distributed in space in regional assimilation researches.Therefore,it is necessary to adopt different inflation factors to different variables or to the same variable at different locations.In the future,it is necessary to develop a time-and-space dependent inflation method and test its capability in real applications.