CAJ | 학술논문

提出将集合平方根滤波(EnSRF)估计的预报误差协方差用于四维变分(4DVAR)的同化方案(文中称混合四维变分同化方法,简称混合方法)来反演土壤湿度廓线,该方法由两个同化时段构成:第一时段为EnSRF,第二时段为4DVAR,此种组合可以充分发挥每一同化方法的优势.通过同化表层土壤湿度观测反演土壤湿度廓线这一理想试验来验证方法的可行性,并与EnSRF和4DVAR的反演结果进行比较,结果表明,混合方法反演的分析时刻土壤湿度廓线都优于EnSRF和4DVAR的结果.与此同时,为了克服小样本在估算背景场误差协方差矩阵时出现的虚假相关对反演的干扰,提出在原有协方差矩阵中加入具有高斯指数函数成分来降低其影响;与修正前结果相比,反演的中下层(地下34～100 cm)土壤湿度的均方根误差从0.036 cm~3/cm~3降到0.016cm~3/cm~3,降幅为55.6%,更重要的是大大降低了部分深度处反演土壤湿度的误差,如地下90 cm处误差从0.085 cm~3/cm~3降到0.024 cm~3/cm~3,降幅达71.8%.
제출장집합평방근려파(EnSRF)고계적예보오차협방차용우사유변분(4DVAR)적동화방안(문중칭혼합사유변분동화방법,간칭혼합방법)래반연토양습도곽선,해방법유량개동화시단구성:제일시단위EnSRF,제이시단위4DVAR,차충조합가이충분발휘매일동화방법적우세.통과동화표층토양습도관측반연토양습도곽선저일이상시험래험증방법적가행성,병여EnSRF화4DVAR적반연결과진행비교,결과표명,혼합방법반연적분석시각토양습도곽선도우우EnSRF화4DVAR적결과.여차동시,위료극복소양본재고산배경장오차협방차구진시출현적허가상관대반연적간우,제출재원유협방차구진중가입구유고사지수함수성분래강저기영향;여수정전결과상비,반연적중하층(지하34～100 cm)토양습도적균방근오차종0.036 cm~3/cm~3강도0.016cm~3/cm~3,강폭위55.6%,경중요적시대대강저료부분심도처반연토양습도적오차,여지하90 cm처오차종0.085 cm~3/cm~3강도0.024 cm~3/cm~3,강폭체71.8%.
A hybrid four-dimensional variational(H4DVAR)data assimilation approach is proposed by combining the Ensemble Square Root Filter(EnSRF)with the Four-Dimensional Variational(4DVAR)data assimilation method,which is composed of two time windows with the first using EnSRF and the second using 4DVAR,and this combination can make good use of both EnSRF and 4DVAR.An Observing System Simulation Experiment(OSSE)is set up to investigate the ability to retrieve the true soil moisture profile with the new method by only assimilating the near-surface soil moisture observations into a land surface model After comparing the performance of the three data assimilation schemes(i.e.,EnSRF,4DVAR,and H4DVAR),it is shown that the H4DVAR is superior to the rest two methods because it can quickly retrieve the soil moisture profile with less error.However,when small ensembles are used to calculate the background error covariance,the spurious long-range vertical error correlation between an observation and a state variable will have a bad influence on the estimation of soil moisture.Therefore the authors propose a method to tackle this issue by adding a correlation matrix with the elements defined by the Gaussian function into the original background error covariance.By this way,the rms error of the estimated soil moisture reduces from 0.036 cm~3/cm~3 to 0.016 cm~3/cm~3 with a relative reduction of 55.6%,and the most important is the large reduction of the errors in some soil moisture estimates,for example,the error at the depth of 90 cm reducing from 0.085 cm~3/cm~3 to 0.024 cm~3/cm~3 with a relative reduction of 71.8%.