CAJ | 학술논문

集合卡尔曼滤波（EnKF）算法在地下水数据同化领域中的应用受到了越来越广泛的关注。作为同化系统的重要组成部分，观测数据的空间/时间密度的配置直接影响滤波运算结果。本文构造了一个理想二维地下水流算例考察空间/时间密度对传统EnKF和局域化EnKF的影响。研究结果表明：随着空间密度的增大，局域化EnKF运算精度增高，而传统EnKF运算精度无此改进倾向。总体趋势上时间密度增大使EnKF运算精度增高，但对不同数目的观测井方案，这种精度增高的幅度有所变化，观测井越多，增高越不明显。由此得出结论：局域化改进EnKF能够有效同化更多的观测井数据，给出更精确的结果；模拟初期水头变化波动较大，观测数据价值较高；在一定时间密度配置下，低空间密度局域化EnKF运算精度可以接近甚至超过高空间密度配置。
집합잡이만려파（EnKF）산법재지하수수거동화영역중적응용수도료월래월엄범적관주。작위동화계통적중요조성부분，관측수거적공간/시간밀도적배치직접영향려파운산결과。본문구조료일개이상이유지하수류산례고찰공간/시간밀도대전통EnKF화국역화EnKF적영향。연구결과표명：수착공간밀도적증대，국역화EnKF운산정도증고，이전통EnKF운산정도무차개진경향。총체추세상시간밀도증대사EnKF운산정도증고，단대불동수목적관측정방안，저충정도증고적폭도유소변화，관측정월다，증고월불명현。유차득출결론：국역화개진EnKF능구유효동화경다적관측정수거，급출경정학적결과；모의초기수두변화파동교대，관측수거개치교고；재일정시간밀도배치하，저공간밀도국역화EnKF운산정도가이접근심지초과고공간밀도배치。
Ensemble Kalman Filter (EnKF) has recently attracted much attention in the field of groundwa-ter data assimilation. As an important component of EnKF data assimilation system, observation data and its time/spatial density can directly affect calculation results. To investigate the effect of time/spatial density on EnKF and covariance localization scheme,a two-dimensional synthetic example is constructed for calculat-ing. The results indicate that with the spatial density increases, covariance localization scheme of EnKF ex-hibits a promotion of calculation accuracy,while the standard EnKF has no such trend. The general trend shows that the increase of time density leads to better calculation results but varies with different numbers of observation wells:the larger the observation well number is, the less remarkable the result will be. In conclusion, localized EnKF can assimilate more observation data and draws a better result. The data value is lager in the early stage of assimilation because the head variation is much larger. Localized EnKF of low-er spatial density can exhibit even better than higher spatial density with a optimized time density.