Liu Zhao, Zhou Yan-Lian, Ju Wei-Min, Gao Ping
School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210093, China.
Ying Yong Sheng Tai Xue Bao. 2011 Nov;22(11):2943-53.
By using an ensemble Kalman filter (EnKF) to assimilate the observed soil moisture data, the modified boreal ecosystem productivity simulator (BEPS) model was adopted to simulate the dynamics of soil moisture in winter wheat root zones at Xuzhou Agro-meteorological Station, Jiangsu Province of China during the growth seasons in 2000-2004. After the assimilation of observed data, the determination coefficient, root mean square error, and average absolute error of simulated soil moisture were in the ranges of 0.626-0.943, 0.018-0.042, and 0.021-0.041, respectively, with the simulation precision improved significantly, as compared with that before assimilation, indicating the applicability of data assimilation in improving the simulation of soil moisture. The experimental results at single point showed that the errors in the forcing data and observations and the frequency and soil depth of the assimilation of observed data all had obvious effects on the simulated soil moisture.
通过使用集合卡尔曼滤波器（EnKF）同化观测到的土壤湿度数据，采用改进的北方生态系统生产力模拟器（BEPS）模型对中国江苏省徐州农业气象站2000 - 2004年生长季冬小麦根区土壤湿度动态进行模拟。同化观测数据后，模拟土壤湿度的决定系数、均方根误差和平均绝对误差分别在0.626 - 0.943、0.018 - 0.042和0.021 - 0.041范围内，与同化前相比模拟精度显著提高，表明数据同化在改善土壤湿度模拟方面具有适用性。单点实验结果表明，强迫数据和观测中的误差以及观测数据同化的频率和土壤深度对模拟土壤湿度均有明显影响。
