基于DEnKF方法的考虑次网格变异性的MODIS雪盖同化

基于通用陆面模型(CoLM)和确定性集合卡尔曼滤波算法发展了一个考虑模型次网格变异性的MODIS雪盖同化方案,提高雪深模拟的估计精度。利用北疆阿勒泰地区5个气象站点2007年11月至2008年4月逐日雪深观测数据对同化结果进行了验证。结果表明,该同化方案不需要对MODIS雪盖观测数据进行扰动,能明显提高雪深模拟的精度。另外,雪深同化结果与地面观测雪深具有一致的时间变化趋势,能准确地反映积雪深度在各个不同时段的变化特性。

DEnKF-based Assimilation of MODIS-Derived Snow Cover Products into Common Land Model Considering the Model Sub-grid Heterogeneity

The use of perturbed observations in the traditional ensemble Kalman filter (EnKF) introduces uncertainties and results in sub-optimal model state estimates. A modified EnKF method, the deterministic ensemble Kalman filter (DEnKF), can approach the analysis error covariance matrix without perturbing observations. As a forecast operator, the common land model (CoLM) is advantageous for sub-grid heterogeneity analysis. To reduce some errors stemming from the uncertainty in snow data assimilation, a new DEnKF-based snow data assimilation method is proposed for considering model sub-grid heterogeneity. The proposed method was used to assimilate the MODIS-derived snow cover products into CoLM for improving simulated snow depth. The daily snow depth of five meteorological stations from November 2007 to April 2008 in Altay is used for validation. The experimental results show that the DEnKF-based assimilation method can improve the simulated snow depth effectively. The improved snow depth does not only show the consistent time trends with in-situ snow depth but also reflects time-varying characteristics for different seasons.