...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of hydrometeorology >Adaptive Soil Moisture Profile Filtering for Horizontal Information Propagation in the Independent Column-Based CLM2.0
【24h】

Adaptive Soil Moisture Profile Filtering for Horizontal Information Propagation in the Independent Column-Based CLM2.0

机译：基于独立列的CLM2.0中用于水平信息传播的自适应土壤水分剖面过滤

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Data assimilation aims to provide an optimal estimate of the overall system state, not only for an observed state variable or location. However, large-scale land surface models are typically column-based and purely random ensemble perturbation of states will lead to block-diagonal a priori (or background) error covariance. This facilitates the filtering calculations but compromises the potential of data assimilation to influence (unobserved) vertical and horizontal neighboring state variables. Here, a combination of an ensemble Kalman filter and an adaptive covariance correction method is explored to optimize the variances and retrieve the off-block-diagonal correlations in the a priori error covariance matrix. In a first time period, all available soil moisture profile observations in a small agricultural field are assimilated into the Community Land Model, version 2.0 (CLM2.0) to find the adaptive second-order a priori error information. After that period, only observations from single individual soil profiles are assimilated with inclusion of this adaptive information. It is shown that assimilation of a single profile can partially rectify the incorrectly simulated soil moisture spatial mean and variability. The largest reduction in the root-mean-square error in the soil moisture field varies between 7% and 22%, depending on the soil depth, when assimilating a single complete profile every two days during three months with a single time-invariant covariance correction.
机译：数据同化的目的是提供整个系统状态的最佳估计值，而不仅仅是针对观察到的状态变量或位置。但是，大型陆地表面模型通常基于列，并且状态的纯随机整体扰动将导致块对角线先验（或背景）误差协方差。这有助于进行滤波计算，但会损害数据同化影响（未观察到）垂直和水平相邻状态变量的可能性。在此，探索了集成卡尔曼滤波器和自适应协方差校正方法的组合，以优化方差并检索先验误差协方差矩阵中的块外对角线相关性。在第一个时间段内，将一个小农业领域中所有可用的土壤水分剖面观测资料同化到社区土地模型2.0版（CLM2.0）中，以找到自适应的二阶先验误差信息。在那段时间之后，只有来自单个土壤剖面的观测值才会被包含该自适应信息同化。结果表明，单一剖面的同化可以部分纠正不正确模拟的土壤水分空间均值和变异性。在三个月中，每三个天每两天用一次时不变协方差校正来吸收一个完整的轮廓时，根据土壤深度的不同，土壤湿度场中均方根误差的最大减少幅度在7％至22％之间。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号