Sequential Assimilation of Soil Moisture from Atmospheric Low-Level Parameters. Part II: Implementation in a Mesoscale Model
Sequential Assimilation of Soil Moisture from Atmospheric Low-Level Parameters. Part II: Implementation in a Mesoscale Model
复制标题
从大气低层参数中连续同化土壤水分。
DOI：
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发表时间：
1993
期刊：
影响因子：
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通讯作者：
J. Noilhan
中科院分区：
文献类型：
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作者：
F. Bouttier;J. Mahfouf;J. Noilhan
摘要提出了一种基于最佳插值的序贯同化技术来初始化大气模式中的土壤湿度。土壤湿度增量与近地面大气温度和相对湿度的预报误差呈线性关系。第一部分表明，土壤湿度可以从地表特征（植被覆盖度、土壤质地）估算。在这一部分中，在一个三维中尺度模式的方法的行为进行检查。该模型包括一个现实的土地表面参数化，土壤水分与大气变量。结果表明，经过48小时的同化，土壤湿度收敛到参考值附近，通过混合大气量的算法。收敛速度几乎与第一个猜测无关。敏感性研究表明，观测误差调制的效率的过程中，与最佳系数的解析公式的结果是接近与Monte…
Abstract A sequential assimilation technique based upon optimum interpolation is developed to initialize soil moisture in atmospheric models. Soil moisture increments are linearly related to forecast errors of near-surface atmospheric temperature and relative humidity. Part I has shown that soil moisture can be estimated from surface characteristics (vegetation coverage, soil texture). In this part, the behavior of the method is examined within a three-dimensional mesoscale model. The model includes a realistic land surface parameterization that relates soil moisture to atmospheric variables. Results reveal that after 48-h assimilations soil moisture has converged near reference values by blending atmospheric quantities in the algorithm. The convergence rate is almost independent of the first guess. Sensitivity studies show that the observational errors modulate the efficiency of the process and that results with an analytic formulation of the optimum coefficients are close to those obtained with a Monte ...