Application of Feature Calibration and Alignment to High-Resolution Analysis: Examples Using Observations Sensitive to Cloud and Water Vapor
Application of Feature Calibration and Alignment to High-Resolution Analysis: Examples Using Observations Sensitive to Cloud and Water Vapor
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特征校准和对准在高分辨率分析中的应用：使用对云和水蒸气敏感的观测的示例
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
R. Hoffman
中科院分区：
文献类型：
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作者：
T. Nehrkorn;B. Woods;T. Auligne;R. Hoffman
摘要对准误差[即，背景中云或降水的相干结构（“特征”）具有位置误差的情况]可能导致大的非高斯背景误差。在这些情况下，使用由变分和/或集合方法得到的加性增量同化云影响的辐射可能是有问题的。为了解决这个问题，这里使用特征校准和对准技术（FCA）来通过置换背景场来校正位置误差。一组二维位移向量应用于预测字段，以改善预测和观测中的特征对齐。这些位移矢量是通过成本函数的非线性最小化获得的，该成本函数测量与观测值的失配，沿着有多个附加约束（例如，位移向量的平滑性和不发散性）以防止非物理解。该方法被应用在一个理想的情况下，使用天气研究和预报模型（WR...
AbstractAlignment errors [i.e., cases where coherent structures (“features”) of clouds or precipitation in the background have position errors] can lead to large and non-Gaussian background errors. Assimilation of cloud-affected radiances using additive increments derived by variational and/or ensemble methods can be problematic in these situations. To address this problem, the Feature Calibration and Alignment technique (FCA) is used here for correcting position errors by displacing background fields. A set of two-dimensional displacement vectors is applied to forecast fields to improve the alignment of features in the forecast and observations. These displacement vectors are obtained by a nonlinear minimization of a cost function that measures the misfit to observations, along with a number of additional constraints (e.g., smoothness and nondivergence of the displacement vectors) to prevent unphysical solutions. The method was applied in an idealized case using Weather Research and Forecasting Model (WR...
影响因子：
5.4
作者：
A. Schöniger;Wolfgang Nowak;H. Franssen
通讯作者：
A. Schöniger;Wolfgang Nowak;H. Franssen