A Hybrid ETKF-3DVAR Data Assimilation Scheme for the WRF Model. Part I: Observing System Simulation Experiment
A Hybrid ETKF-3DVAR Data Assimilation Scheme for the WRF Model. Part I: Observing System Simulation Experiment
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DOI：
10.1175/2008mwr2444.1
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发表时间：
2008-12
影响因子：
3.2
通讯作者：
Xuguang Wang;D. Barker;C. Snyder;T. Hamill
中科院分区：
文献类型：
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作者：
Xuguang Wang;D. Barker;C. Snyder;T. Hamill
摘要介绍了一个用于WRF模式的混合集合变换卡尔曼滤波-三维变分资料同化（ETKF-3DVAR）系统。该系统基于现有的WRF 3DVAR。与WRF 3DVAR不同，WRF 3DVAR利用简单的静态协方差模型来估计预测误差统计，混合系统将集合协方差与静态协方差相结合来估计复杂的、依赖于流量的预测误差统计。在变分最小化过程中，利用扩展控制变量法引入了包络协方差。系综扰动由计算高效的ETKF保持。作为测试和理解新开发的系统的初步尝试，进行了完美模型假设下的观测系统模拟实验（第一部分）和真实的观测实验（第二部分）。在这些试点研究中，WRF在北美域上以粗网格间距（200 km）运行。
Abstract A hybrid ensemble transform Kalman filter–three-dimensional variational data assimilation (ETKF–3DVAR) system for the Weather Research and Forecasting (WRF) Model is introduced. The system is based on the existing WRF 3DVAR. Unlike WRF 3DVAR, which utilizes a simple, static covariance model to estimate the forecast-error statistics, the hybrid system combines ensemble covariances with the static covariances to estimate the complex, flow-dependent forecast-error statistics. Ensemble covariances are incorporated by using the extended control variable method during the variational minimization. The ensemble perturbations are maintained by the computationally efficient ETKF. As an initial attempt to test and understand the newly developed system, both an observing system simulation experiment under the perfect model assumption (Part I) and the real observation experiment (Part II) were conducted. In these pilot studies, the WRF was run over the North America domain at a coarse grid spacing (200 km) t...