Four-dimensional variational data assimilation for mesoscale and storm-scale applications
Four-dimensional variational data assimilation for mesoscale and storm-scale applications
复制标题
中尺度和风暴尺度应用的四维变分数据同化
DOI：
10.1007/s00703-001-0586-7
复制
发表时间：
2003
影响因子：
2
通讯作者：
D. Zupanski
中科院分区：
文献类型：
--
作者：
Seon Ki Park;D. Zupanski
综述了四维变分资料同化（4DVAR）在中尺度/风暴尺度大气现象预报中的应用现状和进展。理论背景提供了每个重要组成部分的4DVAR系统-预测和伴随模型，观测，背景，成本函数，预处理，和最小化。中尺度/风暴尺度4DVAR的具体实际问题的概述，然后提出在高分辨率观测，非线性和不连续性问题，模式误差，横向边界条件的误差，和降水同化。还介绍了有效和简化的4DVAR的实用策略，例如，增量4DVAR、穷人4DVAR和逆3DVAR。提出了一种新的混合方法的概念，结合联合收割机一个有效的4DVAR方案和标准的4DVAR方案，旨在减少所需的计算要求，标准的4DVAR，同时提高精度的简化4DVAR。流体静力学和非流体静力学模型的应用进行了说明，并提供了我们对未来研究的机会和方向的看法。
Summary¶The status and progress of the four-dimensional variational data assimilation (4DVAR) are briefly reviewed focusing on application to prediction of mesoscale/storm-scale atmospheric phenomena. Theoretical background is provided for each important component of the 4DVAR system – forecast and adjoint models, observations, background, cost function, preconditioning, and minimization. An overview of practical issues specific for mesoscale/storm-scale 4DVAR is then presented in terms of high-resolution observations, nonlinearity and discontinuity problem, model error, errors from lateral boundary condition, and precipitation assimilation. Practical strategies for efficient and simplified 4DVAR are also introduced, e.g., incremental 4DVAR, poor man’s 4DVAR, and inverse 3DVAR. A new concept on hybrid approach is proposed to combine an efficient 4DVAR scheme and the standard 4DVAR scheme aiming at reducing computational demand required by the standard 4DVAR while improving the accuracy of the simplified 4DVAR. Applications to both hydrostatic and nonhydrostatic models are illustrated and our vision on opportunities and directions for future research is provided.