期刊名称：Tellus A: Dynamic Meteorology and Oceanography
电子版ISSN：1600-0870
出版年度：2013
卷号：65
期号：1
页码：1-17
DOI：10.3402/tellusa.v65i0.19677
摘要：In this study, we selected a heavy rainfall case over the Korean Peninsula, which was characterised by two localised rainfall maxima. Neither of the two maxima is accurately simulated when no radar data are assimilated or when radar data are assimilated using the three-dimensional variational (3D-Var) method of the Weather Research and Forecasting Data Assimilation (WRFDA) system. Using the four-dimensional variational (4D-Var) method included in the WRFDA system improves the rainfall forecast partially. To obtain further improvements in the rainfall forecast, outer loops and the time-incremental 4D-Var method are used. In the time-incremental 4D-Var method, the length of the assimilation window is increased gradually, and the starting point of the current minimisation task comes from the minimiser of the previous minimisation task. The analysis of the experiment using outer loops or the time-incremental 4D-Var method is closer to the observations than that of the experiment using the 4D-Var method. This is because the first guess is improved progressively by using outer loops or the time-incremental 4D-Var method. The gap between nonlinear and linear growth is reduced via outer loops or the time-incremental 4D-Var method compared to the 4D-Var method, because the nonlinearity is accounted for by consistently updating the nonlinear model trajectory. However, the rainfall forecast is improved only in the experiment using the time-incremental 4D-Var method. Analysis increments of the horizontal wind and convective available potential energy (CAPE) result in proper modifications to the analysis, and finally, an improved subsequent forecast. The quasi-static adjustment in the time-incremental 4D-Var method may contribute to finding the global minimum under the high degree of nonlinearity present in the original minimisation problem.
关键词：time-incremental 4D-Var method ; outer loop ; nonlinearity ; heavy rainfall ; radar data assimilation
