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Feng Chen, Xudong Liang, and Hao Ma


An improved Doppler radar radial velocity assimilation observation operator is proposed based on the integrating velocity–azimuth process (IVAP) method. This improved operator can ingest both radial wind and its spatial distribution characteristics to deduce the two components of the mean wind within a given area. With this operator, the system can be used to assimilate information from tangential wind and radial wind. On the other hand, because the improved observation operator is defined within a given area, which can be uniformly chosen in both the observation and analysis coordinate systems, it has a thinning function. The traditional observation operator and the improved observation operator, along with their corresponding data processing modules, were implemented in the community Gridpoint Statistical Interpolation analysis system (GSI) to demonstrate the superiority of the improved operator. The results of single analysis unit experiments revealed that the two operators are comparable when the analysis unit is small. When the analysis unit becomes larger, the analysis results of the improved operator are better than those of the traditional operator because the former can ingest more wind information than the latter. The results of a typhoon case study indicated that both operators effectively ingested radial wind information and produced more reasonable typhoon structures than those in the background fields. The tangential velocity relative to the radar was retrieved by the improved operator through ingesting tangential wind information from the spatial distribution characteristics of radial wind. Because of the improved vortex intensity and structure, obvious improvements were seen in both track and intensity predictions when the improved operator was used.

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Wei Peng, Xudong Liang, Xin Zhang, Xiangyu Huang, Bing Lu, and Qiao Fu


Generally, the results of data assimilation are not well balanced dynamically due to errors in background, observations, or the model itself. So, initialization methods have been introduced to remove spurious gravity waves from the analysis. One of the initialization methods is digital filter initialization (DFI), which has been used in operational forecast systems, though its physical meaning is not well understood. Other methods eliminate high-frequency noise in optimized initial conditions by introducing physical constraints, such as the model constraint scheme, which minimizes the time tendency of model variables. In this study, a physical filter initialization (PFI) scheme, based on the model constraint scheme, is implemented in the four-dimensional variational data assimilation (4DVar) system of the Weather Research and Forecasting (WRF) Model. The impacts of the PFI scheme are examined by both single-observation and real-data experiments. The results indicate that the PFI scheme can eliminate high-frequency noise effectively, obtain flow-dependent analysis increments, and shorten forecast spinup time. Consequently, the precipitation forecast is improved to a certain extent, especially during the first few hours thanks to the shorter spinup time.

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