| dc.description.abstract | Under two conditions of a cold pool with terrain and a storm without terrain, this study attempts to establish a thermodynamic retrieval method to get all datasets we needed. After getting all retrieval data, we put back all the thermal retrieval fields (p′ and θ′) into model again and continue doing the simulation. Finally, we discuss whether the procedure is feasible and how much advantages we get from the results in the data assimilation. This research uses RASTA (Radar Analysis System for Taiwan Area) 3-D variational method, and all momentum and thermodynamic equations used in the retrieval method are expressed in a terrain-following coordinate system. The model we use here is called ARPS（Advanced Regional Prediction System）, it is created by CAPS (Center for Analysis and Prediction of Storms) and University of Oklahoma.
Due to lack of high-resolution thermodynamic observational data in real atmosphere, this research uses ARPS model to simulate a cold pool with ideal terrain and a storm without terrain, and then tests the performance of this method and estimates the error. The results show that, the thermodynamic fields retrieved from this method have good consistencies in horizontal and vertical structures.
For the data assimilation results, putting the RASTA retrieval data back to the ARPS is feasible, and this method can simulate reasonable p′ and θ′ if we have good initial p′ and θ′. In order to enhance the accuracy of the data assimilation results, first we need to have better p′ and θ′. Therefore, a finer computational formula is required in retrieving thermodynamic structure by the use of three-dimensional wind field. The θ′ can accept higher errors in the assimilation data, however, the p′ is not very ideal, especially when the SCC value of p′ is lower than 0.9. Although the data assimilation method in this research has improvement in the results for two different conditions, but there still has limit. If the difference in the initial fields is large, it is difficult to amend the difference even we have correct thermodynamic fields. In the data assimilation of precipitation, the simulation result changes only a little. We guess the possible reason is that the assimilation does not update the information of moisture. Therefore, if we want to improve the rain field, radar reflectivity should be taken into account in the assimilation procedure.
Over all, the data assimilation method in this research improves both the p′ and θ′ in both case one (cold pool) and case two (Del City storm). | en_US |