- 學位論文
利用雷達觀測直接反演氣象變數進行資料同化以改進短期定量降水預報-2008 SoWMEX IOP8 個案分析
Direct retrieval of meteorological variables using weather radar for assimilation
摘要
雷達觀測具有高時空解析度的優點，常使用於劇烈天氣的監控與觀測。本研究是延續及改善前人的工作，選取2008西南氣流實驗計畫(SoWMEX)的IOP8個案，利用多都卜勒雷達觀測資料，改善模式當時的初始場，增進模式降水定量預報之能力。此方法主要包含三大部分：(1)多都卜勒風場合成、(2)熱動力反演、(3)水氣調整。然而前人的實驗存在一些問題：(1)同化後模式初始場壓力與反演的壓力場有很大差異，顯示壓力場並沒有被完整地同化到模式中；(2)模式預報時邊界會出現錯誤回波；(3)定量降水預報的高估及位置偏差。 為了改善這些問題，吾人更換將反演之壓力場置入模式的方式，及利用新的水氣調整的方法，其結果顯示在預報回波結構及降水預報都有明顯的改善，且能修正前人實驗的問題，令降水預報更加準確。而在各雲微物理方案測試中，以WSM6物理方案較為合適此實驗的降水預報。 當進行熱動力反演時可利用探空資料來提供每個高度層的水平壓力擾動平均，但是當沒有探空資料時，本研究利用模式當時預報場的每層平均壓力擾動來代替探空所提供的水平壓力擾動平均，從而反演出大氣壓力場。本實驗結果顯示沒有使用探空資訊，其降雨預報結果會較有探空時的降水預報為高估，但其預報仍有一定程度的準確性，因此在沒有探空時此方法是可行的。若只利用模式預報而不作任何的雷達資料同化，其降水預報結果十分不理想，也顯示只需兩筆時間的雷達資料並透過此反演同化方法，便可對定量降水預報有很好的改善。 由於雷達觀測的範圍是有限制，因此利用二次雷達資料同化讓模式得到更密集的大氣狀態資訊，可以有效改善降水預報的效果。實驗結果顯示利用二次同化方法，可以修正一次同化的強降雨區位置誤差，也減少了模式預報強回波時出現高估的程度，且二次同化方法在定量降水檢驗的表現優於一次同化，但若把二次同化的時間推延至一次同化預報後兩小時，其預報將有更明顯的改善。
並列摘要
An important advantage of radar observations is their high temporal and spatial resolution data, which are suitable for heavy weather surveillance. The purpose of this study is to improve previous studies, which are to improve the initial field and hence the quantitative precipitation forecast (QPF) of the numerial model by using multiple-Doppler radar observation data. The assimilation technique includes three components: multiple-Doppler radar wind synthesis, thermodynamic retrieval and moisture adjustment. A case during IOP8, Southwest Monsoon Experiment (SoWMEX)2008 is selected in this study. Some problems have not solves in previous studies: such as the pressure field has not been fully assimilated into the model, the boundary of forecast field produces wrong reflectivity, and overestimate of the rainfall. In this study we replace the method of the retrieval pressure embedding the model, and use a newly designed moisture adjustment method. The results show improvement in reflectivity structure and the accuracy precipitation of forecast. In the microphysics schemes test, WSM6 is a reasonable choice. In assimilation test, the model QPF can be significantly improved after assimilating the radar data. In with or without sounding test, it is feasible to use the model outputs to replace the role played by a sounding for estimating the unknown constant at each altitude. In second assimilation study, it can improve the retrieval atmospheric state variables, convection position and QPF in first assimilation. None of the fraction show which strategy is better. These two experiments produce comparable forecast, the model QPF may be more effective when the second DA is postponed until two hours.