使用四維變分都卜勒雷達變分分析系統(VDRAS)與WRF改善短期定量降水預報

Improving short-term QPF by using a 4DVAR radar data assimilation system (VDRAS) and WRF

楊靜伃(Ching-yu Yang)

指導教授 ： 廖宇慶

國立中央大學/地球科學學院/大氣物理研究所/碩士(2012年)

摘要

美國國家大氣研究中心(National Center for Atmospheric Research:NCAR)發展的都卜勒雷達變分分析系統(Variational Doppler Radar analysis System: VDRAS)，是利用四維變分資料同化方法(4DVAR)同化雷達回波和徑向風觀測資料，可反演出天氣系統之三維動力場及熱力場，並進行預報。VDRAS曾用於台灣地區檢視同化雷達資料後模式的預報能力，並將VDRAS之最佳分析場，與WRF(Weather Research and Forecasting)模式結合並進行預報，其結果明顯比VDRAS和WRF的單獨預報佳。本研究使用VDRAS同化多部都卜勒雷達資料，對VDRAS之分析場進行同化窗區敏感度實驗，並改善VDRAS與WRF之結合方式，期望提升短期定量降水預報之能力。
本實驗針對2008年SoWMEX/TiMREX (西南氣流實驗) IOP8(第八次密集觀測期間)中6月14日的梅雨鋒面進行研究分析。第一部分為VDRAS同化窗區敏感度實驗，檢視同化窗區數目對分析場及預報場的影響，進行1~3個同化窗區測試。結果顯示兩個同化窗區的VDRAS在分析場之動力及熱力結構的表現較佳。
第二部分為VDRAS分析場與WRF模式的同化實驗，其中WRF模式使用單一區域及開放式邊界條件進行模擬，以避免同化後在WRF模式邊界所產生的不平滑問題。在此採用「直接取代結合」與「WRF 3DVAR結合」為同化方式，檢視同化方式對預報結果之影響。將VDRAS的分析場與WRF進行直接取代結合，其對定量降水預報的影響可以持續四小時，在降雨分佈的結果中，取代後的WRF模式可以很好的模擬降雨分佈與地形之間的效應，對降雨的預報有明顯改善。其中以兩個窗區的VDRAS分析場與WRF進行取代結合，在四小時的定量降水預報的表現最好。但是以WRF 3DVAR結合VDRAS分析場與WRF模式，對於結合結果的調整能力有限，且此方法需要很長時間的spin up。
本研究提供了一種在台灣地區多山的情況下較有效地使用VDRAS來預報降雨的方案。

關鍵字

都卜勒雷達變分分析系統 ； 四維變分 ； WRF ； 都卜勒雷達 ； 定量降水預報

並列摘要

The NCAR Variational Doppler Radar Analysis System (VDRAS) is a system which uses the 4DVAR technique to assimilate the radar reflectivity and radial wind observations, and is capable of providing the three-dimensional kinematic and thermodynamic fields within a weather system. Since VDRAS is formulated on a Cartesian coordinate, its application to Taiwan where the topography is complicated is expected to be limited. Previous studies in which the analysis fields from VDRAS were merged with WRF showed encouraging results in improving the model Quantitative Precipitation Forecast (QPF). The purpose of this study is to test the sensitivity of the cycling configuration for VDRAS, and find a robust way of combining VDRAS and WRF.
A real case of Mei-Yu front occurred on 14 June 2008 during Southwest Monsoon Experiment (SoWMEX) IOP8 is selected. In the first set of experiment, three tests are designed to examine the sensitivity of the analysis and forecast with respect to VDRAS cycling configuration containing 1-3 cycles in assimilation processes, respectively. The results of the principal kinematic and thermodynamic features reveal that VDRAS with two cycles is better than the other designs. In the second set of experiment, the VDRAS analysis fields are merged with WRF model. Assuming that the synoptic scale influence can be neglected within a period of three to four hours, it is found that using a single domain instead of nested domains can effectively remove the noises generated along the domain boundaries. Two different ways are used to merge VDRAS with WRF, and they are “direct replacement” and “WRF 3DVAR”, respectively.
It is found that when the VDRAS analysis fields generated by 1-3 cycles are directly merged with WRF model, the two-cycle VDRAS analysis field produces the best results. The impact of using VDRAS outputs to improve the QPF can last for about four hours. The accuracy of the predicted 4-hour accumulated rainfall after merging VDRAS and WRF turns out to be significantly higher than that generated by using VDRAS or WRF alone. This can be attributed to the assimilation of meso- and convective scale information, embedded in the radar data into the VDRAS, and to a better treatment of the topographic effects by the WRF model simulation. However, the experiments of using “WRF 3DVAR” to merge VDRAS analysis fields with WRF are not successful, and this method needs a longer time to spin up.
This research suggests an effective way of using VDRAS to forecast rainfall under Taiwan’s mountainous situation.

並列關鍵字

4DVAR ； VDRAS ； WRF ； QPF ； Doppler radar

參考文獻

Crook, N. A., and J. Sun, 2002: Assimilating radar, surface and profiler data for the Sydney 2000 forecast demonstration project. J. Atmos. Oceanic Technol., 19, 888–898.

Crook, N. A., and J. Sun, 2004: Analysis and Forecasting of the Low-Level Wind during the Sydney 2000 Forecast Demonstration Project. Weather and Forecasting, 19, 151-167.

Gal-Chen, T., 1978: A method for the initialization of the anelastic equations: Implications for matching models with observations. Mon. Wea. Rev., 106, 587–606.

Kawabata J., H. Seko, K. Saito, T. Kuroda, K. Tamiya, T. Tsuyuki, Wakazuki, 2007: An assimilation and forecasting experiment of the Nerima heavy rainfall with a cloud-resolving nonhydrostatic 4-dimensiojnal variational data assimilation system, J. Meteor. Soc. Japan, 85, 255-276.

Kawabata, T., T. Kuroda, H. Seko, and K. Saito, 2011：A cloud-resolving 4D-Var assimilation experiment for a local heavy rainfall event in the Tokyo metropolitan area, Mon. Wea. Rev., AMS early release version.