| DC 欄位 |
值 |
語言 |
| DC.contributor | 大氣物理研究所 | zh_TW |
| DC.creator | 呂佳龍 | zh_TW |
| DC.creator | Jia-Long Lue | en_US |
| dc.date.accessioned | 2010-7-28T07:39:07Z | |
| dc.date.available | 2010-7-28T07:39:07Z | |
| dc.date.issued | 2010 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:444/thesis/view_etd.asp?URN=956201017 | |
| dc.contributor.department | 大氣物理研究所 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 梅雨降水為台灣每年最嚴重的氣象災害之一，如何準確預測降水以減少生命與經濟的損失為重要的課題。本研究以WRF (Weather Research and Forecasting)模式三維變分資料同化方法 (3D-Var)來同化GPS掩星折射率、SSM/I衛星、QuikSCAT衛星及其他傳統觀測資料(GTS)和投落送資料加入初始場中，期望藉由資料同化調整初始分析場來修正初始場可能存在的問題，進一步而改善短期降水預報。
本研究將各種同化的資料分為同化時間前後3小時與前後小於3小時兩類，並加上合併第二類中各組資料做資料同化模擬的合成組，與此合成組循環模擬(cycling)的合成組。針對六月初與六月中的三個梅雨個案分別進行三天的預報，並將三天的降水結果以TS (Threat score) 預報得分來量化比較各組改善的實際情形。單一資料組的同化可比較不同觀測資料模擬的差異影響，縮小時間窗區的同化組可以比對在預報上準確度是否有顯著的提升，合成組則是期望藉由合成所有資料的優點，來改善初始場資料不足與降水的模擬結果。模擬的結果顯示，在預報中期的第二天各組TS預報分數相對於未同化時普遍都是較佳的。縮小時間窗區的模擬中發現，除了GTS組外，其他各組TS分數相對上都更佳，符合預期的結果。而在循環模擬也發現，藉由增加初始時間以前6小時內的資料，經過cycling循環模擬後的合成組，在初始場資料量明顯增加的情況下，除了事件1外，對於預報初期(00~24h)都有較好的TS預報分數。
在對於事件1的GPS同化資料模擬的動力分析中，發現造成GPS同化後降水增加的原因，除了模擬前期溫度正增量造成的熱力影響外，主要維持往後兩天半大量降水的原因，為西南方水平輻合的增強造成水氣的迅速凝結抬升而降水。
敏感度測試實驗發現，經過加入投落送資料的GTS組，對於GTS預報初期都有很明顯的改善，投落送資料對初期預報占有很重要的影響。而另一部份，在同化參數敏感度測試方面，發現同化參數組中以單純提高水平尺度的流函數和速度位的部分，對模式預報有相對較好的影響。
| zh_TW |
| dc.description.abstract | Mei-yu precipitation is one of the most serious weather disasters every year in Taiwan. This study uses WRF (Weather Research and Forecasting) model with three dimensional variance data assimilation scheme (3D-Var) to assimilate GPS refractivity, SSM/I satellites, QuikSCAT satellites, other conventional observation soundings and dropsonde data into model initial conditions. All assimilation data with the assimilation window of three hours of the model initial time are assimilated with different combinations of the above data. Three Mei-yu cases in early and middle June were simulated with a forecast length of three days and there results on predicted rainfall are evaluated using by Threat Score (TS) verified against with observation in Taiwan. Modeling results show that in the second forecast day all assimilation experiments generally have better TS compared to the control run (without assimilation). It was found that a reduced time window gives relatively better TS except for the assimilation run with GTS. Furthermore, cycling runs in a window of six hours improve TS in the first for all cases except for case 1, but have no improvement afterwards.
Analysis of GPS data assimilation results for case 1 indicate that increase in rainfall may be mainly related to temperature increase in lower levels in the earlier simulation time, but in response to enhanced horizontal water vapor convergence to the southwest of Taiwan in the later time that facilitates lifted condensation and thus precipitation.
In terms of sensitivity test experiments, it is ascertained that combining dropsonde data into the GTS experiment with exhibits considerable improvement in earlier forecast time, but has no improvement afterwards. Sensitivity tests of assimilation parameters show that increases in the horizontal scale of stream function and velocity potential have more positive impacts on model forecast.
| en_US |
| DC.subject | 三維變分資料同化 | zh_TW |
| DC.subject | three-dimensional variational data assimilation | en_US |
| DC.title | 同化GPS掩星及其他觀測資料對梅雨模擬之影響 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | The Impacts of Assimilation with GPS RO and Other Observation Data on Mei-yu Prediction. | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |