Kidder et al.(2005)提出 TRaP 方法，將衛星反演之降雨分佈平移，迅速估算熱帶氣旋未來可能帶來的降雨。陳(2010)修正 TRaP 方法，考量台灣地形效應的影響，並根據測站歷史降雨資料重新估算颱風降雨，稱為 I-TRaP。由於 I-TRaP 使用反演之降雨分布進行計算，如何獲得更好的颱風降雨分布仍然是很重要的課題。
在先前的研究中僅使用單一衛星的降雨產品，受限於掃描之時間解析度，隨著許多研究的發展，高解析多衛星合成之降雨產品已經有越來越好的表現，本研究比較幾種常見的全球多衛星產品(GSMaP、IMERG、PERSIANN)，考慮於西北太平洋上的颱風強降雨之表現，結果以 GSMaP為最佳。微波反演過程的判定降雨型態，確實會對層狀性降雨與對流性降雨的分類產生錯誤判定，但對於降雨結果的影響不大。進一步討論於強降雨造成誤差的可能原因，與目前的輻射方法仍難以準確估計大氣液態水含量，在降雨誤差越大時大氣液態水含量的差異越大。
使用 GSMaP 降雨產品以 I-TRaP 估算台灣地區的颱風降雨，為了將不同降雨產品的結果突顯而對現行 I-TRaP 的版本進行調整，修正以往僅使用衛星平移後的總降雨量進行回歸，新增以個別資料點回歸，能夠有效增加大雨的預報結果。GSMaP 相較先前使用 SSMIS 的方法能有效預報較大降雨，同時由於 GSMaP 的高解析時空分布，更有利於台灣的颱風降雨預報。

The Tropical Rainfall Potential (TRaP) technique presented by Kidder et al. in 2005, shifting rainfall distribution from satellite retrieval, and forecasting rainfall for tropical cyclone. Chen(2010) improved TRaP rainfall forecast practicality by adding orographic effect with historical rainfall distribution(I-TRaP). Since I-TRaP forecast uses rainfall distribution from satellite, how to get better rainfall distribution is an important issue.
There is only single satellite rainfall product in past study, limited by temporal resolution. For many study, The performance of multi-satellite rainfall products with high spatial-temporal resolution(0.1°-0.25°, 0.5-3h) are getting better recently but less discussed on heavy rainfall especially for typhoon. This study compares few common multi-satellite products (GSMaP, IMERG, PERSIANN) with typhoon heavy rainfall in the North-West Pacific, GSMaP is better. There are different performance between convective and stratiform rainfall. Indeed, the PMW retrieval fail to classification in rainfall type determination during microwave rainfall retrieving, but not cause rainfall error. In addition, compare liquid water content and rainfall error, the PMW retrieval still cannot estimate liquid water accurately in moderate to heavy rainfall.
Apply GSMaP to I-TRaP and calculate typhoon rainfall forecast over Taiwan. In order to highlight satellite rainfall distribution, modify earlier method only revising total rainfall and using historical rainfall distribution, calculate rainfall regression by individual point. This method will predict more heavy rainfall but more false alarm. Compare earlier I-TRaP using SSMIS, GSMaP with high spatial-temporal resolution is more useful for I-TRaP forecast, and more prediction of heavy rainfall.

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