本研究主要是探討WRF模式中次網格地形重力波拖曳（gravity wave drag induced by subgrid-scale
orography，GWDO）參數法對模式預報的影響。研究結果顯示，在 45 公里網格間距下，使用 GWDO
參數法可以有效改善模式冬季在低對流層到200 hPa高度的預報能力。GWDO參數法在低層的改進，主
要分布在青康藏高原的下游區，包括印度、中南半島和華中、華南一帶，在中高層則有效修正了模式在
北緯25°～40° 之間西風帶的系統性誤差。
在 GWDO 參數法的敏感度測試中，選擇接近於模式網格解析度之次網格地形資料可以得到較好的
預報結果。而在使用部分循環更新之資料同化策略中，使用 GWDO 參數法可以得到較大程度的改善。
另外，在季節敏感度的測試中，GWDO參數法對冬季預報的改進幅度要明顯大於夏季的預報。
總體而言，在 WRF 模式使用 GWDO 參數法可以明顯地改進模式的預報誤差，此一改進在冬季中
尤為明顯。然而，模式中 GWDO 參數法對不同重力波物理過程的處理，其影響因地而異，這和平均流
場的結構以及地形的分布有密切的關係，本研究著重在探討 GWDO 參數法對預報的影響，有關影響預
報結果之物理過程的探討，則有賴更進一步的診斷分析。

The impact of the gravity wave drag parameterization induced by the subgrid scale orography (GWDO)
on the forecast was investigated in this study. The results show that the GWDO parameterization is in WRF
model significantly improve the model forecast from lower troposphere to 200 hPa in winter season. In
overall, the geographical distribution of the improvement was over the down-stream area of the Tibetan
Plateau in lower troposphere, including the India, Indo-China Peninsula, and south of Mainland China. Above
500 hPa, the improvement is distributed along the westerlies between 25°N~40°N.
In the sensitivity experiments, the better performance was found as using the sub-grid terrain dataset with
comparable model resolution. The impact of the GWDO parameterization is larger in the partial cycle data
assimilation than that without data assimilation. In addition, the effect of the GWDO parameterization is
larger in wintertime than that in summer.
In summary, the GWDO parameterization scheme in WRF model can improve the model forecast
significantly, in particular in the wintertime. This study is focus on studying the statistical impact of the
GWDO parameterization on the forecasts, the more diagnostic analysis is needed to further understand the
role of the GWDO in individual case.