巢狀區域波譜模式精進之研究

• Main Authors: Lo Ming-Fu, 羅明福
• Other Authors: 廖杞昌
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/73184836778985192595

碩士 === 國防大學理工學院 === 大氣科學碩士班 === 98 === On the meteorological point of view, the ultimate goal of verification is to improve weather forecasting ability and model verification is to take advantage of the characteristics of models completely, in order to amend the error. This research utilizes the earlier (f97) and new versions (NRSM) of the nested regional spectral model developed by the U.S. National Centers for Environmental Prediction (NCEP). This research selects the "northeast monsoon period" and "Mei-Yu season period" case simulation, RSMA is to grasp the large-scale systems and forecast, MSM part is to verify the accuracy of model with the observatory data, checking the accuracy and error of model. By the analysis showed that f97 and NRSM in the "northeast monsoon period" RSMA, surface pressure, temperature, wind and lower height field and humidity field results are presented NRSM better than f97. For the MSM of NRSM, surface pressure field closer to measured values, temperature dropped abnormally, but for MSM of f97 no such phenomenon and closer to the measured value. The wind field, however, shows two models disparity. The " Mei-Yu Season period " RSMA, surface pressure, temperature, wind and lower height field and humidity field results are presented NRSM better than f97; For the MSM of NRSM, surface pressure field closer to measured values, and no " temperature dropped abnormally", the wind field results show that the two models is still little difference. The results show that the NRSM is better than f97 overall, show that the NRSM with a considerable degree of improvement. By the results of comparing with the analysis field, it can make the operation units of using this mode to master and understand the characteristics of the model forecast, and to adjust the properties, and may further adjust the mode set to improve forecasting capabilities, to achieve the purpose of model improvement.