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  本文關(guān)鍵詞：應(yīng)用AMDAR數(shù)據(jù)探尋高空顛簸區(qū)的方法研究　出處：《中國民用航空飛行學(xué)院》2016年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 高空顛簸區(qū) AMDAR Moscat顛簸預(yù)報(bào)指數(shù) 指數(shù)分布圖 顛簸高發(fā)區(qū)

【摘要】：目前顛簸區(qū)的統(tǒng)計(jì)資料主要來自飛行員報(bào)告,報(bào)告數(shù)量少、主觀性強(qiáng),忽視航線以外區(qū)域,導(dǎo)致顛簸區(qū)統(tǒng)計(jì)效果不佳,實(shí)用性差。本文首先利用AMDAR(飛機(jī)氣象數(shù)據(jù)下傳)資料來獲取顛簸案例,進(jìn)而選取了4個(gè)顛簸預(yù)報(bào)指數(shù),基于NCEP再分析數(shù)據(jù),對(duì)采集的顛簸案例進(jìn)行模擬預(yù)報(bào),通過對(duì)預(yù)報(bào)效果的定量評(píng)價(jià),得到最優(yōu)顛簸預(yù)報(bào)指數(shù),然后,繪制最優(yōu)顛簸預(yù)報(bào)指數(shù)分布圖,實(shí)現(xiàn)分布圖的統(tǒng)計(jì),最終得到顛簸高發(fā)區(qū)。具體如下。對(duì)AMDAR資料進(jìn)行時(shí)空特征統(tǒng)計(jì)與質(zhì)量分析,得出：AMDAR是具有高時(shí)空密度、高質(zhì)量的數(shù)據(jù)資料,實(shí)用性極強(qiáng)。通過獲取AMDAR資料中DEVG(等效垂直陣風(fēng))資料,實(shí)現(xiàn)對(duì)顛簸案例的采集。選取4個(gè)顛簸預(yù)報(bào)指數(shù),利用NCEP再分析數(shù)據(jù)集分別對(duì)采集的顛簸案例進(jìn)行模擬預(yù)報(bào)。選取PODy(發(fā)生顛簸正確診斷率)、PODn(未發(fā)生顛簸正確診斷率)、POD(整體預(yù)報(bào)正確率)以及TS (True Skill)評(píng)分4個(gè)評(píng)價(jià)指標(biāo),對(duì)顛簸指數(shù)模擬預(yù)報(bào)的效果進(jìn)行定量評(píng)價(jià),得出,Moscat概率預(yù)報(bào)因子具有更好的預(yù)報(bào)效果�；谧顑�(yōu)顛簸預(yù)報(bào)指數(shù)Moscat概率預(yù)報(bào)因子,繪制特定時(shí)空條件下的最優(yōu)顛簸指數(shù)分布圖,引入灰度圖方法,實(shí)現(xiàn)顛簸指數(shù)分布圖的初步篩選,進(jìn)而采用頻數(shù)統(tǒng)計(jì)的方法,對(duì)篩選后的圖像作最終處理,最終得到特定區(qū)域特定高度層上不同季節(jié)下的顛簸高發(fā)區(qū)具體分布情況。
[Abstract]:At present, the statistical data of the bumpy area mainly come from the pilot report, the number of reports is small, the subjectivity is strong, and the area outside the route is ignored, which leads to the poor statistical effect of the bumpy area. This paper first uses AMDAR data to obtain turbulence cases, and then selects four turbulence forecasting indices, based on NCEP reanalysis data. Through the quantitative evaluation of the forecast effect, the optimal turbulence forecast index is obtained. Then, the distribution map of the optimal turbulence forecast index is drawn to realize the statistics of the distribution map. Finally, we get the bumpy high incidence area. The details are as follows. The space-time characteristic statistics and quality analysis of AMDAR data show that the AMDAR data have high space-time density and high quality data. By obtaining the equivalent vertical gust data from AMDAR data, the acquisition of bumpy cases is realized. Four turbulence prediction indices are selected. The NCEP reanalysis data set is used to simulate and forecast the bumpy cases, and PODyy (correct diagnostic rate for the occurrence of turbulence) is selected. POD (overall prediction accuracy rate) and TS true kill (TS) evaluation index were used to evaluate quantitatively the effect of simulation prediction of turbulence index. The Moscat probability forecast factor has better forecast effect. Based on the Moscat probability forecast factor of the optimal turbulence forecast index, the optimal distribution map of the turbulence index is drawn under the special time and space conditions. The grayscale image method is introduced to realize the preliminary screening of the bumping index distribution map, and then the final processing of the filtered image is made by the method of frequency statistics. Finally, the specific distribution of the bumping high area in different seasons on the specific height layer is obtained.
【學(xué)位授予單位】：中國民用航空飛行學(xué)院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：V321.2

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