本文選題：南京市 + 極端氣溫�。� 參考：《南京信息工程大學(xué)》2014年碩士論文

【摘要】：本文根據(jù)南京市1951-2011年逐日的溫度觀測(cè)資料,采用線性趨勢(shì)分析、灰色關(guān)聯(lián)理論、灰色馬爾可夫預(yù)測(cè)模型,詳細(xì)分析了南京市極端氣溫的變化特征、極端溫度時(shí)間序列與南京市GDP的關(guān)聯(lián)分析以及改變點(diǎn)探索,同時(shí)預(yù)測(cè)2012年的南京市高低溫事件發(fā)生的風(fēng)險(xiǎn)。其中所涉及的氣溫要素包括：平均氣溫、平均最高氣溫、平均最低氣溫、極端最高氣溫、極端最低氣溫、極端最高氣溫頻數(shù)、極端最低氣溫頻數(shù)、高溫日數(shù)、低溫日數(shù)共9個(gè)要素。 (1)對(duì)南京市1951-2011年極端溫度時(shí)間序列的趨勢(shì)分析：首先根據(jù)南京市的逐日最高氣溫、逐日最低氣溫、平均氣溫資料構(gòu)建極端氣溫指數(shù),從而分析南京市61年來的極端溫度變化趨勢(shì),可以得出南京市年平均氣溫、最高氣溫與最低氣溫均呈上升趨勢(shì),且在20世紀(jì)90年代后增加明顯,而年平均最低氣溫增加幅度明顯超過年平均最高氣溫。年平均最高氣溫以及年平均最低氣溫的季節(jié)變化趨勢(shì)除夏季年平均最高氣溫呈下降趨勢(shì)外,其余均為上升趨勢(shì)。年極端最高氣溫呈平穩(wěn)趨勢(shì),年極端最低氣溫呈顯著地增溫趨勢(shì)。南京市極端最高氣溫頻數(shù)以及高溫日數(shù)減少的趨勢(shì)并不明顯,呈平穩(wěn)的狀態(tài),而極端最低氣溫頻數(shù)以及低溫日數(shù)具有顯著的減少趨勢(shì),說明南京的各種溫度指數(shù)都以升溫為主要趨勢(shì)。 (2)對(duì)南京市1951-2011年極端溫度時(shí)間序列與GDP的關(guān)聯(lián)關(guān)系以及改變點(diǎn)探索：南京市極端氣溫變化與GDP增長(zhǎng)率的關(guān)聯(lián)度較大。其中最強(qiáng)的是高溫日數(shù)與低溫日數(shù),關(guān)聯(lián)度為0.7819和0.7798。也就是說南京市的高低溫日數(shù)與GDP的關(guān)聯(lián)度最大,表明當(dāng)?shù)氐亩唐跉夂蜃兓瘜?duì)經(jīng)濟(jì)發(fā)展具有較強(qiáng)的敏感性。反之,短期氣候變化也會(huì)對(duì)經(jīng)濟(jì)發(fā)展造成影響。南京市GDP增長(zhǎng)率與平均氣溫、極端氣溫和極端氣溫頻數(shù)時(shí)間序列的改變點(diǎn)與變化趨勢(shì)基本一致,也就是說南京的經(jīng)濟(jì)發(fā)展與氣候變化之間存在著密切的聯(lián)系。 (3)基于1951-2011年的高低溫日數(shù)預(yù)測(cè)2012年的高低溫風(fēng)險(xiǎn)：基于加權(quán)馬爾可夫和灰色加權(quán)馬爾可夫預(yù)測(cè)方法,通過對(duì)高溫日數(shù)與低溫日數(shù)的持續(xù)時(shí)間劃分,構(gòu)建高低溫賦權(quán)指數(shù),對(duì)南京市高低溫事件發(fā)生的風(fēng)險(xiǎn)進(jìn)行預(yù)測(cè),得出南京市2012年低溫和高溫風(fēng)險(xiǎn)狀態(tài)都為一般風(fēng)險(xiǎn)狀態(tài),產(chǎn)生高低溫事件的風(fēng)險(xiǎn)較小。
[Abstract]:Based on the daily temperature observation data of Nanjing from 1951 to 2011, using linear trend analysis, grey correlation theory and grey Markov prediction model, the variation characteristics of extreme temperature in Nanjing are analyzed in detail. The correlation analysis between extreme temperature time series and GDP in Nanjing and the exploration of change point are also used to predict the risk of high and low temperature events in Nanjing in 2012. The temperature elements involved include: average temperature, mean maximum temperature, average minimum temperature, extreme maximum temperature, extreme minimum temperature, extreme maximum temperature frequency, extreme minimum temperature frequency, high temperature days, The number of low temperature days is 9 elements. (1) the trend analysis of the time series of extreme temperatures in Nanjing from 1951 to 2011: firstly, according to the daily maximum temperature, the daily minimum temperature and the average temperature data of Nanjing, the extreme temperature index was constructed. By analyzing the trend of extreme temperature variation in Nanjing over the past 61 years, it can be concluded that the annual average temperature, the highest temperature and the lowest temperature in Nanjing City are all on the rise, and they have increased obviously since the 1990s. However, the annual mean minimum temperature increased more than the annual mean maximum temperature. The seasonal variation trend of the annual mean maximum temperature and the annual mean minimum temperature except the annual average maximum temperature in summer showed a downward trend and the rest were all upward trends. The annual extreme maximum temperature showed a steady trend, and the annual extreme minimum temperature showed a significant warming trend. The decreasing trend of the frequency of extreme maximum temperature and the number of days of high temperature in Nanjing is not obvious, but the frequency of extreme minimum temperature and the number of days of low temperature have a significant decreasing trend. It shows that the temperature rise is the main trend of all kinds of temperature index in Nanjing. (2) the relationship between extreme temperature time series and GDP from 1951 to 2011 in Nanjing and its change point are explored. The correlation between extreme temperature change and GDP growth rate in Nanjing is great. Among them, the strongest is the high temperature days and the low temperature days, the correlation degree is 0.7819 and 0.7798. In other words, the correlation between high and low temperature days and GDP is the largest in Nanjing, which indicates that local short-term climate change is sensitive to economic development. Conversely, short-term climate change will also have an impact on economic development. The time series of GDP growth rate and mean temperature, extreme temperature and extreme temperature frequency in Nanjing are basically consistent with the change trend, that is to say, there is a close relationship between the economic development of Nanjing and climate change. Forecast of high and low temperature risk for 2012 based on the number of days of high and low temperature from 1951 to 2011: based on weighted Markov and grey weighted Markov forecasting methods, we construct a high and low temperature weighting index by dividing the duration of high temperature days and low temperature days. The risk of high and low temperature events in Nanjing is forecasted. It is concluded that the state of low temperature and high temperature in Nanjing in 2012 are both general risk states, and the risk of producing high and low temperature events is relatively small.
【學(xué)位授予單位】：南京信息工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：F127;P49

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