本文選題：海表溫度 + 時(shí)間序列分析��； 參考：《杭州電子科技大學(xué)》2016年碩士論文

【摘要】：海表溫度是監(jiān)測(cè)海洋現(xiàn)象的重要參量,對(duì)海洋生態(tài)系統(tǒng)有很大的影響和作用,具有重要的研究?jī)r(jià)值,其在海洋動(dòng)力學(xué)、海氣相互作用、漁業(yè)經(jīng)濟(jì)研究和污染檢測(cè)等方面有廣泛應(yīng)用。此前國(guó)內(nèi)外關(guān)于海表溫度的許多研究都基于遙感反演、遙感數(shù)據(jù)重構(gòu),對(duì)其并沒有作進(jìn)一步的研究。海表溫度的預(yù)報(bào)也多以經(jīng)驗(yàn)預(yù)報(bào)方法、數(shù)值預(yù)報(bào)方法和統(tǒng)計(jì)預(yù)報(bào)方法為主,這些預(yù)報(bào)方法精度有限。由于海表溫度受各種因素的影響,使得海表溫度時(shí)間序列呈現(xiàn)出明顯的季節(jié)性變化特征。本文用時(shí)間序列分析的方法,研究包括東海、杭州灣、臺(tái)灣海峽和南海在內(nèi)的中國(guó)近海的海表溫度溫度及其預(yù)報(bào)工作。研究?jī)?nèi)容主要包括下面三個(gè)方面:一、海表溫度時(shí)間序列的預(yù)處理。首先是海表溫度時(shí)間序列的聚類分析,將相似度高的樣本聚為一類,把每個(gè)研究區(qū)域上的576個(gè)樣本劃分為兩個(gè)類,以樣本點(diǎn)多的類為例來研究相關(guān)海域的海表溫度。且本文提出了氣候月的思想,對(duì)時(shí)間序列數(shù)據(jù)按氣候月求月平均海表溫度,提高了預(yù)測(cè)的精度。二、從時(shí)域分析的角度,對(duì)月平均海表溫度時(shí)間序列數(shù)據(jù),經(jīng)過模型識(shí)別、模型估計(jì)和模型的診斷檢驗(yàn),建立SARIMA模型并作預(yù)測(cè)。三、從頻域分析的角度,對(duì)月平均海表溫度時(shí)間序列數(shù)據(jù)做譜分析,觀察其周期圖,建立相應(yīng)的潛周期模型或混合潛周期模型并作預(yù)測(cè)。由擬合的SARIMA模型和潛周期模型分別預(yù)測(cè)2010年3至2011年2月這12個(gè)月的月平均海表溫度,通過實(shí)際值和預(yù)測(cè)值的比較,發(fā)現(xiàn)這兩種預(yù)報(bào)方法的預(yù)測(cè)精度都較高,可以為這些地區(qū)海表溫度的研究提供參考。
[Abstract]:Sea surface temperature (SST) is an important parameter for monitoring ocean phenomena, which has great influence and effect on marine ecosystem, and has important research value in marine dynamics and air-sea interaction. Fishery economic research and pollution detection are widely used. Many previous studies on sea surface temperature are based on remote sensing inversion, remote sensing data reconstruction, and no further research has been done. The forecasting methods of sea surface temperature are mostly empirical, numerical and statistical, and the precision of these forecasting methods is limited. Because sea surface temperature is affected by various factors, the time series of sea surface temperature show obvious seasonal variation characteristics. In this paper, time series analysis is used to study the sea surface temperature (SST) and its prediction work in the East China Sea, Hangzhou Bay, Taiwan Strait and South China Sea, which include the East China Sea, Hangzhou Bay, Taiwan Strait and the South China Sea. The main contents include the following three aspects: first, the pretreatment of sea surface temperature time series. Firstly, the sea surface temperature (SST) time series is analyzed by clustering. The samples with high similarity are grouped into two categories, and the sea surface temperature (SST) in the relevant sea areas is studied by taking the multi-sample groups as an example. The idea of climate month is put forward in this paper, and the prediction accuracy is improved by calculating monthly mean sea surface temperature according to the time series data. Secondly, from the point of view of time domain analysis, the SARIMA model is established and predicted by model identification, model estimation and model diagnosis test for the monthly mean sea surface temperature time series data. Thirdly, from the point of view of frequency domain analysis, the spectral analysis of monthly mean sea surface temperature time series data is made, the period diagram is observed, and the corresponding latent period model or mixed latent period model is established and forecasted. Based on the fitting SARIMA model and the latent period model, the monthly mean sea surface temperature for 12 months from March 2010 to February 2011 is predicted respectively. By comparing the actual values with the predicted values, it is found that the two forecasting methods have higher prediction accuracy. It can provide reference for the study of sea surface temperature in these areas.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：P731.11

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