本文選題：鄉(xiāng)鎮(zhèn) + 氣象數(shù)據(jù)　； 參考：《齊魯工業(yè)大學》2015年碩士論文

【摘要】：本文通過氣象數(shù)據(jù)特征分析,建立數(shù)據(jù)擬合方法,在此基礎上開發(fā)系統(tǒng)軟件,并對實際應用效果校驗和評述。根據(jù)1981-2010年逐日氣壓、氣溫、相對濕度、風速、降水量五要素氣候標準值的分布特征分析,氣壓、氣溫、相對濕度要素變化基本呈正態(tài)分布,風速分布曲線為左偏太,降水量分布曲線大致為指數(shù)。再利用2007-2014年氣象數(shù)據(jù)計算五要素相關性。結合要素分布特征和相關系數(shù)分析,氣壓、氣溫、相對濕度可直接利用線性方程,擬合鄉(xiāng)鎮(zhèn)區(qū)域站氣象資料,風速、降水量需做正態(tài)分布轉換。最后對擬合得到的2014年氣象數(shù)據(jù)與鄉(xiāng)鎮(zhèn)區(qū)域站觀測數(shù)據(jù)進行校驗,只有個別月份氣象數(shù)據(jù)超出儀器標準誤差,說明擬合數(shù)據(jù)可用。其中氣溫數(shù)據(jù)符合最好,誤差基本在0.2℃之內,風速和降水量擬合誤差對小觀測量影響較大。系統(tǒng)結合現(xiàn)有氣象業(yè)務開發(fā),以實用為主,保障擬合的準確性。系統(tǒng)包括系統(tǒng)管理、報文收集轉換、質量控制、數(shù)據(jù)擬合、小麥風險度計算、特色農業(yè)風險度計算、數(shù)據(jù)查詢、應用工具等模塊。利用系統(tǒng)多次插補觀測期數(shù)據(jù)、重復計算回歸方程系數(shù),保障擬合氣象數(shù)據(jù)最接近觀測值。利用農業(yè)氣象災害風險評估系統(tǒng)計算東阿縣農業(yè)災害風險度,得到如下結論:小麥氣象災害風險按旱澇、高溫、低溫、倒伏四項指標計算,小麥綜合農業(yè)氣象災害風險度在0.47~0.51之間,各鄉(xiāng)鎮(zhèn)綜合氣象災害風險度處于中、高風險區(qū)。特色農業(yè)油用牡丹的綜合農業(yè)氣象災害風險度在0.25~0.65之間,處于綜合氣象災害低到高風險區(qū)。課題建立了以鄉(xiāng)鎮(zhèn)為基點的山東省氣象擬合資料集,使氣象資料分布達到0.1°x0.1°,為氣象資料的共享提供了基礎;并在觀測和實驗得到氣象災害數(shù)據(jù)基礎上建立部分特色農業(yè)的氣象災害風險模型,為特色農業(yè)生產提供了技術指導。
[Abstract]:Based on the feature analysis of meteorological data, the method of data fitting is established, and the system software is developed on the basis of which, and the practical application effect is verified and reviewed. According to the analysis of the distribution characteristics of daily air pressure, air temperature, relative humidity, wind speed and precipitation from 1981 to 2010, the variation of air pressure, temperature and relative humidity is basically normal distribution, and the distribution curve of wind speed is too far left. The precipitation distribution curve is approximately exponential. The correlation of the five elements is calculated by using the meteorological data of 2007-2014. Based on the analysis of the distribution characteristics of elements and the correlation coefficient, the linear equation can be directly used to fit the meteorological data of township regional stations. The transformation of normal distribution of wind speed and precipitation is needed. Finally, the fitting meteorological data of 2014 and the observation data of township regional stations are verified. Only the meteorological data in a few months exceed the standard error of the instrument, which shows that the fitting data can be used. Among them, the temperature data is the best, the error is within 0.2 鈩，

本文編號：1923819