气象与减灾研究
氣象與減災研究
기상여감재연구
METEOROLOGY AND DISASTER REDUCTION RESEARCH
2015年
1期
8-15
,共8页
降水趋势%REOF%SST%OLR%气候预测
降水趨勢%REOF%SST%OLR%氣候預測
강수추세%REOF%SST%OLR%기후예측
REOF%SST%OLR%climate prediction
在经验正交函数分解（EOF）分析的基础上，采用旋转经验正交展开（REOF）方法，对江西省81个气象站6月降水进行客观分区，发现前4个主成分的累积方差达到了76.6%，这4个旋转主分量的高值区（绝对值≥0.5）基本覆盖整个江西省地区，且第1、2、3和4旋转主分量的高值区分别位于赣北南部、赣南、赣中及赣北北部，这表明江西省6月的降水可以划分为赣北北部（Ⅳ区）、赣北南部（Ⅰ区）、赣中（Ⅲ区）及赣南（Ⅱ区）4个区域。分别对各区6月降水与上年9月—5月SST和OLR场进行相关分析，找到了对各区6月降水有预报指示意义的因子（前期SST或OLR），并利用多元逐步回归方法建立了各区6月降水的预报模型。利用这些预报模型对1980—2008年各区6月降水进行了模拟，各区模拟值与观测值的吻合较好，两者的相关系数分别为0.55（I区）、0.43（Ⅱ区）、0.58（Ⅲ区）和0.54（Ⅳ区），这表明了这些模型对各区6月的降水有较好的模拟能力。2009—2013年各区6月降水模拟效果检验结果也显示，4个区模拟效果检验5 a中有4 a符号与实况一致。2014年6月预测结果的检验也显示，Ⅰ区、Ⅲ区、Ⅳ区预测的降水距平百分率与实况非常接近，进一步证明了这些模型的模拟性能。
在經驗正交函數分解（EOF）分析的基礎上，採用鏇轉經驗正交展開（REOF）方法，對江西省81箇氣象站6月降水進行客觀分區，髮現前4箇主成分的纍積方差達到瞭76.6%，這4箇鏇轉主分量的高值區（絕對值≥0.5）基本覆蓋整箇江西省地區，且第1、2、3和4鏇轉主分量的高值區分彆位于贛北南部、贛南、贛中及贛北北部，這錶明江西省6月的降水可以劃分為贛北北部（Ⅳ區）、贛北南部（Ⅰ區）、贛中（Ⅲ區）及贛南（Ⅱ區）4箇區域。分彆對各區6月降水與上年9月—5月SST和OLR場進行相關分析，找到瞭對各區6月降水有預報指示意義的因子（前期SST或OLR），併利用多元逐步迴歸方法建立瞭各區6月降水的預報模型。利用這些預報模型對1980—2008年各區6月降水進行瞭模擬，各區模擬值與觀測值的吻閤較好，兩者的相關繫數分彆為0.55（I區）、0.43（Ⅱ區）、0.58（Ⅲ區）和0.54（Ⅳ區），這錶明瞭這些模型對各區6月的降水有較好的模擬能力。2009—2013年各區6月降水模擬效果檢驗結果也顯示，4箇區模擬效果檢驗5 a中有4 a符號與實況一緻。2014年6月預測結果的檢驗也顯示，Ⅰ區、Ⅲ區、Ⅳ區預測的降水距平百分率與實況非常接近，進一步證明瞭這些模型的模擬性能。
재경험정교함수분해（EOF）분석적기출상，채용선전경험정교전개（REOF）방법，대강서성81개기상참6월강수진행객관분구，발현전4개주성분적루적방차체도료76.6%，저4개선전주분량적고치구（절대치≥0.5）기본복개정개강서성지구，차제1、2、3화4선전주분량적고치구분별위우공북남부、공남、공중급공북북부，저표명강서성6월적강수가이화분위공북북부（Ⅳ구）、공북남부（Ⅰ구）、공중（Ⅲ구）급공남（Ⅱ구）4개구역。분별대각구6월강수여상년9월—5월SST화OLR장진행상관분석，조도료대각구6월강수유예보지시의의적인자（전기SST혹OLR），병이용다원축보회귀방법건립료각구6월강수적예보모형。이용저사예보모형대1980—2008년각구6월강수진행료모의，각구모의치여관측치적문합교호，량자적상관계수분별위0.55（I구）、0.43（Ⅱ구）、0.58（Ⅲ구）화0.54（Ⅳ구），저표명료저사모형대각구6월적강수유교호적모의능력。2009—2013년각구6월강수모의효과검험결과야현시，4개구모의효과검험5 a중유4 a부호여실황일치。2014년6월예측결과적검험야현시，Ⅰ구、Ⅲ구、Ⅳ구예측적강수거평백분솔여실황비상접근，진일보증명료저사모형적모의성능。
Based on the decomposition analyses of Empirical Orthogonal Function (EOF), the Rotated Empirical Orthogonal Function (REOF) method is used to analyze the spatial distribution features of Jiangxi precipitation in June by using the 1959-2014 precipitation data of 81 stations. It is found that the accumulated variance of the first four principal components reaches 76.6%, the high value area (the absolute value is larger than 0.5) of this four rotated principal components covers almost the whole Jiangxi province, and the high value areas of the first, the second, the third and the fourth rotated principal components are located in south part of northern Jiangxi, southern Jiangxi, central Jiangxi and north part of northern Jiangxi respectively, which meant that the June precipitation in Jiangxi can be divided to four subareas. With the SST and OLR data, study is undertaken on the relationship between the Jiangxi rainfall in June and SST as well as OLR. It turned out that Jiangxi rainfall in June is significant correlated to the previous (last September-May) SST and OLR at a significant level. The multiple regression method is used to establish the June precipitation forecasting models, the June precipitations for each subarea during 1980-2008 are simulated with these models, results show that the simulated values are in good agreement with those observed values, with the correlation coefficients being 0.55 (the first subarea), 0.43 (the second subarea), 0.58(the third subarea) and 0.54(the forth subarea) respectively, which means that these models have good abilities to simulate the June precipitation in Jiangxi for each subarea. The results of validity check of June precipitation during 2009-2013 for each subarea show that the simulated results of all the four subareas are consistent with the actual ones in four of these five years. Forecast test results also show that the simulated results in the first, the third and the forth areas are consistent with the fact in June 2014. This suggests that these models have a good ability on forecasting June precipitation.
