Please wait a minute...
img

官方微信

遥感技术与应用  2014, Vol. 29 Issue (5): 839-845    DOI: 10.11873/j.issn.1004-0323.2014.5.0839
遥感应用     
基于时间序列的中亚地区云量特征分类及云量变化趋势
王志信1,2,林友明2,黄鹏2,贾秀鹏2
(1.中国科学院大学,北京100049;
2.中国科学院遥感与数字地球研究所,北京100094)
Cloud Amount Feature Classification based on Time Serious and Cloud Amount Change Trends in Central Asia
Wang Zhixin1,2,Lin Youming2,Huang Peng2,Jia Xiupeng2
(1.University of Chinese Academy of Sciences,Beijing 100049,China;
2.Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences,Beijing 100094,China)
 全文: PDF(1755 KB)  
摘要:

利用1984~2009年国际卫星云气候学计划(ISCCP)D2数据集的平均云量(MCA)资料,提取了中亚地区不同区域平均云量的时间序列特征,并利用平均云量时间序列平稳性、季节性等特征将中亚地区云量划分为3类:月平均云量时间序列季节性非平稳类型、月平均云量时间序列普通非平稳类型和月平均云量时间序列平稳类型。从分类结果看,中亚地区云量总体呈现非平稳特征。月平均云量时间序列季节非平稳类型云量主要集中在哈萨克斯坦和新疆地区,月平均云量时间序列普通非平稳类型云量主要分布在哈萨克斯坦北部和中部偏东一直延伸到新疆的条带地区,月平均云量时间序列平稳性类型云量主要分布在阿姆河流域。从平均云量结果看,中亚地区1984~2009年的总体平均云量为 64.69%,最高为1984年的66.50%,最低为1999年的62.38%,最高和最低平均云量之间相差4.12%。中亚地区平均云量变化趋势为先下降后上升,并在中间发生了一次震荡,但总体呈现出下降的趋势。

关键词: ISCCP时间序列云量分类    
Abstract:

Using the Mean Cloud Amount (MCA) data of 1984~2009 annual International Satellite Cloud Climatology Project (ISCCP) D2 data set,time series features of mean cloud amount in different regions of Central Asia are extracted by time series analysis method.Using the time series stationary features of mean cloud amount,cloud amount in Central Asia is divided into two categories:the monthly mean cloud amount time series stationary type and non\|stationary type,and then the non\|stationary type is divided into two categories:the monthly mean cloud amount time series ordinary non\|stationary type and seasonal non\|stationary type by time series seasonal features of mean cloud amount.At last,mean cloud amount in Central Asia is divided into three categories:the monthly mean cloud amount time series stationary type,namely,stationary type,the monthly mean cloud amount time series ordinary non\|stationary type,namely,ordinary non\|stationary type,and the monthly mean cloud amount time series seasonal non\|stationary type,namely,seasonal non\|stationary type.The classification results show that the seasonal non\|stationary type accounts for 62.37% of the study area,the ordinary non\|stationary type and stationary type respectively account for 31.18% and 6.45% of the study area respectly.The overall cloud amount in Central Asia shows non\|stationary characteristics.The seasonal non\|stationary type areas are mainly concentrated in Kazakhstan and Xinjiang region,the ordinary non\|stationary type areas are mainly in the northern and central east Kazakhstan extending to a strip region of Xinjiang and the stationary type areas are mainly in Amu Darya River Basin.The mean cloud amount results show that,the overall mean cloud amount in Central Asia is 64.69% from 1984 to 2009 with a maximum of 66.50% in 1984 and a minimum of 62.38% in 1999.The difference between maximum and minimum mean cloud amount is 4.12%.The mean cloud amount in Central Asia has a downward trend at first,and then has upward trend.There was a shock in the middle,but the overall shows a downward trend.

Key words: ISCCP    Time series    Cloud amount classification
收稿日期: 2013-07-22 出版日期: 2014-11-10
:  TP 79  
基金资助:

国家科技支撑计划课题(201207BAH27B05),国家863计划项目(2012AA12A301)。

作者简介: 王志信(1988-),男,河南林州人,硕士研究生,主要从事遥感数据获取和挖掘研究。Email:wangzhixin@ceode.ac.cn。
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
王志信
林友明
黄鹏
贾秀鹏

引用本文:

王志信,林友明,黄鹏,贾秀鹏. 基于时间序列的中亚地区云量特征分类及云量变化趋势[J]. 遥感技术与应用, 2014, 29(5): 839-845.

链接本文:

http://www.rsta.ac.cn/CN/Y2014/V29/I5/839

[1]Huhtala Y,Karkkainen J,Toivonen H.Mining for Similarities in Aligned Time Series Using Wavelets[J].Data Mining and Knowledge Discovery:Theory,Toolsand Technology,1999,3695(1999):150-163.

[2]Das G,Gunopulos D.Find Similar Time Series[J].Principles of Knowledge Discovery and Data Mining(PKDD),1997,1263(1997):88-100.

[3]Eamonn K,Shruti K.On the Need for Time Series Data Mining Benchmarks:A Survey and Empirical Demonstration[J].Data Mining and KnowledgeDiscovery,2003,7(4):349-371.

[4]Wang Jun,Kong Lingyi.The Application in Economics of Non linear Time Series Analysis and Smooth Transition Autoregression[J].The Journal ofQuantitative&Technical Economics,2006,(1):77-85.[王俊,孔令夷.非线性时间序列分析STAR 模型及其在经济学中的应用[J].数量经济技术经济研究,2006,(1):77-85.]

[5]Sai Xiaoyong,Zhang Zhiying,Xu Dezhong,et al.Application of Time Series Analysis in the Prediction of Schistosomiasis Prevalence in the Areas ofBreaking Dikes or Opening Sluice for Waterstoreo in Dongting Lake[J].Journal of the Fourth Military Medical University,2003,24(24):2297-2300.[赛晓勇,张治英,徐德忠,等.时间序列分析在洞庭湖区双退试点血吸虫病发病预测中的应用[J].第四军医大学学报,2003,24(24):2297-2300.]

[6]Chen Yonghang,Chen Yan,Huang Jianping,et al.Distribution and Variation Trend of Cloud over Northwestern China[J].Plateau Meteorology,2007,26(4):741-748.[陈勇航,陈艳,黄建平,等.中国西北地区云的分布及其变化趋势[J].高原气象,2007,26(4):741-748.]

[7]Bai Jie,Chen Xi,Li Junli,et al.Changes of Inland Lake Area in Arid Central Asia during 1975~2007:A Remote Sensing Analysis[J].Journal of LakeSciences,2011,23(1):80-88.[白洁,陈曦,李均力,等.1975~2007年中亚干旱区内陆湖泊面积变化遥感分析[J].湖泊科学,2011,23(1):80-88.]

[8]Sun Hongbo,Wang Ranghui,Yang Guishan.The Mountain-Oasis-Desert System and Characteristics of Climate in the Arid Zone of Center Asia——A Case Study inthe Northern of Xinjiang in China and the East of Kazakhstan[J].Journal of Arid Land Resources and Environment,2007,21(10):6-11.[孙洪波,王让会,杨桂山.中亚干旱区山地一绿洲一荒漠系统及其气候特征——以中国新疆北部和东哈萨克斯坦为例[J].干旱区资源与环境,2007,21(10):6-11.]

[9]Donald P W,Harold M W.The Diurnal Cycle of Upper-tropospheric Clouds Measured by GOES-VAS and the ISCCP[J].Monthly Weather Review,2002,130(10):171-179.

[10]Maslanik J A,Key J,Fowler C W.Spatial and Temporal Variability of Satellite Derived Cloud and Surface Characteristics During FIRE-ACE[J].Journal ofGeophysical Research Atmospheres,2001,30(1):15233-15249.

[11]Zhang Y,Rockel B,Stuhlmann R.REMO Cloud Modeling Improvements and Validation with ISCCP DX Data[J].Journal of Applied Meteorology,2001,40(3):389-408.[12]Gordon C T,Rosati A,Gudgel R.Tropical Sensitivity of a Coupled Model to Specified ISCCP Low Clouds[J].Journal of Climate,2000,13(13):2239-2260.

[13]Rossow W B,Delo C,Cairns B.Implications of the Observed Mesoscale Variations of Clouds for the Earths Radiation Budget[J].Journal ofClimate,2002,15(2002):557-585.[14]Rossow W B,Alison W W,Leonid C G.Comparison of ISCCP and other Cloud Amounts[J].Journal of Climate,1993,6(1996):2394-2418.

[15]Hatzianastassiou N,Cleridou N,Vardavas I.Polar Cloud Climatologies from ISCCP C2 and D2 Datasets[J].Journal of Climate,2001,14(2001):3851-3862.

[16]Rossow W B,Walker A W,Beuschel D E,et al.International Satellite Cloud Climatology Project (ISCCP) Documentation of New Cloud Datasets[EB/OL].http://isccp.giss.nasa.gov/docs/documents.html,2013-03,2013-06.

[17]Wang Yan.Application of Time Series Analysis[M].Beijing:China Renmin University Press,2012:212-223.[王燕.应用时间序列分析[M].北京:中国人民大学出版社,2012:212-223.]

[1] 汪航,师茁. 基于MODIS时间序列数据的春尺蠖虫害遥感监测方法研究—以新疆巴楚胡杨为例[J]. 遥感技术与应用, 2018, 33(4): 686-695.
[2] 周晓宇,陈富龙. 四川大熊猫栖息地PALSAR时序数据森林覆盖动态监测研究[J]. 遥感技术与应用, 2017, 32(6): 1100-1106.
[3] 姜涛,朱文泉,詹培,唐珂,崔雪锋,张天一. 一种抗时序数据噪声的冬小麦识别方法研究[J]. 遥感技术与应用, 2017, 32(4): 698-708.
[4] 李洛晞,沈润平,李鑫慧,郭佳. 基于MODIS时间序列森林扰动监测指数比较研究[J]. 遥感技术与应用, 2016, 31(6): 1083-1090.
[5] 冯莉,李柳华,郭松,卢荻. HJ-1A NDVI与MODIS NDVI时间序列提取植被物候特征对比研究[J]. 遥感技术与应用, 2016, 31(6): 1158-1166.
[6] 马海萍,冯建刚,窦喜英,李晓峰,张辉. 2016年门源M6.4地震前区域地壳形变特征[J]. 遥感技术与应用, 2016, 31(6): 1167-1173.
[7] 周惠慧,付东杰,张立福,王文生,岑奕,王晋年. 基于数字相机的草地物候模拟及其与气象因子关系的研究[J]. 遥感技术与应用, 2016, 31(5): 966-974.
[8] 晋锐. 中国长时间序列地表冻融状态数据集[J]. 遥感技术与应用, 2016, 31(4): 820-826.
[9] 赵永光,李传荣,马灵玲,唐伶俐,王宁. 一种遥感图像太阳—观测几何归一化方法[J]. 遥感技术与应用, 2016, 31(2): 260-266.
[10] 杜一男,李晓峰,赵凯,武黎黎,郑兴明,姜涛. NASA系列雪参数反演算法在单像元内的时间序列验证与分析[J]. 遥感技术与应用, 2016, 31(2): 332-341.
[11] 王颖洁,刘良云,王志慧. 基于时序Landsat数据的三江平原植被地表类型变化遥感探测研究[J]. 遥感技术与应用, 2015, 30(5): 959-968.
[12] 董淑英,晋锐,亢健,李大治. ASAR GM后散时间序列数据估算黑河上游地表土壤水分[J]. 遥感技术与应用, 2015, 30(4): 667-676.
[13] 刘亚南,肖飞,杜耘. Logistic函数方法拟合多时序NDVI数据的改进研究[J]. 遥感技术与应用, 2015, 30(4): 737-743.
[14] 贾远信,郭建文,刘丰. 基于时间序列相似性的自动观测数据时空异常探测方法研究[J]. 遥感技术与应用, 2015, 30(4): 700-705.
[15] 高应波,柳钦火,李静,杨乐. 基于时序植被指数特征时相识别的多熟制耕地提取新方法[J]. 遥感技术与应用, 2015, 30(3): 431-438.