###
气象:2014,40(3):373-380
本文二维码信息
码上扫一扫!
利用遥感估算区域气温评价站点代表性——以藏东南林芝站点为例
(1 中国气象局中国遥感卫星辐射测量和定标重点开放实验室国家卫星气象中心,北京 100081;2 中国气象局成都高原气象研究所,成都 610071)
Representativeness Analysis of Meteorological Stations Based on Temperature Estimated from MODIS Data
(1 Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration (LRCVES/CMA), Beijing 100081;2 Institute of Plateau Meteorology, China Meteorological Administration, Chengdu 610071)
摘要
图/表
参考文献
相似文献
本文已被:浏览 949次   下载 699
投稿时间:2012-07-17    修订日期:2013-09-16
中文摘要: 遥感地表温度(Land Surface Temperature,LST)产品时序数据可用于分析基准气候站点的环境代表性,但LST空间变异性明显高于气温,容易低估站点代表性,因此提出首先建立面上气温的遥感估算模型,再用模型估算气温(和LST产品具有相同时空分辨率)评估站点代表性的方案。以藏东南林芝国家基准气候站点为例,首先提取气象站点2000—2011年8 d平均日最高气温(T air)和MODIS地表温度产品(MOD11A2,8 d合成,1 km空间分辨率),分析LST与T air之间的相关性以及其他因素对相关性的影响;而后利用Cubist回归树算法,建立了基于LST、日序、晴空日数的T air估算模型(RMSE=1.4718℃,r2 =0.95);最终将模型用于林芝站周围附近区域计算T air,并对林芝站点代表性加以评估,结果表明,新方案得到的结果更加可信,林芝站点代表范围为15 km×15 km,而基于LST计算得到的林芝站代表范围仅为3 km×3 km。
Abstract:Long time series Land Surface Temperature (LST) products can be used to quantify representativeness of meteorological stations. However, spatial heterogeneity of LST is dramatically higher than that of air temperature, which is prone to an under estimation of the representativeness. To obtain more accurate results, this paper proposes a new procedure in which spatially continuous air temperature is first estimated from LST and other parameters, and then used for estimating representativeness of meteorological stations. A case study focusing on Linzhi Meteorological Station located in southeastern Tibetan Plateau of China is presented. First, 8 d averaged maximum temperature (T air) and the corresponding MODIS LST product (MOD11A2,8 d composited with 1 km spatial resolution) of meteorological stations during 2000-2011 are extracted. The correlation coefficients between T air and LST are analyzed under different conditions. Then, a Cubist regression tree model for T air estimation is developed using LST, Julian day, and the number of clear skies as predictors (RMSE=1.4718℃, r2=0.95). Finally, the model is applied to the region around Linzhi Station to estimate T air and representativeness. As a result, it is found that the new procedure can produce more reasonable results: 15 km×15 km can be well represented by Linzhi Station, rather than 3 km×3 km when LTS data was used.
文章编号:     中图分类号:    文献标志码:
基金项目:公益性行业(气象)科研专项(GYHY200906041 02和GYHY201206095)和国家自然科学基金项目(41301461)共同资助
引用文本:
王圆圆,李贵才,闵文彬,张艳,2014.利用遥感估算区域气温评价站点代表性——以藏东南林芝站点为例[J].气象,40(3):373-380.
WANG Yuanyuan,LI Guicai,MIN Wenbin,ZHANG Yan,2014.Representativeness Analysis of Meteorological Stations Based on Temperature Estimated from MODIS Data[J].Meteor Mon,40(3):373-380.