| 利用MODIS/LST产品分析基准气候站环境代表性 |
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| 引用本文： | 王圆圆,李贵才,张艳.利用MODIS/LST产品分析基准气候站环境代表性[J].应用气象学报,2011,22(2):214-220. |
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| 作者姓名： | 王圆圆 李贵才 张艳 |
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| 作者单位： | 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 |
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| 基金项目： | 资助项目：公益性行业（气象）科研专项“国家级台站环境代表性分析技术研究”(GYHY20090604102)，中国气象局气候变化专项“气候变化对中国南部及东南亚森林碳汇影响的遥感评估”（CCSF0909） |
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| 摘 要： | 该文基于2001—2007年地表温度遥感反演产品 (MOD11A2)，以基准气候站对其周围不同大小窗口内地表温度距平序列的解释方差作为度量，评估了我国142个基准气候站的环境代表性，并将代表性与土地覆盖和高程状况进行相关研究。结果显示:以解释方差大于0.75作为区分是否具有代表性的阈值，约41%的站点代表性较好，代表区域范围可超过51×51 km2，多分布于北方地区；约21%的站点代表性较差，代表区域范围不足7×7 km2，多分布于南方地区；其他代表区域范围居中的站点在南、北方均有分布；站点周围的土地覆盖多样性和地形起伏度与站点代表性存在负相关，且相关性随窗口的增大而加强。文中还评估了基准气候站对所属气候区的代表性，发现在气候特征复杂的西南地区和新疆部分地区，站点对气候区的代表性较差。
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| 关 键 词： | 基准气候站 代表性 MODIS/LST产品 |
| 收稿时间： | 2010-06-29 |
Regional Representativeness Analysis of National Reference Climatological Stations Based on MODIS/LST Product |
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| Wang Yuanyuan,Li Guicai and Zhang Yan.Regional Representativeness Analysis of National Reference Climatological Stations Based on MODIS/LST Product[J].Quarterly Journal of Applied Meteorology,2011,22(2):214-220. |
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| Authors: | Wang Yuanyuan Li Guicai and Zhang Yan |
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| Affiliation: | Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center, China Meteorological Administration (LRCVES/CMA), Beijing 100081 |
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| Abstract: | Observations of reference climatological stations plays an important role in climate change research and disaster warning, which requires the stations to be representative enough for their surroundings. Furthermore, representativeness is important in optimizing meteorological observation network and selecting locations for new meteorological stations. However, researches on meteorological stations representativeness are still limited, especially from regional point of view.A new method to quantify reference climatological stations representativeness based on remote sensing data is proposed. The representativeness of a reference climatological station is measured using its explained variances for the LST (land surface temperature) anomaly series extracted from different sizes of windows centered on that station. MODIS/LST products (MOD11A2, 1 km spatial resolution) from 2001 to 2007 are used. MOD11A2 is 8-day average composite of MODIS daily LST product (MOD11_L2) which is produced using split-window algorithm. The product accuracy is better than 1 km according to previous validation, providing quality guarantee. The selected window size ranges from 3×3 km2 to 51×51 km2, with a step of 2 km. For each window size, explained variances of all the 142 national reference climatological stations are calculated. Through investigating the trend of explained variances with window size increments, a threshold is selected, according to which the maximum area a station can represent is determined. When 0.75 is set as the threshold, about 41% stations have good representativeness, representing areas larger than 51×51 km2 which are mainly located in the north regions. About 21% stations have low representativeness, representing areas less than 7×7 km2 that mainly located in the south regions. Other stations with moderate representativeness are found to distribute in both north and south regions. In order to explore the factors influencing representativeness, two indices are defined. One is land cover diversity based on Shannon-Weiner index formula and retrieved from MODIS land cover product. The other one is terrain undulation, which is defined as the difference between the maximum and minimum elevation and retrieved from DEM data. It is found that as far as all reference climatological stations are concerned, land cover diversity and terrain undulation are negatively correlated with representativeness, and when window size increases this correlation strengthens accordingly. Land cover diversity has greater impacts on representativeness than terrain undulation. Using land cover diversity and terrain undulation as independent variables, linear regressions can model representativeness pattern of most stations very well. For several stations whose representativeness cannot be explained well, fast urban expansion maybe an important factor, which needs further research. Finally, the representativeness for climate zone to which a station belongs is also studied. The results reveal that in areas featured with complicated climate, such as southwest region and part of Xinjiang, the representativeness of reference climatological stations are low, suggesting more meteorological stations are needed. |
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| Keywords: | reference climatological station representativeness MODIS/LST product |
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