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Regional Representativeness Analysis of National Reference Climatological Stations Based on MODIS/LST Product
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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
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. -
图 1 解释方差随窗口大小的变化
(所有基准气候站的平均状况)
Fig. 1 Change trend of the average explained variances with increasing window sizes
(averaged for all reference climatological stations)
图 2 基准气候站代表窗口分布示意图
Fig. 2 Illustration of reference climatological stations with different maximum representative window sizes
图 3 不同窗口下站点解释方差与窗口内土地覆盖多样性指数和地形起伏度的相关性
Fig. 3 Correlation coefficients of explained variance to land cover diversity and terrain undulation at different window sizes
图 4 土地覆盖和地形起伏度对站点代表性的影响分布图
Fig. 4 Distribution of different types of meteorological stations categorized based on the magnitude land cover diversity and terrain undulation influences
图 5 基准气候站对所在气候区LST距平序列的解释方差
Fig. 5 Explained variance of reference climatological stations for the LST anomaly series of their own climate zones
表 1 根据代表窗口大小对站点的分类统计
Table 1 Statistics and categorization of the meteorological stations based on the maximum representative window size
代表性 可代表最大窗口大小/km2 站点个数 所占比例/% 很好 ＞51×51 58 41 较好 31×31~49×49 16 11 一般 9×9~29×29 38 27 较差 3×3~7×7 30 21 
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表 2 代表窗口不超过7×7 km2的站点列表 (单位:km2)
Table 2 List of the meteorological stations with maximum representative window size no more than 7×7 km2 (unit:km2)
城市 代表窗口 玉山 7×7 榆社 7×7 石门 7×7 会理 7×7 汉中 7×7 恩施 7×7 酉阳 7×7 昆明 7×7 洪家 7×7 和田 7×7 增城 7×7 永安 7×7 汕头 7×7 海口 7×7 桂林 7×7 南雄 7×7 株洲 5×5 绵阳 5×5 吉首 5×5 腾冲 5×5 郑州 5×5 桐梓 5×5 三穗 5×5 贵阳 5×5 都安 5×5 电白 5×5 武冈 3×3 纳溪 3×3 林芝 3×3 元江 3×3
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[1] 张强, 阮新, 熊安元.近57年我国气温格点数据集的建立和质量评估.应用气象学报, 2009, 20(4):385-393. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20090401&flag=1 [2] 赵天保, 华丽娟.几种再分析地表气压资料在中国区域的适用性评估.应用气象学报, 2009, 20(1):70-79. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20090109&flag=1 [3] 邵全琴, 孙朝阳, 刘纪远, 等.中国城市扩展对气温观测的影响及其高估程度.地理学报, 2009, 64(11):1292-1302. doi: 10.3321/j.issn:0375-5444.2009.11.002 [4] McKitrick R R, Michaels P J. Quantifying the influence of anthropogenic surface processes and inhomogeneities on gridded global climate data. Journal of Geophysical Research (Atmospheres), 2007, 112, D24S09, doi: 10.1029/2007JD008465. [5] Jacobs J D. Spatial representativeness of climatic data from Baffin Island, N.W.T., with implications for muskoxen and caribou distribution. Arctic, 1989, 42 (1): 50-56. https://www.researchgate.net/publication/254963226_Spatial_Representativeness_of_Climatic_Data_from_Baffin_Island_NWT_with_Implications_for_Muskoxen_and_Caribou_Distribution [6] 王一姝, 高晓东.利用Google earth调查气象台站环境.气象与环境科学, 2009, 32(1):88-90. http://www.cnki.com.cn/Article/CJFDTOTAL-HNQX200901021.htm [7] Julien Y, Sobrino J A, Verhoef W. Changes in land surface temperatures and NDVI values over Europe between 1982 and 1999. Remote Sens Environ, 2006, 103: 43-55. doi: 10.1016/j.rse.2006.03.011 [8] Oku Y, Ishikawa H, Haginoya S, et al. Recent trends in land surface temperature on the Tibetan Plateau. J Climate, 2006, 19: 2995-3003. doi: 10.1175/JCLI3811.1 [9] Sandholt I, Rasmussen K, Andersen J. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sens Environ, 2002, 79: 213-224. doi: 10.1016/S0034-4257(01)00274-7 [10] 于敏, 程明虎.基于NDVI-Ts特征空间的黑龙江省干旱监测.应用气象学报, 2010, 21(2):221-228. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20100212&flag=1 [11] Stathopoulou M, Cartalis C. Downscaling AVHRR land surface temperatures for improved surface urban heat island intensity estimation. Remote Sens Environ, 2009, 113: 2592-2605. doi: 10.1016/j.rse.2009.07.017 [12] 申双和, 赵小艳, 杨沈斌, 等.利用ASTER数据分析南京城市地表温度分布.应用气象学报, 2009, 20(4):458-464. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=200904010&flag=1 [13] Wang K C, Li Z Q, Cribb M. Estimation of evaporative fraction from a combination of day and night land surface temperatures and NDVI: A new method to determine the Priestley-Taylor parameter. Remote Sens Environ, 2006, 102: 293-305. doi: 10.1016/j.rse.2006.02.007 [14] 吴晓, 陈维英.利用FY-1D极轨气象卫星分裂窗区通道计算陆表温度.应用气象学报, 2005, 16(1):45-53. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20050106&flag=1 [15] Wan Z, Zhang Y, Zhang Q, et al. Quality assessment and validation of the MODIS global land surface temperature. Int J Remote Sens, 2004, 25: 261-274. doi: 10.1080/0143116031000116417 [16] Wang K C, Liang S L. Evaluation of ASTER and MODIS land surface temperature and emissivity products using long-term surface longwave radiation observations at SURFARD sites. Remote Sens Environ, 2009, 113: 1556-1565. doi: 10.1016/j.rse.2009.03.009 [17] 《中华人民共和国气候图集》编委会.中华人民共和国气候图集.北京:气象出版社, 2002. [18] 赵卫权, 苏维词, 袁俊.基于地形起伏度的贵州省景观空间格局分异特征.水土保持研究, 2010, 17(2):105-110. http://www.cnki.com.cn/Article/CJFDTOTAL-STBY201002024.htm -
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