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  • 软件名称:Himawari-8气溶胶产品的验证及应用
  • 软件大小: 0.00 B
  • 软件评级: ★★★
  • 开 发 商: 韦海宁,王维真,徐菲楠,冯姣姣
  • 软件来源: 《遥感技术与应用》
  • 解压密码:www.gissky.net

资源简介

摘要: 为准确分析中国地区气溶胶空间分布与时间变化特征规律,首先利用中国地区9个AERONET(Aerosol Robotic Network)地基站点观测资料对新一代静止气象卫星Himawari-8气溶胶光学厚度(Aerosol Optical Depth, AOD)产品数据进行一致性验证,并在此基础上选取2015年7月至2018年4月Himawari-8逐小时AOD数据分析了中国地区气溶胶光学厚度时空变化特征。结果表明:①Himawari-8 AOD与AERONET AOD之间相关性很高,9个站点的相关系数R在0.64 ~ 0.91之间,拟合曲线斜率k的范围为0.57 ~ 0.68。②Himawari AOD产品与AERONET AOD的相关性在中午时段较其他时段相对较低;北方地区Himawari-8 AOD冬季反演效果与夏季相比较差,南方地区则相反。③中国地区年平均AOD呈东高西低分布,春、夏两季AOD明显高于秋、冬两季,其中夏季最高,春季次之;地区间AOD月变化差异也较大;大部分地区AOD日变化呈现先下降后上升再下降的趋势,AOD最高值出现在午后14 ~ 16时,最低值出现在18时。研究结果为了解中国地区大气气溶胶的时空变化规律和全天时的大气污染监测方法提供新的参考。 关键词: 气溶胶光学厚度;  Himawari-8;  AERONET;  静止气象卫星;  验证     Abstract: To accurately analyze the spatial distribution and temporal variation of the aerosol in China, firstly, the Himawari-8 Level 3 Aerosol Optical Depth (AOD) products were validated by the Level 1.5 AERONET (Aerosol Robotic Network) sunphotometer measurements at 9 observation sites all over the China. Then, the hourly Himawari-8 AOD products from July 2015 to April 2018 were selected for further analyzing the spatial and temporal variation of AOD in China. The result shows that: (1) The Himawari-8 AOD agreed well with those from the AERONET, with a slope of 0.57~0.68 and a high correlation coefficient R ranging from 0.64~0.91. (2) The correlation between Himawari AOD and AERONET AOD is relatively low at noon compared to other time periods; In winter, the AOD estimates from Himawari 8 in the northern regions of China is relative worse than that in summer, but in the southern regions. (3) The values of annual-averaged AOD in China are highest in the eastern regions and lowest in the western regions; the AOD in spring and summer is obviously higher than that in autumn and winter, with the highest in summer and the second in spring. Moreover, the difference in the variation of monthly-averaged AOD between different regions of China was also significant; in most regions, the diurnal variation of daily-averaged AOD showed a trend of decreasing first, then rising and then decreasing. Besides, the highest value of daily AOD appeared at 14~16 in the afternoon, and the lowest value occurred at 18 o’clock. The results from this study can not only provide a new reference for understanding the spatial and temporal variation of atmospheric aerosols in China, but for the air pollution monitoring methods throughout the day.

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