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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/amtd-4-6643-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amtd-4-6643-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

Submitted as: research article 03 Nov 2011

Submitted as: research article | 03 Nov 2011

Review status
This discussion paper is a preprint. It has been under review for the journal Atmospheric Measurement Techniques (AMT). The revised manuscript was not accepted.

Long-term aerosol optical depth datasets over China retrieved from satellite data

Y. Xue2,1, H. Xu3,1, Y. Li3,1, L. Yang4,5, L. Mei3,1, J. Guang1, T. Hou3,6, X. He3,1, J. Dong3,1, Z. Chen3,6, and Y. Qi7 Y. Xue et al.
  • 1State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Inst. of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Inst. of Remote Sensing Applications, Chinese Academy of Sciences, Beijing, China
  • 2Faculty of Computing, London Metropolitan University, 166-220 Holloway Road, London N78DB, UK
  • 3Graduate University of Chinese Academy of Sciences, Beijing 100049, China
  • 4School of Geography, Beijing Normal University, Beijing 100875, China
  • 5School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China
  • 6Center for Earth Observation and Digital Earth of the Chinese Academy of Sciences, Beijing 100094, China
  • 7School of Economics and Management, Beijing Forestry University, Tianjin, China

Abstract. Nine years of daily aerosol optical depth (AOD) measurements have been derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data using the Synergetic Retrieval of Aerosol Properties (SRAP) method over China for the period from August 2002 to August 2011, comprising AODs at 470, 550, and 660 nm. Then, the variation over China over the nine years was determined from the derived AOD data. Preliminary daily results show the agreement between the Aerosol Robotic Network (AERONET) AOD data and the derived AOD data. From 1219 daily collocations, representing mutually cloud-free conditions, we find that more than 54% of SRAP-MODIS retrieved AOD values comparing with AERONET-observed values within an expected error envelop of 20%. From 222 monthly averaged collocations, representing mutually cloud-free conditions, we find that more than 63% of SRAP-MODIS retrieved AOD values comparing with AERONET-observed values within an expected error envelop of 15% and more than 70% within an expected error envelop of 20%. In addition, the long-term SRAP AOD dataset has been implemented in analysing case studies involving dust storms, haze and the characteristics of AOD variation over China over the past nine years. It was found that areas in China with high AOD values generally appear in the Inner Mongolia, the North China Plain, Tarim Basin, the Sichuan Basin, the Tibetan Plateau and the middle and lower reaches of the Yangtze River and area with low AOD values generally appear in the Fujian Province, the Yungui Plateau, and northeast plain. The seasonal averaged AOD results indicate that AOD values generally reach their maximum in spring and their minimum in winter. The yearly mean and monthly mean SRAP AOD were also used to study the spatial and temporal aerosol distributions over China. The results indicate that the AOD over China exhibited no obvious change. Monthly averaged AOD in August in Beijing experienced one decreasing processes from 2006 to 2010, especially after 2007. The monthly mean AOD decreased from 0.46 in 2007 to 0.29 in 2010.

SRAP AODs were used to study one haze case and dust case. Combining AOD data from the SRAP AOD dataset and HYSPLIT model can forecast the transport of haze. SRAP AOD data are also sensitive enough to reflect the occurrence and intensity of dust weather. Thus, the SRAP AOD dataset can be used to precisely reflect the spatial distribution, concentration distribution and transmission path of dust.

Y. Xue et al.
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Y. Xue et al.
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