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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/amt-2019-26
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2019-26
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 22 Jan 2019

Research article | 22 Jan 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

Investigations into the Development of a Satellite-Based Aerosol Climate Data Record using ATSR-2, AATSR and AVHRR data

Yahui Che1,7, Jie Guang1, Gerrit de Leeuw2,4, Yong Xue1,3, Ling Sun5, and Huizheng Che6 Yahui Che et al.
  • 1Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences (RADI/CAS), Beijing 100094, China
  • 2Finnish Meteorological Institute, Climate Research Programme, P.O. Box 503, 00101 Helsinki, Finland
  • 3Department of Electronics, Computing and Mathematics, College of Engineering and Technology, University of Derby, Derby DE22 1GB, UK
  • 4School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China
  • 5Key Laboratory of Radiometric Calibration and Validation Environmental Satellites (LRCVES/CMA), National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China
  • 6State Key Laboratory of Severe Weather and Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China
  • 7University of Chinese Academy of Sciences, Beijing 100049, China

Abstract. Satellites provide information on the temporal and spatial distributions of aerosols on regional and global scales. With the same method applied to a single sensor all over the world, a consistent data set is to be expected. However, the application of different retrieval algorithms to the same sensor, and the use of a series of different sensors may lead to substantial differences and no single sensor or algorithm is better than any others everywhere and at any time. For the production of long-term climate data records, the use of multiple sensors cannot be avoided. The Along Track Scanning Radiometer (ATSR-2) and the advanced ATSR (AATSR) Aerosol Optical Depth (AOD) data sets have been used to provide a global AOD data record over land and ocean of 17-years (1995–2012), which is planned to be extended with AOD retrieved from a similar sensor, i.e. the Sea and Land Surface Temperature Radiometer (SLSTR) which flies on Sentinel-3A launched in early 2016. However, this leaves a gap of about 4 years between the end of the AATSR and the start of the SLSTR data records. To fill this gap, and to investigate the possibility to extend the ATSR data record to earlier years, the use of an AOD data set from the Advanced Very High Resolution Radiometer (AVHRR) is investigated. AOD data sets used in this study were retrieved from the ATSR sensors using the ATSR Dual View algorithm ADV v2.31 developed by Finnish Meteorological Institute (FMI), and from the AVHRR sensors using the ADL algorithm developed by RADI/CAR. Together these data sets cover a multi-decadal period (1983–2014). The study area includes two contrasting areas, both as regards aerosol content and composition and surface properties, i.e. a region over North-East (NE) China encompassing a highly populated urban/industrialized area (Beijing–Tianjin–Hebei) and a sparsely populated mountainous area.

Ground-based AOD observations available from ground-based sunphotometer AOD data in AERONET and CARSNET are used as reference, together with radiation-derived AOD data at Beijing to cover the time before sunphotometer observations became available in the early 2000s. In addition, MODIS-Terra C6.1 AOD data are used as reference data set over the wide area where no ground-based data are available. All satellite data over the study area were validated versus the reference data, showing the qualification of MODIS for comparison with ATSR and AVHRR. The comparison with MODIS shows that AVHRR performs better that ATSR in the north of the study area (40° N), whereas further south ATSR provides better results. The validation versus sunphotometer AOD shows that both AVHRR and ATSR underestimate the AOD, with ATSR failing to provide reliable results in the winter time. This is likely due to the highly reflecting surface in the dry season, when AVHRR-retrieved AOD traces both MODIS and reference AOD data well. However, AVHRR does not provide AOD larger than about 0.6 and hence is not reliable in the summer season when high AOD values have been observed over the last decade. In these cases, ATSR performs much better, for AOD up to about 1.3. AVHRR-retrieved AOD compares favourably with radiance-derived AOD, except for AOD higher than about 0.6. These comparisons lead to the conclusion that AVHRR and ATSR AOD data records each have their strengths and weaknesses which need to be accounted for when combining them in a single multi-decadal climate data record.

Yahui Che et al.
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Status: open (until 19 Mar 2019)
Status: open (until 19 Mar 2019)
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Yahui Che et al.
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Short summary
The use of AOD data retrieved from ATSR-2, AATSR and AVHRR to produce a very long time series is investigated. The study is made over a small area in northern China with a large variation of AOD values. Sun Photometer data from AERONET and CARSNET and radiance-derived AOD are used as reference. The results show that all data sets compare well. However, AVHRR underestimates high AOD (mainly occurring in summer) but performs better than (A)ATSR in winter.
The use of AOD data retrieved from ATSR-2, AATSR and AVHRR to produce a very long time series is...
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