• Ensemble of ESA/AATSR aerosol optical depth products based on the likelihood estimate method with uncertainties

      Xie, Yanqing; Xue, Yong; Che, Yahui; Guang, Jie; Mei, Linlu; Voorhis, Dave; Fan, Cheng; She, Lu; Xu, Hui; University of Chinese Academy of Sciences; et al. (IEEE, 2017-10-20)
      Within the European Space Agency Climate Change Initiative (CCI) project Aerosol_cci, there are three aerosol optical depth (AOD) data sets of Advanced Along-Track Scanning Radiometer (AATSR) data. These are obtained using the ATSR-2/ATSR dual-view aerosol retrieval algorithm (ADV) by the Finnish Meteorological Institute, the Oxford-Rutherford Appleton Laboratory (RAL) Retrieval of Aerosol and Cloud (ORAC) algorithm by the University of Oxford/RAL, and the Swansea algorithm (SU) by the University of Swansea. The three AOD data sets vary widely. Each has unique characteristics: the spatial coverage of ORAC is greater, but the accuracy of ADV and SU is higher, so none is significantly better than the others, and each has shortcomings that limit the scope of its application. To address this, we propose a method for converging these three products to create a single data set with higher spatial coverage and better accuracy. The fusion algorithm consists of three parts: the first part is to remove the systematic errors; the second part is to calculate the uncertainty and fusion of data sets using the maximum likelihood estimate method; and the third part is to mask outliers with a threshold of 0.12. The ensemble AOD results show that the spatial coverage of fused data set after mask is 148%, 13%, and 181% higher than those of ADV, ORAC, and SU, respectively, and the root-mean-square error, mean absolute error, mean bias error, and relative mean bias are superior to those of the three original data sets. Thus, the accuracy and spatial coverage of the fused AOD data set masked with a threshold of 0.12 are improved compared to the original data set. Finally, we discuss the selection of mask thresholds.
    • Evaluation of the AVHRR DeepBlue aerosol optical depth dataset over mainland China.

      Che, Yahui; Xue, Yong; Guang, Jie; She, Lu; Guo, Jianping; University of Derby; Chinese Academy of Sciences; Chinese Academy of Meteorological Sciences; University of Chinese Academy of Sciences (Elsevier, 2018-12)
      Advanced Very High Resolution Radiometer (AVHRR) on-board NOAA series satellites have been used to observe the Earth’s surface and clouds for almost 40 years. Limited by bands and problematic instrument calibration, aerosol studies using AVHRR data have focused on retrieving data over the ocean. However, continuous developments have made it possible to retrieve aerosol over land as well. The newly developed AVHRR Deep Blue (DB) technique has been applied to process global aerosol datasets over both land and the ocean during 1989–1990, 1995–1999 and 2006–2011. This paper aims to evaluate, in detail, the performance of the AVHRR DB aerosol optical depth (AOD) dataset over mainland China by comparison with both ground-based data and satellite aerosol products. The ground-based validation results show that DB AOD is close to ground-based AOD when AOD is moderate during winter, while DB underestimates AOD when AOD increases over 1.0 during summer over vegetated surfaces. AVHRR DB underestimates dry, urban and transitional surfaces in Western China due to the high uncertainty in low retrievals over bright surfaces. Cross-comparison with the Moderate-resolution imaging spectrometer (MODIS) DB aerosol dataset shows that the disadvantages of the single longer visible channel are greatly increased over bright surfaces. Together with problematic instrument calibration, the differences between the two datasets over most of mainland China are significant. Meanwhile, the differences show strong seasonal variation characteristics.
    • Long-time series aerosol optical depth retrieval from AVHRR data over land in North China and Central Europe

      Xue, Yong; He, Xingwei; de Leeuw, Gerrit; Mei, Linlu; Che, Yahui; Rippin, Wayne; Guang, Jie; Hu, Yincui; University of Chinese Academy of Sciences; University of Derby; et al. (Elsevier, 2017-07-06)
      An algorithm for the retrieval of the aerosol optical depth over land (ADL) using radiances at the top of the atmosphere (TOA) measured by the Advanced Very High Resolution Radiometer (AVHRR) is proposed. AVHRR is the only satellite sensor providing nearly continuous global coverage since June 1979, which could generate the longest aerosol climate data records currently available from operational satellites. In the implementation of the ADL algorithm, an analytical model is used which couples an atmospheric radiative transfer model and a land surface reflectance parameterization. The radiation field can be separated into three parts: direct radiance, single-scattered radiance, and multiple-scattered. Each of these parts is individually parameterized. To obtain the surface reflectance in an automatic retrieval procedure over land for AVHRR, the aerosol scattering effect at 3.75 μm was assumed to be negligible and relationships between the surface reflectances at 0.64 μm and 3.75 μm were evaluated for different surface types and the authors propose to use these to obtain the surface reflectance at the shorter wavelength. The 0.64 μm surface reflectance was then used in a radiative transfer model to compute AOD at that wavelength using six different aerosol types, where optimal estimation (OE) theory is applied to minimize the difference between modeled and measured radiances. The ADL algorithm is applied to re-calibrated Level 1B radiances from the AVHRRs on-board the TIROS-N and the Metop-B satellites to retrieve the AOD over North China and Central Europe. The results show that the AOD retrieved from these two instruments are in agreement with co-located AOD values from ground-based reference networks. Over North China, using AERONET sites, 58% of the ADL AOD values are within an expected error (EE) range of ±(0.05 + 20%) and 53% are within the EE range of ±(0.05 + 15%). For GAW-PFR (World Meteorological Organization, WMO, Global Atmosphere Watch, GAW) sites, part of the European ACTRIS (Aerosols, Clouds, and Trace gases Research InfraStructure) sites, 79% of the ADL AOD values are within the EE range of ±(0.05 + 20%) and 75% are within the EE range of ±(0.05 + 15%). Not surprisingly, the agreement is better over Europe with generally lower AOD values. An additional cross comparison of the AOD results with MODIS (MODerate-resolution Imaging Spectroradiometer) DeepBlue aerosol products shows that the spatial distributions of the two AOD datasets are similar, but with generally lower values for ADL and lower coverage. The temporal variation of the annual mean AOD over selected AERONET sites shows that ADL values are generally between 0.2 and 0.5 over North-Eastern China and trace the MODIS and AERONET data for the overlapping years quite well.
    • Validation of aerosol products from AATSR and MERIS/AATSR synergy algorithms—Part 1: Global Evaluation.

      Che, Yahui; Mei, Linlu; Xue, Yong; Guang, Jie; She, Lu; Li, Ying; University of Derby; Chinese Academy of Sciences; University of Chinese Academy of Sciences; University of Bremen; et al. (MPDI, 2018-09-06)
      The European Space Agency’s (ESA’s) Aerosol Climate Change Initiative (CCI) project intends to exploit the robust, long-term, global aerosol optical thickness (AOT) dataset from Europe’s satellite observations. Newly released Swansea University (SU) aerosol products include AATSR retrieval and synergy between AATSR and MERIS with a spatial resolution of 10 km. In this study, both AATSR retrieval (SU/AATSR) and AATSR/MERIS synergy retrieval (SU/synergy) products are validated globally using Aerosol Robotic Network (AERONET) observations for March, June, September, and December 2008, as suggested by the Aerosol-CCI project. The analysis includes the impacts of cloud screening, surface parameterization, and aerosol type selections for two products under different surface and atmospheric conditions. The comparison between SU/AATSR and SU/synergy shows very accurate and consistent global patterns. The global evaluation using AERONET shows that the SU/AATSR product exhibits slightly better agreement with AERONET than the SU/synergy product. SU/synergy retrieval overestimates AOT for all surface and aerosol conditions. SU/AATSR data is much more stable and has better quality; it slightly underestimates fine-mode dominated and absorbing AOTs yet slightly overestimates coarse-mode dominated and non-absorbing AOTs.