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Improvement of aerosol optical depth retrieval from MODIS spectral reflectance over the global ocean using new aerosol models archived from AERONET inversion data and tri-axial ellipsoidal dust database

Atmospheric Chemistry and Physics (2012) 12(15) 7087-7102
DOI: 10.5194/acp-12-7087-2012
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Abstract

New over-ocean aerosol models are developed by integrating the inversion data from the Aerosol Robotic Network (AERONET) sun/sky radiometers with a database for the optical properties of tri-axial ellipsoid particles. The new aerosol models allow more accurate retrieval of aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) in the case of high AOD (AOD>0.3). The aerosol models are categorized by using the fine-mode fraction (FMF) at 550 nm and the singlescattering albedo (SSA) at 440 nm from the AERONET inversion data to include a variety of aerosol types found around the globe. For each aerosol model, the changes in the aerosol optical properties (AOPs) as functions of AOD are considered to better represent aerosol characteristics. Comparisons of AODs between AERONET and MODIS for the period from 2003 to 2010 show that the use of the new aerosol models enhances the AOD accuracy with a Pearson coefficient of 0.93 and a regression slope of 0.99 compared to 0.92 and 0.85 calculated using the MODIS Collection 5 data. Moreover, the percentage of data within an expected error of ±(0.03 + 0.05×AOD) is increased from 62% to 64% for overall data and from 39% to 51% for AOD>0.3. Errors in the retrieved AOD are further characterized with respect to the Å ngström exponent (AE), scattering angle (2), SSA, and air mass factor (AMF). Due to more realistic AOPs assumptions, the new algorithm generally reduces systematic errors in the retrieved AODs compared with the current operational algorithm. In particular, the underestimation of fine-dominated AOD and the scattering angle dependence of dust-dominated AOD are significantly mitigated as results of the new algorithm's improved treatment of aerosol size distribution and dust particle nonsphericity. © Author(s) 2012. CC Attribution 3.0 License.

Figures

  • Fig. 1. Schematic flowchart of aerosol retrieval by the MODIS C005 algorithm (left column) and the test algorithm (right column). The test algorithm was designed to use the same observation data (“Mean Reflectance Ocean” in “MYD04” files) as the C005 algorithm to evaluate the effects of the new aerosol models only. The major difference between the two algorithms is the aerosol models used to calculate the LUT.
  • Fig. 2. Global distribution of AERONET sun/sky radiometers located in coastal areas (81 stations) used to archive aerosol optical properties for the test algorithm. The colors represent the number of inversion data points at each site. AERONET stations within 7 km from the ocean were chosen as coastal stations.
  • Fig. 3. The number of data points included in each FMF (550 nm) and SSA (440 nm) bin, archived from the AERONET inversion data over the globe (left) and coastal areas (right). The data were sorted into intervals of 0.05 and 0.01 for FMF and SSA, respectively. The AERONET stations in the coastal area are shown in Fig. 2.
  • Table 1. LUT dimensions for the MODIS over-ocean algorithm.
  • Fig. 4. TOA reflectance difference at 860 nm between MODIS aerosol models and new aerosol models with respect to AOD for a given geometry. SZA (θo) and SAZA (θs) are assumed to be 50◦ for both tests and RAA (φ) is assumed to be 90◦ (left) and 170◦ (right), resulting in scattering angle (2) of 114◦ and 172◦, respectively. Corresponding MODIS aerosol models to the new aerosol models are created by combining F2 (“Water Soluble”) and C8 (“Dust-like type”) aerosol models from Remer et al. (2006) for SSA< 0.95 and F4 (“Water Solublt with humidity”) and C8 for SSA> 0.95 using FMF values from new aerosol models. Negative values are shown in line-fill.
  • Table 2. Spectral SSA (upper) and asymmetry factor (lower) with respect to wavelength, Ångström exponent (AE), effective radius (reff), and sphericity of the new aerosol models used for the test algorithm. H models (0.85<SSA< 0.90) and M models (0.90<SSA< 0.95) cover FMF ranging from 0.2 to 1.0, while N models (SSA> 0.95) cover from 0.3 to 1.0. The minimum and maximum values are shown because of AOD dependence. The optical properties are interpolated to spectral response functions of MODIS bands in RTM calculations.
  • Fig. 5. Comparison between input variables (AOD, FMF, SSA) and retrieved variables from the present algorithm in LUT space. Tests are performed with synthesized data including maximum random error of 3 % (upper) and 10 % (lower). Mean and standard deviation values are shown for each calculation point for LUT.
  • Fig. 6. Comparison of AOD between AERONET and MODIS over the global ocean for the period from 2003 to 2010. The MODIS AODs are from the C005 algorithm (left) and the test algorithm (middle and right) with new aerosol models. Two different inversion procedures using standard deviation of spectral AOD (middle) and MODIS operational inversion (right) are applied for the test algorithm. The collocation criteria of ± 30 minutes in time and 25 km in space were used. The gray dots represent all data points, whereas black dots with onestandard deviation interval represent mean AODs in 20 equal-number-of-data bins with respect to the AERONET data. The solid line is from the regression equation, while the dotted and dashed lines are the one-to-one line and the MODIS expected error (EE) line showing ± (0.03 + 0.05×AOD), respectively. Only data points overlapping between the two algorithms are compared. Originally, the number of data points was 3106 for the C005 algorithm and 3578 for the test algorithm. The statistics shown are the Pearson coefficient (R), root mean squared error (RMSE), mean bias (MB), and the number of data points (N ).

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APA

Lee, J., Kim, J., Yang, P., & Hsu, N. C. (2012). Improvement of aerosol optical depth retrieval from MODIS spectral reflectance over the global ocean using new aerosol models archived from AERONET inversion data and tri-axial ellipsoidal dust database. Atmospheric Chemistry and Physics, 12(15), 7087–7102. https://doi.org/10.5194/acp-12-7087-2012

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