Abstract
The retrieval of optimal aerosol datasets by the synergistic use of hyperspectral ultraviolet (UV)–visible and broadband meteorological imager (MI) techniques was investigated. The Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) was used as a proxy for hyperspectral UV–visible instrument data to which the Geostationary Environment Monitoring Spectrometer (GEMS) aerosol algorithm was applied. Moderate-Resolution Imaging Spectroradiometer (MODIS) L1B and dark target aerosol Level 2 (L2) data were used with a broadband MI to take advantage of the consistent time gap between the MODIS and the OMI. First, the use of cloud mask information from the MI infrared (IR) channel was tested for synergy. High-spatial-resolution and IR channels of the MI helped mask cirrus and sub-pixel cloud contamination of GEMS aerosol, as clearly seen in aerosol optical depth (AOD) validation with Aerosol Robotic Network (AERONET) data. Second, dust aerosols were distinguished in the GEMS aerosol-type classification algorithm by calculating the total dust confidence index (TDCI) from MODIS L1B IR channels. Statistical analysis indicates that the Probability of Correct Detection (POCD) between the forward and inversion aerosol dust models (DS) was increased from 72% to 94% by use of the TDCI for GEMS aerosol-type classification, and updated aerosol types were then applied to the GEMS algorithm. Use of the TDCI for DS type classification in the GEMS retrieval procedure gave improved single-scattering albedo (SSA) values for absorbing fine pollution particles (BC) and DS aerosols. Aerosol layer height (ALH) retrieved from GEMS was compared with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data, which provides high-resolution vertical aerosol profile information. The CALIOP ALH was calculated from total attenuated backscatter data at 1064 nm, which is identical to the definition of GEMS ALH. Application of the TDCI value reduced the median bias of GEMS ALH data slightly. The GEMS ALH bias approximates zero, especially for GEMS AOD values of >~0.4 and GEMS SSA values of <~0.95. Finally, the AOD products from the GEMS algorithm and MI were used in aerosol merging with the maximum-likelihood estimation method, based on a weighting factor derived from the standard deviation of the original AOD products. With the advantage of the UV–visible channel in retrieving aerosol properties over bright surfaces, the combined AOD products demonstrated better spatial data availability than the original AOD products, with comparable accuracy. Furthermore, pixel-level error analysis of GEMS AOD data indicates improvement through MI synergy.
Highlights
Atmospheric aerosol monitoring by active and passive satellite sensors has been undertaken globally since 1972 [1,2,3] due to the significant impact of aerosols on Earth’s climate system, both directly and indirectly [4]
Results of a Geostationary Environment Monitoring Satellite (GEMS) 443 nm aerosol optical depth (AOD) validation test with Ozone Monitoring Instrument (OMI) Level 1B (L1B) data, 2005–2007, are shown in Figure 4, with Aerosol Robotic Network (AERONET) Level 2 (L2) direct-sun AOD data being used as true references
Calculated root mean square error (RMSE) values for GEMS AOD simulated with OMI L1B data, and Moderate-Resolution Imaging Spectroradiometer (MODIS) dark target (DT) AOD data for the three years 2005–2007 are given in Table 5, which indicates that the RMSE values satisfactorily indicate characteristics of GEMS and MODIS AOD
Summary
Atmospheric aerosol monitoring by active and passive satellite sensors has been undertaken globally since 1972 [1,2,3] due to the significant impact of aerosols on Earth’s climate system, both directly and indirectly [4]. Due to the high temporospatial variability of atmospheric aerosols and their short residence times, there is a high demand for global aerosol monitoring using both low (LEO; see Appendix A for acronyms) and geostationary (GEO) Earth orbit satellite measurements. Requirements for atmospheric aerosol products include the following: Data intervals should be
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
