Validation of SOAR VIIRS Over‐Water Aerosol Retrievals and Context Within the Global Satellite Aerosol Data Record

This study validates aerosol properties retrieved using a Satellite Ocean Aerosol Retrieval (SOAR) algorithm applied to Visible Infrared Imaging Radiometer Suite (VIIRS) measurements, from Version 1 of the VIIRS Deep Blue data set. SOAR is the over‐water complement to the over‐land Deep Blue algorithm and has two processing paths: globally, 95% of pixels are processed with the full retrieval algorithm, while the 5% of pixels in shallow or turbid (mostly coastal) waters are processed with a backup algorithm. Aerosol Robotic Network (AERONET) data are used to validate and compare the midvisible (550 nm) aerosol optical depth (AOD), Ångström exponent (AE), and fine mode fraction of AOD at 550 nm (FMF). AOD uncertainty is shown to be approximately ±(0.03 + 10%) for the full and ±(0.03 + 15%) for the backup algorithms, with a small positive median bias around 0.02. When AOD is below about 0.2, the AE and FMF have small negative offsets from AERONET around −0.15 and −0.04, respectively. For higher AOD, AE is less offset and the magnitudes of differences versus AERONET are about ±0.2 and ±0.14, respectively. Aerosol‐type classifications provided by SOAR are found to be reasonable, matching optical‐based classifications from AERONET over 80% of the time. Spatial and temporal patterns of AOD and AE are also compared with those of other contemporary over‐water satellite aerosol data sets; dependent on region, the satellite data sets show varying levels of consistency, with SOAR broadly in‐family, and the largest discrepancies in regions with persistent heavy cloud cover.

[1]  T. Eck,et al.  Spectral discrimination of coarse and fine mode optical depth , 2003 .

[2]  Jianglong Zhang,et al.  Minimum aerosol layer detection sensitivities and their subsequent impacts on aerosol optical thickness retrievals in CALIPSO level 2 data products. , 2017, Atmospheric measurement techniques.

[3]  Alexander Smirnov,et al.  Multiangle Imaging SpectroRadiometer global aerosol product assessment by comparison with the Aerosol Robotic Network , 2010 .

[4]  Yoram J. Kaufman,et al.  An Emerging Global Aerosol Climatology from the MODIS Satellite Sensors , 2008 .

[5]  J. Randerson,et al.  Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997-2009) , 2010 .

[6]  Jin Huang,et al.  Enhanced Deep Blue aerosol retrieval algorithm: The second generation , 2013 .

[7]  Lorraine Remer,et al.  A critical examination of the residual cloud contamination and diurnal sampling effects on MODIS estimates of aerosol over ocean , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Mark A. Vaughan,et al.  The Retrieval of Profiles of Particulate Extinction from Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) Data: Algorithm Description , 2009 .

[9]  Barbara J. Gaitley,et al.  An analysis of global aerosol type as retrieved by MISR , 2015 .

[10]  Michael Schulz,et al.  Will a perfect model agree with perfect observations? The impact of spatial sampling , 2016 .

[11]  C. Zerefos,et al.  Interannual variability of cirrus clouds in the tropics in El Niño Southern Oscillation (ENSO) regions based on International Satellite Cloud Climatology Project (ISCCP) satellite data , 2011 .

[12]  T. Eck,et al.  Modified angström exponent for the characterization of submicrometer aerosols. , 2001, Applied optics.

[13]  Alexander Smirnov,et al.  Cloud-Screening and Quality Control Algorithms for the AERONET Database , 2000 .

[14]  Michael D. King,et al.  A flexible inversion algorithm for retrieval of aerosol optical properties from Sun and sky radiance measurements , 2000 .

[15]  D. Winker,et al.  Seasonally transported aerosol layers over southeast Atlantic are closer to underlying clouds than previously reported , 2017, Geophysical research letters.

[16]  J. Kar,et al.  Evaluation of CALIOP 532 nm aerosol optical depth over opaque water clouds , 2015 .

[17]  Alexander Smirnov,et al.  SeaWiFS Ocean Aerosol Retrieval (SOAR): Algorithm, validation, and comparison with other data sets , 2012 .

[18]  Oleg Dubovik,et al.  Angstrom exponent and bimodal aerosol size distributions , 2006 .

[19]  Anne Garnier,et al.  Extinction and optical depth retrievals for CALIPSO's Version 4 data release , 2018, Atmospheric Measurement Techniques.

[20]  Clive D Rodgers,et al.  Inverse Methods for Atmospheric Sounding: Theory and Practice , 2000 .

[21]  Michael J. Garay,et al.  New approach to the retrieval of AOD and its uncertainty from MISR observations over dark water , 2017 .

[22]  Zhanqing Li,et al.  Quality, compatibility, and synergy analyses of global aerosol products derived from the advanced very high resolution radiometer and Total Ozone Mapping Spectrometer , 2005, Journal of Geophysical Research.

[23]  N. C. Hsu,et al.  Evaluation of NASA Deep Blue/SOAR aerosol retrieval algorithms applied to AVHRR measurements , 2017, Journal of geophysical research. Atmospheres : JGR.

[24]  David M. Winker,et al.  Investigating enhanced Aqua MODIS aerosol optical depth retrievals over the mid‐to‐high latitude Southern Oceans through intercomparison with co‐located CALIOP, MAN, and AERONET data sets , 2013 .

[25]  Ana Maria Silva,et al.  Some considerations about Ångström exponent distributions , 2007 .

[26]  Jean-François Léon,et al.  Application of spheroid models to account for aerosol particle nonsphericity in remote sensing of desert dust , 2006 .

[27]  S. Stehman,et al.  Accuracy Assessment , 2003 .

[28]  Alexander Smirnov,et al.  A Pure Marine Aerosol Model, for Use in Remote Sensing Applications , 2012 .

[29]  N. C. Hsu,et al.  AERONET‐Based Nonspherical Dust Optical Models and Effects on the VIIRS Deep Blue/SOAR Over Water Aerosol Product , 2017, Journal of geophysical research. Atmospheres : JGR.

[30]  Soo Chin Liew,et al.  Observing and understanding the Southeast Asian aerosol system by remote sensing: An initial review and analysis for the Seven Southeast Asian Studies (7SEAS) program , 2013 .

[31]  Yong Xue,et al.  Development, Production and Evaluation of Aerosol Climate Data Records from European Satellite Observations (Aerosol_cci) , 2016, Remote. Sens..

[32]  Andrew M. Sayer,et al.  Validation and uncertainty estimates for MODIS Collection 6 “Deep Blue” aerosol data , 2013 .

[33]  Yujie Wang,et al.  Multiangle implementation of atmospheric correction (MAIAC): 2. Aerosol algorithm , 2011 .

[34]  A. Smirnov,et al.  AERONET-a federated instrument network and data archive for aerosol Characterization , 1998 .

[35]  Menghua Wang,et al.  Uncertainties in satellite remote sensing of aerosols and impact on monitoring its long-term trend: a review and perspective , 2009 .

[36]  B. Holben,et al.  An Accuracy Assessment of the CALIOP/CALIPSO Version 2/Version 3 Daytime Aerosol Extinction Product Based on a Detailed Multi-Sensor, Multi-Platform Case Study , 2011 .

[37]  T. Eck,et al.  Characterizing the 2015 Indonesia Fire Event Using Modified MODIS Aerosol Retrievals , 2018 .

[38]  Roy G. Grainger,et al.  A sea surface reflectance model for (A)ATSR, and application to aerosol retrievals , 2010 .

[39]  M. Chin,et al.  Biomass burning aerosol transport and vertical distribution over the South African‐Atlantic region , 2017 .

[40]  T. Eck,et al.  Accuracy assessments of aerosol optical properties retrieved from Aerosol Robotic Network (AERONET) Sun and sky radiance measurements , 2000 .

[41]  F. D’Ortenzio,et al.  The colour of the Mediterranean Sea: Global versus regional bio-optical algorithms evaluation and implication for satellite chlorophyll estimates , 2007 .

[42]  Didier Tanré,et al.  Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations , 2010 .

[43]  A. Nenes,et al.  Effects of Ocean Ecosystem on Marine Aerosol-Cloud Interaction , 2010 .

[44]  Bernard Pinty,et al.  Techniques for the retrieval of aerosol properties over land and ocean using multiangle imaging , 1998, IEEE Trans. Geosci. Remote. Sens..

[45]  Joseph M. Prospero,et al.  Characterizing the annual cycle of African dust transport to the Caribbean Basin and South America and its impact on the environment and air quality , 2014 .

[46]  Alexei Lyapustin,et al.  Earth Observations from DSCOVR/EPIC Instrument. , 2018, Bulletin of the American Meteorological Society.

[47]  B. Martinsson,et al.  Volcanic impact on the climate – the stratospheric aerosol load in the period 2006–2015 , 2018, Atmospheric Chemistry and Physics.

[48]  Lorraine A. Remer,et al.  Suomi‐NPP VIIRS aerosol algorithms and data products , 2013 .

[49]  Andrew K. Heidinger,et al.  A global survey of the effect of cloud contamination on the aerosol optical thickness and its long‐term trend derived from operational AVHRR satellite observations , 2013 .

[50]  T. Marbach,et al.  The 3MI mission: multi-viewing-channel-polarisation imager of the EUMETSAT polar system: second generation (EPS-SG) dedicated to aerosol and cloud monitoring , 2015, SPIE Optical Engineering + Applications.

[51]  Xavier Briottet,et al.  Results of POLDER in-flight calibration , 1999, IEEE Trans. Geosci. Remote. Sens..

[52]  Steffen Beirle,et al.  A global aerosol classification algorithm incorporating multiple satellite data sets of aerosol and trace gas abundances , 2015 .

[53]  S. Piketh,et al.  A seasonal trend of single scattering albedo in southern African biomass‐burning particles: Implications for satellite products and estimates of emissions for the world's largest biomass‐burning source , 2013 .

[54]  Yujie Wang,et al.  Exploring systematic offsets between aerosol products from the two MODIS sensors. , 2018, Atmospheric measurement techniques.

[55]  Robert E. Holz,et al.  Towards a long-term global aerosol optical depth record: applying a consistent aerosol retrieval algorithm to MODIS and VIIRS-observed reflectance , 2015 .

[56]  Yujie Wang,et al.  Exploring systematic offsets between aerosol products from the two MODIS sensors. , 2018, Atmospheric measurement techniques.

[57]  M. Schulz,et al.  On the spatio-temporal representativeness of observations , 2017 .

[58]  A. J. Miller,et al.  Factors affecting the detection of trends: Statistical considerations and applications to environmental data , 1998 .

[59]  Sara Basart,et al.  Status and future of numerical atmospheric aerosol prediction with a focus on data requirements , 2018, Atmospheric Chemistry and Physics.

[60]  A. Kokhanovsky,et al.  Satellite Aerosol Remote Sensing Over Land , 2009 .

[61]  D. Tanré,et al.  Remote sensing of aerosol properties over oceans using the MODIS/EOS spectral radiances , 1997 .

[62]  T. Eck,et al.  AERONET-based models of smoke-dominated aerosol near source regions and transported over oceans, and implications for satellite retrievals of aerosol optical depth , 2014 .

[63]  David M. Winker,et al.  The CALIPSO Version 4 Automated Aerosol Classification and Lidar Ratio Selection Algorithm. , 2018, Atmospheric measurement techniques.

[64]  Oleg Dubovik,et al.  GRASP: a versatile algorithm for characterizing the atmosphere , 2014 .

[65]  N. C. Hsu,et al.  Satellite Ocean Aerosol Retrieval (SOAR) Algorithm Extension to S‐NPP VIIRS as Part of the “Deep Blue” Aerosol Project , 2018, Journal of geophysical research. Atmospheres : JGR.

[66]  M. Chin,et al.  Online simulations of global aerosol distributions in the NASA GEOS‐4 model and comparisons to satellite and ground‐based aerosol optical depth , 2010 .

[67]  D. Winker,et al.  The CALIPSO Automated Aerosol Classification and Lidar Ratio Selection Algorithm , 2009 .

[68]  Zhaoyan Liu,et al.  CALIPSO Lidar Calibration at 532 nm: Version 4 Nighttime Algorithm. , 2018, Atmospheric measurement techniques.

[69]  Edith Rodriguez,et al.  Collocation mismatch uncertainties in satellite aerosol retrieval validation , 2017 .

[70]  Yan Yu,et al.  How Long should the MISR Record Be when Evaluating Aerosol Optical Depth Climatology in Climate Models? , 2018, Remote. Sens..

[71]  Otto P. Hasekamp,et al.  Retrieval of aerosol properties over the ocean from multispectral single‐viewing‐angle measurements of intensity and polarization: Retrieval approach, information content, and sensitivity study , 2005 .

[72]  T. Eck,et al.  Wavelength dependence of the optical depth of biomass burning, urban, and desert dust aerosols , 1999 .

[73]  T. Eck,et al.  A review of biomass burning emissions part III: intensive optical properties of biomass burning particles , 2004 .

[74]  Johannes Quaas,et al.  Estimates of aerosol radiative forcing from the MACC re-analysis , 2012 .

[75]  E. Fetzer,et al.  Indonesian fire activity and smoke pollution in 2015 show persistent nonlinear sensitivity to El Niño-induced drought , 2016, Proceedings of the National Academy of Sciences.

[76]  Brent N. Holben,et al.  Retrieving near‐global aerosol loading over land and ocean from AVHRR , 2017 .

[77]  T. Eck,et al.  Comparison of Moderate Resolution Imaging Spectroradiometer (MODIS) and Aerosol Robotic Network (AERONET) remote-sensing retrievals of aerosol fine mode fraction over ocean , 2005 .

[78]  David M. Winker,et al.  CALIPSO lidar level 3 aerosol profile product: version 3 algorithm design , 2018, Atmospheric measurement techniques.

[79]  G. Mann,et al.  Large contribution of natural aerosols to uncertainty in indirect forcing , 2013, Nature.

[80]  Brent N. Holben,et al.  An analysis of the collection 5 MODIS over-ocean aerosol optical depth product for its implication in aerosol assimilation , 2010 .

[81]  Roy G. Grainger,et al.  Some implications of sampling choices on comparisons between satellite and model aerosol optical depth fields , 2010 .

[82]  Matthew S. Johnson,et al.  A multiparameter aerosol classification method and its application to retrievals from spaceborne polarimetry , 2014 .

[83]  Ralph A. Kahn,et al.  Detecting Thin Cirrus in Multiangle Imaging Spectroradiometer Aerosol Retrievals , 2010 .

[84]  Amit Angal,et al.  Terra and Aqua moderate-resolution imaging spectroradiometer collection 6 level 1B algorithm , 2013 .

[85]  M. Deeter,et al.  Satellite-observed pollution from Southern Hemisphere biomass burning. , 2006 .

[86]  Robin J. Leatherbarrow,et al.  On to the second generation , 1990, Nature.

[87]  Alexander Smirnov,et al.  Maritime Aerosol Network as a component of Aerosol Robotic Network , 2009 .

[88]  G. McFarquhar,et al.  Thin and Subvisual Tropopause Tropical Cirrus: Observations and Radiative Impacts , 2000 .

[89]  Lawrence E. Flynn,et al.  How long do satellites need to overlap? Evaluation of climate data stability from overlapping satellite records , 2017 .

[90]  Seasonally Transported Aerosol Layers over Southeast Atlantic , 2017 .

[91]  L. Remer,et al.  The Collection 6 MODIS aerosol products over land and ocean , 2013 .

[92]  Didier Tanré,et al.  Aerosol Remote Sensing , 2013 .

[93]  Robert C. Levy,et al.  Correcting for trace gas absorption when retrieving aerosol optical depth from satellite observations of reflected shortwave radiation , 2018, Atmospheric Measurement Techniques.

[94]  D. Winker,et al.  Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms , 2009 .

[95]  N. C. Hsu,et al.  Cross-calibration of S-NPP VIIRS moderate resolution reflective solar bands against MODIS Aqua over dark water scenes. , 2017, Atmospheric measurement techniques.

[96]  B. Holben,et al.  A spatio‐temporal approach for global validation and analysis of MODIS aerosol products , 2002 .

[97]  Jeffrey S. Reid,et al.  MODIS aerosol product analysis for data assimilation: Assessment of over‐ocean level 2 aerosol optical thickness retrievals , 2006 .

[98]  B. Holben,et al.  Estimating Marine Aerosol Particle Volume and Number from Maritime Aerosol Network Data , 2012 .

[99]  N. C. Hsu,et al.  Retrieval of aerosol optical depth under thin cirrus from MODIS: Application to an ocean algorithm , 2013 .