MISR research-aerosol-algorithm refinements for dark water retrievals

Abstract

Abstract. We explore systematically the cumulative effect of many assumptions made in the Multi-angle Imaging SpectroRadiometer (MISR) research aerosol retrieval algorithm with the aim of quantifying the main sources of uncertainty over ocean, and correcting them to the extent possible. A total of 1129 coincident, surface-based sun photometer spectral aerosol optical depth (AOD) measurements are used for validation. Based on comparisons between these data and our baseline case (similar to the MISR standard algorithm, but without the "modified linear mixing" approximation), for 558 nm AOD < 0.10, a high bias of 0.024 is reduced by about one-third when (1) ocean surface under-light is included and the assumed whitecap reflectance at 672 nm is increased, (2) physically based adjustments in particle microphysical properties and mixtures are made, (3) an adaptive pixel selection method is used, (4) spectral reflectance uncertainty is estimated from vicarious calibration, and (5) minor radiometric calibration changes are made for the 672 and 866 nm channels. Applying (6) more stringent cloud screening (setting the maximum fraction not-clear to 0.50) brings all median spectral biases to about 0.01. When all adjustments except more stringent cloud screening are applied, and a modified acceptance criterion is used, the Root-Mean-Square-Error (RMSE) decreases for all wavelengths by 8-27 % for the research algorithm relative to the baseline, and is 12-36 % lower than the RMSE for the Version 22 MISR standard algorithm (SA, with no adjustments applied). At 558 nm, 87 % of AOD data falls within the greater of 0.05 or 20 % of validation values; 62 % of the 446 nm AOD data, and > 68 % of 558, 672, and 866 nm AOD values fall within the greater of 0.03 or 10 %. For the Ångström exponent (ANG), 67 % of 1119 validation cases for AOD > 0.01 fall within 0.275 of the sun photometer values, compared to 49 % for the SA. ANG RMSE decreases by 17 % compared to the SA, and the median absolute error drops by 36 %.

New approach to the retrieval of AOD and its uncertainty from MISR observations over dark water

“…Recently, Kahn and Gaitley (2015) provided a thorough verification of MISR's aerosol type retrieval capabilities and Lee et al (2016) demonstrated that the MISR aerosol particle climatologies regionally showed good agreement with the results of chemical transport models. The current 74 mixture set, however, is not complete and has some documented deficiencies Kalashnikova et al, 2013;Limbacher and Kahn, 2014). A more comprehensive set of aerosol types and mixtures will be considered in a future release of the MISR's aerosol algorithm.…”

“…The most frequently used metric that quantitatively characterizes the quality of a particular AOD dataset as a whole is the error envelope (EE) (Bréon et al, 2011;Garay et al, 2017;Kahn et al, 2010;Levy et al, 2010Levy et al, , 2013Limbacher and Kahn, 2014;Omar et al, 2013;Remer et al, 2008;Sayer et al, 2012Sayer et al, , 2013Witek et al, 2013). EE results from a validation study where a particular satellite AOD dataset is compared against another AOD dataset, typically ground-based information from the Aerosol Robotic Network (AERONET) (Dubovik et al, 2000;Holben et al, 1998), which is considered to represent the "truth".…”

A theoretical study of the effect of subsurface oceanic bubbles on the enhanced aerosol optical depth band over the southern oceans as detected from MODIS and MISR

“…For practical applications, the uncertainties in whitecap estimates (fractional coverage and spectral reflectance, e.g., Frouin et al, 1996;Anguelova et al, 2006) need to be fully considered and incorporated into the analysis. The theoretical derivations used to estimate wind speed effects on surface reflectance for MODIS and MISR satellite aerosol retrievals explicitly include whitecaps and bubble rafts but not subsurface bubbles (Levy et al, 2013;Limbacher and Kahn, 2014). For future applications that require accurate estimations of atmospheric aerosol concentrations from satellite observations, oceanic bubble concentration is a factor that needs to be taken into consideration for ocean regions with strong near-surface winds.…”

“…Some Level 3 products (e.g., Zhang and Reid, 2008;Shi et al, 2011a) include an empirical correction for wind-speedrelated bias to retrieved AOD. Some Level 2 satellite retrievals (e.g., Sayer et al, 2010Sayer et al, , 2012Jackson et al, 2013;Levy et al, 2013;Limbacher and Kahn, 2014) also incorporate wind speed data into the radiative transfer calculations using parameterizations of wind effects on whitecaps and bubble rafts. The current study uses a unique combination of data sets to further investigate the mechanics of the ocean lower boundary condition.…”

Updated MISR dark water research aerosol retrieval algorithm – Part 1: Coupled 1.1 km ocean surface chlorophyll &lt;i&gt;a&lt;/i&gt; retrievals with empirical calibration corrections

“…Prior to Limbacher and Kahn (2014), the MISR RA simulated ocean surface was modeled as an isotropic (wind-speed-dependent only) Fresnel reflector (Cox-Munk), with whitecap reflectance included. In Limbacher and Kahn (2014), we made adjustments to the whitecap reflectance and added an ocean under-light term that includes molecular and particulate attenuation.…”