An improved method for retrieving nighttime aerosol optical thickness from the VIIRS Day/Night Band

Abstract

Abstract. Using Visible/Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) data, a method, dubbed the "variance method", is developed for retrieving nighttime aerosol optical thickness (τ) values through the examination of the dispersion of radiance values above an artificial light source. Based on the improvement of a previous algorithm, this updated method derives a semi-quantitative indicator of nighttime τ using artificial light sources. Nighttime τ retrievals from the newly developed method are inter-compared with an interpolated value from late afternoon and early morning ground observations from four AErosol RObotic NETwork (AERONET) sites as well as column-integrated τ from one High Spectral Resolution Lidar (HSRL) site at Huntsville, AL during the NASA Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) campaign, providing full diel coverage. Sensitivity studies are performed to examine the effects of lunar illumination on VIIRS τ retrievals made via the variance method, revealing that lunar contamination may have a smaller impact than previously thought, however the small sample size of this study limits the conclusiveness thus far. VIIRS τ retrievals yield a coefficient of determination (r2) of 0.60 and a root-mean-squared-error (RMSE) of 0.18 when compared against straddling daytime-averaged AERONET τ values. Preliminary results suggest that artificial light sources can be used for estimating regional and global nighttime aerosol distributions in the future.

Document Details

Document Type
Pub Defense Publication
Publication Date
May 20, 2015
Source ID
10.5194/amtd-8-5147-2015

Entities

People

  • Edward Hyer
  • J. S. Reid
  • Jianglong Zhang
  • R. E. Kuehn
  • Steven D. Miller
  • Theodore M. McHardy

Organizations

  • National Aeronautics and Space Administration
  • Office of Naval Research

Tags

Fields of Study

  • Environmental science

Readers

  • Atmospheric Remote Sensing.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Autonomy
  • Space