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Geoinformatics PhD student Meng Zhou 1st Place in Oral Presentation at AMS

Meng Zhou presenting at an earlier in-person poster session

PhD student Meng Zhou -- Interdisciplinary Graduate Program in Informatics (IGPI) | Geoinformatics -- advised by Jun Wang was selected as a First Place Winner 
     in the Oral Presentation Category of the Joint Conference [11R2O and 17OESS] Student Competition
     at the 2021 American Meteorological Society (AMS) 101st Annual Meeting (Virtual).

The winning presentation’s title was "Retrieval of Nighttime Aerosol Optical Depth Using Moonlight Measurement of VIIRS DNB"

Zhou is a member of Iowa's Atmospheric and Environmental Research Lab, lead by Wang.


Abstract

Atmospheric aerosols play a critical role in Earth's environment, climate change, and public health. Satellite remote sensing of aerosols is the only way for providing the daily global aerosol information and has received continuous attention and development during the past few decades. While various algorithms exist to retrieve daytime Aerosol Optical Depth (AOD) from satellite observations, there are no operational algorithms currently in existence to conduct nighttime AOD retrievals daily and globally. By measuring visible light at night from space, the Day/Night Band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard respectively on the Suomi National Polar-orbiting Partnership (Suomi NPP) and National Oceanic and Atmospheric Administration 20 (NOAA-20) satellites provides the research and operational communities the capability to explore nighttime aerosol properties and thus to monitor surface air quality. Using moonlight measured by VIIRS DNB, this study retrieves the nighttime AOD for rural regions. The radiative transfer calculation of this study is done by the UNified and Linearized Radiative Transfer Model (UNL-RTM) with newly developed capabilities for nighttime scenarios. Clouds are screened out by utilizing the observation from the Moderate Resolution Bands and DNB. Rural and city pixels are classified based on a pre-calculated city light database. Fire pixels are identified by a refined fire detection algorithm which take advantage of the nighttime visible light to detect active fire. The surface spectral reflectance for DNB ranging from 400 to 900 nm is estimated by a random forest which is trained using the surface spectral reflectance from the United States Geological Survey and from the Advanced Spaceborne Thermal Emission Reflection Radiometer spectral library. A Gaussian process neural work is trained using the radiative transfer calculation done by the UNL-VRTM to accelerate the convolution of the look-up table and the retrieval process. We apply the algorithm to the fire season of 2017 and the FIREX-AQ Campaign 2019. DNB AOD shows good agreements with AOD measured by AERONET and CALIOP.

Authors

  • Meng Zhou
    • Atmospheric and Environmental Research Laboratory
  • Jun Wang
    • Univ. of Iowa
  • Xi Chen
    • Univ. of Iowa