Nighttime AOT Retrieval for Asian Dusts from MODIS IR Measurements: An Artificial Neural Network Approach

Abstract

In order to estimate MODIS-equivalent aerosol optical thicknesses (AOTs) for dust particles during the nighttime over East Asia, an Artificial Neural Network (ANN) model approach was used to combine MODIS-measured infrared (IR) brightness temperatures (BTs) and visible (VIS) AOTs. For training the ANN model, IR BTs were used together with surface type and geometrical information as inputs to predict MODIS-derived AOTs as target data during the daytime when VIS-based AOTs are available. The training was done exclusively over dust-laid pixels during the spring (MarchMay) of 2006 over the East Asian domain (20It should be noted that the obtained daytime AOTs from the ANN model are in good agreement with MODIS-derived AOTs, with a correlation coefficient of 0.77 over the analysis domain. Although a weaker correlation is found during the nighttime when derived AOTs are compared against AOTs from CALIPSO, the case study indicates that the developed ANN method appears to effectively depict both the evolutionary features and intensity of the Asian dust plume during the nighttime. Results further indicate that nighttime VIS-like AOTs can be readily used for monitoring dust movement and its intensity during the nighttime, filling the gap between consecutive daytime AOT distributions.

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

“…Recently, observationally based nighttime aerosol studies have drawn increasing attention from the research community (e.g., Zhang et al, 2008b;Berkoff et al, 2011;Lee and Sohn, 2012;Campbell et al, 2012;Johnson et al, 2013). Berkoff et al (2011) and Barreto et al (2013) present surface-based methods to estimate nighttime aerosol optical properties through measuring the attenuated moonlight using a modified version of sun photometers.…”

“…Based on Berkoff et al (2011), the NASA AErosol RObotic NETwork (AERONET, Holben et al, 1998) team is developing an operational nighttime aerosol retrieval capability (B. Holben, personal communication, 2015). While mineral dust can be detected and in part quantified from space by infrared means (e.g., Miller, 2003;Hansell et al, 2007;Lee and Sohn, 2012), the recently launched Visible Infrared Imaging Radiometer Suite (VIIRS) instrument, equipped with a calibrated Day/Night Band (DNB), opens a new door for enabling shortwave nighttime aerosol retrievals from spaceborne observations (e.g., Johnson et al, 2013).…”

Preliminary investigations toward nighttime aerosol optical depth retrievals from the VIIRS Day/Night Band

“…Furthermore, large uncertainties exist in converting CALIOP measurements of attenuated backscatter to physical quantities such as aerosol optical depth (e.g., Campbell et al, 2010;Winker et al, 2009). Lee and Sohn (2012) demonstrate the utility of thermal infrared (IR) channels in retrieving nighttime aerosol properties; however, such algorithms are only capable of detecting coarse-mode aerosols, and the performance of these retrieval methods could degrade when dust plumes are near the surface.…”

Retrieving Asian dust AOT and height from hyperspectral sounder measurements: An artificial neural network approach

“…The model has a single hidden layer with five neurons, and a hyperbolic tangent sigmoidal function is used to link their nonlinear responses. More detailed explanation of training procedures is found in Lee and Sohn [].…”