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A Dynamic Enhancement With Background Reduction Algorithm: Overview and Application to Satellite‐Based Dust Storm Detection
Author(s) -
Miller Steven D.,
Bankert Richard L.,
Solbrig Jeremy E.,
Forsythe John M.,
Noh YooJeong,
Grasso Lewis D.
Publication year - 2017
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1002/2017jd027365
Subject(s) - multispectral image , remote sensing , emissivity , satellite , context (archaeology) , storm , computer science , dust storm , satellite imagery , algorithm , sky , environmental science , radiometer , reduction (mathematics) , geostationary orbit , meteorology , geography , mathematics , physics , geometry , astronomy , optics , archaeology
This paper describes a Dynamic Enhancement Background Reduction Algorithm (DEBRA) applicable to multispectral satellite imaging radiometers. DEBRA uses ancillary information about the clear‐sky background to reduce false detections of atmospheric parameters in complex scenes. Applied here to the detection of lofted dust, DEBRA enlists a surface emissivity database coupled with a climatological database of surface temperature to approximate the clear‐sky equivalent signal for selected infrared‐based multispectral dust detection tests. This background allows for suppression of false alarms caused by land surface features while retaining some ability to detect dust above those problematic surfaces. The algorithm is applicable to both day and nighttime observations and enables weighted combinations of dust detection tests. The results are provided quantitatively, as a detection confidence factor [0, 1], but are also readily visualized as enhanced imagery. Utilizing the DEBRA confidence factor as a scaling factor in false color red/green/blue imagery enables depiction of the targeted parameter in the context of the local meteorology and topography. In this way, the method holds utility to both automated clients and human analysts alike. Examples of DEBRA performance from notable dust storms and comparisons against other detection methods and independent observations are presented.