A picture being worth a thousand words is not always such a good thing! When a complex environmental scene contains too much information, it can be hard for analysts operating in time-critical environments to digest it all.
The rich spatial, spectral, and temporal resolution offered by next-generation geostationary satellites such as the Himawari-8 Advanced Himawari Imager and the GOES-16 Advanced Baseline Imager, comes with an underlying challenge—how best to sip from this proverbial firehose of data. Simple attempts to distill the information into colorful graphical displays and enhance a certain feature of interest can be helpful, but sometimes they can do more harm than good. These techniques rely upon the existence of ‘spectral fingerprints’ to isolate the parameter of interest. Problems arise when the fingerprint is not unique, and other parts of the image produce false alarms, causing confusion.
Miller et al. [2017] present an elegant new way of separating the wheat from the chaff—reducing the chances of those troublesome false alarms happening to begin with—by accounting for them in advance. The Dynamic Enhancement Background Reduction Algorithm (DEBRA), is a versatile technique applied here to the notoriously diffiucult problem of detecting dust storms from satellite-based multispectral imaging radiometers.
A chief concern among forecasters has been that there are far too many dust-detection products, many of which are difficult to interpret. DEBRA shows promise in alleviating these frustrations. It accounts for land surfaces that masquerade as dust and adjusts the sensitivity of its detection tests accordingly—enhancing the important signals where present, while suppressing the noise to improve the overall detection accuracy and clarity of display. The result is a numerical gauge of confidence in the presence of lofted dust above various surfaces, making it useful for downstream quantitative applications.
DEBRA can also be communicated as visually intuitive imagery, where the only colors involved pertain to the feature of interest—the rest of the scene is portrayed as gray scale, preserving the meteorological context. The final enhanced picture may no longer be worth a thousand words, as they say, but its added value to end-users speaks volumes.
Citation: Miller, S. D., Bankert, R. L., Solbrig, J. E., Forsythe, J. M. & Noh, Y.-J. [2017]. DEBRA—A Dynamic Enhancement with Background Reduction Algorithm: Overview and Application to Satellite-Based Dust Storm Detection. Journal of Geophysical Research: Atmospheres, 122. https://doi.org/10.1002/2017JD027365
—Zhanqing Li, Editor, JGR: Atmospheres
from Eos https://eos.org/editor-highlights/addition-by-subtraction-raising-the-bar-for-satellite-imagery?utm_source=rss&utm_medium=rss&utm_content=addition-by-subtraction-raising-the-bar-for-satellite-imagery
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