Improved Cloud Cover Layers with Machine Learning
A manuscript titled “Low Cloud Detection in Multilayer Scenes using Satellite Imagery with Machine Learning Methods” has been accepted for publication in the Journal of Atmospheric and Oceanic and Technology (JTECH; https://doi.org/10.1175/JTECH-D-21-0084.1). This work describes a new method to detect low-level clouds from GOES ABI satellite data using decision trees and artificial neural networks. Low clouds are especially relevant for aviation users, but are traditionally difficult to detect, especially in multilayer scenes. This new method, now incorporated into CIRA’s SLIDER, significantly increases detection rates of these low clouds, especially when upper cloud layers are present. (J. Haynes, Y. J. Noh, S. Miller, K. Haynes, I. Ebert-Uphoff, CIRA; A. Heidinger, NESDIS; POC: John.Haynes@colostate.edu). Funding: GOES-R.
Download Weekly Report (PDF)Posted on: December 17, 2021
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