Extrapolating shortwave geostationary satellite imagery of clouds into nighttime using longwave observations

The lack of shortwave (SW, visible, and near-infrared) geostationary satellite data at night results in degradation of many weather forecasts and real-time diagnostic products. We present a method to extrapolate SW GOES-16 advanced baseline imager data through night using nighttime longwave (LW, infrared) observations and the relationships between LW and SW data observed during the previous day. The method is not a forecast since it requires LW nighttime observations but can provide continuity through day, night, and satellite terminator hours. To provide performance statistics, the algorithm is applied during the day so the SWextrapolations can be compared to observations. Typical mean absolute errors (MAEs) range from 1.0% to 12.7% reflectance depending on the SW channel. These MAEs can be predicted using a diagnostic metric called 0-hMAE which quantifies the quality of the algorithm's input data. In addition to quantitative error statistics, three case studies are presented, including an animation of extrapolated imagery from dusk through dawn. Considerations for future improvements include use of convolutional neural networks and/or object-based extrapolations where mesoscale features are extrapolated individually. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.

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Author Rugg Stebbins, Allyson Lyman Rugg
Haggerty, Julie A.
Adriaansen, Daniel
Smith, W. L.
Publisher UCAR/NCAR - Library
Publication Date 2021-07-08T00:00:00
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Topic Category geoscientificInformation
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Metadata Date 2025-07-11T16:14:05.959844
Metadata Record Identifier edu.ucar.opensky::articles:24643
Metadata Language eng; USA
Suggested Citation Rugg Stebbins, Allyson Lyman Rugg, Haggerty, Julie A., Adriaansen, Daniel, Smith, W. L.. (2021). Extrapolating shortwave geostationary satellite imagery of clouds into nighttime using longwave observations. UCAR/NCAR - Library. https://n2t.org/ark:/85065/d7c82dqf. Accessed 02 August 2025.

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