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Regional and Mesoscale Meteorology Branch

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GOES 3.9 µm Channel


Dan Lindsey








Last Updated:


Contributors: Dan Bikos, Jeff Braun, Don Hillger, Bernie Connell, Scott Bachmeier and Scott Lindstrom


This is an introductory session meant to complement CIRA’s 3.9 µm tutorial, but is a good review for any forecaster.


  1. Review the science behind the 3.9 µm channel
  2. Discuss the many possible applications, including fog detection, fire detection, and snow/low cloud differentiation
  3. Look at a number of examples
  4. Preview what’s to come with future sensors

Training Session Options

NOAA/NWS students – to begin the training, use the web-based video, YouTube video, or audio playback options below (if present for this session). Certificates of completion for NOAA/NWS employees can be obtained by accessing the session via the Commerce Learning Center.

  1. Audio playback (recommended for low-bandwidth users) – This is an audio playback version in the form of a downloadable VISITview and can be taken at anytime.

    Create a directory to download the audio playback file (56 MB) from the following link:

    After extracting the files into that directory click on either the visitplay.bat or visitauto.bat file to start the lesson. If both files are present, use visitauto.bat

  2. YouTube video:

References/Additional Links

  • Talking points are available for this lesson and may be printed out to easily review the session in detail at any time.
  • Ellrod, G. P., 1995: Advances in the Detection and Analysis of Fog at Night Using GOES Multispectral Infrared Imagery Wea. Forecasting, 606–619.
  • Lindsey, D. T., D. Hillger, L. Grasso, J. Knaff, and J. Dostalek, 2006: Climatology and analysis for thunderstorms with enhanced 3.9 µm reflectivity. Mon. Wea. Rev., in press. Email me if you want a copy.
  • CIRA’s 3.9 µm tutorial
  • COMET’s GOES Channel Selection Tutorial
  • Modification of the Fog Product in AWIPS
This course is Basic

There are no prerequisites


Dan Bikos

Page Contact

Bernie Connell


Unless otherwise noted, all content on the CIRA RAMMB: VISIT, SHyMet and VLab webpages are released under a Creative Commons Attribution 3.0 License.