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Synthetic Imagery in Forecasting Severe Weather


Dan Bikos

Dan Lindsey



Satellite Proving Ground





Last Updated:



This training session is part of a series that focuses on applications of synthetic imagery from the NSSL 4-km WRF-ARW model. In this training session we’ll consider applications of the synthetic imagery towards severe weather events. The primary motivation for looking at synthetic imagery is that you can see many processes in an integrated way compared with looking at numerous model fields and integrating them mentally.

This is an experimental GOES-R Proving Ground Product designed to foster GOES-R readiness.

This session was updated in March 2015 to reduce the length from 50 to 30 minutes, and add some notes regarding the synthetic 4-km NAM-Nest imagery interpretation. The previous (50 minute) version contains more cases, and can be found below in the “References/Additional Links” section.

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 Learn 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 (131 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

    Links to old (50 minute) version of this lesson which contains more cases:

This course is Intermediate

It’s recommended that you take the following training session first: Water Vapor Imagery Analysis for Severe Weather


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.