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Regional and Mesoscale Meteorology Branch

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GOES Imagery for Forecasting Severe Weather

Instructors:

Dan Bikos

|

Topic:

Severe/Sat

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Developed:

2008

Contributors: Scott Lindstrom, Jonathan Finch, Jeff Braun, Dan Lindsey, Al Pietrycha, Melissa Goering.

Introduction


Objectives:

  1. Assessment of model performance – how to gain confidence in particular model solution(s) with observational data.
  2. Air mass identification.
  3. Identification of changes in the pre-storm environment.
  4. Monitor the changing environment during the nowcast to WDM time period.

There is a post-training WES simulation jobsheet associated with this training.

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 (185 MB) from the following link: http://rammb.cira.colostate.edu/training/visit/training_sessions/goes_imagery_for_forecasting_severe_weather/goes_imagery_for_forecasting_severe_weather_audio.exe

    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.
  • Mazur, R.J., J.F. Weaver, and T.H. Vonder Haar. Observations of inflow feeder clouds and their relation to severe thunderstorms. 22nd Conference on Weather Analysis and Forecasting, Park City, Utah, 25-29 June 2007. Boston, MA, American Meteorological Society, P2.19.
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Contact

Dan Bikos

Dan.Bikos@colostate.edu

Page Contact

Bernie Connell

bernie.connell@colostate.edu

970-491-8689

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