Regional and Mesoscale Meteorology Branch

Introduction:

This module is part of the satellite foundational course for GOES-R (SatFC-G). This particular module covers multi-channel interpretation approaches, including band differences and RGBs (Red-Blue-Green). Learning objectives include:

1. Bands on ABI, and what they are useful for.

2. How can you use multiple bands to reveal atmospheric phenomena (band differences and comparisons).

3. AWIPS capabilities for channel combinations.

4. RGBs: what are they? How are they created? What are some simple examples?

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. Web-based video that can be taken at anytime (streamed, not recommended for low-bandwidth users). Be sure to have your speakers on and the volume loud enough to hear the presentation.
2. YouTube video:

1. Download video (recommended for low-bandwidth users) – This is an audio playback version in the form of an Articulate Presenter video and can be taken at anytime. Create a directory to download the video file from the following link: http://rammb.cira.colostate.edu/training/visit/training_sessions/multi_channel_interpretation_of_goes_r_imagery/multi_channel_interpretation_of_goes_r_imagery_articulate.zip After extracting the files into that directory click on either the presentation.html OR index.html file and the video will begin to play in your browser.

Original Pre-Launch Version

This version of the training module debuted in the Fall of 2016 which included Himawari and pre-GOES-R era GOES imagery. Formats include:

• Articulate Presenter Video
• Articulate Download (zip file)
• Youtube video

References / Additional Links:

List of quantitative products available using GOES-R data to complement multi-channel approaches

Powerpoint file

GOES-R ABI Fact Sheets

Split Window Difference journal article by Lindsey et al.

Dust Detection using Split Window Difference

RGBs from the Japanese Meteorological Agency