Regional and Mesoscale Meteorology Branch

Introduction:

This session consists of two parts. Both parts must be completed to get a certificate of completion as there are different points made in each part. For the “Live VISIT teletraining,” the two parts are in different files. For the live training for part 1, please follow this link to Utilizing GOES Imagery to Forecast Winter Storms – Part 1.

Objectives:

• Blend GOES imagery with other available AWIPS products to help improve forecast skill in precipitation associated with winter storms
• Analyze case studies over various parts of the CONUS

Topics:

• Conveyor belt identification
• Synoptic features (e.g. dry slot, short waves, jet streaks etc.)
• Model divergence
• Mesoscale banding
• Topographic effects
• Shear zones
• Precipitation type (with other datasets)

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

PLEASE NOTE: For the “Audio playback” version of this session, both of the 2 parts are contained in the same files linked to on this page: Utilizing GOES Imagery to Forecast Winter Storms – Part 1. For the “Live VISIT teletraining,” the two parts are in different files, and the link for Part 2 is below.

1. Audio playback containing both parts 1 and 2 is located on this page: Utilizing GOES Imagery to Forecast Winter Storms – Part 1
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.
• Bader, M.J., G.S. Forbes, J.R. Grant, R.B.E. Lilley, and A.J. Waters, 1995: Images in Weather Forecasting. University Press, Cambridge, Great Britain, 499 pp.
• Johnston, E.C. 1995: Updated Satellite Techniques to Forecast Heavy Snow. Wea. Forecasting, 10, 219-228.
• Snow to liquid water ratio climatology for each CWA
• TROWAL Identification
• An Ingredients-Based Methodology for Forecasting Midlatitude Winter Season Precipitation
• Water Vapor Channel Satellite Imagery
• Precipitation Type Forecasting
• Snow to liquid water ratio estimate based on a neural network