Regional and Mesoscale Meteorology Branch

Introduction:

This training session will build on data fusion methodologies from the Data Fusion section of the WOC Severe Course (updated in FY21).  We will apply these data fusion concepts to dust storm warning decision making.  This training consists of two parts, we recommend that you take part 1 prior to taking part 2.

There is a quiz after each part.  Be sure to take the quiz and score a passing grade to earn training completion credit on the NOAA Commerce Learning Center (CLC).

Part 1: Introduction (~30 minutes)

Learning objectives:

1. What data sources are useful when making decisions on dust storm warnings
2. NWS blowing dust products
3. Important considerations in analyzing different data types
4. Options for data display
5. Variability in visual observations
6. Best practices

Part 2: 6 October 2022 Convective Outflow Blowing Dust Exercise (~40 minutes)

Data fusion techniques are utilized to learn how to blend all available data which can be applied to warning decision making for dust storm warnings.  This is an interactive exercise with survey questions to illustrate variable priorities on observational data types important for dust storm warning decision making.

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.

• Web-based video: Part 1
• Web-based video: Part 2

References / Additional Links:

Satellite-Based Blowing Dust Detection Products and Climatology for Operational Weather Forecasters

Data fusion section of the WOC Severe Track – this training session will build on data fusion methodologies from the Data Fusion section of the WOC Severe Course (added with the FY21 update).

AWIPS Procedures used in this training

COMET Lesson: Forecasting Dust Storms

New Polygon-based Dust Storm Warning and Dust Advisory Product (2018)