GOES-R Series Faculty Virtual Course: Severe Storms: This course is part of a webinar series to provide university faculty and others with a solid basis for using GOES-R/16 data in coursework and student research projects. This module explains how GOES-R/16 can help improve forecasts of severe storms and provide forecasters with real-time information about lightning, flooding potential and other hazards.
GOES-16 Case Exercise: 8 May 2017 Colorado Hail Event This lesson harnesses GOES-16’s increased temporal and spatial resolutions to identify convective development and intensity signatures on traditional longwave IR and visible band imagery, and compares the experience to using legacy GOES products. The lesson is geared toward early-career forecasters, those forecasters wanting more experience using high-resolution satellite data to forecast convection, and will be useful to aviation forecasters, meteorology major students and instructors, and weather enthusiasts.
Day Convection RGB: Originally developed by EUMETSAT and referred to as the Severe Storms RGB, this product was renamed and adapted for GOES ABI. Convection can be seen in several of the single channels from ABI, but it is severe convection from very strong updrafts that is the focus of this RGB. Examples from ABI during the spring of 2018 are presented over the CONUS as well as marine regions. The objective of this lesson is to apply the Day Convection RGB to identify where strong updrafts are occurring and associated severe convection is likely to result in hazards such as large hail, severe wind, and heavy precipitation. Download Day Convection RGB Quick Guide (PDF).
UAH GOES-R CI: This 12-minute module briefly describes the latest operational version of UAH GOES-R CI, a 0-2 convective initiation satellite nowcasting product developed at UA Huntsville and transitioned by NASA SPoRT. GOES-R CI uses a number of algorithms to track cloud objects, identify cloud properties like growth and glaciation (and rates of change in these cloud properties), and incorporates environmental data from the RAP model to produce a likelihood, or probability, of convection for identified cloud objects. This product is available for both the GOES East and GOES West domains.
Convective Cloud-top Cooling: This teletraining describes the University of Wisconsin Convective Initiation (UWCI) product, which tracks cloud top temperatures and cloud types to determine when a particular cloud pixel is growing in the vertical.
GOES-R Boundary-Forced Convection: This module is part of the GOES-R Satellite Foundational Course and covers boundary-forced convection. The primary learning objectives of this module are to identify boundaries utilizing new capabilities of the GOES-R Series and identify convection forced by boundaries in GOES-R proxy imagery.
GOES-R Cumulous Growth: This module is part of the GOES-R Satellite Foundational Course and the primary learning objective is how to make optimal use of GOES-R Series capabilities in analysis of cumulus congestus/growth.
GOES-R Discrete Storms: This module is part of the GOES-R Satellite Foundational Course and the primary learning objective is to provide an introduction to how GOES-R Series capabilities can be utilized to identify and track discrete thunderstorms.
GOES-R Introduction to Mesoscale and Synoptic Sections: This module is part of the GOES-R Satellite Foundational Course and outlines the mesoscale/convection and synoptic sections of the course. The primary learning objectives of this module are to understand the structure of the mesoscale/convection and synoptic sections and also introduce specific GOES-R Series capabilities applicable to these sections.
GOES-R Marine and Polar Mesolows: This module is part of the GOES-R Satellite Foundational Course and the primary learning objective is to learn how to make optimal use of GOES-R Series capabilities for identification of marine and polar mesolows.
GOES-R Mesoscale Convective Systems: This module is part of the GOES-R Satellite Foundational Course and the primary learning objective is to provide an introduction to how GOES-R Series capabilities can be utilized to identify and monitor the evolution of mesoscale convective systems (MCSs).
GOES-R Pre-Convective Cloud Features: This module is part of the GOES-R Satellite Foundational Course and covers pre-convective cloud features. The primary learning objective of this module is to provide an introduction to how GOES-R Series capabilities can be utilized to identify various pre-convective cloud features, including cumulus streets, stable wave clouds, and the undular bore.
GOES-R Pre-Convective Environment: This module is part of the GOES-R Satellite Foundational Course and introduces GOES-R series capabilities for identification of the pre-convective environment. Specific topics include surface conditioning (differential heating), the elevated mixed layer (EML) and elevated cold fronts.
Objective Satellite-Based Overshooting Top and Enhanced-V Anvil Thermal Couplet Signature Detection: In this basic course, learn how satellite data can be used objectively to detect features associated with strong thunderstorms and how overshooting tops and thermal couplets are related to severe weather.
Synthetic Imagery in Forecasting Severe Weather: This training session considers 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 session is helpful in learning how to use/interpret the GOES-R Proving Ground "Cloud and Moisture Imagery" products.
Utility of GOES Satellite Imagery in Forecasting/Nowcasting Severe Weather: Includes information on assessing model performance, air mass identification, identification of changes in the pre-storm environment, and monitoring the changing environment.
The UW Nearcasting Product: This teletraining describes the University of Wisconsin CIMSS' NearCasting product, which is derived from Lagrangian model output. The variable that is predicted by the model is equivalent potential temperature at multiple levels. Thus, the model produces near-term forecasts of convective destabilization.