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Convection Training Resources

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Cooperative Program for Operational Meteorology, Education, and Training (Comet)

COMET logo and Link The Cooperative Program for Operational Meteorology, Education, and Training supports, enhances, and stimulates the communication and application of scientific knowledge of the atmospheric and related sciences for the operational and educational communities. COMET’s Web-based self-paced training materials serve earth science education and training needs by providing interactive experiences for learners at a distance. Experts at both the Cooperative Institute for Meteorological Satellite Studies (CIMSS) and the Cooperative Institute for Research in the Atmosphere (CIRA) have contributed to many of these lessons.

GOES-R Series Faculty Virtual Course: Severe Storms

GOES-R Series Faculty Virtual Course: Severe Storms

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.

NASA’s Short-term Prediction Research and Transition Center (SPoRT)

COMET logoNASA’s Short-term Prediction Research and Transition Center (SPoRT) provides training about specific products, discussing the strengths and weaknesses, with the goal of successfully transitioning products to operations. This training is built from surveys and direct communication with our partners. With this paradigm, the forecasters are an integral component of the transition process and not a passive recipient of data. SPoRT Product Training Modules include: Pseudo Geostationary Lightning Mapper, Lightning Mapping Array, MODIS Fog Products, GOES Fog Depth, and more.

Link to UAH GOES-R CI lessons

UAH GOES-R CI

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 UAHuntsville 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.

Virtual Institute for Satellite Integration Training (VISIT)

COMET logoVirtual Institute for Satellite Integration Training (VISIT) is a joint effort involving NOAA Cooperative Institutes, the National Environmental Satellite Data and Information Service (NESDIS), and the National Weather Service (NWS). The primary mission of VISIT is to accelerate the transfer of research results based on atmospheric remote sensing data into NWS operations using distance education techniques. Training sessions include topics on Satellite Meteorology, Severe Weather, Climate, Numerical Weather Prediction, and more. Many of these modules were developed in collaboration with the Cooperative Institute for Meteorological Satellite Studies (CIMSS) and the Cooperative Institute for Research in the Atmosphere (CIRA). VISIT also provides monthly Satellite Chats to demonstrate satellite products that can be applied to operational forecasting and identify new training topics based on specific participant needs. See the VISIT Training Calendar for upcoming VISIT Satellite Chats.

A selection of VISIT training sessions specific to GOES is listed below:

Convective Cloud-top Cooling

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

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

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

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

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

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

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

GOES-R Pre-Convective Cloud Features: 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.

GOES-R Pre-Convective Environment

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

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

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

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

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.