GOES-R Series Faculty Virtual Course: Multispectral RGB Composites: 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 discusses the capabilities of the GOES-R/16 red-green-blue (RGB) composites. Multispectral or RGB composites are qualitative, false color images designed to enhance specific features in the atmosphere that are important to forecasters, aviators, mariners, and emergency response officials.
GOES-R Series Faculty Virtual Course: RapidScan Imaging: 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 presents GOES-16/GOES-R 30-second and 1-minute rapid scan imagery to demonstrate unprecedented views of convection, wildfire, storm intensification, and other quickly-evolving features.
RGB Products Explained: This module provides an overview of meteorological and environmental RGB products, namely, how they are constructed and how to use them. This module is also available in Spanish.
Satellite Feature Identification: Blocking Patterns: Examines how water vapor imagery can be used to help diagnose blocking patterns and their dissipation. Four major blocking patterns are covered in this module: blocking highs, cut-off lows, Rex blocks, and Omega blocks. This module is also available in French and Spanish.
Satellite Feature Identification: Cyclogenesis: This module uses water vapor satellite imagery to present a satellite perspective of basic features associated with the formation and development of extratropical cyclones.
Satellite Feature Identification: Inferring Three Dimensions from Water Vapour Imagery: Water vapor imagery can help us break out of flatland and move to more dimensions. This imagery holds so much under-utilized potential. We can actually see three-dimensional structures evolving in near-real-time. This module is also available in French.
Air Mass RGB: This micro-lesson is focused on an application of the Air Mass RGB to anticipate rapid cyclogenesis and high impact winds. The case presented is the development of a storm force low in the Western Atlantic and the use of the RGB to anticipate changes in cyclone intensity and associated impacts such as high wind. Basic interpretation and description of the R-G-B components are presented. Download Air Mass RGB Quick Guide (PDF).
Day Land Cloud Fire RGB: The focus of this micro-lesson is on the Natural Color Fire RGB product. This RGB is similar to the Day Land Cloud or Natural Color RGB; however, one of the channels is replaced to specifically focus on fire hot spots. The RGB allows one to view the smoke and fire hot spots within the same product. The case used for this object is the October 2017 Northern California Firestorm. The objective for this item is to have the forecaster demonstrate the ability to identify various hot spots within the Day Land Cloud Fire RGB imagery. Download Day Land Cloud Fire RGB Quick Guide (PDF).
Dust RGB: This micro-lesson demonstrates the value of applying Dust RGB imagery from the GOES-R ABI via an event from March 2017 in the U.S. Southwest. The Dust RGB is compared to traditional visible and infrared single-channel imagery to show the operational value of the multi-channel RGB imagery for analyzing blowing dust plumes during both day and night. Download Dust RGB Quick Guide (PDF).
Dust RGB identifies aviation ceiling hazard at KFMN: This seven-minute micro-lesson of the dust RGB application to a mesoscale event that impacted the ceiling conditions at the Farmington, New Mexico, airport (i.e. TAF site). Observations at the site and the changes to the TAF are highlighted. The lesson also illustrates the value of the dust RGB with the GOES visible and MODIS/VIIRS true color imagery.
Nighttime Microphysics RGB: This micro-lesson demonstrates the operational use of the Nighttime Microphysics RGB both to increase the lead time or to avoid a ‘false alarm’ of advisory products related to fog hazards. The RGB is compared to the traditional split window difference of the long and short-wave infrared channels for the identification of low clouds and fog. Other mid- and upper-level clouds are apparent in the RGB, but they are not the focus of this particular training. Download Nighttime Microphysics RGB Quick Guide (PDF)
Comparing NWP Synthetic/Simulated Satellite Imagery to Observed Satellite Imagery This module is part of the GOES-R Satellite Foundational Course and covers simulated/synthetic satellite imagery.
Cyclogenesis: Analysis Utilizing Geostationary Satellite Imagery: This module examine various conceptual models of cyclogenesis (basic, split flow, cold air, instant occlusion and in-stream) and helps forecasters learn to utilize a blend of conceptual models, satellite imagery, and NWP output in diagnosing cyclogenesis.
GOES-R Multi-Channel Interpretation Approaches: This module is part of the GOES-R Satellite Foundational Course and covers multi-channel interpretation approaches, including band differences and RGBs (red-blue-green).
GOES-R TROWAL Formation: This module is part of the GOES-R Satellite Foundational Course and describes the TROWAL phenomenon and suggests methods of identifying TROWALS in water vapor imagery.
Synthetic Imagery in Forecasting Cyclogenesis: This training session is part of a series that focuses on applications of synthetic imagery from the NSSL 4-km WRF-ARW model. In this training session we'll consider applications of the synthetic imagery in forecasting extratropical cyclogenesis. This training session builds off the previous VISIT Cyclogenesis course which stressed a blend of conceptual models, NWP output and GOES satellite imagery.
Synthetic Imagery in Forecasting Orographic Cirrus: Forecasting orographic cirrus is important because of their influence on temperature forecasts. Utilizing synthetic imagery generated from a model is a useful way to anticipate orographic cirrus. Orographic cirrus can be more easily visualized on synthetic imagery compared to model output fields such as relative humidity over some layer. This session is helpful in learning how to use/interpret the GOES-R Proving Ground cloud and moisture imagery products.
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.
Water Vapor Imagery Analysis for Severe Weather: The primary objective of this session is to maximize the information available from the GOES water vapor imagery during severe weather episodes, and how to effectively utilize this information with other available datasets.