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, Satellite Feature Identification: Cyclogenesis, 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. Come see the atmosphere in 3D using water vapor imagery! This module is also available in French.
Dust RGB identifies aviation ceiling hazard at KFMN: This 7-minute micro-lesson of the Dust RGB application to a meso-scale 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.
RGB Air Mass (pdf): A two-page reference document that describes the fundamental aspects of the RGB Air Mass imagery product and demonstrates color interpretation of the multi-channel imagery.
RGB Dust (pdf): A two-page reference document that describes the fundamental aspects of the RGB Dust imagery product and includes a large dust event.
RGB Nighttime Microphysics (pdf): A three-page reference document that describes the fundamental aspects of the RGB Nighttime Microphysics imagery product. Included are two examples: a coastal event and a multi-cloud scene.
SPoRT Hybrid MODIS-GOES Imagery for the GOES-R Proving Ground: The SPoRT hybrid imagery for the GOES-R Proving Ground is a combination of high-resolution MODIS imagery and standard GOES imagery.
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.