GOES-R Series Faculty Virtual Course: Tropical Cyclones: 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 demonstrates the GOES-R series' new capabilities for real-time tropical cyclone analysis and monitoring, which will contribute significantly to improved hurricane track and intensity forecasts.
GOES-R Tropical to Extratropical Transition: This lesson uses water vapor satellite imagery from Himawari-8 to describe the typical extratropical transition of a tropical cyclone. The Himawari-8 imager previews comparable capabilities coming online with the GOES-R series Advanced Baseline Imager.
GOES-16 and S-NPP/JPSS Case Exercise: Hurricane Harvey Surface Flooding: Satellite data are important tools for analyses and short-term forecasts of surface floodwater. This lesson will highlight the August 2017 flooding associated with Hurricane Harvey in southeastern Texas, one of the most costly weather disasters in U.S. history. Through the use of interactive exercises the learner will become familiar with use and interpretation of satellite imagery in regions with surface flooding. The lesson will use data from both the S-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) and the GOES-16 Advanced Baseline Imager (ABI). The satellite-derived flood map and the data that go into the flood map will both be highlighted in the lesson. Examples of floodplain inundation, interbasin transfer, and water pooling in reservoirs will be shown along with issues related to spatial and temporal resolution.
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.
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 Baseline Product: Derived Motion Winds: This module is part of the GOES-R Satellite Foundational Course and covers the derived motion winds product.
GOES-R Baseline Product: Hurricane Intensity Estimate: This module is part of the GOES-R Satellite Foundational Course and provides an introduction to the Hurricane Intensity Estimate (HIE) product.
GOES-R Cyclogenesis Life Cycle: This module is part of the GOES-R Satellite Foundational Course and covers the life cycle of extra-tropical cyclogenesis.
GOES-R Cyclogenesis Potential Vorticity Concepts: This module is part of the GOES-R Satellite Foundational Course and covers potential vorticity concepts applied to extra-tropical cyclogenesis.
GOES-R General Circulation Patterns: 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 imagery to identify and monitor basic circulation features, including ridges, troughs and jets.
GOES-R Low-Level Jet Features: 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 various types of low-level jets.
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.
Tropical SHyMet Introduction: The Tropical track of the SHyMet course will cover satellite imagery interpretation and application of satellite derived products in the tropics as well as the models used at NHC for tropical cyclone forecasting.