MODIS Fog Product: This is a brief, seven-minute training module to highlight the use of the MODIS spectral difference, or fog product. The module presents user-provided examples gathered during the fall 2008 evaluation period.
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.
Valley fog through mid/high clouds in Southern Appalachians: This 8-minute micro-lesson demonstrates the value of multispectral (i.e. RGB imagery) to the analysis of fog and low clouds in valleys of the southern Appalachians, particularly compared to channel differencing.
Forecaster Training for the GOES-R Fog/low stratus (FLS) Products: Learn how the GOES-R fog/low stratus product improves upon the traditional brightness temperature difference (BTD) product, understand how the GOES-R FLS product is created, and see examples of how the product should be used in different geographic regions.
GOES-R Cloud and Microphysical Properties, fog and low stratus: This module is part of the GOES-R Satellite Foundational Course and covers GOES-R series cloud products, including fog and low stratus. GOES-R cloud products, such as cloud mask, cloud-top properties (height, pressure, temperature), cloud optical depth and cloud phase can help a forecaster more completely describe cloud attributes and anticipate weather events related to specific cloud types.
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 Fog/Low Clouds: Formation and Dissipation: This module is part of the GOES-R Satellite Foundational Course and covers fog and low clouds. Learning objectives include ABI bands and products for detecting fog/low cloud formation and dissipation.
GOES-R Mountain Waves and Orographic Enhancement: This module is part of the GOES-R Satellite Foundational Course and covers mountain waves and orographic enhancement. The primary learning objective of this module is to introduce GOES-R Series capabilities in identification of mountain wave clouds, also known as orographic cirrus or lee wave clouds.
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.
Regional Satellite Cloud Composites from GOES: This module reflects how one can create and use cloud composites on a regional scale to assist with everyday forecasting tasks. Cloud composites refer to a shorter time span than cloud climatology. This module highlights simple techniques used to create the cloud composites and regional applications used to visualize weather patterns - all from the diurnal geostationary satellite.
Synthetic Imagery in Forecasting Low Clouds and Fog: This 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 low clouds and fog. 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.
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 cloud and moisture imagery products.