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SPoRT Training Resources

Click on the thumbnail images or links below to launch/redirect to each training course.

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

NASA Sport NASA’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.

Aviation Forecasting RGB Products

Aviation Forecasting RGB Products

Aviation Forecasting RGB Products: This eight-minute micro-lesson reviews some specific utilities of the Nighttime Microphysics RGB, the Hybrid LEO/GEO Fog and Low Cloud product, and the VIIRS Day-Night Band RGB relative to aviation weather forecasting. Included are a review of each of the products, as well as examples on their use individually and in tandem to help improve fog and low cloud forecasting for terminal aerodome forecasts (TAFs) and other aviation requirements.

CIRA Blended TPW and Anomaly Products

CIRA Blended TPW and Anomaly Products

CIRA Blended TPW and Anomaly Products: This 30 minute module presents the CIRA Blended Total Precipitable Water (TPW) and associated TPW Anomaly products.

Dust RGB identifies aviation ceiling hazard at KFMN

Dust RGB identifies aviation ceiling hazard at KFMN

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.

MODIS Fog Product

MODIS Fog Product

MODIS Fog Product: This brief, 7-minute training module highlights the use of the MODIS spectral difference, or fog product. The module presents user-provided examples gathered during the Fall 2008 evaluation period.

NESDIS GOES-R QPE

NESDIS GOES-R QPE

NESDIS GOES-R QPE (pdf):   This short reference guide describes the NESDIS GOES-R Quantitative Precipitation Estimation product suite in the context of operations. GOES-R QPE uses both IR and microwave data to retrieve rainfall rates in both the GOES-East and GOES-West domains, and the mechanics of this algorithm are briefly described. The rainrate and accumulation products are shown in a number of operationally-based examples, demonstrating the uses of GOES-R QPE in different regions and environments.

Pseudo Geostationary Lightning Mapper

Pseudo Geostationary Lightning Mapper

Pseudo Geostationary Lightning Mapper:  This module is an update to the original 2010 training module with new information, graphics, and content. This module introduces SPoRT's Pseudo Geostationary Lightning Mapper Flash Extent Density product and variants for use in the GOES-R Proving Ground. The Pseudo GLM is intended as a training product for forecasters ahead of the GOES-R era and to prepare forecasters for the more robust GLM proxy product under development by the Algorithm Working Group.

RGB Air Mass

RGB Air Mass

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

RGB Dust

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

RGB Nighttime Microphysics

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

SPoRT Hybrid MODIS-GOES Imagery for the GOES-R Proving Ground

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.

Total Lightning Application in a Tornado Event

Total Lightning Application in a Tornado Event

Total Lightning Application in a Tornado Event: Total lightning trends provide a proxy to storm stage and a “jump” in activity can be a precursor to severe weather. This case demonstrates the application of total lightning source density (similar to ‘Events’ from GLM) along with radar data to anticipate severe weather, which resulted in a tornado event.
Total Lightning Operational Uses: Additional Scenarios

Total Lightning Operational Uses: Additional Scenarios

Total Lightning Operational Uses: Additional Scenarios: This is a follow-up to the Total Lightning Training: Part 1 module and assumes a basic knowledge of total lightning. This sub-section focuses on other cases that may not fight directly with the other two sub-sections. This will cover an "obvious" severe weather event (i.e., a large outbreak) and a null cases where severe weather occurred, but no lightning jump was observed. The full module includes the Safety and Severe Weather sub-sections.

Total Lightning Operational Uses: Safety

Total Lightning Operational Uses: Safety

Total Lightning Operational Uses: Safety: This is a follow-up to the Total Lightning Training: Part 1 module and assumes a basic knowledge of total lightning. This sub-section focuses on the use of total lightning for lightning safety needs. This ranges from receiving lead time ahead of the first cloud-to-ground strike to incident support. The full module includes the Severe Weather and Additional Scenarios sub-sections.

Total Lightning Operational Uses: Severe Weather

Total Lightning Operational Uses: Severe Weather

Total Lightning Operational Uses: Severe Weather: This is a follow-up to the Total Lightning Training: Part 1 module and assumes a basic knowledge of total lightning. This sub-section focuses on the most common use of total lightning; severe weather decision support. This module focuses on the use of lightning jumps for three different events. These include a severe wind and hail event as well as a tornadic event. The full module includes the Safety and Additional Scenarios sub-sections.

Total Lightning Training: Part 1

Total Lightning Training: Part 1

Total Lightning Training: Part 1: This module introduces the user to total lightning and the source density product provided by NASA SPoRT.

Tracking Tool basics for total lightning

Tracking Tool basics for total lightning

Tracking Tool basics for total lightning: Quick Guide to applying the Tracking Meteogram Tool to total lightning data in order to look for sharp increases in the trend of flashes and hence precursors to severe weather.

UAH GOES-R CI

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.

Valley fog through mid/high clouds in Southern Appalachians

Valley fog through mid/high clouds in Southern Appalachians

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.

WRF Model Lightning Forecast Algorithm (LFA)

WRF Model Lightning Forecast Algorithm (LFA)

WRF Model Lightning Forecast Algorithm (LFA): This tutorial provides background information on the development, calibration, and application of the Lightning Forecast Algorithm (LFA), as implemented into the Weather Research and Forecasting (WRF) numerical weather prediction model. The LFA is a demonstration product for use in the GOES-R Proving Ground to develop model proxy fields of total lightning that could be used in future data assimilation applications of the Geostationary Lightning Mapper.