The Applications Integration Meteorologist, the Nexus between the Research and Operations Communities at NASA SPoRT

A significant part of the culture of the Huntsville, Alabama meteorology community involves the co-location of the National Weather Service (NWS) office, the University of Alabama in Huntsville, and the NASA Short-term Prediction Research and Transition (SPoRT) Center. In order to better bridge the communications divide between the needs and capabilities of research and operations, the NWS established a liaison forecaster position at NWS Huntsville initially in 2002, but this was later formalized as the Applications Integration Meteorologist in 2010. These meteorologists have served both as an operational meteorologist and a Research-To-Operations (“R2O”) nexus for the SPoRT program, ensuring effective product transitions from the NASA research community to NWS offices. In the case of the AIM, this requires serving not only as a consulting forecaster, but also as an information technology officer (ITO) and trainer. As a consulting forecaster, the AIM has the opportunity to work with both NWS colleagues to identify forecast problems and with NASA and UAH researchers to solve them. In the role of ITO, the AIM can specifically be tasked with leading transition efforts for products and data in the Automated Interactive Weather Processing System (AWIPS). Often, this will take place initially at the NWS Huntsville Office for testing. As a trainer, the AIM, along with researchers at SPoRT and other entities, helps to develop training materials and formal assessments for products and their intended applications.

This presentation will include perspectives and experiences of three meteorologists who have served in this role since planning for the NWS Huntsville office began in 2002. This discussion will include involvement of the liaison forecaster and AIM in coordinated R2O/O2R efforts that span more than 20 years, beginning with the transition of total lightning data into operations as early as 2003, through the GOES-R and JPSS Proving Grounds, to present-day machine learning efforts such as streamflow and probabilistic lightning forecasts.