This presentation describes the recent research and transition activities as part of the University of Alabama in Huntsville's (UAH) portion of the Advanced Satellite Aviation Weather Products (ASAP) initiative. For ASAP, UAH is collaborating directly with the University of Wisconsin Cooperative Institute for Meteorological Satellite Studies (UW-CIMSS), MIT Lincoln Labs (MIT-LL), and the Research Applications Laboratory at NCAR, on research and development of products designed to be used to diagnose and nowcast (0-6 hours) significant aviation weather hazards related to convective storm development and turbulence. The ASAP initiative represents a commitment between the National Aeronautics and Space Administration (NASA) and the Federal Aviation Administration's Aviation Weather Research Program (FAA AWRP) to enhance and extend the use of satellite data sets for applications in aviation weather. Through ASAP, UAH, UW-CIMSS, MIT-LL and NCAR RAL are working with various AWRP Product Development Teams (PDTs). The collaborative effort represents an opportunity, through NASA sponsorship, to assist the AWRP PDTs in making better use of existing satellite data sets. It will also be used to facilitate an early involvement of the AWRP PDTs in the development process for the next generation of satellite sensors and speed the use and incorporation of these new technologies into the Nation's aviation safety programs.

Activities lie in two main areas related to the processing of satellite data: 1) convection initiation and trend nowcasting, and 2) development of algorithms that detect various forms of tropospheric turbulence caused by thunderstorms and mountain waves. For the convection work, ongoing efforts are to nowcast convective initiation (CI) at 1 km resolution up to 60 min in advance based on the identification of key indicators in visible and infrared (IR) GOES satellite data, and validate this algorithm. Other research relates these IR trends to radar information (mainly for validating our CI algorithms) and total lightning flash rates. For atmospheric turbulence, pattern recognition and data mining techniques are being applied toward automatically identifying mountain waves - those that accompanying convection (from the CI work), and turbulence source regions related to cumulus updrafts. This talk will overview all of these research areas, and describe the validation activities associated with them as well.