The Advanced Satellite Aviation Weather Products (ASAP) initiative: Phase I efforts at the University of Alabama in Huntsville

This presentation describes 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) on research and development of products designed to be used to diagnose, nowcast (0-6 hours) and forecast (beyond 6 hours) significant aviation weather hazards. 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 and UW-CIMSS 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.

At UAH, ASAP will draw together scientists in the Marshall Space Flight Center (MSFC) involved in the use of lightning [the National Lightning Detection Network (NLDN) and the northern Alabama Lightning Mapping Array (LMA)], as well as those involved in the NASA Short-term Prediction Research Transition (SPoRT) Center.

Phase I activities (mid 2003-late 2005) for ASAP lie in three main areas: 1) convection initiation and trend nowcasting, 2) development of algorithms that detect various forms of tropospheric turbulence, and 3) cloud base estimation. For the convection work, ongoing efforts are to nowcast convective initiation (CI) at 1 km resolution up to 45 minutes in advance based on the identification of key indicators in visible and infrared (IR) GOES satellite data. New work has begun to relate 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 tropopause folding. All forms of turbulence are being developed into a 5 km resolution grid that may be used by various AWRP PDTs. All convection and turbulence work is being coordinated with UW-CIMSS as products are made available to the PDTs.