A multi-platform satellite tropical cyclone wind analysis system

Despite the fairly recent advances in satellite-based wind estimate techniques and their real-time availability, there have been very few attempts to create a combined wind analysis in and around tropical cyclones (TCs). The lack of such analyses is primarily due to the lack of techniques that would estimate the strong near core winds (i.e., within 200km of the cyclone center). Such capabilities are now possible from the routinely available IR-imagery and it is now possible to create a low-level wind field like those created when aircraft reconnaissance data is available, but without aircraft reconnaissance. These TC wind fields are constructed from all available satellite wind datasets including IR-imagery, AMSU, SSMI, Quikscat, and cloud drift/water vapor winds as input to variational wind analysis. The variational analysis is performed in a cylindrical coordinate framework, makes use of speed only information, and performs gross error checking. A simple flight-level to 10-m level reduction is preformed. Such an analysis has been available since mid-July 2005 in a semi-operational and timely manner (six-hourly) for all tropical cyclones warned by the National Hurricane Center and the Joint Typhoon Warning Center. It has provided vital information concerning the structure of the wind field as well as the location(s) and extent of the maximum winds - information that previously would only be available via aircraft reconnaissance.

As it stands, the current TC fix is no longer state-of-the-art and by using currently available real-time satellite datasets, a low-level wind field – similar to that obtained by reconnaissance aircraft can be estimated when aircraft reconnaissance is unavailable. This presentation will discuss the development of a multi-platform satellite tropical cyclone wind analysis system, which can provide two-dimensional estimates of the tropical cyclone wind field structure along with a real-time example or two from the 2005 hurricane season.