An Improved Method to Estimate Tropical Cyclone Surface Wind Fields from Routine Satellite Reconnaissance

The estimation of surface winds associated with tropical cyclones is important to a variety of public, private, and governmental stakeholders and applications. However the surface wind field of tropical cyclones is rarely, if ever, instantaneously measured with sufficient resolution to provide enough detail for most users and applications. The best depictions of the TC wind field come from storm-centric temporal composite analyses that make use of aircraft reconnaissance-based (and other) observations over a relatively short time period. Fortunately many of the high-temporal resolution (≥10 Hz) digital records of flight-level wind vectors and more recently surface wind speed estimates have been archived and exist in sufficient quantities that analyses of flight-level wind fields can be analyzed for a variety of tropical cyclone cases. Several studies have also provided guidance as how flight-level wind speeds are related to surface wind speeds (e.g., Powell and Black 1990, Franklin et al 2003, Powell et al. 2009) and what the surface wind inflow angles are as a function of azimuth and radius (Zhang and Uhlhorn 2012) based on dropwindsonde and SFMR data. Collectively, these data and techniques allow for the estimation of the surface wind fields associated with a large sample of TCs. Geostationary satellites have also provided over thirty years of routine infrared (IR) window (~ 11 µm) observations of TCs in the Atlantic and Eastern North Pacific where most of the digital aircraft reconnaissance data is available. The coincidence of both the IR and aircraft reconnaissance data led to the development of methods to estimate the azimuthally averaged flight-level wind profiles from the azimuthally averaged IR brightness temperature profiles (Mueller et al. 2006, Kossin et al. 2007). Using simple assumptions of wind asymmetries as a function of motion, both methods produce estimates of two-dimensional flight-level winds. From those flight-level wind estimates and a flight-level to surface wind reduction, a surface wind field can be estimated. Both methods provide IR proxies to flight-level winds within 200 km of the TC center that cannot be estimated from other satellite platforms. Thus, these IR proxy flight-level wind estimate methods represented a new satellite data capability. For instance, the Mueller et al (2006)-based wind estimates are used to generate the National Environmental Satellite Data and Information Services' (NESDIS) Multi-platform Tropical Cyclone Surface Wind Analysis (MTCSWA; Knaff et al. 2011)that provides operational 6-hourly surface wind field estimates for all active global tropical cyclones – without aircraft reconnaissance. While these methods represented a new satellite wind estimate capability, they represented the two-dimensional wind field in a rather crude manner. The only azimuthal asymmetries in the wind field were due to motion and those asymmetries were not a function of radius. The accuracy and minimum size of the radius of maximum winds was also hampered by radial resolution and rather smooth depiction of the flight-level wind analyses used in both Mueller et al (2006) and Kossin et al. (2007). In this paper, we look to improve upon these two methods and create an improved depiction of the flight-level and surface wind fields by 1) using several more years of aircraft reconnaissance data, 2) using a higher resolution wind analysis system to create the developmental wind fields, one that can better depict the radial and azimuthal variations of the winds, and 3) statistically relate two-dimensional IR information to the amplitude and phase of wavenumber 0,1 and 2 of the TC wind field on a TC-motion relative polar analysis grid. To go one step further and estimate the surface winds, we will rely on recent guidance on flight-level to surface wind reduction and inflow angles based on dropwindsonde and SFMR data and generalize the technique to the Southern Hemisphere. Finally we will show how the wind fields estimated by this new method could be incorporated into and improve the surface wind estimates produced by the operational MTCSWA.

DISCLAIMER: The views, opinions, and findings contained in this report are those of the authors and should not be construed as an official National Oceanic and Atmospheric Administration or U.S. Government position, policy, or decision.