S43 Using Buoy and Radar Data to Study Sudden Wind Gusts Over Coastal Regions

Sunday, 22 January 2017
4E (Washington State Convention Center )
Georgios Priftis, Univ. of Alabama, Huntsville, AL; and T. Chronis and T. J. Lang

Buoys have been extensively used as ground-truth to assess (accuracy, quality control) and compare radar-derived surface winds. This analysis is based on the buoy dataset provided by the National Data Buoy Centre, for the period 2013 - 2015. The scope of this research is to identify cases for which there has been an abrupt increase in the wind speed and identify a radar-derived proxy for these events, given that they represent hazardous conditions for small-sized boats but also ground activities in coastal regions. The criterion for the wind increase is set to 10 m/s between buoy consecutive measurements. The study period was chosen to include the NEXRAD dual-polarization capabilities. In all of the cases the sudden wind increases were coincident with an abrupt change in the wind direction and, in most cases with a respective temperature change. Two cases are presented: one in 2015 (May 25th) and one in 2014 (August 26th), which are differentiated by the presence or absence of storms. For the first case, located in Cape May (NJ), the change in the wind speed was found to be 11.8 m/s, followed by a change in the direction and temperature of (-)48˚ and (-)1.5˚C, respectively. The radar proxy that best characterizes the onset of the wind increase was found to be the vertically integrated liquid (VIL) water. In the second case, located over Houston (TX), in the absence of any marine storm at least 100 km around the radar site, the correlation coefficient was the only proxy which identified a propagating wave-like pattern, moving towards the buoy location. The change in the wind speed was 11.5 m/s, while the change in the direction was (-)140˚ and an increase of dew point temperature of 0.5˚C. To further simulate and investigate the environmental conditions, the Weather Research and Forecasting (WRF) model was implemented for both cases. Additional cases have been analyzed in order to find a consistent pattern to identify these events.
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