The 12th Americas Conference on Wind Engineering (12ACWE) Seattle, Washington, USA, June 16-20, 2013 H*Wind Hurricane Time History Extraction for Defined Locations Seyed Armin Madani a , Mohammad Baradaranshoraka b , Carol J. Friedland c a Graduate Research Assistant, Engineering Science,, Louisiana State University, Baton Rouge, LA, USA b Graduate Research Assistant, Engineering Science, Louisiana State University, Baton Rouge, LA, USA c Assistant Professor of Construction Management, Louisiana State University, Baton Rouge, LA, USA ABSTRACT: Two dimensional linear interpolations to approximate the wind speed and wind direction values for a specific location by using nearby known points can be a complicated or computationally intensive process when the of points are included in the calculation is a variable. By using the corrective smoothed particle hydrodynamic method (CSPM), the number of points included in the calculations can be easily increased or decreased to achieve more computationally efficient results at unknown points. This method is implemented to derive hurricane time histories from published H*Wind datasets or other regularly or irregularly distributed wind data for any desired location. This paper presents the CSPM methodology to interpolate data points and extract a wind hazard time history from published H*Wind datasets. KEYWORDS: NOAA H*Wind, time history, wind speed, wind direction 1 INTRODUCTION AND BACKGROUND Wind measurement stations may not maintain continuous operation during severe wind events. To evaluate the impact of a wind event on individual points of interest (e.g., buildings, infrastructure), it is important to understand the magnitude of the hazard over the duration of the event. Many times, obtaining wind information for a specific location during a large scale event can be difficult, if not impossible. The Hurricane Research Division (HRD) of the National Oceanic & Atmospheric Administration (NOAA) synthesizes the best available wind speed information from a hurricane event to represent large scale wind speeds and directions [1]. These data are available for the entire event in the form of time-stepped footprints at 3 or 6 hour intervals, as well as maximum ensemble wind swaths [2]. Spatial interpolation methods may be used to synthesize the time-stepped datasets to determine the temporal variation in wind speed and wind direction for a defined location. However, there are several interpolation methods that may be used, and method selection is very important from computational and accuracy perspectives. To derive the time history of wind speed or wind direction for a specific location, a suitable interpolation technique must be chosen through considerations of different of wind dataset characteristics (e.g., regular or irregular distribution of data points, scarcity or abrupt changes of values for each time period). Several geostatistical and deterministic interpolation methods have been used to estimate wind speed or wind direction in unknown locations or to create an integrated surface over storm domains. Table 1 shows a brief review of advantages and disadvantages of a number of the most commonly used interpolation methods for windstorms.