While Hurricane Harvey will best be remembered for record rainfall that led to widespread flooding in southeastern Texas and western Louisiana, the storm also produced some of the most extreme wind speeds ever to be captured by an adaptive mesonet at landfall. This paper describes the unique tools and the strategy used by the Digital Hurricane Consortium (DHC), an ad hoc group of atmospheric scientists and wind engineers, to intercept and collect high-resolution measurements of Harvey’s inner core and eyewall as it passed over Aransas Bay into mainland Texas. The DHC successfully deployed more than 25 observational assets, leading to an unprecedented view of the boundary layer and winds aloft in the eyewall of a major hurricane at landfall. Analysis of anemometric measurements and mobile radar data during heavy convection shows the kinematic structure of the hurricane at landfall and the suspected influence of circulations aloft on surface winds and extreme surface gusts. Evidence of mesoscale vortices in the interior of the eyewall is also presented. Finally, the paper reports on an atmospheric sounding in the inner eyewall that produced an exceptionally large and potentially record value of precipitable water content for observed soundings in the continental United States.

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This paper presents a generalized approach for predicting (i.e., interpolating) the magnitude and distribution of roof pressures near separated flow regions on a low-rise structure based on freestream turbulent flow conditions. A feed-forward multilayer artificial neural network (ANN) using a backpropagation (BP) training algorithm is employed to predict the mean, root-mean-square (RMS), and peak pressure coefficients on three geometrically scaled (1:50, 1:30, and 1:20) low-rise building models for a family of upwind approach flow conditions. A comprehensive dataset of recently published boundary layer wind tunnel (BLWT) pressure measurements was utilized for training, validation, and evaluation of the ANN model. On average, predicted ANN peak pressure coefficients for a group of pressure taps located near the roof corner were within 5.1, 6.9, and 7.7% of BLWT observations for the 1:50, 1:30, and 1:20 models, respectively. Further, very good agreement was found between predicted ANN mean and RMS pressure coefficients and BLWT data.

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