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Kyle Andrew Poyar and Nancy Beller-Simms

quantifying population and assets vulnerable to storm surge (e.g., Houston); mapping exposed populations using lidar and other technologies (e.g., Miami, Maryland); presenting plausible future climate scenarios across a range of sectors (e.g., King County); and projecting municipal financial damages associated with climate change, such as more frequent replacement of property and equipment (e.g., Chicago). d. Evaluation criteria used with recommended adaptation strategies The recommendations presented in

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Christopher A. Fiebrich, Jadwiga R. Ziolkowska, Phillip B. Chilson, and Elizabeth A. Pillar-Little

, sodars, lidars, and radiometers ( NASEM 2018b ); however, the “data gap” still persists. The initial application of WxUAS for meteorological studies was achieved via fixed-wing aircraft ( Elston et al. 2015 ). These aircraft successfully made targeted observations of meteorological phenomena otherwise too small in scale to be sampled or too difficult to access by traditional radiosondes. Examples of WxUAS applications in the atmospheric sciences include the characterization of mesoscale airmass

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Saeideh Maleki, Saeid Soltani Koupaei, Alireza Soffianian, Sassan Saatchi, Saeid Pourmanafi, and Vahid Rahdari

function that takes on the values of −1, 0, and 1 according to the sign of ( x i − x j ). That is, sign ( x i − x j ) is equal to −1 for ( x i − x j ) < 0, equal to 0 for ( x i − x j ) = 0, and equal to 1 for ( x i − x j ) > 0. The variance of S (Var[ S ]) is calculated by the following equation: (2) Var ⁡ [ S ] = n ⁡ ( n − 1 ) ⁡ ( 2 n + 5 ) − ∑ p = 1 q t p ⁡ ( t p − 1 ) ⁡ ( 2 t p + 5 ) 18 , where tp is the number of observations the for the p th group and q is the number of tied

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