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\title{Engineering Applications of the Newly Available Roughness-Length Measurements by AOML at 213 ASOS Stations}
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\begin{document}

             \author[1]{S. A.  Hsu}

             \affil[1]{  Louisiana State University}

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\date{\small \em Received: 8 December 2012 Accepted: 1 January 2013 Published: 15 January 2013}

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\begin{abstract}
        


Most recently, the Hurricane Research Division of the U. S. Atlantic Oceanographic and Meteorological Laboratory (AOML) has made extensive surveys of the roughness length (Zo) in each of the 213 Automated Surface Observation Stations (ASOS) located in tropical-cyclone prone regions. The original 8 values of Zo for each of the 45 degree segments within the 360 degree compass in each ASOS station are averaged geometrically to obtain one typical value for each of these 213 ASOS stations. Six ASOS stations are verified independently by the gust factor method during 5 hurricanes. Since the difference is within the 10 % composite accuracy for field measurements in wind speed, the computed geometric mean Zo values for each of the 213 ASOS stations are recommended for practical use. Applications of the proposed geometric mean Zo value to estimate the 3-second gust, peak gust, and peak factor during Hurricane Katrina are also provided for engineers as an example.

\end{abstract}


\keywords{roughness length; hurricanes; asos stations, turbulence intensity; power-law exponent; gust factor; peak factor.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
ost recently, in its "Tropical Cyclone Wind Exposure Documentation Project", the Hurricane Research Division (HRD) at the Atlantic Oceanographic and Meteorological Laboratory (AOML), U. S. National Oceanic and Atmospheric Administration (NOAA), has made an extensive survey and monitoring of the roughness length (Zo) at 213 ASOS are located at hurricane-prone airports (see www.aoml.noaa.gov/hrd/asos/index.html). Because Zo is a parameter needed for wind and turbulence estimates for civil, structural and environmental engineers (see, e.g., \hyperref[b2]{Hsu, 2013)}, the purpose of this study is to utilize these newly available Zo measurements by AOML for engineering applications. 
\section[{II.}]{II.} 
\section[{Geometric Mean zo for each ASOS Environment}]{Geometric Mean zo for each ASOS Environment}\par
According to AOML the 360 degree compass for the wind direction measurement is divided into 8 segments\par
Author: Professor Emeritus and Certified Consulting Meteorologist Coastal Studies Institute, Louisiana State University. e-mail: sahsu@lsu.edu so that there is one Zo value for each 45 degrees at each ASOS. These 8 Zo values may be needed for aviation safety reasons. However, since the wind direction in a tropical cyclone is rotational in nature and since the strongest wind may come from any direction, it is not necessary for practical operation to have 8 Zo for each ASOS. Instead, a typical Zo value or the geometric mean for each ASOS is needed for most engineering applications. Therefore, the original list which consists of 8 Zo values for each ASOS is geometric averaged. Our results are provided in the Appendix with one geometric mean Zo for each of the 213 ASOS. 
\section[{III.}]{III.}\par
Validating the Relation between zo, Gust Factor and Turbulence Intensity\par
According to  {\ref Panofsky and Dutton (1984, pp.130-131)}, it is common in engineering practice to describe the variation of the wind speed with height, i.e. the wind profile with a power law such that \par
Where U 2 and U 1 are the wind speed at height Z 2 and Z 1 Now, for each ASOS Station the appropriate value of p based on Eq. ( 2) is also provided in the Appendix.\par
, respectively, p is the power-law exponent, and Zo is the roughness length.\par
According to Hsu (2013), for 5 second gust over the 2 minute duration, which is available routinely from the wind speed measurements by ASOS, we haveG = 1 + 2.04 P (3) = 1 + 2.04 TI (\textbf{4})\par
Where G is the gust factor (the ratio of 5-s gust to 2 -min sustained wind speed) and TI represents the longitudinal turbulence intensity. A forementioned equations are validated as follows :\par
wind speed measurements. According to U. S. National Data Buoy Office (see http://www.ndbc.noaa.gov/rsa. shtml), The composite accuracy of field measurements for the wind speed and wind gust is +/-10 \%. In other words, if the difference between measurements and estimates related to wind and gust characteristics is within 10 \%, one may accept those estimates as reasonable. Note that this 10 \% margin of error can also be related to the different anemometers used in the field. An example is shown in Table \hyperref[tab_0]{1}. On the basis of Tables \hyperref[tab_2]{2 and 3} and Fig. \hyperref[fig_1]{1}, we can say that the geometric mean Zo for KILM as listed in the Appendix is valid for engineering applications. Furthermore, it is shown that p =TI. (Data Source : Schroeder, 1999).  In 2005 Hurricane Rita passed near Lake Charles, Louisiana, USA. On the basis of Fig. \hyperref[fig_2]{2} and Eq.\par
(3), P = 0.172. According to the Appendix for KLCH, p = 0.182. Since the difference between these two p values is 5.5 \%, we can say that the mean geometric Zo value and the computed p value are validated.   {\ref 1}), we have p = 0.2996. Since the difference between 0.29 and 0.2996 is approximately 3 \%, we can say that Eq. (  {\ref 1}) is further verified. Now, according to Fig. \hyperref[fig_3]{3} and Eq. (  {\ref 3}), p = 0.180. Since this value is nearly equal to that of 0.177for KHOU as shown in the Appendix, we can say that the geometric mean Zo for KHOU is validated.   Since the information on both 3-second and peak gusts are needed for wind load analyses (see, e.g., \hyperref[b3]{Irwin, 2006)} and since some data during Katrina are available, we can use Katrina as a case study. This is done as follows : According to the Hurricane Katrina Post-Tropical Cyclone Report (http://www.srh.noaa.gov/lix/? n=psh\textunderscore katrina) by the National Weather Service (NWS) in New Orleans,LA, there was an ASOS station located at 50 feet (or 15.2 m) over Lake Pontchartrain. That station recorded max 2-min sustained wind speed of 68 knots (35.1 m/s) and 5-second gust of 86 knots (44.3 m/s). Therefore, according to Eq. (  {\ref 3}), p = 0.130. According to \hyperref[b2]{Hsu (2013)} and Fig.  {\ref 6}, the gradient height over the Lake was 309 m so that the wind speed at 309 m is estimated to beU 309m = U 15.2m\par
Now, according to the Appendix, p = 0.225 for New Orleans International Airport (KMSY). Substituting gradient height over KMSY is estimated to be 467m. Therefore, based on Eq. (  {\ref 6}), the 2-minute sustained wind speed over KMSY at the elevation of 467m was 51.9 m/s during Katrina. Although much of the data were not available due power failure during Katrina, there were two peak wind speed measurements located at \par
International Airport during Katrina as provided in the website as quoted above. This is done in Table \hyperref[tab_4]{4}.Since the difference between estimated and measured is 5.5 \% or less, the methods provided in this study should be useful in engineering applications.    {\ref 9}), we have max This estimated value is in good agreement with those measured value which ranged from 53.6 to 55.2 m/s or from 104 to 107 knots. Therefore, the answer to the questions raised by the civil and structural engineers is that those "peak gust" measurements in the Eastern New Orleans area as provided in its Hurricane Katrina -Post Tropical Cyclone Report by the National Weather Service in New Orleans were in fact not the 3-second gust but the maximum instantaneous gust, which represents the 3 standard deviation or within the top 1 \% probability.\par
=33.3*(1+3*0.198)=53.0 m/s=103 knot (10) c) Application to estimate peak factor Depending on anemometer system and averaging period, each dataset for the wind speed measurement consists of the duration of sampling such as 1 minute (e.g. see Table \hyperref[tab_1]{2}), 2 minutes (such as from ASOS station),10 minutes, or even one hour. Within this sampling duration, there is a maximum or peak gust, which represents the shortest period of measurement such as 0.2 second as shown in Table \hyperref[tab_1]{2}. Therefore, the generic formula similar to Eq. (  {\ref 9}) isU peak = U duration Or, A = (U (1+ A p) (11) peak /U duration\par
Where "A" is the peak factor.\par
-1)/ p (12)\par
An example is provided as follows: According to Table \hyperref[tab_1]{2}, the maximum 1-min wind speed was 25.0 m/s and the max 0.2-second 38.2 m/s.\par
A question was raised by some civil and According to Table \hyperref[tab_0]{1}, p= 0.185, substituting these values into Eq. (  {\ref 12}), we get A = 2.85. Since the difference between 2.85 and 3 (see Eq. 3) is 5 \%, we can say that the 0.2-second gust measurement is near the top one per cent during a one minute period. Statistically, one can also get this "A" value from the ratio of 0.2 second and one minute such that 0.2/60 = 0.0033 or within the top 1 \% probability. Furthermore, from statistics (see, e.g.,  {\ref Spiegel, 1961, p.343)}, (1-0.2/60)/2 = 0.4983 so that "A" = 2.93 for areas under standard normal curve from zero to z, where z is our peak factor. Note that this value of 2.93 is even closer to 3 as shown in Eq. (  {\ref 9}).\par
V. 
\section[{Conclusions}]{Conclusions}\par
On the basis of aforementioned analyses and discussions, several conclusions may be drawn: 1. Because of the instrument response and system design the composite accuracy of the anemometer for field application is illustrated to be approximately within 10 \%. 2. The roughness length (Zo) measurements around the 360 compass in each of the 213 ASOS stations located in tropical-cyclone prone regions have been averaged geometrically. Appendix : A list of geometric mean for Zo and power-law exponent for p.  
\section[{Station}]{Station}\begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_1}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-3.png}
\caption{\label{fig_2}Figure 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-4.png}
\caption{\label{fig_3}Figure 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4}\includegraphics[]{image-5.png}
\caption{\label{fig_4}Figure 4 :}\end{figure}
      \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.4158273381294964\textwidth}P{0.1039568345323741\textwidth}P{0.10089928057553957\textwidth}P{0.22931654676258992\textwidth}}
\tabcellsep \tabcellsep \tabcellsep b) Validation during Hurricane Bonnie in 1998\\
\multicolumn{4}{l}{turbulence intensity (TI) from different anemometers}\\
\multicolumn{4}{l}{during Hurricane Bonnie in 1998}\\
(1). UVW\tabcellsep (2). Propeller-\tabcellsep Difference\tabcellsep Mean TI\\
anemometer\tabcellsep Vane\tabcellsep between\tabcellsep between\\
\tabcellsep anemometer\tabcellsep (1) and (2)\tabcellsep (1) and (2)\\
0.175\tabcellsep 0.195\tabcellsep 10 \%\tabcellsep 0.185\\
\multicolumn{3}{l}{(Data source: Schroeder, 1999)}\tabcellsep \end{longtable} \par
 
\caption{\label{tab_0}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.6830357142857143\textwidth}P{0.05464285714285714\textwidth}P{0.11232142857142857\textwidth}}
\multicolumn{3}{l}{ASOS and Texas Tech University at Wilmington Airport}\\
\multicolumn{3}{l}{(KILM), North Carolina, USA, during Hurricane Bonnie in}\\
1998\tabcellsep \tabcellsep \\
\tabcellsep ASOS\tabcellsep Texas Tech\\
\multicolumn{2}{l}{Station}\tabcellsep Station\\
0.2-Second Gust (m/s)\tabcellsep NA\tabcellsep 38.2\\
3-Second Gust (m/s)\tabcellsep NA\tabcellsep 33.6\\
5-Second Gust (m/s)\tabcellsep 32.9\tabcellsep 33.5\\
1-minute Sustained (m/s)\tabcellsep NA\tabcellsep 25.0\\
2-minute Sustained (m/s)\tabcellsep 25.2\tabcellsep 24.4\end{longtable} \par
 
\caption{\label{tab_1}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.24089439655172412\textwidth}P{0.13098060344827586\textwidth}P{0.07327586206896552\textwidth}P{0.003663793103448276\textwidth}P{0.05495689655172414\textwidth}P{0.004579741379310345\textwidth}P{0.3297413793103448\textwidth}P{0.007327586206896552\textwidth}P{0.004579741379310345\textwidth}}
\tabcellsep 25.00 30.00 35.00\tabcellsep \multicolumn{3}{l}{R² = 0.920 y = 1.420x}\tabcellsep \\
\tabcellsep 20.00\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep 15.00\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep 10.00\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep 5.00\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep 0.00\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \multicolumn{2}{l}{0.00}\tabcellsep \multicolumn{2}{l}{5.00}\tabcellsep 10.00\tabcellsep 15.00\tabcellsep 20.00\tabcellsep 25.00\\
\tabcellsep 5\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{5}{l}{: A comparison of measurements against 3}\tabcellsep \\
\multicolumn{6}{l}{estimates of p using Eq. (3) and the geometric mean of}\\
\multicolumn{5}{l}{Zo from Appendix at Wilmington Airport during}\tabcellsep \\
\tabcellsep \multicolumn{2}{l}{Hurricane Bonnie in 1998}\tabcellsep \tabcellsep \tabcellsep \\
Source\tabcellsep (1). P based\tabcellsep \multicolumn{2}{l}{(2). P from}\tabcellsep \multicolumn{2}{l}{Difference}\tabcellsep (68.4/47) = (350/77) \textasciicircum  p\\
\tabcellsep on either\tabcellsep \multicolumn{2}{l}{Appendix}\tabcellsep between\tabcellsep \tabcellsep So that p = Ln (68.4/47)/Ln (350/77) = 0.248\tabcellsep (5)\\
UVW, Table 1 Pro peller-vane, Table 1\tabcellsep measured or estimated 0.175 0.195\tabcellsep for KILM 0.185 0.185\tabcellsep \tabcellsep \multicolumn{2}{l}{(1) and (2) 0.054 0.051}\tabcellsep Since this value is identical to that at KASD for Slidell Airport, LA (which is not very far from Pass Christian), as provided in the Appendix, we can say that the geometric mean Zo for KASD is verified for practical\\
ASOS, Table 2\tabcellsep 0.150\tabcellsep 0.185\tabcellsep \tabcellsep 0.189\tabcellsep \tabcellsep use. Note that, during Katrina, nearly all surface wind\\
TTU, Table 2\tabcellsep 0.183\tabcellsep 0.185\tabcellsep \tabcellsep 0.011\tabcellsep \\
Fig. 1\tabcellsep 0.206\tabcellsep 0.185\tabcellsep \tabcellsep 0.102\tabcellsep \\
Mean\tabcellsep 0.182\tabcellsep 0.185\tabcellsep \tabcellsep 0.016\tabcellsep \\
\multicolumn{5}{l}{c) Validation during Hurricane Katrina in 2005}\tabcellsep \end{longtable} \par
 
\caption{\label{tab_2}Table 3}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.3546676096181046\textwidth}P{0.49533239038189536\textwidth}}
\multicolumn{2}{l}{Engineering Applications of the Newly Available Roughness-Length Measurements by AOML at 213ASOS}\\
\multicolumn{2}{l}{Stations}\\
Year 2013\tabcellsep \\
14\tabcellsep \\
rsion I\tabcellsep \\
XIII Issue v VII Ve\tabcellsep \\
Volume\tabcellsep \\
D D D D ) E\tabcellsep \\
(\tabcellsep \\
Global Journal of Researches in Engineering\tabcellsep coming ashore near Pass Christian, MS, the aircraft measure-ments of maximum wind speed was 68.4 m/s at 350 m and at the near-surface (77m) it dropped down to 47 m/s. Therefore, according to Eq. (1), we have\\
\tabcellsep measurements were not available because of massive\\
\tabcellsep power failure. Therefore, these aircraft measurements by\\
\tabcellsep U.S. Air Force Hurricane-Hunters are greatly\\
\tabcellsep appreciated.\\
\tabcellsep d) Validation during Hurricane Rita in 2005\\
According to Henning (see http://ams.confex.-\tabcellsep \\
com/ams /pdfpapers/108816.pdf), when Katrina was\tabcellsep \end{longtable} \par
 
\caption{\label{tab_3}-second gust, m/s 2 -minute sustained wind speed, m/s}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.29507772020725387\textwidth}P{0.2510362694300518\textwidth}P{0.15854922279792746\textwidth}P{0.14533678756476684\textwidth}}
\multicolumn{4}{l}{second gust around New Orleans International Airport}\\
\tabcellsep \multicolumn{2}{l}{during Katrina}\tabcellsep \\
Height,\tabcellsep Estimated,\tabcellsep Measured,\tabcellsep Difference\\
m\tabcellsep m/s\tabcellsep m/s\tabcellsep In per cent\\
36.6\tabcellsep 42.7\tabcellsep 43.8\tabcellsep 2.5 \%\\
\tabcellsep from Eq.(7)\tabcellsep From NWS\tabcellsep \\
9.1\tabcellsep 31.2\tabcellsep 33.0\tabcellsep 5.5 \%\\
\tabcellsep From Eq.(8)\tabcellsep From NWS\tabcellsep \end{longtable} \par
 
\caption{\label{tab_4}Table 4 :}\end{figure}
 			\footnote{© 2013 Global Journals Inc. (US) © 2013 Global Journals Inc. (US)} 			\footnote{© 2013 Global Journals Inc. (US)} 		 		\backmatter  			 
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\end{document}
