Gust Factors: Meteorologically Stratified Climatology, Data Artifacts, and Utility in Forecasting Peak Gusts

Gust Factors: Meteorologically Stratified Climatology, Data Artifacts, and Utility in Forecasting... AbstractGust factors in Milwaukee, Wisconsin, are investigated using Automated Surface Observing System (ASOS) wind measurements from 2007 to 2014. Wind and gust observations reported in the standard hourly ASOS dataset are shown to contain substantial bias caused by sampling and reporting protocols that restrict the reporting of gusts to arbitrarily defined “gusty” periods occurring during small subsets of each hour. The hourly ASOS gust reports are found to be inadequate for describing the gust characteristics of the site and ill suited for the study of gust factors. A gust-factor climatology was established for Milwaukee using the higher-resolution, 1-min version of the ASOS dataset. The mean gust factor is 1.74. Stratified climatologies demonstrate that Milwaukee gust factors vary substantially with meteorological factors, with wind speed and wind direction exerting the strongest controls. A variety of modified gust-factor models were evaluated in which the peak wind gust is estimated by multiplying a gust factor by the observed, rather than forecast, wind speed. Errors thus obtained are entirely attributable to utility of the gust factor in forecasting peak gusts, having eliminated any error associated with the wind speed forecast. Results show that gust-factor models demonstrate skill in estimating peak gusts and improve with the use of meteorologically stratified gust factors. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Applied Meteorology and Climatology American Meteorological Society

Gust Factors: Meteorologically Stratified Climatology, Data Artifacts, and Utility in Forecasting Peak Gusts

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1558-8432
D.O.I.
10.1175/JAMC-D-17-0133.1
Publisher site
See Article on Publisher Site

Abstract

AbstractGust factors in Milwaukee, Wisconsin, are investigated using Automated Surface Observing System (ASOS) wind measurements from 2007 to 2014. Wind and gust observations reported in the standard hourly ASOS dataset are shown to contain substantial bias caused by sampling and reporting protocols that restrict the reporting of gusts to arbitrarily defined “gusty” periods occurring during small subsets of each hour. The hourly ASOS gust reports are found to be inadequate for describing the gust characteristics of the site and ill suited for the study of gust factors. A gust-factor climatology was established for Milwaukee using the higher-resolution, 1-min version of the ASOS dataset. The mean gust factor is 1.74. Stratified climatologies demonstrate that Milwaukee gust factors vary substantially with meteorological factors, with wind speed and wind direction exerting the strongest controls. A variety of modified gust-factor models were evaluated in which the peak wind gust is estimated by multiplying a gust factor by the observed, rather than forecast, wind speed. Errors thus obtained are entirely attributable to utility of the gust factor in forecasting peak gusts, having eliminated any error associated with the wind speed forecast. Results show that gust-factor models demonstrate skill in estimating peak gusts and improve with the use of meteorologically stratified gust factors.

Journal

Journal of Applied Meteorology and ClimatologyAmerican Meteorological Society

Published: Dec 15, 2017

References

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