On the limits of estimating the maximum wind speeds in hurricanes

David S Nolan, Jun A. Zhang, Eric W. Uhlhorn

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

This study uses an observing system simulation experiment (OSSE) approach to test the limitations of even nearly ideal observing systems to capture the peak wind speed occurring within a tropical storm or hurricane. The dataset is provided by a 1-km resolution simulation of an Atlantic hurricane with surface wind speeds saved every 10 s. An optimal observing system consisting of a dense field of anemometers provides perfect measurements of the peak 1-min wind speed as well as the average peak wind speed. Suboptimal observing systems consisting of a small number of anemometers are sampled and compared to the truth provided by the optimal observing system. Results show that a single, perfect anemometer experiencing a direct hit by the right side of the eyewall will underestimate the actual peak intensity by 10%-20%. Even an unusually large number of anemometers (e.g., 3-5) experiencing direct hits by the storm together will underestimate the peak wind speeds by 5%-10%. However, the peak winds of just one or two anemometers will provide on average a good estimate of the average peak intensity over several hours. Enhancing the variability of the simulated winds to better match observed winds does not change the results. Adding observational errors generally increases the reported peak winds, thus reducing the underestimates. If the average underestimate (negative bias) were known perfectly for each case, it could be used to correct the wind speeds, leaving only mean absolute errors of 3%-5%.

Original languageEnglish (US)
Pages (from-to)2814-2837
Number of pages24
JournalMonthly Weather Review
Volume142
Issue number8
DOIs
StatePublished - 2014

Fingerprint

anemometer
hurricane
wind velocity
surface wind
simulation
experiment

ASJC Scopus subject areas

  • Atmospheric Science

Cite this

On the limits of estimating the maximum wind speeds in hurricanes. / Nolan, David S; Zhang, Jun A.; Uhlhorn, Eric W.

In: Monthly Weather Review, Vol. 142, No. 8, 2014, p. 2814-2837.

Research output: Contribution to journalArticle

Nolan, David S ; Zhang, Jun A. ; Uhlhorn, Eric W. / On the limits of estimating the maximum wind speeds in hurricanes. In: Monthly Weather Review. 2014 ; Vol. 142, No. 8. pp. 2814-2837.
@article{88460de96caa4bdaac6188d46ce5326a,
title = "On the limits of estimating the maximum wind speeds in hurricanes",
abstract = "This study uses an observing system simulation experiment (OSSE) approach to test the limitations of even nearly ideal observing systems to capture the peak wind speed occurring within a tropical storm or hurricane. The dataset is provided by a 1-km resolution simulation of an Atlantic hurricane with surface wind speeds saved every 10 s. An optimal observing system consisting of a dense field of anemometers provides perfect measurements of the peak 1-min wind speed as well as the average peak wind speed. Suboptimal observing systems consisting of a small number of anemometers are sampled and compared to the truth provided by the optimal observing system. Results show that a single, perfect anemometer experiencing a direct hit by the right side of the eyewall will underestimate the actual peak intensity by 10{\%}-20{\%}. Even an unusually large number of anemometers (e.g., 3-5) experiencing direct hits by the storm together will underestimate the peak wind speeds by 5{\%}-10{\%}. However, the peak winds of just one or two anemometers will provide on average a good estimate of the average peak intensity over several hours. Enhancing the variability of the simulated winds to better match observed winds does not change the results. Adding observational errors generally increases the reported peak winds, thus reducing the underestimates. If the average underestimate (negative bias) were known perfectly for each case, it could be used to correct the wind speeds, leaving only mean absolute errors of 3{\%}-5{\%}.",
author = "Nolan, {David S} and Zhang, {Jun A.} and Uhlhorn, {Eric W.}",
year = "2014",
doi = "10.1175/MWR-D-13-00337.1",
language = "English (US)",
volume = "142",
pages = "2814--2837",
journal = "Monthly Weather Review",
issn = "0027-0644",
publisher = "American Meteorological Society",
number = "8",

}

TY - JOUR

T1 - On the limits of estimating the maximum wind speeds in hurricanes

AU - Nolan, David S

AU - Zhang, Jun A.

AU - Uhlhorn, Eric W.

PY - 2014

Y1 - 2014

N2 - This study uses an observing system simulation experiment (OSSE) approach to test the limitations of even nearly ideal observing systems to capture the peak wind speed occurring within a tropical storm or hurricane. The dataset is provided by a 1-km resolution simulation of an Atlantic hurricane with surface wind speeds saved every 10 s. An optimal observing system consisting of a dense field of anemometers provides perfect measurements of the peak 1-min wind speed as well as the average peak wind speed. Suboptimal observing systems consisting of a small number of anemometers are sampled and compared to the truth provided by the optimal observing system. Results show that a single, perfect anemometer experiencing a direct hit by the right side of the eyewall will underestimate the actual peak intensity by 10%-20%. Even an unusually large number of anemometers (e.g., 3-5) experiencing direct hits by the storm together will underestimate the peak wind speeds by 5%-10%. However, the peak winds of just one or two anemometers will provide on average a good estimate of the average peak intensity over several hours. Enhancing the variability of the simulated winds to better match observed winds does not change the results. Adding observational errors generally increases the reported peak winds, thus reducing the underestimates. If the average underestimate (negative bias) were known perfectly for each case, it could be used to correct the wind speeds, leaving only mean absolute errors of 3%-5%.

AB - This study uses an observing system simulation experiment (OSSE) approach to test the limitations of even nearly ideal observing systems to capture the peak wind speed occurring within a tropical storm or hurricane. The dataset is provided by a 1-km resolution simulation of an Atlantic hurricane with surface wind speeds saved every 10 s. An optimal observing system consisting of a dense field of anemometers provides perfect measurements of the peak 1-min wind speed as well as the average peak wind speed. Suboptimal observing systems consisting of a small number of anemometers are sampled and compared to the truth provided by the optimal observing system. Results show that a single, perfect anemometer experiencing a direct hit by the right side of the eyewall will underestimate the actual peak intensity by 10%-20%. Even an unusually large number of anemometers (e.g., 3-5) experiencing direct hits by the storm together will underestimate the peak wind speeds by 5%-10%. However, the peak winds of just one or two anemometers will provide on average a good estimate of the average peak intensity over several hours. Enhancing the variability of the simulated winds to better match observed winds does not change the results. Adding observational errors generally increases the reported peak winds, thus reducing the underestimates. If the average underestimate (negative bias) were known perfectly for each case, it could be used to correct the wind speeds, leaving only mean absolute errors of 3%-5%.

UR - http://www.scopus.com/inward/record.url?scp=84905687122&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84905687122&partnerID=8YFLogxK

U2 - 10.1175/MWR-D-13-00337.1

DO - 10.1175/MWR-D-13-00337.1

M3 - Article

VL - 142

SP - 2814

EP - 2837

JO - Monthly Weather Review

JF - Monthly Weather Review

SN - 0027-0644

IS - 8

ER -