Peak Gust Wind Research Articles

Investigating the Potential of Using Mixdown Altitudes to Forecast Peak Wind Gusts

Abstract Forecast models based on the gust factor, the ratio of peak gust to sustained wind speed, have shown promise in predicting peak wind gusts in recent years. These models assume that turbulent vertical transport forced by wind flowing over upstream terrain mixes high-momentum air aloft down to the surface. A recently constructed database of hourly peak gusts, together with approximately coincident, nearby upper-air wind observations, is used to identify mixdown altitudes, the altitudes from which peak gusts descend, during different weather scenarios at 16 locations across the United States. Median mixdown altitudes generally ranged from 50 to 450 m AGL with occasional exceptions, particularly for convective gusts and at mountainous locations where terrain effects are likely to amplify gusts. A mixdown model in which surface peak gusts are predicted by obtaining forecast upper-air winds within this altitude interval was developed and tested. Our results suggest that a mixdown model methodology for forecasting peak gusts may be feasible at locations and during weather conditions where terrain-forced turbulent mixing is the principal cause of wind gusts.

A decision-tree-based measure–correlate–predict approach for peak wind gust estimation from a global reanalysis dataset

Abstract. Peak wind gust (Wp) is a crucial meteorological variable for wind farm planning and operations. However, for many wind farm sites, there is a dearth of on-site measurements of Wp. In this paper, we propose a machine-learning approach (called INTRIGUE, decIsioN-TRee-based wInd GUst Estimation) that utilizes numerous inputs from a public-domain reanalysis dataset and, in turn, generates multi-year, site-specific Wp series. Through a systematic feature importance study, we also identify the most relevant meteorological variables for Wp estimation. The INTRIGUE approach outperforms the baseline predictions for all wind gust conditions. However, the performance of this proposed approach and the baselines for extreme conditions (i.e., Wp>20 m s−1) is less satisfactory.

Advancing parcel-level hurricane regional loss assessments using open data and the regional resilience determination tool

Hurricanes are a major driver of losses in the United States and thus are the focus of risk assessment capacity building efforts in the public and private sectors, as well as in the scholarly community. Capabilities for loss modeling have been particularly advanced through the development of open-source scientific workflows that conduct site-specific, building-specific, and even component-level loss assessments across entire regions. Notable among these is the Natural Hazards Engineering Research Infrastructure's Computational Modeling and Simulation Center's (NHERI SimCenter) Regional Resilience Determination (R2D) tool. However, the modular architecture of R2D's computational scaffolding has only been described and illustrated through testbed applications thus far. This study presents the first replication and extension of the R2D tool to conduct parcel-level and component-level hurricane performance assessments outside of the SimCenter's testbed locations. The study first details how building inventories that capture time-evolving building characteristics and regional construction practices can be generated using updated heuristic rulesets that guide the integration of tax assessor data with other open data sources. These rulesets and supporting data are then utilized to generate building inventory information for a set of single family homes located in Florida's Bay County, the landfall site of Hurricane Michael in 2018. HAZUS-compatible, parcel-level damage and loss assessments are then conducted, considering Hurricane Michael's peak gust wind speeds. Finally, a set of custom fragilities, empirically-derived from multiple regional post-disaster datasets, are incorporated into R2D to conduct the first component-level damage assessment of buildings under hurricanes using the SimCenter's regional loss modeling workflows. In total, this represents an important first step in operationalizing replicable regional risk assessments down to the parcel level to provide more granular risk information to key stakeholders.

Convective-gust nowcasting based on radar reflectivity and a deep learning algorithm

Abstract. Convective wind gusts (CGs) are usually related to thunderstorms, and they may cause great structural damage and serious hazards, such as train derailment, service interruption, and building collapse. Due to the small-scale and nonstationary nature of CGs, reliable CG nowcasting with high spatial and temporal resolutions has remained unattainable. In this study, a novel nowcasting model based on deep learning – namely, CGsNet – is developed for 0–2 h lead times of quantitative CG nowcasting, achieving minute–kilometer-level forecasts. CGsNet is a physics-constrained model established by training on large corpora of average surface wind speed (ASWS) and radar observations; it can produce realistic and spatiotemporally consistent ASWS predictions in CG events. By combining the gust factor (1.77, the ratio of the observed peak wind gust speed (PWGS) to the ASWS) with the ASWS predictions, the PWGS forecasts are estimated with a spatial resolution of 0.01∘ × 0.01∘ and a 6 min temporal resolution. CGsNet is shown to be effective, and it has an essential advantage in learning the spatiotemporal features of CGs. In addition, quantitative evaluation experiments indicate that CGsNet exhibits higher generalization performance for CGs than the traditional nowcasting method based on numerical weather prediction models. CG-nowcasting technology can be applied to provide real-time quantitative CG forecasts.

Biases in wind speed measurements due to anemometer changes

This research presents a case study of the biases and discontinuities that were introduced in observed long-term mean wind-speed and gust data-series due to anemometer changes in a meteorological station in northern Spain, operated by the Spanish State Meteorological Agency: San Sebastian-Igueldo. Field and wind-tunnel experiments with predefined conditions have been presented in the literature, however this research uses a real case study to assess the impact of anemometer changes on wind speed measurements due to three factors being: (i) the 3-cup anemometer model (SEAC vs. THIES companies); (ii) sensor height (∼19.95 m vs. ∼20.45 m) and (iii) sensor age (20-years old vs. new). Our results show (a) substantial biases in the measured wind speed and daily peak wind gusts, with the new THIES anemometer reporting stronger surface winds than the old SEAC anemometer; (b) opposing biases under weak (negative) and moderate-strong (positive) winds; and (c) significant breakpoints in the long-term wind data-series, which highlight the importance of data homogenization. National Weather Services and climate assessment groups will benefit from these findings since errors in wind speed and gust measurements can be minimized by implementing systematic observation protocols. Robust anemometer observations provide a basis for accurate quantification of the magnitude of changes and the variability of surface winds.

A novel framework to study community-level social and physical impacts of hurricane-induced winds through synthetic scenario analysis

Strong hurricane winds often cause severe infrastructure damage and pose social and economic consequences in coastal communities. In the context of community resilience planning, estimating such impacts can facilitate developing more risk-informed mitigation plans in the community of interest. This study presents a new framework for synthetically simulating scenario-hurricane winds using a parametric wind field model for predicting community-level building damage, direct economic loss, and social consequences. The proposed synthetic scenario approach uses historical hurricane data and adjusts its original trajectory to create synthetic change scenarios and estimates peak gust wind speed at the location of each building. In this research, a stochastic damage simulation algorithm is applied to assess the buildings’ physical damage. The algorithm assigns a damage level to each building using the corresponding damage-based fragility functions, predicted maximum gust speed at the building’s location, and a randomly generated number. The monetary loss to the building inventory due to its physical damage is determined using FEMA’s direct loss ratios and buildings’ replacement costs considering uncertainty. To assess the social impacts of the physical damage exposure, three likely post-disaster social disruptions are measured, including household dislocation, employment disruption, and school closures. The framework is demonstrated by its application to the hurricane-prone community of Onslow County, North Carolina. The novel contribution of the developed framework, aside from the introduced approach for spatial predicting hurricane-induced wind hazards, is its ability to illuminate some aspects of the social consequences of substantial physical damages to the building inventory in a coastal community due to the hurricane-induced winds. These advancements enable community planners and decision-makers to make more risk-informed decisions for improving coastal community resilience.

Predicting Peak Wind Gusts during Specific Weather Types with the Meteorologically Stratified Gust Factor Model

Abstract Wind gusts present challenges to operational meteorologists, both to forecast accurately and also to verify. Strong wind gusts can damage structures and create costly risks for diverse industrial sectors. The meteorologically stratified gust factor (MSGF) model incorporates site-specific gust factors (the ratio of peak wind gust to mean wind speed) with wind speed and direction forecast guidance. The MSGF model has previously been shown to be a viable operational tool that exhibits skill (improvement over climatology) in forecasting peak wind gusts. This study assesses the performance characteristics of the MSGF model by evaluating peak gust predictions during several types of gust-producing weather phenomena. Peak wind gusts were prepared and verified for seven specific weather conditions over an 8-yr period at 16 sites across the United States. When coupled with two forms of model output statistics (MOS) wind guidance, the MSGF model generally shows skill in predicting peak wind gusts at forecast projections ranging from 6 to 72 h. The model performed best during high pressure and nocturnal conditions and was also skillful during conditions involving snow. The model did not perform well during the “rain with thunder” weather type. The MSGF model is a viable tool for the operational prediction of peak gusts for most gust-producing weather types. Significance Statement Wind gusts are an important and potentially costly environmental hazard. Wind gusts affect many industrial sectors, including transportation, power generation, forestry, construction, and insurance, but predicting gusts remains a challenging component of weather forecasting. Recent studies have demonstrated that the meteorologically stratified gust factor (MSGF) model shows skill in predicting peak gusts. This study shows that the MSGF model is skillful at predicting peak gusts during specific types of gust-producing weather phenomena at forecast projections up to 72 h, providing further confirmation that the MSGF model is a viable tool for the operational prediction of peak gusts.

Meteorologically Stratified Gust Factors for Forecasting Peak Wind Gusts across the United States

ABSTRACTWind gusts are common to everyday life and affect a wide range of interests including wind energy, structural design, forestry, and fire danger. Strong gusts are a common environmental hazard that can damage buildings, bridges, aircraft, and trains, and interrupt electric power distribution, air traffic, waterways transport, and port operations. Despite representing the component of wind most likely to be associated with serious and costly hazards, reliable forecasts of peak wind gusts have remained elusive. A project at the University of Wisconsin–Milwaukee is addressing the need for improved peak gust forecasts with the development of the meteorologically stratified gust factor (MSGF) model. The MSGF model combines gust factors (the ratio of peak wind gust to average wind speed) with wind speed and direction forecasts to predict hourly peak wind gusts. The MSGF method thus represents a simple, viable option for the operational prediction of peak wind gusts. Here we describe the results of a project designed to provide the site-specific gust factors necessary for operational use of the MSGF model at a large number of locations across the United States. Gust web diagrams depicting the wind speed– and wind direction–stratified gust factors, as well as peak gust climatologies, are presented for all sites analyzed.

Radar-Based Comparison of Thunderstorm Outflow Boundary Speeds versus Peak Wind Gusts from Automated Stations

AbstractStraight-line winds are arguably the most challenging element considered by operational forecasters when issuing severe thunderstorm warnings. Determining the potential maximum surface wind gust prior to an observed, measured gust is very difficult. This work builds upon prior research that quantified a relationship between the observed outflow boundary speed and corresponding measured wind gusts. Whereas this prior study was limited to a 30-case dataset over eastern Colorado, the current study comprises 943 cases across the contiguous United States and encompasses all times of day, seasons, and regions while representing various convective modes and associated near-storm environments. The wind gust ratios (WGRs), or the ratio between a measured wind gust and the associated outflow boundary speed, had a nationwide median of 1.44, mean of 1.68, 25th percentile of 1.19, and 75th percentile of 1.91. WGRs varied considerably by region, season, time of day, convective mode, near-storm environment, and outflow boundary speed. WGRs tended to be higher in the plains, Intermountain West, and southern coastal regions, lower in the cool season and during the morning and overnight, and lower in linear convective modes relative to supercell and disorganized modes. Environments with stronger mean winds and low- to midlevel shear vector magnitudes tended to have lower WGRs, whereas those with steeper low-level lapse rates and other thermodynamic characteristics favorable for momentum transfer and evaporative cooling tended to have higher WGRs. As outflow boundary speed increases, WGRs—and their variability—decrease. Applying these findings may help operational meteorologists to provide more accurate severe thunderstorm warnings.

Climatology of Near‐Surface Daily Peak Wind Gusts Across Scandinavia: Observations and Model Simulations

AbstractAn observed daily peak wind gusts (DPWG) dataset over Scandinavia, consisting of time series from 127 meteorological stations across Finland, Norway and Sweden, has been created. This dataset provides high‐quality and homogenized near‐surface DPWG series for Scandinavia, spanning the longest available time period (1996–2016). The aim of this study is to evaluate the ability of two regional climate models (RCMs) in simulating DPWG winds. According to the observed DPWG climatology, meteorological stations are classified into three regions for which wind conditions are influenced by similar physical processes: coast, inland and mountain. Smaller‐scale DPWG features of the three regions are only captured when coarser general circulation models or reanalyses are downscaled by a RCM. Dynamic downscaling is thus needed to achieve more realistic simulations of DPWG when compared to their driving models. The performances of the RCMs are found to be more dependent on model dynamics and physics (such as gust parametrization) than on the boundary conditions provided by the driving models. We also found that the RCMs cannot accurately simulate observed DPWG in inland and mountainous areas, suggesting the need for higher horizontal resolution and/or better representation of relevant boundary‐layer processes.

A Decline of Observed Daily Peak Wind Gusts with Distinct Seasonality in Australia, 1941–2016

AbstractWind gusts represent one of the main natural hazards due to their increasing socioeconomic and environmental impacts on, for example, human safety, maritime–terrestrial–aviation activities, engineering and insurance applications, and energy production. However, the existing scientific studies focused on observed wind gusts are relatively few compared to those on mean wind speed. In Australia, previous studies found a slowdown of near-surface mean wind speed, termed “stilling,” but a lack of knowledge on the multidecadal variability and trends in the magnitude (wind speed maxima) and frequency (exceeding the 90th percentile) of wind gusts exists. A new homogenized daily peak wind gusts (DPWG) dataset containing 548 time series across Australia for 1941–2016 is analyzed to determine long-term trends in wind gusts. Here we show that both the magnitude and frequency of DPWG declined across much of the continent, with a distinct seasonality: negative trends in summer–spring–autumn and weak negative or nontrending (even positive) trends in winter. We demonstrate that ocean–atmosphere oscillations such as the Indian Ocean dipole and the southern annular mode partly modulate decadal-scale variations of DPWG. The long-term declining trend of DPWG is consistent with the “stilling” phenomenon, suggesting that global warming may have reduced Australian wind gusts.

Signatures of Oceanic Wind Events in Geostationary Cloud Top Temperature and Lightning Data

AbstractA total of 13 ocean-based wind events from 2018, detected by buoys and Coastal-Marine Automated Network (C-MAN) stations, were analyzed using 1-min mesoscale sector Advanced Baseline Imager (ABI) cloud top brightness temperature (CTTB) data, as well as 1-min Geostationary Lightning Mapper (GLM) lightning data. The ABI and GLM instruments are located on the Geostationary Operational Environmental Satellite-16 (GOES-16) satellite. An oceanic wind event was defined as a buoy or C-MAN station-recorded peak wind gust of at least 14 m s−1, associated with a convective storm. The wind gust was required to exceed the wind speed by at least 4 m s−1 at the time of the event, but not exceed the corresponding wind speed by at least 4 m s−1 for more than 30 min. This study hypothesized that prior to a wind event, there should be unique signatures in ABI CTTB and GLM lightning datasets. The presumption was that the minimum CTTB and maximum flash rate should occur near the same time and prior to the event. The minimum CTTB occurred an average of 10.5 min and a median of 7 min prior to events, with a range from 29 min prior to 1 min after the event. Changes in CTTB were often subtle. A maximum flash rate occurred within 5 min of the minimum CTTB for 11 of the 12 events with lightning and did not exceed 11 flashes per minute for 9 of the 12 events with lightning. Operational weather forecasters might use CTTB and lightning trends to help identify storms capable of producing significant oceanic wind events.

Reduced gust factor for extreme tropical cyclone winds over ocean

Information of gust factor (ratio between a short-duration peak gust wind speed to a long-duration mean) for extreme winds is of paramount importance for structural design and risk assessment. However, in-situ measurements were generally made for weak winds, and observations of gust factors for 10-min mean wind speeds over 50 ​m/s have been rarely reported, especially over the ocean. On August 23, 2017, the center of Super Typhoon Hato passed directly over an offshore island named Huangmaozhou in the South China Sea. An anemometer at the weather station on the island recorded a maximum 3-s gust wind speed of 84.2 ​m/s, 1-min mean wind speed of 74.0 ​m/s, and 10-min mean wind speed of 70.9 ​m/s during the typhoon. Based on the wind records from the anemometer, the variation of the gust factor with wind speed is examined in this paper. It was observed that for 1-min mean wind speeds between 10 and 70 ​m/s, the gust factor initially increased with increasing wind speed and plateaued at wind speed between 30 and 50 ​m/s before exhibiting a decreasing trend until 70 ​m/s. The reduced gust factor at very high wind speeds over ocean was observed and reported for the first time, which may have strong implications in tropical cyclone studies and engineering applications.

Forecasting Peak Wind Gusts Using Meteorologically Stratified Gust Factors and MOS Guidance

Abstract Gust prediction is an important element of weather forecasting services, yet reliable methods remain elusive. Peak wind gusts estimated by the meteorologically stratified gust factor (MSGF) model were evaluated at 15 locations across the United States during 2010–17. This model couples gust factors, site-specific climatological measures of “gustiness,” with wind speed and direction forecast guidance. The model was assessed using two forms of model output statistics (MOS) guidance at forecast projections ranging from 1 to 72 h. At 11 of 15 sites the MSGF model showed skill (improvement over climatology) in predicting peak gusts out to projections of 72 h. This has important implications for operational wind forecasting because the method can be utilized at any location for which the meteorologically stratified gust factors have been determined. During particularly windy conditions the MSGF model exhibited skill in predicting peak gusts at forecast projections ranging from 6 to 72 h at roughly half of the sites analyzed. Site characteristics and local wind climatologies were shown to exert impacts on gust factor model performance. The MSGF method represents a viable option for the operational prediction of peak wind gusts, although model performance will be sensitive to the quality of the necessary wind speed and direction forecasts.

URBAN INFRASTRUCTURE AND BUILDINGS IN RUINS: DAMAGE SEVERITY MAPPING OF NEIGHBORHOODS AFFECTED BY THE JUNE 2018 WINDSTORM IN BAUCHI

Abstract. Bauchi for the first time in history experienced a horrific windstorm that lasted for not more than 2 hours, but destroyed more than 20 lives and thousands urban infrastructure. This study examines the monumental damage on buildings and structures as a result of the June, 2018 windstorm disaster event in Bauchi. Handheld GPS was used in taking the location information 1662 structures affected by the windstorm. GIS was used in assessing the spatial pattern and as well mapping the extent of the damage. The results of the Average Nearest Neighbor indicate a clustered pattern with the index (ANN ratio) at 0.30 less than 1%. Similarly, the study reveals that most of the affected structures are residential land use with of 91.2% identified as damaged while the least is the recreational land use with only 0.3% structures identified as damaged by the disaster. On a district level, Jahun is the worst affected district with a total of 46.6% damaged buildings and structures. Finally, variability in annual peak wind gust trend in the last decade suggests the evidence of climate change footprints in Bauchi.

Probabilistic performance assessment of power distribution infrastructure under wind events

The electric power delivery infrastructure directly contributes to essential societal functions, the economy, and the general quality of life. Recent extreme wind events, such as Hurricanes Irma and Maria, and more recently Hurricane Michael resulted in power outages that affected millions of customers and led to major social and economic disruptions throughout communities. The longer the outage duration, the greater the incurred losses. Power distribution systems have proven to be highly vulnerable to such events and responsible for 90% of the outages. Distribution structures are built according to safety standards to ensure the safety of assets in operational and extreme conditions, but the dynamic nature of the wind loading is often overlooked or only considered empirically. Therefore, in this paper, such effects are included in a comprehensive risk-informed framework to assess the performance of electric power distribution components. This methodology explicitly accounts for the inevitable uncertainty in predictions of the component response. The focus is on characterizing wind events and the component-level risk analysis of physical components of the electric power system. For this purpose, a power pole–conductor system is modeled in which wind events are simulated as one-dimensional multivariable stochastic processes along the height of the poles and the conductors. Three-second peak gust wind speeds are used as a modeling reference. A probabilistic framework is developed to estimate the capacity of the distribution poles under different aging mechanisms. The response of the power distribution poles due to the simulated wind speeds is then computed using a finite element analysis. Fragility functions are generated to estimate the probability of exceedance for different damage states. A set of hazard mitigation strategies, the associated costs, and estimated benefits are implemented, and the improved fragility functions for elements are generated. A probabilistic life-cycle cost analysis is used to assess the long-term benefits of investing in different components of the power distribution system.

Use of High-Resolution Numerical Models and Statistical Approaches to Understand New Zealand Historical Wind Speed and Gust Climatologies

AbstractThis paper describes how a combination of high-resolution numerical modeling, a robust homogenization algorithm, and local pressure observations have been used to understand and reconcile time series of daily, seasonal, and annual peak wind gusts recorded at observing sites in the Cook Strait region of New Zealand. The homogenization algorithm consists of corrections for the relocation of masts, changes in instrumentations, data acquisition and signal processing, surrounding surface roughness, and measurement heights. In addition, a statistical method, penalized maximal F test (PMFT), was used to assess the homogeneity of the wind speed time series and detect and eliminate all remaining, undocumented, artificial (i.e., nonclimatic) breakpoints. A three-dimensional time-dependent computational fluid dynamics (CFD) simulation was carried out using the Gerris model to characterize the turbulence environment at the mast sites and to estimate topographic speedup effects. Trends in magnitudes and frequencies of the homogenized seasonal and annual peak wind gusts are evaluated and presented for the Cook Strait region. The pressure gradients between pairs of stations were used to study the correlation between the gust wind speeds and the pressure field. A high-resolution convection-resolving numerical weather prediction model [the New Zealand Convective-Scale Model (NZCSM)] was employed to aid the interpretation of results and analyze wind trends. The trend in gust speeds is also shown to be consistent with larger-scale NCEP–NCAR reanalysis pressure trends. The homogenization algorithm showed promising results in eliminating the artificial breakpoints and trends. Overall, strong correlations were found between the homogenized gust speeds, the pressure field across the region, and NZCSM predictions.

Evacuee Perception of Geophysical Hazards for Hurricane Irma

AbstractHurricane Irma was one of the strongest Atlantic hurricanes in history before landfall and caused a large evacuation. A total of 155 evacuees at interstate rest areas were asked to rank their concern about damage at their residence for six different geophysical hurricane hazards. Additionally, they were asked about their perceived maximum wind speeds (PMWS) and the wind speeds at which they thought damage would occur (DW) at their residence. These wind speeds were then compared to the actual peak wind gusts (APG) nearest to each resident’s location. Results show a significantly greater concern for wind and storm size, compared to other hazards (tornadoes, rainfall/flooding, storm surge, falling trees). The mean PMWS of evacuees was greater than the mean APG, suggesting widespread misperception of wind speeds. Furthermore, the mean APG was less than the mean DW, and the mean PMWS was also higher than the DW. Additional tests found no significant differences in wind perception between residents with previous storm experiences and no experience, and no significant differences between those who resided in mandatory evacuation zip codes and those who did not. These results suggest that wind speed risk is poorly understood, even though it is a high concern for evacuees from hurricanes. The communication of wind speed risk in forecasts should possibly be modified by placing greater emphasis on postlandfall impacts, wind speed decay after landfall, and wind speeds that cause damage to different types of residences.

An approach to homogenize daily peak wind gusts: An application to the Australian series

Daily Peak Wind Gust (DPWG) time series are important for the evaluation of wind‐related hazard risks to different socioeconomic and environmental sectors. Yet, wind time series analyses can be impacted by several artefacts, both temporally and spatially, which may introduce inhomogeneities that mislead the study of their decadal variability and trends. The aim of this study is to present a strategy in the homogenization of a challenging climate extreme such as the DPWG using 548 time series across Australia for 1941–2016. This automatic homogenization of DPWG is implemented in the recently developed Version 3.1 of the R package Climatol. This approach is an advance in homogenization of climate records as it identifies 353 break points based on monthly data, splits the daily series into homogeneous subperiods, and homogenizes them without needing the monthly corrections. The major advantages of this homogenization strategy are its ability to: (a) automatically homogenize a large number of DPWG series, including short‐term ones and without needing site metadata (e.g., the change in observational equipment in 2010/2011 was correctly identified); (b) use the closest reference series even not sharing a common period with candidate series or presenting missing data; and (c) supply homogenized series, correcting anomalous data (quality control by spatial coherence), and filling in all the missing data. The NCEP/NCAR reanalysis wind speed data were also trialled in aiding homogenization given the station density was very low during the early decades of the record; however, reanalysis data did not improve the homogenization. Application of this approach found a reduced range of DPWG trends based on site data, and an increased negative regional trend of this climate extreme, compared to raw data and homogenized data using NCEP/NCAR. The analysis produced the first homogenized DPWG dataset to assess and attribute long‐term variability of extreme winds across Australia.

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

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.