Intense and Extreme Wind Speeds Observed by Anemometer and Seismic Networks
Barthelmie, R J |
ABSTRACT
The scale and intensity of extreme wind events have tremendous relevance to determining the impact on infrastructure and natural and managed ecosystems. Analyses presented herein show the following. 1) Wind speeds in excess of the station-specific 95th percentile are coherent over distances of up to 1000 km over the eastern
1. Introduction
Intense and extreme wind events are observed on every continent, derive from multiple scales of forcing, and are manifest across a range of spatial scales. The occurrence of intense and extreme wind speeds (defined here as wind speeds in excess of 10 m s~x at 10 m AGL and above the 95th percentile, respectively) is associated with a range of ecological impacts [e.g., seed dispersal (Bullock et al. 2012), forest health (Blennow et al. 2010;
Windstorms are the sixth costliest natural disasters in
The overarching objective of this research is to im- prove understanding of the causes and scales of intense and extreme wind speeds, with a specific focus on the eastern
1) The analysis is designed to quantify the spatial co- herence of sustained high wind speeds. In addition to offering critical information regarding the dynamical causes of high-wind events, the relevance of extreme wind speeds to socioeconomic sectors such as the insurance industry is dictated in part by the spatial scale over which damage is caused and thus the spatial scales over which high winds are "simultaneously" observed (Chandler et al. 2001).
2) The analysis is designed to quantify characteristics of high-wind events associated with cold-frontal passages. Extratropical cyclones are a major source of high-wind events, and the highest winds are frequently observed at, or close to, cold fronts. Thus mean sustained (2-min average) wind speeds U and 3-s gust magnitudes during 35 cold-frontal passages are analyzed to quantify
(i) the relationship between both sustained and gust wind speeds ug and frontal strength,
(ii) the duration of intense sustained wind speeds and gusts, and
(iii) the relationship between mean wind speed and gust magnitudes and to describe the probability distribution of gust factors (GF; defined as the ratio of maximum gust speed to the correspond- ing mean wind speed).
3) The analysis is designed to assess whether seismic data from the "USArray," a primary program within the
These three objectives thus provide the structure of this paper and are addressed in sections 2, 3, and 4, re- spectively. All analyses presented herein are based on wind measurements at 10 m AGL. Use of measurements at this height affords the best spatial coverage and en- ables use of long-term datasets, but it is acknowledged that these near-surface wind speeds are also subject to the greatest inhomogeneity as a result of local surface conditions (Azorin-Molina et al. 2014).
2. Analyses of spatial coherence</p>
a. Data and method
Wind speeds from 85 stations within the National Cli- matic Data Center's "TD-6421" dataset (Groisman 2002) are used in the analysis of spatial coherence. This dataset was selected because it is of relatively long du- ration (>30yr of data from some stations), and has been subject to detailed data-homogenization and quality - control procedures and has well-documented data- quality flags. The original observations are reported at hourly intervals, and they reflect the mean wind speed [to the nearest knot (kt); 1 kt « 0.51 m s~x] over a 2-min period during the hour. Data availability increases in 1973 to a point at which >95% of stations have more than two-thirds of hourly observations available on a daily basis (see Fig. SI in the supplemental material). Thus herein we use data from 1973 to 2000 (the end of the dataset).
The spatial coherence of extreme wind events is quantified by selecting a reference station and event threshold to identify the occurrence of "events," and then observations from other stations are examined to de- termine whether they also exceeded that threshold within a given time period of the event (Pfahl and Wernli 2012; Ricciardulli and Sardeshmukh 2002). This approach quantifies the degree to which the occurrence of extreme values at two or more sites is coherent in time and thus is fundamentally different from spatial correlation analyses that test the degree of coherency of the entire time series. Given the spatial variability of wind speed regimes across the eastern
Because CR are strongly influenced by the choice of the reference station (and are not symmetric for a given reference and target station pair), in this analysis five reference stations (with low numbers of missing data) are used to allow investigation of spatial variability across the study domain by using stations with very low numbers of missing data. The specific locations used as reference stations are as follows: Louisville Standiford Field (
Temporal autocorrelation in the time series of thresh- old exceedance is relatively weak and is neglected in the analyses to determine the spatial coherence ratios that would derive from random chance. Weibull distributions having shape and scale factors calculated from the wind speed data at the five reference stations are used with a random-number generator to derive 1000 X 5 simulated daily maximum wind speed time series of 10200 value duration (i.e., 1000 realizations of 28 yr of data from each of the five stations). These time series are then used to determine coherence ratios. The results indicate that CR > 0.08 is observed in less than 1% of analyses of the sim- ulated time series, and thus CR > 0.08 are considered to be statistically different from 0 at the 99% confidence level.
b. Results
Same-calendar-day coherence ratios for the most centrally located reference station,
Consistent with expectations that are based on extra- tropical cyclone frequencies, tracks, and translational speeds (Coleman and Klink 2009), spatial coherence ra- tios for a lag of +1 day for Pierre (726686) are > 0.08 for a swath of stations to the east (Fig. 2a), but stations ex- hibiting coherence ratios of >0.08 for a + 2-day lag are generally limited to the
Although the spatial patterns of same-day CR in the current analysis vary from site to site, analyses for these five reference stations indicate an approximately expo- nential decay with distance and a drop in CR to below 0.1 at spherical distances of approximately 1200 km (Fig. 3a). Thus the spatial scales of coherence reported herein are larger than those derived from analyses of damage patterns associated with winter windstorms in the contiguous
Higher spatial coherence of intense winds is observed during the cold season than during the entire year (cf. Fig. 3a and Fig. 3b), particularly in the Southeast (cf. Fig. 2e and Fig. 4e). When averaged across all sta- tions, coherence ratios decline almost exponentially with station separation (distance) and are slightly higher for cold-season events than in events from the entire year (Fig. 3b). This is also true for individual stations except in data from
3. Analysis of sustained and gust wind speeds associated with cold fronts
a. Data and method
The high degree of spatial coherence (i.e., coherence on scales of hundreds of kilometers) in the occurrence of intense wind speeds is consistent with a priori expecta- tions regarding the importance of extratropical cyclones and cold-frontal passages to the occurrence of high wind speeds over the eastern
b. Results Although maximum wind speeds and gusts increase with frontal strength, linear regression fits of both maximum sustained wind speeds and wind gusts with dTIdn exhibit variance explanation r2 of <0.02 (Fig. 5a). Therefore, this metric of frontal intensity is a very weak predictor of wind extremes. This finding may be due to use of a single value to describe frontal strength (thus neglecting variability along the front and/or temporal evolution of the front). It may also reflect the diversity of factors that determine the surface thermal gradient across a front (including local land cover variability and the presence/absence of cold cloud; Segal et al. 1993), or that mechanisms responsible for vertical momentum transport and thus for high near-surface wind speeds (Brasseur 2001) are not strongly correlated with this measure of frontal intensity. Forest damage can begin at wind speeds as low as 10ms-1 ( Gust factors (typically defined as iig/U) are used exten- sively in wind-load design codes (although the structural elemental under consideration dictates the GF-loading constants; Greenway 1979). For example, a GF of 1.43 (for 3-s gust to 10-min sustained wind speeds at 10-m height) is typically used in estimating dynamic loads exerted by wind gusts on components of an electrical transmission line (Wong and Miller 2010). The GF are a function of the turbulence length scales of the flow (Greenway 1979) [and thus the surface over which the flow is moving, atmo- spheric stratification, and measurement height (Paulsen and Schroeder 2005)], instrument response (Kristensen 1998), and the averaging period that is used to characterize gust and sustained mean wind speed (Kristensen et al. 1991) (Table 1). For the 2661 gust observations in this study, the mode and median GF = 1.5 and the mean = 1.57 (Fig. 5c). Although differences in averaging period confound detailed comparisons with prior studies, these values are in reasonable accord with previous estimates of GF for extratropical cyclones (Table 1) and corrobo- rate research that has suggested that GF from extra- tropical cyclones may exceed those from tropical systems (Paulsen and Schroeder 2005). The probability distribu- tion of GF (at 10-m AGL) is highly positively skewed and is best described by a lognormal distribution (Fig. 5c). Prognostic gust forecasting, although of high potential utility, is extremely challenging (Thorarinsdottir and Johnson 2012), in part because the relationship between GF and U is highly nonlinear and is a function of mea- surement height (Agustsson and Olafsson 2004; Cheng et al. 2012; Krayer and Marshall 1992; Paulsen and Schroeder 2005). Consistent with prior research, the majority of high GF are associated with relatively weak mean sustained wind speeds. For U > 10ms-1 all GF are <2 (Fig. 5d). The relationship between maximum daily G [G = (uJU) - 1, where ug = peak daily gust velocity and U = mean daily wind speed] and U (kt) in a prior study of measurements in the 4. Can seismic data be used to identify and quantify extreme wind events? a. Data and method Even the approximately 900 stations that compose the The Transportable Array (TA) network comprises 400 seismic stations deployed within vault enclosures (-2.1 m deep, with inner diameter « 1 m) that are se- quentially deployed with a Cartesian grid spacing of -70 km throughout the contiguous Data from two case studies ( b. Results Measuring turbulent pressure fluctuations is chal- lenging (Wilczak and Bedard 2004), but all power spectra of data from microbarographs deployed at each TA station show increased variance in a broad fre- quency range centered between 0.1 and 0.01 Hz during high-wind periods that is not present during the period of quiescent winds (see Fig. S2 in the supplemental material). Data from all seismic channels show the emergence of a more distinct variance peak at a fre- quency centered at -0.04 Hz (i.e., periods of -25 s) during the gust/frontal-passage periods that is not pres- ent during relatively quiescent winds (see Table 2 and Fig. 6). The frequencies of enhanced variability are consistent between the two case studies and are dis- placed to lower frequencies than gust phenomena (time scales =" 3-5 s). Further, this peak is recorded at dis- tinctly different frequencies than is the peak of high variance (centered on periods of -100-400 s), referred to historically as "the earth's hum," that is thought to arise from the interaction of oceanic waves on conti- nental shelves (De Angelis and Bodin 2012). Seismic responses to wind gusts exhibit the clearest signal in the BHZ channel (associated with vertical motion; Figs. 6c,f). The amplitude of the variance maximum in BHZ that is associated with frequencies of -0.04-0.06 Hz is larger in the 5. Concluding remarks On the basis of the analyses presented herein, the following inferences are drawn regarding the causes and scales of intense and gust wind events over the eastern 1) Exceedance of site-specific 95th-percentile wind speeds is coherent over distances up to 1000 km and exhibits the largest "footprints" during the cold season. These results thus emphasize the importance of synoptic-scale drivers of intense wind events. Co- herence ratios exhibit spatial patterns that are in accord with a priori expectations regarding trans- lational tracks and speeds of extratropical cyclones, indicate that deep convection lowers CR during the warm season (particularly in the southeastern 2) Cold fronts are a major cause of high-wind speed events, but-although maximum 2-min sustained and 3-s gust wind speeds tend to increase with frontal intensity (temperature gradient across the front)- the relationship is not statistically significant at the 80% confidence level. Three-second gusts exceeding 10 ms-1 occur over 2 or more consecutive hours at a given site in over 50% of cases and can extend up to 18 h in duration, again indicating a large-scale driver of intense wind speeds. Gust factors associated with frontal cyclones exhibit a nonlinear relationship with sustained wind speed. The mean GF over the eastern 3) Although intense wind events exhibit high spatial coherence, there is clear evidence for subsynoptic- scale variability, some of which is not captured even by the dense NWS ASOS network. Seismic data from the USArray exhibit well-defined spectral signatures that are associated with wind gusts (with variance maxima that appear to scale with wind-gust intensity) and thus indicate the potential to use these data to aid in spatially mapping and characterizing gust events. Further work is warranted to evaluate the generalizability of the results that are presented here and for this potential use to be realized. Acknowledgments. 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