Extreme wind speed estimation: Issues and improvements

Transcription

1 Extreme wind speed estimation: Issues and improvements Franklin T. Lombardo a, Emil Simiu b a National Institute of Standards and Technology, Gaithersburg, MD, USA, franklin.lombardo@nist.gov b National Institute of Standards and Technology, Gaithersburg, MD, USA, emil.simiu@nist.gov 1 INTRODUCTION ASCE 7 (ASCE, 2010) bases its design wind speeds on data that were combined without regard for storm type, and combines weather stations in superstations with the intent of smaller sampling errors (Peterka and Shahid, 1998). However, the importance of separating storm types (i.e. hurricane, convective, non-convective) for wind loading has been clearly demonstrated (Gomes and Vickery, 1977). Each storm type is characterized by its own distribution, and the extreme wind speeds at any one station are described by a mixed distribution based on the distributions of those storm types. In addition, location effects (e.g. surface roughness) can also be significant. Today, the number of weather reporting stations is increasing. This includes Automated Surface Observing Stations (ASOS), high resolution networks such as the West Texas Mesonet and portable platforms such as StickNet (Schroeder and Weiss, 2008). These high resolution platforms are able to capture extreme wind speeds that were not accounted for in previous analyses due to the small spatial and temporal scales associated with these events. In addition, records at ASOS stations specifically indicate storm type, and are now including higher resolution data enabling more routine and accurate roughness estimations. It is therefore possible to significantly improve estimates of extreme wind speeds and create an updated extreme wind climatology for the US. 2 ISSUES 2.1 Data Volume Basic (design) wind speeds outlined in ASCE (2010) were based on data from only ~500 stations throughout the US over durations of mostly years. This scarcity of data both spatially and temporally motivated grouping of individual stations into superstations. In theory a valid approach, grouping into superstations must be performed by using sound and tested methods described below. The extreme speed estimation methods in ASCE 7 use maximum annual values (i.e., an epochal approach). This approach limits the amount of data points available for extreme value analysis to the number of years associated with the record. 2.2 Spatial Characteristics Micrometeorological characteristics, such as terrain roughness, when unaccounted for, can have significant effects on wind speed estimates. In fact, a recent study (Masters et al., 2010) has shown that errors can be in upwards of 40% for terrain assumptions alone using ASOS data. Some superstations included data combined from large urban centers to open airport exposure in small communities (Simiu et al., 2003). Terrain roughness was assumed to be uniform for all stations used in the current ASCE 7 wind map (Peterka and Shahid, 1998).

2 Wind Speed (knots) Observations from high resolution platforms have revealed the existence of small scale thunderstorm winds that would not have been accounted for in previous analyses and can significantly affect the extreme wind climate (Lombardo, 2009). Furthermore, the grouping of stations from hundreds of miles apart may integrate data having different wind climates. 2.3 Storm Type Implicit in ASCE 7 outside of hurricane regions is the assumption is that thunderstorm and largescale synoptic winds have the same probability distribution. This assumption was shown to be incorrect, especially for short intervals (e.g years) (Lombardo et al., 2009; Vega, 2008). An example of the different probability distributions associated with different storm types is shown in Figure 1. Separating the wind speeds by thunderstorm (T) and non-thunderstorm (NT) winds illustrates the differences from the current approach (C), where all values were grouped together. Accounting for both T and NT independently in a mixed distribution (M), shows that failure to properly account for all probability distributions (storm types) can lead to less conservatism in extreme wind speed estimation M: Mixed T: Thunderstorm NT: Non-Thunderstorm C: Commingled M T C NT Return Period (years) Figure 1. Estimates of wind speed for LaGuardia Airport using a mixed distribution (M). Also shown are estimates based on thunderstorm wind speeds (T), non-thunderstorm wind speeds (NT), and current method (C). 2.4 Extreme Value Statistics There has been much debate on the type of extreme value distribution to use for extreme wind speeds. Cook and Harris (2004) state the belief that the Generalized Extreme Value (GEV) Type III distribution is inappropriate and that its best fit is due to the unavailability of sufficiently large data sets. According to Vega (2008) the extremes, regardless of storm type, are described by a GEV-Type I, or Gumbel distribution. The current design wind speed map uses Gumbel analysis, although Peterka and Shahid (1998) mention that GEV-Type III may in fact be appropriate. Harris (2009) suggests estimating extremes directly from the parent distribution, avoiding the GEV distribution altogether, while others have considered approaches that use all wind speeds exceeding an appropriate threshold to reduce uncertainties (Vega, 2008; An and Pandey, 2005). Regardless, the choice of appropriate distributional models can affect estimates of extreme wind speeds significantly (Peterka and Shahid, 1998; Cook and Harris, 2004). 3 CURRENT/FUTURE IMPROVEMENTS 3.1 Data Volume ASOS has now over 2000 stations across the US, over four times that of the previous analysis as well as nearly 20 additional years of data. Fixed platforms such as the West Texas Mesonet

3 (WTM, 2009), and portable platforms specifically designed to capture extreme wind events (Schroeder and Weiss, 2008) have increased observations dramatically in the past 10 years. The advantages of increased data volume are illustrated in the following sections using peak wind speed and direction data extracted (Lombardo et al., 2009) from 28 ASOS airport stations for the state of Kansas. All 28 stations, at a minimum, had 6 years of wind speed and direction data. On average, the 28 stations contained around 32 years of data. 3.2 Spatial Characteristics Statistical methods have been employed to convert 1-minute ASOS data to a standardized micrometeorological regime (Masters et al., 2010). Using the expected gust factor (GF) and roughness length (z o ) relationship for statistically stationary conditions (i.e., 600 s) as developed in Masters et al. (2010), and the relationship between mean wind speeds at different heights and roughness regimes in Simiu and Miyata (2006), a gust speed at a given averaging time, height and roughness measured using a specific anemometer (assuming distance constant is known) can be converted to a gust wind speed using the standardized values of 3 s averaging time, 10 m height and z o = 0.03 m from the following general equation: (1) Where t,z,zo is the actual wind gust recorded by a specific measuring device and micrometeorological conditions. The variable is the ratio of the GF s from an anemometer with zero distance constant (i.e., sonic) to some anemometer that has some inertial limitations (i.e, cup, prop), GF t,z,zo is the expected GF from statistically stationary conditions for a given averaging time, height and roughness, and is the roughness length calculated from an ASOS station in this case from a chosen wind direction sector, θ. The previous three variables are from relationships developed in Masters et al. (2010). In general the GF relationship is dependent on the mean wind speed magnitude causing differing anemometer response, but the difference between wind speed values is typically, at most, less than 1%. For this application, the mean wind speed was set to 10 m/s and the subsequent = The relationship between mean wind speeds in different micrometeorological conditions is represented by the right two terms in the denominator of Equation 1. Other methods can be used to relate the mean wind speeds in different heights and roughness regimes (Simiu and Scanlan, 1996). As an example of Equation 1, the relationship between a 5-s block averaged gust measured from a Belfort anemometer with distance constant of 8 m at 6 m height and z o = 0.1 m and a standardized 3 s gust is (t=3s, z = 10 m, zo = 0.03 m) 1.3 5,6,0.1. Also, thunderstorm GF s, which are largely non-stationary, can be accounted for using Equation 1 because an actual mean wind speed value is not needed and thunderstorm gust properties at small averaging times (< 30 s) were shown to be similar to stationary (synoptic) winds (Lombardo et al., in review). For the Kansas stations, 12 of the 28 had 1-minute data available to calculate roughness in each of, for this paper, 8 direction sectors. Roughness was assumed temporally homogeneous over the length of the record. Further study will be devoted to analyzing temporal changes in roughness at a particular site and/or sector. Averaging time and anemometer height were known for all 28 stations. All other stations were assumed to be in open terrain (z o = 0.03 m) as was done for previous extreme wind climatologies of the US. However given the distribution of mean z o values for the 12 stations as shown in Figure 2, the assumption of open terrain perhaps is not always the best. Although the mean z o value in all 96 sectors is around 0.04, 61 of the values

4 No. of Values exceed the value prescribed for open terrain and 14 values excced a z o = 0.1, of which an example of the error associated with this roughness is shown in the above paragraph. Future work will take into account the estimation of z o values from aerial photos calibrated from stations that already have roughness measurements based on the calculations in Masters et al. (2010). Preliminary analysis suggests that as expected terrain features correlate very highly with the calculated z o values. The standardized data from Kansas will be used in the following sections. 35 z o values for 12 KS stations Roughness Length, z o (m) Figure 2. Histogram of z o estimates for 12 airport stations in Kansas. The greater number of ASOS stations and high resolution data from mesonets and storm intercepting programs mentioned in Section 3.1 can also help to address spatial characteristics. Shown in Figure 3 is an example of the wind speed calculations using additional ASOS data compared to a map in ASCE The calculations were performed without using the superstation approach in the state of Texas outside of hurricane prone regions. Using additional data shown in Figure 3 reveals areas of wind speed variability that is not seen in the map in ASCE. Other related issues are further discussed in Sections 3.3. and 3.4. Figure 3. Estimated 50 year wind speeds using current methodology (left) and using additional data (right).

5 The West Texas Mesonet (WTM), a higher resolution network than ASOS, in combination with ASOS has shown 7 exceedances to the basic wind speed (90 mph, 3 second gust) in the previous version of ASCE (ASCE 7-05) over an approximate 9 year span in the West Texas region. Using a Poisson process, the probability of this occurring, assuming a homogeneous wind climate over the West Texas domain is essentially nil (P 4 x 10-8 ). 3.3 Storm Type ASOS data also denotes whether peak winds occurred during a thunderstorm, a phenomenon which dominates the extreme wind climate in much of the US (Holmes, 2001). Automated software (Lombardo et al., 2009) has enabled efficient separation of storm types for further analysis, as archived records contain a significant volume of data. As shown in Figure 1, the ability to separate by storm type leads to different conclusions about the extreme wind climate of a particular location. Figure 1 also reveals that for mean recurrence intervals (MRI) not dominated by a particular storm type, the contribution from both storm types (i.e. mixed distribution) is significant and should be accounted for. To illustrate further, the 28 Kansas stations were used as an example of a wind climate that has a high frequency of relatively strong non-thunderstorm (synoptic) and thunderstorm winds. Figure 4 shows that for the Kansas stations, using a Gumbel distribution, the estimates for thunderstorm winds are generally higher than those of non-thunderstorm wind speeds as also shown in Figure 1. As a qualitative example, the 10 highest non-standardized gust wind speeds in the entire state of Kansas over the recording period were due to thunderstorms. A thunderstorm gust wind speed of approximately 108 mph was not included in this analysis because it was not saved in the archived ASOS data, but has been verified by meteorological studies of the particular event (Smith, 1993) and will be used in further iterations. In addition, a general increase in the estimated wind speed from east to west is noted for both storm types including considerable variability from station to station as also noted in Figure 3. This variability could be due to a number of factors including non-standardization (i.e., assuming open terrain) and short record lengths of some stations. As is also obvious from Figure 4, additional data from other neighboring locations is needed to develop a clear picture of the overall extreme wind climate. Figure 4. Estimated 50 year wind speeds for non-thunderstorm (NT) data left and thunderstorm (T) data right for Kansas. Wind speeds are in miles per hour. 3.4 Extreme Value Statistics The influx of additional data in combination with novel statistical methods as discussed in the previous three sections should aid in estimation of extreme wind speeds. Using annual maxima from ~50 WTM stations and separating by 0.5 and 4 day periods for thunderstorms (TS) and non-thunderstorms (NTS) respectively yielded 145 TS and 29 NTS independent events over a 7 year data record. It is estimated that the 50 year wind speed in West

6 Wind Speed (3-s, mph) 50 YEAR Texas is approximately an 120 mph, 3 second gust (Figure 3) using a Type I distribution (Vega, 2008). It should be noted that even though all data was grouped, only a time period (T) equal to 7 years was used as to estimate a regional probability or a physical process over the 7 year time scale. Figure 5 shows the differences when using T = 145 years for thunderstorm events considering all stations independent in a superstation approach. Assuming independence leads to estimate of around 100 mph, which is coincident with Vega (2008). This aspect of the analysis will be further studied ~ loglog (P) Figure 5. Estimated thunderstorm wind speeds for West Texas showing the differences between non-independent and independent wind speed data. 4 CONCLUSIONS ASCE 7 currently bases its design wind speeds on data in which multiple stations are combined without regard for storm type, and assumes open terrain all for relatively short data sets. Since the development of the last comprehensive US extreme wind climatology for wind loading, many of these issues have been improved, however much further study is needed in all areas mentioned. The number of ASOS and other high resolution stations and quality and length of records have increased substantially, leading to higher confidence in estimation procedures both spatially and temporally. Statistical methods have been developed that allow for standardization of ASOS data to specific micrometeorological conditions. Not accounting for standardization procedures could easily lead to errors in upwards of 30-40%, as shown. Estimating roughness data from select Kansas stations show that assuming a single value for open terrain is likely not warranted in most cases, although most values would be generally considered in open terrain. Spatial analysis of wind speed data over a larger scale shows considerable variabililty between stations and a general trend related to geographical regions. High resolution stations are able to capture additional extreme wind events not traditionally captured by ASOS, leading to questions about station independence and probability of exceedance. Thunderstorm wind speeds in the examples shown in this paper dominated the extreme wind climate for longer MRI s, as expected, however mixed distributions may be important for select MRI s.

7 REFERENCES American Society of Civil Engineers (ASCE) (2010). Minimum Design Loads for Buildings and Other Structures, ASCE/SEI 7-10, 2006, Reston, VA. An, Y. and Pandey, M.D. (2005). A comparison of methods of extreme wind speed estimation, J. Wind Eng. Ind. Aerodyn., 93, Cook, N. and Harris, R.I. (2004). Exact and general FT1 penultimate distributions of extreme wind speeds drawn from tail-equivalent Weibull parents, Structural Safety, 26, Gomes, L. and Vickery, B.J. (1977). Extreme wind speeds in mixed climates, J. Ind. Aerodyn., 2, Harris, R.I. (2009). XIMIS, a penultimate extreme value method suitable for all types of wind climate, J. Wind. Eng. Ind. Aerodyn., 97, Holmes, J.D. (2001). Wind Loading on Structures, Spon Press, NY-London Lombardo, F.T., Main, J.A., Simiu, E. (2009). Automated extraction and classification of thunderstorm and nonthunderstorm wind data for extreme value analysis, J. Wind Eng. Ind. Aerodyn., 97, 3-4, Lombardo, F.T. (2009). Location and Storm Type Effects in the Estimation of Extreme Wind Speeds, Proc., 11 th Americas Conf. on Wind Engineering, June 22-26, San Juan, Puerto Rico. CD-ROM Lombardo,F.T., Smith, D.A., Schroeder, J.L. and Mehta, K.C. (in review). Thunderstorm Characteristics of Importance to Wind Engineering Part II: Profiles, Gust Factors and Other Observations, submitted to J. Wind Eng. Ind. Aerodyn. Masters, F., Vickery, P., Bacon, P. And Rappaport, E. (2010). Towards objective, standardized intensity estimates from surface wind speed observations, Bull. Amer. Met. Soc., 12, Peterka, J.A. and Shahid, S. (1998). Design Gust Wind Speeds in the United States, J. Struct. Eng., 124, Schroeder, J.L. and Weiss, C.C. (2008). Integrating research and education through measurement and analysis, Bull. Amer. Met. Soc., 89, Smith, B.E. (1993). The Concordia, KS downburst of 8 July 1992: A case study of an unusually long-lived windstorm, Preprints, Annual Conf. on Severe Local Storms, St. Louis, Simiu, E. and Scanlan, R.H. (1996). Wind Effects on Structures, Wiley, New York Simiu, E., Wilcox, R., Sadek, F., and Filliben, J.J. (2003). Wind speeds in ASCE 7 standard peak gust map: assessment. J. Struct. Eng., 129(4), Vega, R. (2008). Wind Directionality: A Reliability Based Approach, Ph.D. Dissertation, Texas Tech University West Texas Mesonet (WTM) (2009). (Accessed August 1, 2009)