Full-Scale Strong Winds from a Time-Varying Perspective

A field experiment was conducted to collect high spatial and temporal resolution data of various types of high wind events. Winds sampled include those generated from a thunderstorm outflow, hurricane, cold front, and two surface lows. The data were analyzed using the Empirical Mode Decomposition to assess the turbulent and transient nature of the flows. Traditional statistical methods were also used to compare their effectiveness. To complement the analyses, wind tunnel data was evaluated in a similar fashion. The preliminary findings are presented. INTRODUCTION Windstorms account for nearly 80% of insured losses worldwide since 1950 [1]. This substantial amount of loss prompts the necessity to better understand high wind events physically and statistically. Researchers have been modeling extreme winds for decades using laboratory simulations and numerical models, and considerable information has been obtained [2]-[6]. However, a major impediment for researchers has been the lack of full-scale data from strong to extreme wind events. Much of the publicly available data consists of low resolution radar or tower data, which is insufficient for engineering validation purposes. Additionally, researchers struggle with statistically analyzing inherently nonlinear and nonstationary phenomenon such as highly transitory thunderstorm outflows. The Synoptic Wind And Thunderstorm Hurricane (SWATH) field experiment was conducted in 2004 and 2005 to acquire high resolution wind data enabling characterization of the turbulent and transient nature of a variety of wind events, and allow comparison to wind tunnel data [7]. Traditional and non-traditional analysis techniques are utilized, and preliminary results of the turbulence statistics are presented. OVERVIEW OF FIELD CAMPAIGN SWATH was conducted in three phases during 2004-2005 to target events ranging from synoptic windstorms to thunderstorm outflows to hurricanes. Each phase utilized a different tower configuration with up to ten 10m towers deployed in close proximity. Phases 1 and 2 took place at the Reese Technology Center near Lubbock, TX, while Phase 3 involved deployments at a number of locations along the Gulf Coast to obtain measurements from landfalling hurricanes. Phase 1 was conducted from 15 April to 15 July 2004. The towers were deployed in a linear array from north to south utilizing 10 towers spaced 65-130m apart. A satellite image of the deployment location and tower configuration is shown in Figure 1. Phase 2 took place from 5 January to 7 July 2005. Nine towers were deployed in a “T” configuration with the primary axis aligned north to south and the secondary axis aligned west to east as shown in Figure 2. The towers along the primary axis were spaced 65m apart, while those in the secondary axis were spaced 40m apart. Phase 3 involved deployments for Hurricanes Dennis, Emily, and Katrina in 2005. However, only the deployment for Hurricane Katrina was successful in capturing the rightfront quadrant of the storm, and thus the onshore flow that was preferred. Three towers were deployed 50m apart at the Stennis International Airport in Mississippi, as shown in Figure 3. Figure 1. Tower configuration during SWATH Phase 1 at the TTU Wind Science and Engineering field site. Figure 2. Tower configuration for SWATH Phase 2 at the TTU Wind Science and Engineering field site. Figure 3. Tower configuration for SWATH Phase 3 during Hurricane Katrina at the Stennis International Airport. ANALYSIS METHODOLOGY Traditional analysis techniques assume data stationarity, but wind data is inherently nonstationary. It is common practice to remove low-order trends to force stationarity, but for this study the Hilbert-Huang Transform (HHT) will be used to decompose the data into its intrinsic modes, from which the data can be analyzed from a time-varying perspective. The HHT is a two part method made up of the Empirical Mode Decomposition (EMD) and the Hilbert Transform (HT) [8]-[10]. EMD is a sifting process to break the signal into intrinsic mode functions (IMF), which are then used to create a time-varying mean for acquiring turbulence statistics. The IMF components are also processed through the HT to generate a time-frequency depiction of the parent signal and associated physical processes. This method has been applied to several cases obtained during SWATH, and compared to traditional statistical analysis methods. For this study, one 60min segment of 10Hz data from a single tower was selected from each event. The first step was to determine a time-varying mean from the lower frequency IMF components and residual that resulted from the EMD sifting process (Figures 4-5). Determining the appropriate TVM was accomplished both in a subjective and objective manner. When the time-varying mean (TVM) was subtracted from the original signal, the mean of the perturbations must be within + or – 0.05 units (e.g. m/s, degrees, etc.) of 0. The TVM was then visually examined to ensure that a good fit was achieved, as there were some cases where the mean criterion was met, but the TVM was a poor fit. For all cases, the residual plus the lowest three or four IMF components typically generated a suitable TVM. However, this is highly dependent on the length of the time history and the sampling frequency (in this case 60min at 10Hz). The resulting TVM was comparable to a 30-40s moving average filter, while maintaining the physical components of the signal, as shown in Figure 6. This result corresponds well with what has been shown previously in the literature [11]. Figure 4. Wind speed time history of a thunderstorm outflow and the resulting IMF components. Figure 5. Wind speed time history with time-varying mean (top) and perturbations (bottom). Figure 6. TVM from Surface Low 1 compared to moving averages. The TVM was then utilized to convert from a vector magnitude (WS) and direction (θ) into alongwind (U) and crosswind (V) components: )] ( ) ( cos[ ) ( t t t WS U     (1) )] ( ) ( sin[ ) ( t t t WS V     (2) and to compute time-dependent gust factor (3), turbulence intensity (4), and length scale (5) statistics: ) ( ) ( ˆ t U t u GU  (3) ) (t U I U U   (4)