MEASUREMENT, ANALYSIS, AND SIMULATION OF WIND DRIVEN RAIN

Transcription

1 MEASUREMENT, ANALYSIS, AND SIMULATION OF WIND DRIVEN RAIN By CARLOS R. LOPEZ A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 211 1

2 211 Carlos R. Lopez 2

3 To my parents, Amanda Duarte and Alfonso Lopez 3

4 ACKNOWLEDGMENTS I would like to thank my advisor, Forrest J. Masters, Ph.D., P.E. and committee members Kurtis R. Gurley, Ph.D., David O. Prevatt, Ph.D., P.E., Peter N. Adams, Ph.D., and Katja Friedrich, Ph.D. for their guidance, advice, and support throughout my graduate career. I would also like to extend my appreciation to George Fernandez, Jason Smith, James Austin, Juan Balderrama, Scott Bolton, Jimmy Jesteadt, Dany Romero, Abraham Alende, and Alon Krauthammer for their assistance in my experiments. This research was made possible by the financial support of the Insurance Institute for Business & Home Safety, National Science Foundation under grants ATM (Friedrich) and AGS (Friedrich), and the University of Florida Alumni Fellowship Program. 4

5 TABLE OF CONTENTS 5 page ACKNOWLEDGMENTS... 4 LIST OF TABLES... 9 LIST OF FIGURES... 1 ABSTRACT CHAPTER 1 INTRODUCTION Scope of Research Summary of Research Thrusts Thrust 1: Development of a Portable Weather Observing System to Characterize Raindrop Size and Velocity in Strong Winds Thrust 2: Characterization of the Raindrop Size Distribution in Atlantic Tropical Cyclones and Supercell Thunderstorms in the Midwest U.S Thrust 3: Development of a Wind-Driven Rain Simulator for Implementation In a Full-Scale Test Facility Capable of Subjecting a Low-Rise Building to Windstorm Conditions Importance of the Study Organization of Document LITERATURE REVIEW Precipitation Precipitation Events Precipitation Types Raindrop Size Distribution Rainfall and Wind-Driven Rain... 3 Quantification of the Rain Deposition on the Building Façade Factors Affecting Rain Deposition Rate on the Building Façade Rainfall intensity Influence of the wind on raindrop trajectory Terrain characteristics Building characteristics Modeling of Rain Deposition on the Building Façade Semi-Empirical Models Numerical Models... 4 Full Scale Experiments Measurement of Wind-Driven Rain Instrumentation Limitations of optical disdrometers... 45

6 Instrumentation Used In This Study OTT PARSIVEL optical disdrometer Droplet Measurement Technologies Precipitation Imaging Probe Summary DESIGN, PROTOTYPING, AND IMPLEMENTATION OF ARTICULATING RAIN PARTICLE SIZE MEASUREMENT PLATFORMS Motivation for the Development of an Articulating Instrument Platform Raindrop Size Distribution Verification Using the Oil Medium Test Bearing Drop Test Instrument Performance in High Wind Speeds... 6 Articulating Instrumentation Platforms Articulating Instrumentation Platform Components RM Young sonic anemometer Articulating support structure Data acquisition system Power Substructure Portable Instrument Platform Operation Quality Control Algorithm... 7 Description of Weather Station T Comparison of Collocated Stationary and Articulating Instruments in a Supercell Thunderstorm Summary CHARACTERIZATION OF WIND-DRIVEN RAIN IN STRONG WINDS Field Research Programs Verification of the Origins of Rotation in Tornadoes Experiment 2 (VORTEX2) Overview Deployment details Florida Coastal Monitoring Program (FCMP) Overview Deployment details... 8 Effect of Wind Velocity and Turbulence Intensity on Raindrop Diameter Wind Velocity and Turbulence Intensity Dependency of the Raindrop Size Distribution... 9 Comparison of Raindrop Size Distribution Models to Measured Raindrop Size Distribution Data in Multiple Wind Velocities Peak to Mean Ratio of Rainfall Intensities Comparison of Ground Measured Rainfall Intensity and Estimated Reflectivity to Weather Surveillance WSR-88D Estimated Rainfall Intensity and Measured Reflectivity Summary

7 5 DEVELOPMENT OF A WIND-DRIVEN RAIN SIMULATION SYSTEM FOR THE WATER PENETRATION RESISTANCE EVALUATION OF LOW-RISE BUILDINGS IN A FULL-SCALE WIND TUNNEL Design Specifications for the Rain Simulator in the IBHS Research Center Spray Uniformity and the Effect of Wind Velocity on the Raindrop Size Distribution Characterization of the Raindrop Size Distribution of a Spay Nozzles in Stagnant Air: A Proxy for Full-Scale Testing Specimen Matrix Experimental Configuration Analysis Validation of the Wind-Driven Rain Simulation System at the IBHS Research Facility Summary SUMMARY, CONCLUSIONS,AND RECOMMENDATIONS Characterization of Extreme Wind-Driven Rain Events Proof of Concept Conclusions from Field Data Results Effect of wind velocity and turbulence intensity on raindrop diameter wind velocity and turbulence intensity dependency of the raindrop size distribution Comparison of Measured Data to Raindrop Size Distribution Models Comparison of Measured Data to WSR-88D Data Peak to Mean Ratio of Rainfall Intensities Design and Implementation of a Full Scale Wind-Driven Rain System Nozzle Characterization Full Scale Implementation Recommendations for Future Research Recommendations for Instrumentation Recommendations for Full Scale and Numerical Models Recommendations for the Morphological Image Processing Algorithm APPENDIX: ADDITIONAL INFORMATION AND DATA Theoretical Proof of Greater Accuracy from an Articulating Instrument Nozzle Selection Measured Diameter Wind Relationships Hurricane Ike Data Hurricane Irene (Beaumont) Data Hurricane Irene (Deal) Data Florida Coastal Monitoring Program Hurricane Ike Data Verification of the Origins of Rotation in Tornadoes Experiment 2 Articulating Instrument Platform Data Measured Nozzle Characteristics

8 Uniformity Measured Nozzle Raindrop Size Distribution REFERENCES BIOGRAPHICAL SKETCH

9 LIST OF TABLES Table page 2-1 PARSIVEL diameter classes PARSIVEL velocity classes Trajectory angles of multiple diameter drops in multiple wind speeds Steady wind test matrix Verification of the Origins of Rotation in Tornadoes Experiment 2 deployment details Peak to mean ratios of U and R Comparison of Z-R models Rainfall Intensities Spray nozzles evaluated in this study

10 LIST OF FIGURES Figure page 1-1 Articulating precipitation measurement platform and stationary platform Portable FCMP weather station with the actively controlled positioning system Insurance Institute for Business & Home Safety Research Center Influence of the modified three-parameter gamma model parameters Components of the rain intensity vector Drop size and shape Trajectory of drops released at m/s Distance required for the trajectory angle to reach 95% of the terminal angle Deposition of smaller drops left and larger drops right PARSIVEL drop diameter and velocity determination PARSIVEL measurement and output Sample picture from morphological image processing algorithm Comparison of different raindrop size distribution measurement techniques Angled trajectory experiment configuration PARSIVEL measured diameters at multiple trajectory angles PARSIVEL measured velocities at multiple trajectory angles Steady wind instrument configuration PARSIVEL measured raindrop size distributions for the tests in steady wind flow PARSIVEL measured drop diameters and velocities for the tests in steady wind flow Articulating instrument platform Articulating instrument platform automation

11 3-11 Drag coefficient of a cylinder dependant of Reynolds number of flow Instrument platform control and data diagram Quality control filter T-3 Precipitation Imaging Probe turret system and Gill anemometers at Measured raindrop size distribution by stationary instrumentations and articulating instrumentation Comparison of rainfall intensities measured by stationary and articulating instrument platforms Comparison of estimated reflectivity by stationary and articulating instrument platforms VORTEX2 instrument deployment VORTEX2 data collection sites Effect of longitudinal wind velocity on drop diameter observed in VORTEX2 data Effect of longitudinal turbulence intensity on drop diameter observed in VORTEX2 data Effect of lateral turbulence intensity on drop diameter observed in VORTEX2 data Effect of longitudinal wind velocity on drop diameter observed in FCMP data Effect of longitudinal turbulence intensity on drop diameter observed in FCMP data Effect of lateral turbulence intensity on drop diameter observed in FCMP data VORTEX2 gamma parameters observed in multiple wind conditions and rainfall intensities FCMP Hurricane Ike and Irene gamma parameters observed in multiple wind conditions and rainfall intensities FCMP and VORTEX2 gamma parameters observed in multiple wind conditions and rainfall intensities Observed shape slope relation

12 4-13 Model raindrop size distribution and measured raindrop size distribution comparisons Model raindrop size distribution and measured raindrop size distribution comparisons Model raindrop size distribution and measured raindrop size distribution comparisons Mean square error values of raindrop size distribution models stratified by U and R Mean square error values of raindrop size distribution models stratified by TI U and R Mean square error values of raindrop size distribution models stratified by TI V and R Peak to mean ratios of VORTEX2 data Peak to mean ratios of FCMP data Comparison of disdrometer estimated and radar measured reflectivity Comparison of disdrometer measured and radar estimated rainfall intensity Observed Z-R relationship Comparison of observed and recommended Z-R relationships Apparatus to measure the spray uniformity of a single nozzle Comparison of raindrop size distributions measured in stagnant air and in a steady wind by the PARSIVEL and PIP PARSIVEL test locations for nozzle characterization Count of drops radially outward from nozzle centerline Determining initial velocity using high speed footage Raindrop size distribution of BETE WL3 in stagnant air conditions Instrument arrangement at the Insurance Institute for Business & Home Safety Research Center Measured raindrop size distributions at multiple heights and wind velocities Comparison of measured raindrop size distributions and Best (195) model

13 A-1 Droplet formation process A-2 Types of nozzles and drop size relationship

14 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy MEASUREMENT, ANALYSIS, AND SIMULATION OF WIND DRIVEN RAIN Chair: Forrest J. Masters Major: Civil Engineering By Carlos R. Lopez December 211 This study presents a new experimental approach to collect wind-driven rain data and overcome known issues associated with field measurements during strong winds. Simultaneous measurements of wind and wind-driven rain were collected using a novel tracking system that continuously reorients a raindrop size spectrometer (or disdrometer) to maintain correct alignment with the rain trajectory. Experiments were conducted in multiple supercell thunderstorms during the Verification of the Origins of Rotation in Tornadoes Experiment 2 (VORTEX2), and Hurricanes Ike (28) and Irene (211) with the Florida Coastal Monitoring Program (FCMP, fcmp.ce.ufl.edu). The results of the data analysis appear promising for the continued use of the system and others based on the same concept. The final component of the study consisted of the design and implementation of a wind-driven rain simulation system at a full-scale test facility to evaluate the water penetration resistance of low-rise structures. 14

15 CHAPTER 1 INTRODUCTION This study presents a new experimental approach to collect wind-driven rain data and overcome known issues associated with field measurements during strong winds. Simultaneous measurements of wind and wind-driven rain were collected using a novel tracking system that continuously reorients a raindrop size spectrometer (or disdrometer) to maintain correct alignment with the rain trajectory. Experiments were conducted in multiple supercell thunderstorms during the Verification of the Origins of Rotation in Tornadoes Experiment 2 (VORTEX2), and Hurricanes Ike (28) and Irene (211) with the Florida Coastal Monitoring Program (FCMP, fcmp.ce.ufl.edu). The results of the data analysis appear promising for the continued use of this system and others based on the same concept. The final component of the study consisted of the design and implementation of a wind-driven rain simulation system at a full-scale test facility to evaluate the water penetration resistance of low-rise structures. The motivation for this research is the extensive damage caused by tropical cyclones annually. In the last two decades, Atlantic tropical cyclones have caused more than $113 billion (29 dollars) in insured losses (Insurance Information Institute, 211). Post-storm investigations (e.g., Mehta et al.,1983; NIST, 25; FEMA, 25; FEMA, 26; Guillermo et al., 21; Gurley and Masters, 211) have found that building envelope failures are a leading cause of damage. A critical, recurring problem in residential construction is water ingress through the building envelope. Wind related failures causing mismanaged or unmanaged water infiltration can result in loss of function and costly damage to building contents (Lstiburek, 25; Mullens et al., 26; IBHS, 29). WDR deposition on the building 15

16 façade can cause moisture accumulation in porous wall components (Bondi and Stefanizzi, 21; Abuku et al., 29; Bomberg et al., 22; Tsongas et al., 1998; Lang et al., 1999), deterioration of wood frame wall systems (Hulya et al., 24; Lacasse et al., 23), water infiltration through the secondary water barrier of roof systems (Bitsuamlak et al., 29) and the water penetration resistance of residential wall systems with integrated fenestration (Salzano et al., 21; Lopez et al., 211). Wind-driven rain is an active research subject in the atmospheric science, engineering, and building science disciplines. Engineering and building science research has primarily focused on modeling wind-driven rain deposition on building façades (Blocken and Carmeliet, 21; Blocken and Carmeliet, 24; Choi, 1994; Choi, 1994; Surry et al., 1994), hygrothermal performance and drying (Teasdale-St-Hilaire and Derome, 27; Abuku et al., 29; Cornick and Dalgliesh, 29), and to a lesser extent, fragility modeling (Dao and Van de Lindt, 21). In atmospheric science, extensive research has been directed toward the improvement of a) radar- and satellitederived estimations of rainfall intensity (e.g., Wilson and Brandes, 1979; Rosenfeld et al., 1993; Kedem et al., 1994) and b) microphysical models for numerical weather prediction (Chen and Lamb, 1994; Gaudet and Cotton, 1998; Stoelinga et al., 23). Raindrop size distribution (RSD) is a critical variable in both fields. One view of meteorology considers the RSD as a microphysical signature of much larger scale features aloft, while engineering uses the RSD as a probabilistic input for modeling raindrop trajectories and rain deposition rates on buildings. In both applications the relative difference between the number and concentration of small and large drops is also critical. In atmospheric science, remote measurements of the radar reflectivity of 16

17 weather systems are particularly sensitive to the drop size. In engineering, the wetting pattern and the rain deposition rate on the building façade is, in part, a function of the RSD. Since the 194s, a significant amount of RSD data has been collected during stratiform and convective precipitation events in many locations around the world. However, in-situ wind-driven rain data are scarce. The knowledge base is largely built from radar and aircraft observations or adapted from in-situ measurements collected in little-to-no wind. For example, the Best (195) RSD, which is widely used in computational wind engineering, was not calibrated with RSD data collected in high winds yet it is still used to model wind-driven rain deposition on the building façade. This research directly addresses this issue through multiple thrusts, which are described in the next section. Scope of Research The study addresses wind-driven rain (WDR) in an interdisciplinary context, with emphasis on the characterization of field observations (atmospheric science) and the simulation of a wind-driven rain field to evaluate the water penetration resistance of the building envelope (engineering). First, a portable weather observation system was developed to obtain reliable particle size distributions in strong winds. Second, the system was field tested during an in-situ data collection campaign throughout the southern and central Plains as part of the Verification of the Origins of Rotation in Tornadoes Experiment 2 (VORTEX2). Data obtained during the VORTEX2 campaign is also compared to measurements collected during Hurricane Ike (28, separate study) and Hurricane Irene (211, led by the author). Third, a rain simulation system for a fullscale test facility was developed; the design criteria were established from the results of 17

18 the first and second thrusts. The purpose of this thrust is to assist the Insurance Institute for Business & Home Safety (IBHS) in the commissioning of its full-scale test facility to simulate wind-driven rain effects. A spray system was designed, installed, and tested based on the results obtained from the first and second thrusts. Summary of Research Thrusts Thrust 1: Development of a Portable Weather Observing System to Characterize Raindrop Size and Velocity in Strong Winds The first contribution of this research was to design, prototype and successfully field evaluate a portable weather observation system to quantify hydrometeor size and velocity in strong winds (Figure 1-1 left). The system operates by continuously readjusting the orientation of an OTT PARticleSIze and fall VELocity disdrometer (PARSIVEL) to maintain optimal alignment with the raindrop trajectory. Disdrometers function by measuring the voltage drop from a photodiode, or series of photodiodes, caused by a raindrop passing through a light band (Loffler-Mang and Joss, 2). Reliable rain data acquisition via optical disdrometers requires that the hydrometeors travel nearly perpendicular to the light plane. In little-to-no wind, this presents no issue for a stationary instrument with the light plane parallel to the ground (Figure 1-1 right). In the presence of strong winds, advection is a dominant component of the particle trajectory. A stationary instrument loses accuracy as the wind speed increases. Thus, an outstanding experimental challenge has been the development and implementation of an observational system capable of accurately quantifying the RSD during an extreme wind event. While actively aligning the disdrometer with the mean rain vector was previously considered by Grifftihs (1974), this research presents the first such known effort to successfully address this issue. Two systems using different instruments 18

19 were implemented. The first system employs a Droplet Measurement Technologies Precipitation Imaging Probe (DMT PIP), while the second utilizes a PARSIVEL disdrometer. 1. PARSIVEL based systems: A PARSIVEL disdrometer and a sonic anemometer were installed on articulating platforms with actively controlled positioning systems. Figure 1-1 depicts the PARSIVEL disdrometers mounted on articulating and stationary platforms; both platforms were deployed repeatedly throughout a six week field campaign in the southern and central Plains during 21 for the VORTEX2 experiment. In 211 both systems (the PIP and PARSIVEL based systems) were deployed during the passage of Hurricane Irene. 2. PIP based system: A DMT PIP was installed on an actively controlled mechanized turret on a Florida Coastal Monitoring Program (FCMP) weather station (Figure 1-2). This instrument was first deployed successfully during Hurricane Ike (28). Figure 1-1. Articulating precipitation measurement platform (left) and stationary platform (right, photo courtesy of author) 19

20 Figure 1-2. Portable FCMP weather station with the actively controlled positioning system (photo courtesy of author) Thrust 2: Characterization of the Raindrop Size Distribution in Atlantic Tropical Cyclones and Supercell Thunderstorms in the Midwest U.S. With the use of the new instrument platforms, multiple measurements were taken in supercell thunderstorms and in tropical cyclones as part of the VORTEX2 and FCMP field campaigns. These data, to the author s knowledge, are the first set of RSD measurements in high winds. Thus, the analysis and results of these data are presented in this document to address the following questions in an effort to advance the WDR knowledge base: 1. Does wind velocity and turbulence intensity affect rainfall characteristics (e.g. mean drop diameter, RSD, etc.)? 2. Are existing RSD models based on data collected in little-to-no wind applicable to WDR occurring in an extreme wind events? 3. What is the relationship between the peak short duration rainfall intensity to a long term average and how does this affect current WDR specifications? 2

21 4. Does the precipitation algorithm in the radar product generator used by the National Weather Service (NWS) Weather Surveillance Radar exhibit any biases that manifest in extreme wind events? 5. Based on the results of 1-4, what are the implications for computational and experimental simulation and specification of design requirements for water penetration resistance of building products and systems? Thrust 3: Development of a Wind-Driven Rain Simulator for Implementation In a Full-Scale Test Facility Capable of Subjecting a Low-Rise Building to Windstorm Conditions In addition to answering the preceding questions, the data was also used to develop a method to design a WDR simulation system to be used by the Insurance Institute for Business & Home Safety (IBHS). IBHS recently constructed a 3 MW fullscale test facility that can replicate windstorm conditions at a sufficient scale to evaluate the performance of a two-story building subjected to hurricane conditions (Figure 1-3). The results obtained from the field research were used to determine a realistic RSD for the simulations. Tests were then performed to determine the effectiveness of the PARSIVEL as a reference instrument, and once the results were verified, a range of commonly available nozzles were evaluated to determine the optimal choice for the facility. Figure 1-3. Insurance Institute for Business & Home Safety Research Center (photo courtesy of IBHS) 21 IBHS.ORG

22 Importance of the Study The research findings have the potential to impact atmospheric sciences and wind engineering. In atmospheric research, calibrating radar precipitation estimate algorithms to ground level rain gauges has led to better predictions and estimations of rainfall intensity and accumulation (Habib et al., 29). Few such gauges exist (Linsley et al., 1992), and most are not designed to operate in high winds. In contrast, this study deployed multiple instrument stations in a user-defined array. These data can be used to select rainfall reflectivity and rainfall intensity (Z-R) relationships for extreme wind event precipitation systems. Furthermore, the ground level data will complement cloud level radar data and could be used to model cloud to ground RSD evolution (Wilson and Brandes, 1979). In computational wind engineering research, to model WDR trajectories, current analysis techniques employ the following aspects: (1) the wind flow pattern is calculated by solving the three dimensional Reynolds Averaged Navier-Stokes equations, the continuity equation, and the equations of the realizable k-ε turbulence model; (2) a choice of drag coefficient formulas for drops or experimentally derived drag coefficients from Gunn and Kinzer (1949); (3) the use of models to simulate turbulence dispersion of drops; and (4) a spatially and temporally constant RSD, modeled after work performed by Best (195). Choi (1994), Blocken and Carmeliet (22), among others (Rodgers et al., 1974; Inculet and Surry, 1994; Nore et al., 27) have shown that drop size affects the trajectory of particles near a bluff-body. Thus, the data collected were compared with established models (e.g., Marshall and Palmer 1948, Best 195, Willis and Tattleman 1989 and the three parameter gamma model using the mean of the parameters calculated from the gathered data) to determine their appropriateness for 22

23 simulating extreme WDR conditions. This information will be made available to the American Society of Civil Engineers (ASCE) Task Committee on Wind Driven Rain Effects, which is currently preparing a state-of-the-art report for the ASCE Technical Council on Wind Engineering. Experimental wind engineering will also benefit from new data gathered in high wind by setting more comprehensive simulation requirements. This is an emerging subdiscipline and only a few WDR studies have been conducted at full-scale (e.g., Salzano 21, Bitsuamlak 29) and in the wind tunnel (e.g., Inculet 1994). In the full scale experiments, the primary focus was replicating dynamic wind loading and façade wetting rates determined from current test standards (e.g., ASTM E331-, ASTM E547-, ASTM 115-5); therefore, correct RSD simulation was not a concern. The RSD prescribed in the wind tunnel experiments was determined from models (Best, 195; Marshall and Palmer, 1948) based on data collected in little to no wind. In the design of the IBHS WDR simulation system, the collected data was used to determine the required RSD. This methodology can ultimately lead to improved performance evaluation of the water penetration resistance of building products and systems. Organization of Document Chapter 2 provides an overview of precipitation, the definition of WDR, factors that influence WDR, different WDR measurement techniques, and a review of previous computational and experimental simulation methodologies. Chapter 3 explains the design, prototyping, and field evaluation of the articulating instrumentation platform. Chapter 4 presents the characterization of WDR in strong winds. Chapter 5 discusses the technical approach that was taken to design a full scale WDR system, and Chapter 23

24 6 summarizes the research efforts and provides conclusions and recommendations for future research. 24

25 CHAPTER 2 LITERATURE REVIEW This chapter presents fundamental concepts of precipitation and wind-driven rain (WDR). A description of the factors that influence the deposition of rain on the building façade is also provided. Finally, raindrop size distribution (RSD) measurement techniques and previous computational and experimental simulation methodologies are reviewed. Precipitation Events Precipitation Precipitation results from the cooling of warm air, generally occurring as warm air rises over cold air masses. As the air cools, water vapor condenses on particulate matter in the air (e.g. dust and salt particles). Drops then grow by either collision and coalescence (in warm conditions) or ice crystal growth (i.e. the Bergeron-Findeisen process; Houze, 1994). Drop growth continues until the hydrometeors are large enough to overcome updrafts and precipitation occurs as they fall under the influence of gravity. Orographic effects, frontal systems, or convection are some of the processes that cause lifting of air masses in the atmosphere. Orographic lift forces result from the upward deflection of horizontally moving air masses that encounter large orographic obstructions. As the air mass is deflected upward, it cools allowing condensation and subsequently precipitation. Frontal systems occur when a cold air mass approaches a warm air mass (cold front) or vice versa (warm front). Cold fronts generate precipitation that is usually high intensity, short duration, and occurs over a limited area. Severe thunderstorms are associated with this type of frontal system. Conversely, warm fronts generate mild, long term, and widespread precipitation. Convection occurs when solar 25

26 radiation warms the earth surface and subsequently the adjacent air. As the air mass warms, it rises, and precipitation occurs (Houze, 1994). This is the process that forms convective thunderstorms in the rainbands and eyewall of tropical cyclones (Gray, 1979). Precipitation Types Precipitation is generally classified as stratiform or convective. The difference is attributed to the characteristic vertical air velocity in the cloud structure. In stratiform precipitation, the vertical air velocity is much less than the fall velocity of the hydrometeors. In convective precipitation, the vertical air velocity is on the order of the fall velocity of the hydrometeors. As a result, hydrometeors in convective precipitation spend less time airborne (Houze, 1994), and are smaller than their stratiform counterparts. Convective precipitation generally produces more intense rainfall rates over a shorter duration than stratiform precipitation (Tokay et al., 1999). In addition, convective precipitation can produce a greater concentration of small to medium sized drops and fewer concentrations of large drops than stratiform precipitation (Tokay and Short, 1996). Raindrop Size Distribution The raindrop size distribution (RSD) refers to the concentration of all drop sizes for a given sample volume. Early models of the RSD were developed under the assumption that a reference bulk variable usually horizontal rainfall intensity (defined in the next section) is the governing parameter (cf. Torres et al. 1994). Marshall and Palmer (1948) were the first to develop a rainfall-dependent RSD ( ) model: (2-1) 26

27 (2-2) (2-3) where is the intercept parameter, is the slope parameter, is the horiztonal rainfall intensity. The most widely used model in building science and wind engineering is the Best (195) model: (2-4) (2-5) (2-6) where is the fraction of liquid water in the air consisting of raindrops of radii < (mm), (mm/hr) is the horizontal rainfall intensity, and (mm3/ m3) is the volume of liquid water per unit of volume of air. The main difference between the Best (195) and the Marshall and Palmer (1948) models is that Best model does not assume a constant intercept parameter and is not linear in log space. Ulbrich (1983) demonstrated that RSDs can vary significantly under different types of rainfall conditions. He proposed a modified three-parameter gamma distribution (2-7), which has become a widely accepted method for fitting RSDs. (2-7) where is the intercept parameter (mm-3 m-1), is the shape parameter (dimensionless), and is the slope parameter (mm-1). Moments (2-8) of the measured RSD are used to estimate the three parameters (,, and ). For this study the M246 moment estimator method is employed (Cao and Zhang, 29): 27

28 (2-8) (2-9) (2-1) (2-11) (2-12) where is the moment order, is the ratio of moments and is the gamma function defined as: (2-13) Figure 2-1 demonstrates the model sensitivity to each parameter. The dark line depicts RSD derived from the FCMP data acquired during Hurricane Ike.,, and were estimated for each time history; the 5th percentiles of the parameters estimates characterize the empirical distribution shown (dark line). The sensitivity to each parameter is illustrated by independently changing,, and to the 5th and 95th percentiles of the parameter estimates. The intercept and slope parameters indicate the shift and rotation of the distribution, respectively, and the shape parameter indicates the concavity of the distribution. 28

29 Figure 2-1. Influence of the modified three-parameter gamma model parameters Willis and Tattleman (1989) expanded the gamma model by researching the large fluctuations associated with high rainfall intensities. They developed a method for estimating the three parameters using rainfall intensity as the only reference variable and calibrated to data collected in extreme events (Hudson, 197; >1mm/hr). The Wills-Tattleman model (1989) uses empirically derived formulas for water content ( ) and median volume diameter ( ). The equations to estimate the three parameters were developed using a fit to the normalized data: 29

30 (2-14) (2-15) (2-16) (2-17) (2-18) Validation of the Willis and Tattelman (1989) model was accomplished via a comparison to approximately 14, ten second samples collected from hurricanes and tropical storms from at 3 m (9843 ft) and 45 m (1476 ft). The results indicate that the model reasonably characterized the observed distributions collected in rainfall rates of up to 225. mm/hr (8.9 in/hr). Rainfall and Wind-Driven Rain Horizontal rainfall intensity refers to the accumulated volume of rain caused by the flux of rain through a horizontal plane. Wind-driven rain (WDR) occurs when windinduced drag forces impart a horizontal component of motion to the falling particles. The components of the rain intensity vector (,, and ) are defined as follows: = the oblique rain vector = the accumulated volume of rain, over a specified amount of time, caused by the flux of rain through a horizontal plane. the accumulated volume of rain, over a specified amount of time, caused by the flux of rain through a vertical plane,, and are illustrated in Figure 2-2 (after Blocken and Carmeliet, 22). 3

31 Horiz. Rainfall Intensity (R H ) Wind Velocity Driving Rain Intensity(R WDR ) Figure 2-2. Components of the rain intensity vector Quantification of the Rain Deposition on the Building Façade Methods for quantifying wind-induced wetting of the building façade were developed by Choi (1993), Straube and Burnett (2), and Blocken and Carmeliet (22). The WDR deposition on the building is defined as the ratio of wetting on the building at a specified location on the façade to the driving rain intensity ( ). The terminology used in this document is based on Choi (1994). The Local Effect Factor ( ) is the ratio at time of the WDR intensity ( ) at a particular location on the façade to the unobstructed horizontal rainfall intensity ( ) in the free-stream for a single hydrometeor of diameter : (2-19) The equivalent parameter for the deposition of all raindrop sizes at a location on the façade is the Local Intensity Factor ( ). The is obtained by integrating the s over all hydrometeor diameters (Choi, 1994): 31

32 (2-2) The wetting of the building façade is highly non-uniform. RSD, wind, terrain, and building characteristics are factors that influence the wind flow around the building, the drop trajectories, and consequently, the wetting of the building facade. These effects are now discussed. Factors Affecting Rain Deposition Rate on the Building Façade Rainfall intensity The amount of rain impinging on the building surface is primarily dependent on the amount of precipitation in the boundary layer. This quantity is defined as the unobstructed horizontal rainfall intensity. Ground measurements of horizontal rainfall intensity have been primarily collected for hydrological and agricultural purposes. The sampling interval of these data is seldom less than three to six hours and more commonly between daily and monthly (Willis and Tattelman, 1989). These time scales are inadequate for engineering applications (Blocken and Carmeliet,25) that require continuous, high-resolution time histories. Most test methods for the water penetration resistance of building systems (e.g. ASTM E331-, ASTM E115-, ASTM E2268-4, and ASTM E547-) prescribe a wetting rate of 23 mm/hr; this quantity reflects the rate required to cause water to sheet over a curtain wall. The National Weather Service (NOAA, 1977) provides 5- to 6- minute precipitation frequency atlases for the eastern and central United States, in which the maximum rainfall intensity for the South Eastern United States is 274 mm/hr for a 1 year return 5 min rain event. 32

33 Influence of the wind on raindrop trajectory Rainfall trajectories are influenced by body forces (e.g., gravity) and surface forces (e.g., wind-induced drag). Condensation of water vapor on particulate matter in the air during drop synthesis produces small drops, nearly spherical due to the surface tension dominating over pressure forces. Throughout their freefall, drops continuously collide, coalesce, and break up yielding different sized drops. Smaller sized drops are susceptible to evaporation while larger drops are subjected to unequal pressure distributions that cause distortion (Figure 2-3). This deviation from the spherical assumption can lead to over-estimation of drag coefficients, particularly at high velocity, high Reynolds number flow (Hu & Srivastava, 1995). Figure 2-3. Drop size and shape Raindrops traveling through the boundary layer in the free-stream are assumed to have a horizontal velocity component that asymptotically approaches the wind speed due to drag forces and a fall velocity (vertical component) equal to their terminal velocity. Figure 2-4 and Figure 2-5 depict drop trajectories for various drop diameters and wind velocities; the trajectories were modeled assuming a steady flow of marked velocity, and the drop drag coefficients and terminal velocities given by Gunn and Kinzer (1949, model is explained in detail in Chapter 5). Figure 2-5 indicates that the distance at which drops have achieved 95% of the theoretical trajectory angle the 33

34 Distance (m) angle at which the horizontal component of the drop velocity is equal to the wind velocity is less than 55 m. Masters et al. (21) reported a minimum mean and standard deviation values of ± 43. m, 74.4 ± 31.9 m, and 12.4 ± 54.4 m for 15 minute integral scales (at an elevation of 1 m) during Hurricanes Katrina, Rita, and Wilma in 25, respectively. Thus, assuming that the drop travels at the gradient wind speed in tropical cyclones is valid. As raindrops approach the building façade, higher wind speeds increase the horizontal component of motion. With higher horizontal velocities, more drops are susceptible to striking the building surface. Choi (1994) found that changing wind velocity from 5. m/s to 3. m/s can increase LIF values up to 1 times for the top quarter of a 4:1:1 ratio building. Therefore, increasing wind velocity will increase the effect of all raindrop sizes on the building façade, particularly in the top quarters. Droplet Trajectory at 7 m/s Wind Speed -1 A) mm 4. mm mm 2. mm mm.5 mm.1 mm Distance (m) 34

35 Angle (deg) Distance (m) Distance (m) Droplet Trajectory at 35 m/s Wind Speed -1 B) mm 4. mm mm 2. mm mm.5 mm.1 mm Distance (m) Droplet Trajectory at 15 m/s Wind Speed -5 C) 5. mm mm 3. mm 2. mm mm.5 mm.1 mm Distance (m) Figure 2-4. Trajectory of drops released at m/s Droplet Trajectory Angle at 7 m/s Wind Speed 5. mm 4. mm 3. mm 2. mm 1. mm.5 mm.1 mm 95% of terminal angle A) Distance (m) 35

36 Angle (deg) Angle (deg) Droplet Trajectory Angle at 35 m/s Wind Speed 5. mm 4. mm 3. mm 2. mm 1. mm.5 mm.1 mm 95% of terminal angle B) Distance (m) Droplet Trajectory Angle at 15 m/s Wind Speed 5. mm 4. mm 3. mm 2. mm 1. mm.5 mm.1 mm 95% of terminal angle C) Distance (m) Figure 2-5. Distance required for the trajectory angle to reach 95% of the terminal angle Terrain characteristics Terrain characteristics affect the wetting of the building façade by changing the characteristics of the flow upwind, particularly the mean wind speed and turbulence intensity. Karagiozis et al. (1997) described the terrain characteristics affecting the flow conditions upwind of the building façade; these characteristics range from ground surface roughness dictating the global terrain exposures and overall flow conditions (e.g., open, suburban, urban) to larger obstructions introducing local disturbances to the 36

37 upwind flow (e.g., close vicinity building obstruction). Significant distortions of the flow pattern directly upwind of the building resulting from the introduction of high turbulence and mixing due to blockage and shielding effects of building obstructions at close vicinity causes the distribution of wetting on the building façade to deviate from what is commonly expected (Choi, 1993). When no large obstructions are directly upwind, the greatest effect to the wind flow pattern is due to the aerodynamic roughness length ( ). The aerodynamic roughness length is defined as the height at which the mean velocity is zero assuming a logarithmic velocity profile (Weber, 1999): (2-21) Where is the mean hourly wind velocity, is the height above the ground, and is the friction velocity calculated per Weber (1999): (2-22) where is the eddy covariance between the longitudinal and vertical fluctuating components. Surface roughness influences the boundary layer flow by decreasing the mean wind speed and increasing the turbulence intensity as the elevation above the ground decreases (Counihan, 1975). The change in mean wind speed and turbulence will affect the temporal and spatial deposition of rain on the building façade (Blocken and Carmeliet, 22). Building characteristics Immersion of a building in a wind flow creates turbulence in the form of frontal vortices, separations at the building edges/corners, corner streams, recirculation zones, shear layers and the far wake (Bottema, 1993). The flow pattern is dependent on 37

38 upstream conditions, building orientation in the flow field, and building geometric shape. When raindrops approach the bluff body the trajectories become complicated; the result is non-uniform deposition on the building facade. Trajectories of small particles change sharply; however, the higher inertia larger drops are less susceptible to local flow disturbances and bluff body aerodynamic effects. As a result, the deposition contours on the building façade of large drops are less affected than those corresponding to smaller drops (Figure 2-6, after Blocken and Carmeliet, 22). Figure 2-6. Deposition of smaller drops left and larger drops right (arrows indicate decreasing drop diameter) The effect of varying building geometry, in particular width to height ratios, changes the blockage effect on the wind flow. The number of drops diverted away from the structure increases with higher aspect ratios. Choi (1994) experimentally verified this phenomenon in an investigation of a narrow (H:W:D=4:1:1) building that exhibited higher LIF values than a wider (H:W:D= 4:8:2) building (assuming similar drop sizes). Semi-Empirical Models Modeling of Rain Deposition on the Building Façade Measurements from vectopluviometers and surface mounted instruments have shown that is directly related to wind speed and horizontal rainfall intensity (Lacy, 1951; Hoppestad, 1955). This relationship has been the basis for multiple semi- 38

39 empirical models and has been used to derive regional WDR exposure from current standard meteorological data provided by existing weather stations. Hoppestad (1955) expressed the WDR intensity ( ) as a function of a WDR coefficient (, the inverse of drop terminal velocity), the wind velocity ( ), and the horizontal rain intensity ( ). His work provided the basis for current semi-empirical models. (2-23) Lacy (1965) used empirical relationships that express the median raindrop size as a function of horizontal rainfall intensity and terminal velocity data to develop a single WDR coefficient. The outcome was a refined equation that satisfies most WDR scenarios (Lacey, 1965): (2-24) Empirical models have evolved to include the effect of the complex flow around the building and output spatially varying rain deposition rates. Two of the most common models are the Straube and Burnett (2) model and the British Standard BS EN ISO (29). Both models implement: (2-25) where is the WDR coefficient dependent on location on building and is the angle between the wind direction and the perpendicular of the wall surface. A comprehensive comparison of these methods performed by Blocken et al. (21) found that while the ISO model is more accurate than the Straube and Burnett model, neither accurately model the blockage effect of the tested buildings. 39

40 For the purpose of this study, wetting rates were estimated in accordance with the ISO model (BS EN ISO :29): (2-26) Where is the rain intensity reaching the building façade, is the roughness coefficient representative of roughness of the terrain upwind of a wall (conservatively assumed to be one), is the topography coefficient that accounts for wind speed up over isolated hills and escarpments (conservatively assumed to be one), is the obstruction coefficient that accounts for obstructions (e.g. buildings, fences, trees, etc.) close to and upwind of building façade (conservatively assumed to be one), and is the wall factor which is calculated as the ratio of water reaching the building façade to the quantity passing through an equivalent unobstructed space (conservatively assumed to be four, according to Table 4 in the BS EN ISO :29). Numerical Models The work by Choi (1993; 1994) has remained the foundation for most modern techniques. Choi (1993, 1994) proposed a method for calculating WDR deposition on the building façade that includes: (1) calculating wind flow under steady state conditions by solving the Navier-Stokes equations with the k-ε turbulence model, and (2) calculating drop trajectories at every point for each raindrop size by iteratively solving their equations of motion 2-27 through 2-29, and 2-19 through 2-2 calculating and values at different locations of the building. (2-27) 4

41 (2-28) (2-29) In the equations of motion ( ) is mass of the drop, is radius, is the air density, is the water density, is the air viscosity,,, and are along, across, and vertical wind directions, respectively. Blocken and Carmeliet (22) extended this work by introducing a temporal component to the wind flow and examining the spatial and temporal WDR deposition on the facade of the VLIET (Flemish Impulse Programme for Energy Technology) test building (Blocken and Carmeliet, 22). Full Scale Experiments Experimental wind engineering research directed at WDR effects on buildings is limited. Only a few projects have been conducted in the wind tunnel (Flower and Lawson, 1972; Rayment and Hilton, 1977; Inculet and Surry, 1994) and at full-scale (e.g., Salzano, 29; Bitsumalak, 29; Lopez et al., 211). Wind tunnel experiments have been used to characterize wetting patterns on the façade of multiple scale models. Due their complexity and high cost, wind tunnel tests of WDR have been limited. Major difficulties encountered during these experiments include (1) short duration of tests to not saturate the water sensitive paper, (2) counting and measurement of drops on the water sensitive paper was extremely labor intensive, (3) large variability between tests as a result of short test durations, and (4) obtaining a uniform rain distribution was difficult due to the small size of the scaled drops (Inculet and Surry, 1994). 41

42 Currently there are few methods for full scale simulation of WDR. These methods are primarily employed in the product approval process, at the state level, to ensure a minimum water infiltration resistance of building components. The testing procedures require the application of static and cyclic pressures while a constant wetting rate is applied to the exterior of a singular building component; these tests do not directly investigate the holistic behavior of the building envelope, rather the behavior of each component in isolation. In response, research efforts by RDH Building Engineering Limited (22), Florida International University (Bitsuamlak et al., 29), and the University of Florida (Masters et al.,28; Salzano et al., 21; Lopez et al., 211) to simulate WDR on full scale structures have been undertaken. RDH Building and Engineering Ltd. (22) sought to identify the adequacy of building codes, standards, testing protocols, and certification processes towards wind driven rain resistance of fenestration. The study analyzed the performance of 113 laboratory and 127 field window specimens. Field specimens were subjected to a constant impinging jet while a constant rain rate was applied. The research at the University of Florida (Salzano et al., 21; Lopez et al., 211) expanded on the RDH study by evaluating water penetration resistance of window/wall assemblies subjected to wind loading calibrated to tropical cyclone field data collected by the Florida Coastal Monitoring Program (FCMP). This emerging sub-discipline will greatly benefit from new insights regarding the physical simulation of WDR and can ultimately lead to improved performance evaluation of the water penetration resistance of building products and systems. 42

43 Measurement of Wind-Driven Rain Instrumentation In the earliest studies, WDR measurements were made using directional pluviometers (known as vectopluviometers) and wall mounted collection chambers. Vectopluviometers are fixed instruments that obtain directional quantities of WDR in the free-stream with four or eight compartments of similar size openings each facing the cardinal or cardinal and ordinal directions, respectively and an additional compartment facing the vertical direction (Lacy, 1951; Choi,1996). The implementation of vectopluviometers in various research projects (cf. Blocken and Carmeliet, 24) has produced directional WDR data that has been used for the development of WDR maps (Lacy,1965), as a tool for estimating WDR deposition on building façades, and as basis for the assumption that the increases proportionally with wind speed and (discussed in semi-empirical simulation methods). In the obstructed flow, collection chambers are attached to the walls of buildings to directly measures the quantity of WDR deposited on the façade. These instruments are collection chambers, of multiple sizes, mounted flush to a wall with an attached reservoir to collect the deposited rain (Straube et al., 1995). Although implementation of these instruments facilitated research of the spatial variability of WDR on the building surfaces and the effects of building aspect ratio, wind velocity, and horizontal rainfall intensity, they are unable to measure the RSD and wind characteristics. Modern precipitation measurement instruments more accurately determine the characteristics of rain but have limitations. Measurement by radar can produce detailed precipitation data for an extensive area from a single location; however, measurements are taken at heights where the RSD can vary from the ground level RSD, and the RSD 43

44 is not directly measured. RSD determination by radar is based on reflectivity relationships (Richter and Hagen, 1997; Schafer et al., 22) for which discrepancies have been investigated by Marshall et al. (1947), Wilson and Brandes (1979), and Medlin et al. (27). Disdrometers can accurately measure ground level RSD data at temporal resolutions that are useful for WDR research (Joss and Waldvogel, 1967; Loffler-Mang and Joss, 2). Impact and optical disdrometers are the two most commonly used rain sensors. Impact disdrometers, such as the Joss and Waldvogel disdrometer, measure the induced voltage from the displacement of an aluminum covered styrofoam sensor (Joss and Waldvogel, 1967). The voltage is amplified, and the drop size is interpreted by fitting the voltage to predetermined voltage ranges corresponding to drop diameters (Sheppard, 199). The Joss and Waldvogel disdrometer can measure drop sizes between.3 mm and 5 mm with resolutions varying from.1 mm for small drops to.5 mm for larger drops. Inherent limitations of the instrument manifest in high wind scenarios, because the measuring algorithm expects drops to be falling at terminal velocity (Sheppard, 199; Tokay et al., 28; Tokay et al.,23). Optical disdrometers function by creating a light band and measuring the voltage drop from a photodiode, or series of photodiodes, as a drop passes through the light band and a fraction of the light is obstructed from the photodiode (Loffler-Mang and Joss, 2). These instruments are capable of higher temporal resolution measurements due to the lack of a sensor head which requires time to return to its original position. Optical disdrometers are also more accurate than impact disdrometers in measuring particles less than.7mm (Loffler-Mang and Joss, 2). 44

45 Limitations of optical disdrometers Currently, optical disdrometers are the instrument of choice for measuring ground level RSD; however, these instruments have limitations that must be considered. The major limitations of optical disdrometers include fringe effects, coincident measurement of multiple drops, splashing effects, and errors associated with high wind. Fringe effects refer to the partial measurement of a drop s diameter as the drop grazes the sensitive area; thus, the drop is recorded as a having an incorrect smaller diameter and travelling much faster than correctly measured drops. The error in velocity measurement propagates to an incorrect sample volume calculation which leads to an under-estimation of and among other precipitation characteristics (Grossklaus et al., 1998). Errors associated with the coincident crossing of multiple drops occur if the optical disdrometer cannot discriminate between the light extinction from one or multiple drops. When multiple small drops simultaneously cross the sensitive area, a singular large drop is recorded travelling at the velocity of the smaller drops. Thus, the sample volume is erroneously small and the incorrectly measured large drop has an exaggerated contribution to the rain parameters including and (Grossklaus et al., 1998). Splashing effects occur when drops strike the instrument, breakup, and the smaller remnants that bounce off, travel through the sensitive area at a much slower velocity than the terminal velocity (Tokay et al., 21). The measured drops are not characteristic of the precipitation event and thus contaminate the data. The slow velocity results in a small sample volume and the splashed drops have an exaggerated contribution to the rain parameters including and. 45

46 The errors associated with high wind manifest as the incorrect diameter and velocity measurement due to the oblique trajectory of drops as they travel through the sample area. As wind velocities increase, the trajectory of the drops becomes more horizontal; thus, they remain in the sensitive area longer and, through their passage, occupy more of the horizontally aligned light band, leading to larger sampled areas (Loffler-Mang and Joss, 2). Additionally, depending on the structure of the disdrometer, the distortion of the wind around the sensitive area significantly affects the trajectory of drops reducing the quantity of drops crossing the sensitive area (Nespor et al., 2). Many attempts have been made to mitigate these effects (Donnadieu, 198; Hauser et al., 1984; Grossklaus et al., 1998; Nespor et al., 2; Tokay et al., 21). Disdrometers have been developed with the capability of sensing drops at the edge of the sampling area. Illingworth and Stevens (1987) and Grossklaus et al. (1998) developed disdrometers that employed an annular sensitive area rather than a flat sheet. With this advancement, drops that graze the sensitive area are characterized by a single voltage reduction and are thus excluded from the data. Drop Measurement Technologies implemented multiple photodiodes to measure the voltage drop across flat light sheet in the design of their Precipitation Imaging Probe (instrument is discussed in the next section). In this arrangement, when the outer most photodiodes sense a drop in the sensitive area, the drop is excluded from data. Other researchers (Donnadieu, 198; Hauser et al., 1984; Tokay et al., 21) mitigate the effect of fringe effects, coincident measurement of multiple drops and splashing effects limitations by employing quality control algorithms that filter data according to the measured drop velocities and whether 46

47 or not they fall within a velocity threshold based on the work done by Gunn and Kinzer (1949). These quality control algorithms typically exclude less than 2% of the data (Tokay et al., 21) and yield realistic results. Errors associated with high wind speed, are addressed with the same algorithms and the exclusion of the data above a certain velocity threshold (Tokay et al., 28); however, instruments employing an annular sensitive area, such as the Illingworth and Stevens (1987) and the Institut fur Meereskunde (IfM) disdrometer (Grossklaus et al., 1998) were designed for operation in high wind, but are not commercially available and to the authors knowledge, have only been employed on ships for rainfall measurement over open ocean. The instrument platforms used in this research (described in Chapter 3), employed an OTT PARSIVEL disdrometer (Loffler-Mang and Joss, 2) and a Drop Measurement Technologies Precipitation Imaging Probe capable of high resolution measurements, reporting precipitation at ten and one second intervals, respectively. The instruments are described in the following section. Instrumentation Used In This Study OTT PARSIVEL optical disdrometer Loffler-Mang and Joss (2) produced the PARSIVEL an easy to handle, robust, and low cost disdrometer that lends the ability to implement a network of disdrometers to investigate small-scale variability (Loffler-Mang and Joss, 2). The OTT PARSIVEL records the count, diameter, and velocity of hydrometeors that pass through a 3 mm X 16mm X 1 mm laser field. It functions by focusing the laser on a single photodiode at the receiving end and measuring the analog voltage output (Figure 2-7). As particles pass through the laser field they obstruct a band of light, corresponding to their diameter, from arriving to the photodiode and lower the measured 47

48 voltage from the steady state 5 V. The voltage time history is inverted, amplified, filtered, and the DC component is removed (Loffler-Mang and Joss, 2) such that the diameter of the particle is estimated from voltage drop (ΔV). The duration of light absence (Δt), from when the particle enters and exits the laser band, is used to estimate the particle velocity. Particle diameters and velocities are then binned into diameter and velocity classes (Table 2-1 and Table 2-2), and digitally output as time sampled matrices (Figure 2-8). The particle shapes are assumed to be symmetric in the horizontal plane and have linearly varying axis ratios of 1 to 1.3 for diameters 1 mm to 5 mm, 1 (spherical) for diameters < 1 mm, and 1.3 for diameters > 5 mm. Table 2-1. PARSIVEL diameter classes Class Number Class Average (mm) Class Spread (mm)

49 Table 2-1. Continued Class Number Class Average (mm) Class Spread (mm) Table 2-2. PARSIVEL velocity classes Class Number Class Average (m/s) Class Spread (m/s)

50 Figure 2-7. PARSIVEL drop diameter and velocity determination.3 mm drops (class 3) traveling.34 m/s (class 3) PARSIVEL Output:.7 mm drops (class 6) traveling.6 m/s (class 7) Class V Class D Figure 2-8. PARSIVEL measurement and output 5

51 Droplet Measurement Technologies Precipitation Imaging Probe The Droplet Measurement Technologies (DMT) Precipitation Imaging Probe (PIP) is a 2-D optical disdrometer that functions in the same manner as the OTT PARSIVEL but has a higher resolution due to the use of 64 photodiodes instead of one. It measures and counts drop diameters with.1 mm resolution up to 6.2 mm in a sample area of 26. mm x 6.4 mm. The PIP does not measure drop velocity; it assumes that all drops are moving at the wind velocity and thus requires a continuous wind velocity input. Additionally, the PIP is intended to be mounted on aircraft and operated at flight speeds. Thus, the sample area is smaller than the OTT PARSIVEL sample area leading to a smaller sample volume. Rain parameters for both disdrometers are calculated as follows: (2-3) (2-31) (2-32) (2-33) (2-34) (2-35) 51

52 (2-36) The variables introduced in Equations 2-3 to 2-36 are defined as follows: is the number of diameter drops is the sample volume is the depth of field of the instrument is the width of the diode array is the velocity of drop through the laser plane is the sample time of the instrument is the density of water is the third moment as defined in (2-8 is the equivalent radar reflectivity is the mean measured drop diameter is the volume weighted mean drop diameter (Testud et al., 21). Summary This chapter presented an overview of the WDR phenomenon, simulation methodologies adopted by other researchers, and instruments used to measure precipitation. Information of the disdrometers used in this research has been presented, and their applicability for WDR measurement will be discussed in Chapter 3. 52

53 CHAPTER 3 DESIGN, PROTOTYPING, AND IMPLEMENTATION OF ARTICULATING RAIN PARTICLE SIZE MEASUREMENT PLATFORMS This chapter presents the development and implementation of novel instrument platforms intended for characterizing the raindrop size distribution (RSD) in strong winds. To the author s knowledge, this is the first known effort to successfully reduce wind induced errors associated with ground based disdrometers. Thus, the data collected provides a new resource for the study of wind driven rain (WDR). This chapter is organized into three sections. First, the motivation for the development of an instrument platform which actively aligns the disdrometer perpendicular to the mean rain direction is discussed. This section demonstrates that in the absence of wind (i.e., drops travelling perpendicular to the sensitive area) measurements made by the PARSIVEL disdrometer and measurements made using a laborious but highly accurate procedure, the Oil Medium Test, are similar. This section also demonstrates the limitations of the instrument in the presence of wind (i.e., drops travelling obliquely through the sensitive area). Second, details of the WDR measurement system are presented. Finally, a comparison of collocated stationary and actively aligned disdrometers (henceforth referred to articulating instruments) is presented. Motivation for the Development of an Articulating Instrument Platform Optical disdrometers are the instrument of choice for quantifying RSDs; however, due to the limitations of these instruments (discussed in Chapter 2), data recorded in high winds are usually excluded (Tokay et al., 28). Lack of high-quality data has left questions regarding the character of WDR in high wind events. Griffiths (1975) proposed that actively aligning a disdrometer parallel to the mean rain direction could theoretically improve accuracy by showing that the sample volume of an articulating 53

54 sensor was greater than that of the sensor in the stationary, horizontally aligned, configuration (the proof is shown in the Appendix). Bradley and Stow (1975) attempted such an experiment but found that device was unmanageable in high wind, dripped water into the sensitive area when tilted, and were not certain how to define a mean rain direction. Since then, disdrometers and servomotor systems have become lighter, easier to use, and less expensive. Thus, the author investigated the option of aligning disdrometers to the mean rain direction by performing a series of laboratory tests intended to reduce measurement errors associated with high wind speeds. Raindrop Size Distribution Verification Using the Oil Medium Test The Oil Medium Test was performed to demonstrate that when properly employed (i.e. drops travelling perpendicular to the sample area), the PARSIVEL disdrometer yields a reliable estimate of the RSD. The test was performed under ideal stagnant air conditions where the RSDs recorded by the PARSIVEL were compared to RSDs obtained from water drops collected in oil. The PIP was not used to calculate the stagnant air RSD because the instrument requires continuous wind velocity feed back to calculate the sample volume, rather than using the drop velocity. Raindrop size distributions from drops collected in oil were calculated using a morphological image processing algorithm (van den Boomgaard and van Balen, 1992; Adams, 1993; Jones and Soille, 1996). The experimental setup included setting a single nozzle 3. m above a collection dish containing the oil medium, setting the water pressures, and exposing the collection dish to five seconds of continuous spray. A 2 megapixel, RAW format, picture of the collection dish was then immediately taken using a Canon EOS 5D Mark II Digital SLR Camera with a Canon Telephoto EF 1mm f/2.8 USM macro lens with the ISO set to 32, relative aperture set to 7.1, white balance 54

55 set to 63K, and shutter speed set to 1/1 sec. To maximize the contrast between the dish and drops, a blue dye (Cole Parmer #298-18) was used. Care was also taken in the selection of the oil medium, by selecting a clear mineral oil with a specific density similar to water. The oil properties prevented distortion of the shape of the drops and essentially captured the drops as they were when falling freely. The photographs were filtered using Adobe Photoshop to obtain a high contrast black and white image. The black and white images were then analyzed by the morphological image processing algorithm to obtain the size of each drop by scaling the pixel counts (Figure 3-1). The data was then binned using PIP specifications and an RSD was obtained. In this experiment, six measurements were taken by the PARSIVEL and images of 15 oil medium tests were processed by the morphological image processing algorithm, yielding the RSDs shown in Figure 3-2. In general the PARSIVEL measured a smaller number of drops larger than 2 mm in diameter; however, the source of the difference is unclear and for practical purposes the two RSDs are reasonably similar. 1x Magnification Reference 1. cm x 1. cm Area 1. mm 3x Magnification.15 mm Figure 3-1. Sample picture from morphological image processing algorithm 55

56 Concentration (mm -1 m -3 ) PARSIVEL RSDs Mean PARSIVEL RSD RSDs from Algorithm Mean RSD from Algorithm Figure 3-2. Comparison of different raindrop size distribution measurement techniques Bearing Drop Test Diameter (mm) The bearing drop test was performed to replicate the effect of high wind speeds on stationary PARSIVEL measurements by dropping 2 high tolerance steel bearings(1 mm, 2 mm, and 3 mm) from 6.1 m height (Figure 3-3) through the laser plane at multiple elevation angles (9, 8, 7, 6, 5, 45, 4 ). The results of this test are found in Figure 3-4 and Figure 3-5. The figures demonstrate that the PARSIVEL correctly measured the bearing diameters regardless of trajectory angle; however, at 5 or lower, incorrect measurements of the velocity indicate that the measured velocity is dependent of the trajectory angle and can deviate significantly from the theoretical velocity. Small diameter measurements (much less than 1mm) are assumed to be margin fallers. Similar results were observed by Friedrich et. al (211) in which 2-6 mm drops were recorded as having reduced fall velocities when travelling obliquely through the sensitive area; however, at more extreme elevation angles, unrealistically high drop diameters were recorded indicating that incorrect drop diameter measurements may 56

57 occur high wind speeds, even though it was not observed in this experiment. Thus, the observed results may not be representative of every natural scenario. Bearing Direction 6.1m Drop Height Laser Band θ Figure 3-3. Angled trajectory experiment configuration To relate the trajectory angle to specific wind speeds, 3-1 was used (Based on the Lacy 1965 Equation): (3-1) where is the terminal velocity of the drop and is the wind velocity. Table 3-1 demonstrates the sensitivity of the trajectory angle to drop diameter and wind velocity. The table indicates that at 1 m/s wind speeds, trajectory angles of the majority of the drops (< 5 mm) will be far below the 5 threshold. This implies that at wind speeds of 1 m/s drop velocities can be underestimated by at least 4%, yielding incorrect RSD measurements. The effect of oblique trajectory angles was further investigated in winds generated by the UF Windstorm Simulator. Table 3-1. Trajectory angles of multiple diameter drops in multiple wind speeds Drop Diameter (mm) V t (m/s) U = 5 m/s U = 1 m/s U = 15 m/s U = 2 m/s

58 Count Count Count Count Count Count Count Count Count Count Count Count Count Count Count Count Count Count Count Count Count 1 mm Bearing measured at 9 deg Measured Diameter (mm) 1 mm Bearing measured at 8 deg Measured Diameter (mm) 1 mm Bearing measured at 7 deg Measured Diameter (mm) 1 mm Bearing measured at 6 deg Measured Diameter (mm) 1 mm Bearing measured at 5 deg Measured Diameter (mm) 1 mm Bearing measured at 45 deg Measured Diameter (mm) 1 mm Bearing measured at 4 deg Measured Diameter (mm) 2 mm Bearing measured at 9 deg Measured Diameter (mm) 2 mm Bearing measured at 8 deg Measured Diameter (mm) 2 mm Bearing measured at 7 deg Measured Diameter (mm) 2 mm Bearing measured at 6 deg Measured Diameter (mm) 2 mm Bearing measured at 5 deg Measured Diameter (mm) 2 mm Bearing measured at 45 deg Measured Diameter (mm) 2 mm Bearing measured at 4 deg Measured Diameter (mm) 3 mm Bearing measured at 9 deg Measured Diameter (mm) 3 mm Bearing measured at 8 deg Measured Diameter (mm) 3 mm Bearing measured at 7 deg Measured Diameter (mm) 3 mm Bearing measured at 6 deg Measured Diameter (mm) 3 mm Bearing measured at 5 deg Measured Diameter (mm) 3 mm Bearing measured at 45 deg Measured Diameter (mm) 3 mm Bearing measured at 4 deg Measured Diameter (mm) Figure 3-4. PARSIVEL measured diameters at multiple trajectory angles 58

59 Count Count Count Count Count Count Count Count Count Count Count Count Count Count Count Count Count Count Count Count Count 1 mm Bearing measured at 9 deg Measured Velocity (m/s) 1 mm Bearing measured at 8 deg Measured Velocity (m/s) 1 mm Bearing measured at 7 deg Measured Velocity (m/s) 1 mm Bearing measured at 6 deg Measured Velocity (m/s) 1 mm Bearing measured at 5 deg Measured Velocity (m/s) 1 mm Bearing measured at 45 deg Measured Velocity (m/s) 1 mm Bearing measured at 4 deg Measured Velocity (m/s) 2 mm Bearing measured at 9 deg Measured Velocity (m/s) 2 mm Bearing measured at 8 deg Measured Velocity (m/s) 2 mm Bearing measured at 7 deg Measured Velocity (m/s) 2 mm Bearing measured at 6 deg Measured Velocity (m/s) 2 mm Bearing measured at 5 deg Measured Velocity (m/s) 2 mm Bearing measured at 45 deg Measured Velocity (m/s) 2 mm Bearing measured at 4 deg Measured Velocity (m/s) Figure 3-5. PARSIVEL measured velocities at multiple trajectory angles (dashed line indicates theoretical velocity) 59 3 mm Bearing measured at 9 deg Measured Velocity (m/s) 3 mm Bearing measured at 8 deg Measured Velocity (m/s) 3 mm Bearing measured at 7 deg Measured Velocity (m/s) 3 mm Bearing measured at 6 deg Measured Velocity (m/s) 3 mm Bearing measured at 5 deg Measured Velocity (m/s) 3 mm Bearing measured at 45 deg Measured Velocity (m/s) 3 mm Bearing measured at 4 deg Measured Velocity (m/s)

60 Instrument Performance in High Wind Speeds The option of orienting the PARSIVEL into the mean rain direction was further investigated by recording the RSD with PARSIVELs oriented at., 22.5, and 45. elevation angles; a nozzle array operating at 68.9, 13.4, and kpa water pressures (corresponding to approximately 4, 475, and 55 mm/hr); and steady wind velocities of 8.9, 17.9, and 35.8 m/s. Three PARSIVEL measurements were taken for each test configuration listed in Table 3.2. The PIP measurements were used as the reference measurement, given that this instrument is intended to be used in high wind speeds and is more accurate. The instruments were mounted on a gantry system shown in Figure 3-6. The PARSIVEL disdrometer exhibited no noticeable inaccuracies due to increasing rainfall intensities; however, inaccuracies were observed at the high wind velocities (Figure 3-7). As expected, at high wind velocities, the instruments oriented at 22.5 and 45. recorded drops travelling slower than the instrument at. (Figure 3-8). The incorrectly measured velocities decrease the sample volume over which the RSDs are calculated; therefore, data from these instruments yield incorrect RSDs (as shown in Figure 3-7). This experiment also demonstrated that the PARSIVEL measured a smaller number of large drops (larger than 2 mm) when compared to the PIP; however, for practical purposes, when the PARSIVEL is properly employed, the measured RSD is similar to that of the PIP. These results demonstrate that, as Griffiths (1974) proposed, aligning the disdrometer with the mean rain direction improves accuracy. Thus, a novel approach was taken to continuously align the disdrometer with the mean rain vector; described in the following section. 6

61 Table 3-2. Steady wind test matrix Test Angle (deg) Wind Speed (m/s) Water Pressure (kpa) Number Figure 3-6. Steady wind instrument configuration (photo courtesy of author) 61

62 U = 35.8 m/s Concentration (mm -1 m -3 ) Concentration (mm -1 m -3 ) Concentration (mm -1 m -3 ) U = 17.9 m/s Concentration (mm -1 m -3 ) Concentration (mm -1 m -3 ) Concentration (mm -1 m -3 ) U = 8.9 m/s Concentration (mm -1 m -3 ) Concentration (mm -1 m -3 ) Concentration (mm -1 m -3 ) R wdr ~ 4 mm/hr deg 22.5 deg 45 deg PIP Measured RSD R wdr ~ 475 mm/hr R wdr ~ 55 mm/hr Diameter (mm) Diameter (mm) Diameter (mm) Diameter (mm) Diameter (mm) Diameter (mm) Diameter (mm) Diameter (mm) Figure 3-7. PARSIVEL measured raindrop size distributions for the tests in steady wind flow Diameter (mm) 62

63 35.8 m/s Velocity (m/s) Velocity (m/s) Velocity (m/s) 17.9 m/s Velocity (m/s) Velocity (m/s) Velocity (m/s) 8.9 m/s Velocity (m/s) Velocity (m/s) Velocity (m/s) 15 deg 5 BETE WL1-1/2 Nozzle Grid at ~ 4 mm/hr 22.5 deg deg Diameter (mm) Diameter (mm) Diameter (mm) Diameter (mm) Diameter (mm) Diameter (mm) Diameter (mm) Diameter (mm) Figure 3-8. PARSIVEL measured drop diameters and velocities for the tests in steady wind flow Diameter (mm)

64 Articulating Instrumentation Platforms An instrumentation platform was designed, prototyped, and implemented in supercell thunderstorms and Atlantic hurricanes to supplement FCMP weather station T-3 which contains a similar actively controlled system that aligns the PIP to the mean rain direction. The platforms are capable of high temporal resolution measurements of wind and rain. This section describes the components of the articulating instrumentation platform (illustrated in Figure 3-9), its operation, the quality control algorithm, and a description of FCMP weather station T-3. Articulating Instrumentation Platform Components The articulating instrument platform consists of the following six components, which are discussed in the following subsections: 1. An OTT PARSIVEL optical disdrometer (described in Chapter 2) 2. An RM Young Model 85162D Sonic Anemometer 3. An articulating instrument support structure that is driven by two IMS M-17 stepper motors with an integrated controller and encoder (Model MDI4MRQ17C4-EQ-G1C3) 4. A Labview 8.5 software data acquisition system, which consists of a laptop computer connected to a four port RS-485 National Instruments serial interface (Model No. NI USB-485/4) 5. A battery array of 12V 35 AH Power Sonic (PS-1235 NB) batteries to supply power to the instruments 6. A substructure to provide lateral stability and to transfer the gravity and wind loads to the ground 64

65 Figure 3-9. Articulating instrument platform RM Young sonic anemometer Wind velocity and direction are measured by an RM Young Model 8516Sonic Anemometer. A sonic anemometer was chosen for its compact size, lack of moving parts, and superior performance in low winds when compared to a mechanical (plate or cup) anemometer. Mechanical anemometers have a distance constant on the order of 2-1m, and for practical purposes, the distance constant of sonic anemometers is considered to be zero. Sonic instruments operate by measuring the time it takes for ultrasonic pulses to travel between four transducers. The 8516 is capable of measuring wind velocities up to 7 m/s ±.1 m/s and direction to 36 ± 2 at 4 Hz. Data are output on independent analog channels (-5V) and on a digital channel to the motion control systems and computer, respectively. 65

66 Articulating support structure The instrumentation array is continuously rotated by two Intelligent Motion Systems (IMS M17Plus) motion control systems (stepper motor, driver and controller) to align the optical disdrometer with the ten second mean rain vector (Figure 3-1). The motor controllers are programmed in a proprietary programming language (MCode) to read the -5V analog signal for wind speed and direction from the sonic anemometer. The voltage is interpreted as the angle required to rotate the instrument. The elevation equations were based on the Lacy relationship (197, Equation 2.6) for a 1.2 mm particle. Wind/Rain Direction Parsivel will slowly rotate into alignment with the mean rain vector Instrument array will slowly rotate into alignment with the mean wind velocity vector Figure 3-1. Articulating instrument platform automation To ensure that the platform will resist high wind loads it was designed to withstand the expected peak gust wind load caused by a minimal Category 5 tropical cyclone ( ), as defined by the Saffir-Simpson Scale (Simpson 1974, Simpson and 66

67 Riehl 1984). The structural system was idealized as a system of cylinders acted on by drag forces computed using: (3-2) (3-3) Where is air density, is air velocity, is cylinder diameter, is cylinder length, is drag coefficient, and is kinematic viscosity. Drag coefficients were found using Figure 3-11 and (3-3 for a given Reynolds number,. The moment at was found to be while the moment required to overturn the instrument was found to be. Figure Drag coefficient of a cylinder dependant of Reynolds number of flow (Adapted from R Panton, Incompressible Flow 3rd ed.) Data acquisition system Data from the disdrometer, anemometer, and internal encoders on the stepper motors are transmitted digitally via RS-485 lines and recorded on a laptop computer running a custom written data logger operating on the National Instruments LabView 8.5 platform. The control and data diagram is shown in Figure The system is also 67

68 Wind Direction Analog -1V Wind Speed Analog -1V capable of streaming summary data via a cellular connection, should future needs warrant an upgrade. Control Algorithm Azimuth Digital Signal Wind Data Stepper Motor Elevation Angle Stepper Motor Digital Signal Elevation Angle Digital Signal Azimuth Digital Signal Rain Data Data Logger/ National Instruments Labview 8.5 Software Figure Instrument platform control and data diagram Power The system was designed to run on battery power for a period of time sufficient to record the passage of a tropical cyclone. With capacity of each, three Power Sonic (PS-1235 NB) batteries will supply the mean amperage draw of 4 amps for over 24 hrs. Substructure The instrument platform is placed above a heavy duty tripod with wide set legs to maximize the overturn capacity. At the base of each tripod leg a 69.6 mm (24 in) steel stake is embedded in the ground. A guide wire can also be used to secure the system to an earth screw placed below the center of gravity. Once the tripod is secured, the 68

69 data and power cables are connected to ports on the enclosure box and the software is set to operate. The use of a collapsible tripod that assembles in the field permitted the system s portability and rapid deployment. During the VORTEX2 deployment the instrument was assembled, operational, and recording in less than three minutes. Portability of the instrument array is required to allow the set up of multiple systems in storm paths expected to contain the most rain. Rapid deployment is crucial since weather conditions can quickly worsen and pose unsafe conditions for the operator. Portable Instrument Platform Operation System operation, as illustrated in Figure 3-12, begins with anemometer measurement of wind velocity and direction. The anemometer outputs the -5 V analog signals to the motion control systems which continuously calculate the 1 second average. The azimuth motion control system interprets the deviation from 2.5V the voltage corresponding to a wind direction perpendicular to the instrumentation platform as a number of steps for the stepper motors to rotate. When the motion is complete the control system re-samples and rotates. This process continuously aligns the instrument array perpendicular to the wind direction. The elevation motion control system interprets the analog signal and assigns a number of steps to rotate based on the programmed formula relating wind velocity to drop trajectory (Lacy 197, Equation 2-6 for 1.2 mm particle). This aligns the disdrometer to the mean rain vector. The motion control systems digitally output their location, via RS-485 channels, at the completion of each prescribed rotation. The disdrometer and anemometer digitally output their measurements via RS-485 channels every 1 seconds and at 14 Hz, respectively. The 69

70 Velocity (m/s) data acquisition system records the measurements from all of the instruments, as previously described. Quality Control Algorithm To mitigate fringe effects, coincident measurement of multiple drops, and splashing effects (described in Chapter 2) a quality control algorithm was employed. The algorithm filters data that fall below 5% of the expected terminal velocity (Gunn and Kinzer, 1949) and above the maximum expected velocity ( ), defined as the resultant of the terminal velocity ( ) and wind speed ( ) plus one standard deviation. Additionally, the algorithm only considers data above 5 mm/hr and drops smaller than 1 mm (Figure 3-13). The algorithm ensures that drops that graze the sensitive area (characterized by small measured diameter at a high speed), multiple drops (characterized by a large drop at low speed), and drops resulting from splashing (characterized by small drops at low speed) are mostly excluded from the analyzed dataset. U = m/s Considered Observed D-V Combinations V T V R A) Diameter (mm/hr) 7

71 Velocity (m/s) U = 1 m/s Considered Observed D-V Combinations V T V R B) Diameter (mm/hr) Figure Quality control filter at U = m/s and U = 1 m/s Description of Weather Station T-3 The FCMP portable weather stations are 1 m structural steel lattice towers mounted on dual axel trailers for rapid deployment (Poss, 2). The tower system and outriggers unfold and are operational in minutes. The stations are equipped with three fixed axis anemometers (RM Young 2716R) at 5 m and 1 m to measure the 3D wind speed and direction. At the 1 m location is an additional anemometer (RM Young 513V) that measures the resultant of the lateral and longitudinal component of wind and serves as a redundant measurement (Balderrama et al., 211). FCMP tower T-3 is the only of the six weather stations that is outfitted for precipitation measurement. Rainfall measurements are made by a DMT PIP (described in Chapter 2) at a 3 m height. In order for the PIP operate correctly, the laser plane must be oriented perpendicular to the rain vector. To achieve this, the PIP is mounted on an automated 71

72 turret that continuously aligns the disdrometer (Figure 3-14). The azimuth angle of the turret is actively controlled by a positioning system that continuously samples the gill anemometers at 5. m. The elevation angle is governed by the elevation equations derived from work by Lacy (197, Equation 2.6 for 1.2 mm particle). Wind and rain are reported at 1 Hz and 1Hz, respectively. Results from data gathered by both platforms during supercell thunderstorms and Atlantic hurricanes are discussed at length in the Chapter 4. Figure T-3 Precipitation Imaging Probe turret system and Gill anemometers at 5. m (photo courtesy of author) 72

73 Drop Diameter (mm) Comparison of Collocated Stationary and Articulating Instruments in a Supercell Thunderstorm Data from a supercell thunderstorm were collected with collocated stationary and articulating instrumentation platforms (Figure 1-1). A difference in accuracy was confirmed in measurements of the RSDs between the two instrumentation platforms. The stationary instrumentation platform measured unrealistic large number concentrations of drop diameters larger than 4 mm (Figure 3-15 top) in high wind velocities (> 1 m/s). This phenomenon was not apparent in the articulating instrument platforms (Figure 3-15 center). The unrealistic large number concentrations were found to be attributable to erroneously low measured drop velocities (generally less than 2 m/s). Incorrect measurements of the RSD subsequently lead to incorrect estimates of all rainfall parameters in the stationary instrumentation platforms (rainfall intensity - Figure 3-16 and reflectivity - Figure 3-17). The articulating instrument platform did not exhibit similar jumps in the data, indicating reliable measurements. A) Measured RSD on Jun 1, 21 1:17Z 1:25Z 1:33Z 1:41Z 1:49Z 1:58Z Time

74 Rainfall rate(mm/hr) Wind Speed(m/s) Drop Diameter (mm) C) B) Measured RSD on Jun 1, 21 1:17Z 1:25Z 1:33Z 1:41Z 1:49Z 1:58Z Time :17Z 1:25Z 1:33Z 1:41Z 1:49Z 1:58Z Time Figure Measured raindrop size distribution by stationary instrumentations (A) and articulating instrumentation (B) Measured Rainfall Rate Comparison of Data Collected on Jun 1, Articulating Instrument 1 Stationary Instrument :17Z 1:25Z 1:33Z 1:41Z 1:49Z 1:58Z Time Figure Comparison of rainfall intensities measured by stationary and articulating instrument platforms

75 Reflectivity(dBZ) 1 8 Reflectivity Comparison of Data Collected on Jun 1, 21 Articulating Instrument Stationary Instrument :17Z 1:25Z 1:33Z 1:41Z 1:49Z 1:58Z Time Figure Comparison of estimated reflectivity by stationary and articulating instrument platforms Summary This chapter presented the known inaccuracies in RSD characterization caused by strong winds. As a result, an articulating instrument platform was designed, prototyped, and evaluated in the field. The articulating instrument platform did not exhibit the errors observed in the stationary instrument; thus, the approach taken to measure WDR in strong winds was validated. The next chapter presents the analysis and results of data collected during the VORTEX2 and FCMP field campaigns. 75

76 CHAPTER 4 CHARACTERIZATION OF WIND-DRIVEN RAIN IN STRONG WINDS This chapter addresses field research conducted during the Verification of the Origins of Rotation in Tornadoes Experiment 2 (VORTEX2) and Florida Coastal Monitoring Program (FCMP) field deployments during Hurricanes Ike (29) and Irene (211). This portion of the research analyzed the wind-driven rain (WDR) data collected in extreme events. The goals were to: 1. Determine the effect of wind on raindrop size distribution (RSD) 2. Determine if the RSD models used in computational wind engineering are appropriate for modeling the rain deposition rate on buildings during a designlevel wind event 3. Characterize the peak to mean ratio of rainfall intensity and determine the impact on design for water penetration resistance 4. Investigate if the precipitation algorithm in the radar product generator used by the National Weather Service (NWS) Weather Surveillance Radar exhibits any biases that manifest in extreme wind events This chapter is organized into six sections: (1) a description of the field research programs, (2) the effect of wind on the drop diameter, (3) the effect of wind on the RSD, (4) comparison of RSD models to RSDs measured in multiple wind velocities, (5) calculation of the peak to mean ratios of rainfall intensity ( ), and (6) comparison of measured rain data to WSR-88D data. Field Research Programs Verification of the Origins of Rotation in Tornadoes Experiment 2 (VORTEX2) Overview The Verification of the Origins of Rotation in Tornadoes Experiment 2 (VORTEX2) Project a continuation of the original VORTEX project described by Rasmussen et al. (1994) was an interdisciplinary multi-agency effort to investigate tornado genesis, 76

77 dynamics, kinematics, demise, supercell near-ground wind field, and how the environment regulates storm structure. VORTEX2 assets included 1 mobile radars, 12 mobile mesonet instrumented vehicles, and 38 deployable instruments including, disdrometers, surface level wind measurement stations, weather balloon launching vans, and unmanned aircraft that were deployed in the projected path of supercell thunderstorms minutes prior to their arrival. Data collected will assist scientists in better understanding tornadic behavior and improving tornado forecasting. Deployment details As part of VORTEX2, a disdrometer team was tasked with the repeated deployment of eight instrument stations (articulating and stationary) for the collection of RSD data. During each day of field collection, team leaders would determine locations exhibiting favorable conditions for storm formation. The teams would then mobilize and target a supercell thunderstorm that exhibited potential for tornadic activity. The instruments were deployed perpendicular to storm motion in the path of the hook appendage minutes before the approaching storm passed through the area (Figure 4-1). Spacing between instrument stations was.5 km to 2 km based on storm velocity and the projected storm path. Upon storm passage instruments were collected and relocated in the projected path of the same storm in the same configuration. A total of 144 measurements were collected during 32 supercell thunderstorms with eight instrument platforms (Table 4-1) in the states shown in Figure 4-2 through the period of May 1 to June 15, 21. Data admitted for analysis consists of the 25 observations collected with the articulating instrumentation platforms (denoted by * in Table 4-1); the observations consist of 16 Hz wind velocity and.1 Hz rain time histories (see Chapter 77

78 3). The data was collected over open terrain, clear of any obstructions, and at a sufficient distance from roads to minimize errors associated with vehicle spray. Table 4-1. Verification of the rigins of Rotation in Tornadoes Experiment 2 deployment details Time (UTC, Date HHMM) Location Instrument Platforms 6-May 1-58 Oberlin, KS CU1 CU2 1-May Chandler, OK CU1 CU2 *11-May Clinton, OK CU1 CU2 UF1* UF4 UF5 UF6 UF7 12-May Willow, OK CU1 UF4 UF5 UF6 UF7 14-May Odessa, TX UF7 14-May Odessa, TX CU1 UF4 UF5 UF6 UF7 14-May Odessa, TX CU1 UF4 UF5 UF6 UF7 15-May Artesia, NM CU1 UF4 UF5 UF6 UF7 *17-May Artesia, NM CU1 UF1* UF4 UF5 UF6 UF7 *18-May Dumas, TX CU1 UF1* UF4 UF5 UF6 UF7 19-May Watonga, OK CU1 *21-May Mitchell, NE CU1 UF1* UF4 UF5 UF6 UF7 *24-May Ogallala, NE CU1 UF1* UF4 24-May Ogallala, NE CU1 UF4 UF5 UF6 25-May 5-1 Ogallala, NE CU1 UF5 UF6 UF7 *29-May Thedford, NE CU1 UF1*UF3* UF4 UF5 UF6 UF7 *2-Jun Benkelman, NE CU1 UF1*UF3* UF5 UF6 *3-Jun 2-1 Wagner, SD UF1*UF3* UF5 UF6 *5-Jun Des Moines, IA CU1 UF1*UF3* UF5 UF6 *6-Jun Ogallala, NE CU1 UF1* UF5 UF6 *6-Jun Ogallala, NE CU1 UF1* UF5 UF6 *7-Jun Scottsbluff, NE CU1 UF1*UF3* UF5 UF6 UF7 8-Jun 1-2 Scottsbluff, NE CU1 UF5 UF6 UF7 *9-Jun 4-24 Scottsbluff, NE CU1 UF1*UF3* UF5 UF6 UF7 *1-Jun Wiggins, CO CU1 UF1*UF3* UF5 UF6 UF7 *11-Jun Limon, CO CU1 UF1*UF3* UF5 UF6 UF7 *12-Jun Gruver, TX CU1 UF1* UF5 UF6 UF7 *13-Jun Darrouzett, TX CU1 UF1* UF6 *13-Jun Darrouzett, TX CU1 UF5 UF6 UF7 *13-Jun Darrouzett, TX CU1 UF5 UF6 UF7 *14-Jun Post, TX CU1 UF5 UF6 UF7 78

79 Figure 4-1. VORTEX2 instrument deployment Figure 4-2. VORTEX2 data collection sites Florida Coastal Monitoring Program (FCMP) Overview The Florida Coastal Monitoring Program is a collaborative research program between multiple universities (University of Florida, Clemson University, Florida International University, and Florida Institute of Technology) and the insurance industry (IBHS) focusing on the full-scale experimental study of tropical cyclone ground level 79

80 wind fields and wind loads on residential structures (Balderrama et al., 211). Since 1998, the FCMP has deployed portable weather stations to collect ground level meteorological observations and roof pressure sensors on single family residences to measure wind induced roof uplift pressures in tropical cyclones. FCMP data are used widely by meteorologists and emergency management, as well as university researchers developing CFD and numerical models, and conducting boundary layer wind tunnel and experimental full-scale experiments. Deployment details During the 28 and 211 Atlantic hurricane seasons, the FCMP deployed WDR measurement stations for Hurricanes Ike (28) and Irene (211). Three data records were collected in the Greater Houston Area, the Outer Banks of North Carolina, and Deal, New Jersey. The following is a brief narrative of the deployments. Ike became a tropical storm on September 1, 28, approximately 13 km west of the Cape Verde Islands and steadily intensified to a Category 4 storm over the following two days as it moved west-northwestward over the tropical Atlantic. By September 7 Hurricane Ike made landfall at the southeastern Bahamas and by September 8 its center had reached Cabo Lucrecia, Cuba. The storm emerged from Cuba near Cabo Lucrecia into the Gulf of Mexico on September 9th as a Category 1 storm. Hurricane Ike slowly intensified through the gulf and made landfall at the north end of Galveston Island, Texas as a Category 2 storm on September 13. FCMP assets (including weather station T-3) were deployed in Baytown, Texas ( N, W) approximately seven hours prior to landfall. The weather station recorded 18 hours of wind and rain data prior to, during, and after eye wall passage. The terrain 8

81 exposure for the weather station was primarily open to the east and suburban elsewhere. Topography for the region was flat. Irene became a tropical storm on August 2, 211, approximately 44 km East of Martinique. As it travelled West-Northwest Irene intensified into a Category 1 hurricane on August 22, 5 km north of Isabela, Puerto Rico. By August 24, the storm quickly intensified into a Category 3 hurricane as it passed over the Turks and Caicos Islands and the Bahamas. By August 26, Irene had passed over the Bahamas and was approximately 3 km east of Florida. As Irene continued northward it encountered unfavorable conditions and weakened to Category 1 storm before making landfall less than 5 km from Beaufort, NC at approximately 12 UTC on August 27. The FCMP deployed an articulating instrument platform in Beufort, NC ( N, W) approximately eight hours before landfall. The articulating instrument platform recorded 15 hours of wind and rain data prior to, during, and after eye wall passage. While the articulating instrument platform recorded data, a second team continued northward towards Deal, NJ ( N, W) where weather station T-3 was deployed at 3 UTC on August 28. Weather station T-3 recorded 13 hours of wind and rain data approximately 11 km from the storm center as it weakened to tropical storm status. The terrain exposure for the articulating instrument platform was primarily suburban in all directions. Terrain exposure for weather station T-3 was primarily open to the east and suburban elsewhere. Topography for both regions was flat. To compare data collected by weather station T-3 to data gathered using the articulating instrument platform, 1 second segmental averages of disdrometer data and 81

82 the mean velocity ( ), longitudinal turbulence intensity ( ), and lateral turbulence intensity ( ) for the 6 seconds leading up to each segment were calculated. The following section investigates the effect of wind on raindrop diameter. Effect of Wind Velocity and Turbulence Intensity on Raindrop Diameter The effects of longitudinal wind velocity ( ), longitudinal turbulence intensity ( ), and lateral turbulence intensity ( ) on raindrop sizes were investigated by comparing the shapes of calculated probability density functions (PDFs) of raindrop diameter ( ), mean raindrop diameter (, Equation 2-35), and volume weighted mean raindrop diameter (,Equation 2-36). Turbulence intensity is a measure of the variation in wind speed about the mean and is defined as the coefficient of variation (i.e., ratio of the standard deviation and mean): The standard deviations of the longitudinal and lateral wind components are (4-1) (4-2) respectively denoted as and, and the mean longitudinal wind speed is denoted as. It was hypothesized that rate of break up and coalescence may be affected by turbulence; subsequently, the drop diameters would be related to turbulence intensity. Typically turbulence intensities are calculated using one hour records (Harper et al, 29); however, a rain event can fluctuate significantly over a much shorter time period; thus, 6 sec segments were selected for this analysis. Calculated PDFs of VORTEX2 data indicate that and (Figure 4-3) are dependent on. The series of and PDFs show that as the longitudinal wind speed increases, standard deviation decreases, and the mode shifts left. This behavior 82

83 indicates that probability of smaller and increases as the longitudinal wind speed increases. The relationship is further evident in observing the.25,.5, and.75 quantiles of and which demonstrate that there is a general negative trend of and as the longitudinal wind speed increases. PDFs and quantiles of VORTEX2 data also indicate that as both and increase, increases slightly. Similar trends were observed in the FCMP dataset. The PDFs of are shown in Figure 4-6; the probability of a smaller increases as the longitudinal wind speed increases. Similarly, the quantile plots indicate a positive trend between and longitudinal wind speed. Turbulence intensity trends were also observed in this dataset. and were observed to decrease in the presence of higher and (Figure 4-7 and Figure 4-8). The remaining figures of both datasets indicate that does not significantly change with, or. The increase in or with increased, or the decrease in or in the presence of higher or is attributed to one of the following issues: (1) it is possible that in increased wind velocities the high drag forces acting on the drops overcome the surface tension holding the shape together and breakup occurs or (2) high turbulence intensities (either or ) cause the probability of coalescence to increase. Given that these trends were observed, the next step in the research was to investigate the effect of wind on the RSD, discussed in the next section. 83

84 Figure 4-3. Effect of longitudinal wind velocity on drop diameter observed in VORTEX2 data 84

85 Figure 4-4. Effect of longitudinal turbulence intensity on drop diameter observed in VORTEX2 data 85

86 Figure 4-5. Effect of lateral turbulence intensity on drop diameter observed in VORTEX2 data 86

87 Figure 4-6. Effect of longitudinal wind velocity on drop diameter observed in FCMP data 87

88 Figure 4-7. Effect of longitudinal turbulence intensity on drop diameter observed in FCMP data 88

89 Figure 4-8. Effect of lateral turbulence intensity on drop diameter observed in FCMP data 89

90 Wind Velocity and Turbulence Intensity Dependency of the Raindrop Size Distribution The influence of wind on the RSD was investigated by stratifying the data statistics into different wind speed and turbulence intensity regimes. The gamma parameters describing the RSD and the mean wind speed ( ) and turbulence intensities ( and ) were calculated for each one minute segment. The dependency of the three parameters on the wind velocity and turbulence intensities is shown in Figure 4-9 and Figure 4-1. These data indicate that,, and remained constant with increasing, or and that the variance of each of the variables decreased with increasing. Figure 4-9 and Figure 4-1 also demonstrates that the variability of,, and decreased with increasing R. The significance is that while a variability of drop diameter was observed in multiple,, and regimes, the RSD was independent of,, and Another observation was made regarding and. A comparison of the mean values of, and approximately -1.2 and 1.3 mm -1, respectively was significantly different than 3.6 and 3.4 mm -1 observed from typhoon data collected by Chang et al. (29); however, Figure 4-12 demonstrates that the relationship between the measured and is similar to the empirical relationship established by Zhang et al. (23), (4-3. (4-3) Possible sources for the differences in mean values are that: (1) the RSD data gathered by Chang were observed in wind speeds < 8. m/s, (2) there is less noise in the data when implementing an articulating instrument platform, or (3) there are too few one minute measurements for a reasonable comparison. Presently the reason for the dissimilarity is unclear and more data would be necessary to make an assessment. 9

91 (mm -1 ) (mm -1 ) (mm -1 ) (mm -1 ) N (mm -1 m -3 ) N (mm -1 m -3 ) N (mm -1 m -3 ) N (mm -1 m -3 ) Data in U <= 15 m/s U (m/s) TI U TI V R (mm/hr) U (m/s) TI U TI V R (mm/hr) U (m/s) TI U TI V R (mm/hr) Figure 4-9. VORTEX2 gamma parameters observed in multiple wind conditions and rainfall intensities 91

92 (mm -1 ) (mm -1 ) (mm -1 ) (mm -1 ) N (mm -1 m -3 ) N (mm -1 m -3 ) N (mm -1 m -3 ) N (mm -1 m -3 ) Data in U <= 15 m/s Data in U > 15 m/s U (m/s) TI U TI V R (mm/hr) U (m/s) TI U TI V R (mm/hr) U (m/s) TI U TI V R (mm/hr) Figure 4-1. FCMP Hurricane Ike and Irene gamma parameters observed in multiple wind conditions and rainfall intensities 92

93 (mm -1 ) (mm -1 ) (mm -1 ) (mm -1 ) N (mm -1 m -3 ) N (mm -1 m -3 ) N (mm -1 m -3 ) N (mm -1 m -3 ) Data in U <= 15 m/s Data in U > 15 m/s U (m/s) TI U TI V R (mm/hr) U (m/s) TI U TI V R (mm/hr) U (m/s) TI U TI V R (mm/hr) Figure FCMP and VORTEX2 gamma parameters observed in multiple wind conditions and rainfall intensities 93

94 14 12 Data Collected in U <= 15 m/s Data Collected in U > 15 m/s Zhang et al Figure Observed shape slope relation (mm -1 ) 94

95 Comparison of Raindrop Size Distribution Models to Measured Raindrop Size Distribution Data in Multiple Wind Velocities RSD models serve as a simple method for acquiring an adequate RSD for WDR models; thus, one of the thrusts of this project was to validate the application of these models in extreme WDR scenarios. The models include the Marshall and Palmer (1948), Best (195), and Willis and Tattleman (1989) dependent RSD models and the three parameter gamma model using the mean of the parameters calculated from the gathered data (,, and ). VORTEX2 and FCMP Hurricane Ike data were stratified into two (< 15 and > 15 m/s), two (<.2 and >.2), two (<.2 and >.2), and five ( 2, 2 4, 4 6, 6 8, and 8+ mm/hr) regimes illustrated in Figure Figure These figures demonstrate qualitatively that there is no apparent relationship between the RSD and,, or and that throughout all regimes the Best model is most accurate. To validate these observations, mean square error ( ) values were calculated and listed in Figure Figure Each value was computed as follows: where is the measured RSD and is the fit RSD. The values confirm that (4-4) there was no significant difference in the performance of the dependant models below or above 15 m/s, below or above.2, or below or above. 2. values also verify that the Best model is the most accurate, yielding the least error. 95

96 R = 8 mm/hr + N D (mm -1 m 3 ) R = 6-8 mm/hr N D (mm -1 m 3 ) R = 4-6 mm/hr N D (mm -1 m 3 ) R = 2-4 mm/hr N D (mm -1 m 3 ) R = - 2 mm/hr N D (mm -1 m 3 ) VORTEX2 FCMP Best Gamma Willis and Tattleman Marshall Palmer U = m/s - 15 m/s U = 15m/s N VORTEX2 = N VORTEX2 = N FCMP = N FCMP = N VORTEX2 = N VORTEX2 = N FCMP = N FCMP = N VORTEX2 = N VORTEX2 = N FCMP = N FCMP = N VORTEX2 = N VORTEX2 = N FCMP = N FCMP = N VORTEX2 = N FCMP = Diameter (mm) 1 6 N VORTEX2 = Diameter (mm) Figure Model raindrop size distribution and measured raindrop size distribution comparisons (N indicates the number of averaged records) N FCMP =

97 R = 8 mm/hr + N D (mm -1 m 3 ) R = 6-8 mm/hr N D (mm -1 m 3 ) R = 4-6 mm/hr N D (mm -1 m 3 ) R = 2-4 mm/hr N D (mm -1 m 3 ) R = - 2 mm/hr N D (mm -1 m 3 ) VORTEX2 FCMP Best Gamma Willis and Tattleman Marshall Palmer N VORTEX2 = N FCMP = TI U = N VORTEX2 = N FCMP = TI U = N VORTEX2 = 247 N 1 2 FCMP = N VORTEX2 = 386 N 1 2 FCMP = N VORTEX2 = N FCMP = N VORTEX2 = N FCMP = N VORTEX2 = 113 N 1 2 FCMP = N VORTEX2 = 122 N 1 2 FCMP = N VORTEX2 = N FCMP = Diameter (mm) N VORTEX2 = N FCMP = Diameter (mm) Figure Model raindrop size distribution and measured raindrop size distribution comparisons (N indicates the number of averaged records)

98 R = 8 mm/hr + N D (mm -1 m 3 ) R = 6-8 mm/hr N D (mm -1 m 3 ) R = 4-6 mm/hr N D (mm -1 m 3 ) R = 2-4 mm/hr N D (mm -1 m 3 ) R = - 2 mm/hr N D (mm -1 m 3 ) VORTEX2 FCMP Best Gamma Willis and Tattleman Marshall Palmer TI V = -.2 TI V = N VORTEX2 = N FCMP = N VORTEX2 = N FCMP = N VORTEX2 = 365 N 1 2 FCMP = N VORTEX2 = 268 N 1 2 FCMP = N VORTEX2 = N FCMP = N VORTEX2 = N FCMP = N VORTEX2 = 158 N 1 2 FCMP = N VORTEX2 = 77 N 1 2 FCMP = N VORTEX2 = N FCMP = Diameter (mm) N VORTEX2 = N FCMP = Diameter (mm) Figure Model raindrop size distribution and measured raindrop size distribution comparisons (N indicates the number of averaged records) 98

99 Mean Square Error Values 99 Model Marshall and Palmer Best Willis and Tattleman Gamma VORTEX2 FCMP VORTEX2 FCMP VORTEX2 FCMP VORTEX2 FCMP U (m/s) U (m/s) U (m/s) U (m/s) U (m/s) U (m/s) U (m/s) U (m/s) Rainfall (mm/hr) < 15 > 15 < 15 > 15 < 15 > 15 < 15 > 15 < 15 > 15 < 15 > 15 < 15 > 15 < 15 > Mean Model Mean Figure Mean square error values of raindrop size distribution models stratified by U and R Mean Square Error Values Rainfall (mm/hr) Model Marshall and Palmer Best Willis and Tattleman Gamma VORTEX2 FCMP VORTEX2 FCMP VORTEX2 FCMP VORTEX2 FCMP TI U TI U TI U TI U TI U TI U TI U TI U <.2 >.2 <.2 >.2 <.2 >.2 < Mean Model Mean Figure Mean square error values of raindrop size distribution models stratified by TI U and R >.2 <.2 >.2 <.2 >.2 <.2 >.2 <.2 >.2

100 Mean Square Error Values Rainfall (mm/hr) Model Marshall and Palmer Best Willis and Tattleman Gamma VORTEX2 FCMP VORTEX2 FCMP VORTEX2 FCMP VORTEX2 FCMP TI V TI V TI V TI V TI V TI V TI V TI V <.2 >.2 <.2 >.2 <.2 >.2 < Mean Model Mean Figure Mean square error values of raindrop size distribution models stratified by TI V and R >.2 <.2 >.2 <.2 >.2 <.2 >.2 <.2 >.2 1

101 Peak to Mean Ratio of Rainfall Intensities Currently, the widely accepted standard used to estimate the design wetting rate on building façades (BSI EN ISO ) is based on a minimum of ten years of hourly and data. The design free-stream WDR ( ) is calculated as the 67th percentile of the values determined from 4-5 (based on Lacy, 1965): (4-5) where is the hourly mean wind speed, is the hourly rainfall total, is the hourly mean wind direction from north, and is the wall orientation relative to north. Designing for extreme WDR events may require a more stringent method due to the stochastic nature of both wind and rain in time scales less than one hour. Figure 4-2 illustrates the peak to mean ratios of FCMP Hurricane Ike data from one minute to one hour (Durst, 196). This figure demonstrates that designing for a shorter duration peak can significantly increase the design Hurricane Ike data the. For instance, using hourly mean FCMP ; however, if the 1 minute peak is used,. The peak to mean ratios of can be useful in determining the WDR load on building components; thus, the effect of wind and precipitation type on the peak to mean ratios was investigated. Figure 4-19 and Figure 4-2 illustrate the peak to mean ratios of VORTEX2 and FCMP Hurricane Ike data, respectively. VORTEX2 data only contains peak to mean ratios beyond ten seconds (due to the temporal resolution of the instrumentation) and under 15 m/s. In both figures, it is clear that peak to mean ratios observed in convective precipitation are lower. To verify this observation, a two sample 11

102 t-test was conducted to compare the datasets at one and ten seconds (VORTEX2 and FCMP data, respectively) from convective and stratiform precipitation. The t-test indicated that difference of the peak to mean ratios in different precipitations types are statistically significant at the 95% significance level. Figure 4-2 also indicates that there are lower peak to mean ratios in U > 15 m/s. A t-test was conducted and indicated that the difference of peak to mean ratios in < 15 m/s and > 15 m/s is statistically significant at the 95% significance level. The significance of different peak to mean ratios could ultimately lead to design that are orders of magnitude different. The applicability of a peak to mean ratio would then depend on site specific probabilities of type of precipitation and wind speed. The study of peak to mean ratios of rainfall intensities could yield results that are useful in design practice. Table 4-2. Peak to mean ratios of U and R Peak Duration (sec) U Peak to Mean Ratio (Durst, 196) R Peak to Mean Ratio R Multiplier FS WDR of 1-hr Value

103 U < All 15 U m/s Stratiform (< 4 dbz) N = th Percentile 5th Percentile 75th Percentile 3 Convective (> 4 dbz) N = 22 3 Stratiform and Convective N = Duration (sec) Duration (sec) Duration (sec) Figure Peak to mean ratios of VORTEX2 data (N indicates number of one minute segments) 13

104 All U U > 15 m/s U < 15 m/s 25th Percentile 5th Percentile 75th Percentile 1 8 Stratiform (< 4 dbz) N = Convective (> 4 dbz) N = Stratiform and Convective N = N = N = N = N = N = N = Duration (sec) Duration (sec) Duration (sec) Figure 4-2. Peak to mean ratios of FCMP data (N indicates number of one minute segments) 14

105 Comparison of Ground Measured Rainfall Intensity and Estimated Reflectivity to Weather Surveillance WSR-88D Estimated Rainfall Intensity and Measured Reflectivity FCMP Hurricane Ike measurements of rainfall intensity ( ) and estimates of reflectivity ( ) were compared with remotely sensed estimates of and measurements of reflectivity from the National Weather Service WSR-88D Doppler Radar KHGX. WSR-88D Doppler radar data consisted of reflectivity measurements and rainfall estimates at locations along a radial grid extending from the radar location (~ 4km). A linear interpolation of the data from the four nearest grid locations was performed to estimate both the rainfall intensity and reflectivity of the cell above the disdrometer (elevation from 478 m to 54 m). The temporal resolution of the data was approximately five minutes therefore the collected data was divided into five minute segmental averages. VORTEX2 data was not compared to WSR-88D Doppler Radar data due to the short durations of each of the 22 records (<1 minutes). The comparison between the disdrometer estimated reflectivity and radar measured reflectivity is illustrated in Figure Generally, there is good agreement between the two. The mean absolute error, 7. 6 dbz, calculated as: (4-6) where is the number of observations, is the radar measured reflectivity, and is the disdrometer estimated reflectivity, confirms this observation. Rainfall intensities measured by the PIP and estimated by radar are compared in Figure This figure shows that there is relatively good agreement below 1 mm/hr. Above 1 mm/hr the default relationship, employed at KHGX, appears to underestimate the observed. Mean absolute errors below and above the 1 mm/hr 15

106 Z (dbz) regimes are 2.8 mm/hr and 23.3 mm/hr, respectively. Figure 4-23 demonstrates the relationship between the measured ground level reflectivites and rainfall intensities, and the best fit curve. Figure 4-24 confirms that the default Z-R model and the best fit curve agree reasonably well below 1 mm/hr. Commonly used empirical Z-R relationships, as recommended by the WSR-88D Operational Support Facility, are found in Table 4-3. The Rosenfeld tropical relationship was the best predictor for the ground level estimated reflectivity, with an value of.58. For the range of reflectivity values observed in this storm, all relationships underestimated the observed rainfall intensity (Figure 4-24). Table 4-3. Comparison of Z-R models Model Best Fit Curve Rosenfeld tropical Default WSR-88D Marshall/Palmer Z mm m 3 6 Z (dbz) R KGHX Data FCMP Data :Z 6:Z 9:Z 12:Z 15:Z 18:Z Time (UTC) Figure Comparison of disdrometer estimated and radar measured reflectivity 16

107 Reflectivity (dbz) R (mm/hr) 5 4 KGHX Data FCMP Data :Z 6:Z 9:Z 12:Z 15:Z 18:Z Time (UTC) Figure Comparison of disdrometer measured and radar estimated rainfall intensity FCMP Data Best fit Z = log(R) R (mm/hr) Figure Observed Z-R relationship 17

108 R (mm/hr) Measured Data Best Fit Curve Default Z-R Rosenfeld Marshall and Palmer Reflectivity (dbz) Figure Comparison of observed and recommended Z-R relationships Summary This chapter presented the analysis and results from data collected during the VORTEX2 and FCMP field campaigns. Results from the analysis proved that while a relationship between drop diameter and wind was observed, the RSD was unaffected by wind. It was discovered that the Best model (195) was the model that best fit the observed data. Observations were also made regarding the application of one hour data in WDR design as well as commonly used empirical Z-R relationships. The next chapter applies the findings from this analysis to the design and development of a WDR simulation system for the water penetration resistance evaluation of low-rise buildings in a full-scale wind tunnel. 18

109 CHAPTER 5 DEVELOPMENT OF A WIND-DRIVEN RAIN SIMULATION SYSTEM FOR THE WATER PENETRATION RESISTANCE EVALUATION OF LOW-RISE BUILDINGS IN A FULL-SCALE WIND TUNNEL This chapter presents a new method to design a wind-driven rain (WDR) simulation system to achieve a prescribed rain deposition rate on a low-rise structure in a full-scale wind tunnel test facility. The method was successfully applied during the commissioning of the Full-Scale Test Facility at the Insurance Institute for Business & Home Safety (IBHS) Research Center. Essentially, the facility is a wind tunnel with a cross-section that is large enough to test a full-size two story building. IBHS requested guidance on the addition of a water injection system to simulate WDR in the test chamber. The water injection system consists of a grid of spray nozzles located at the entrance of the test chamber. The primary design objective was to achieve 23 mm/hr rain deposition rate on the windward wall of a single-story building located 1 m downwind of the spray system. The second objective was to select a conventional, inexpensive spray nozzle that produced a raindrop size distribution (RSD) representative of measurements in tropical cyclones and supercell thunderstorms. The Best (195) model was selected for this application, based on the results presented in Chapter 4. Research was carried out in four stages. First, the wetting uniformity of a single nozzle was evaluated to determine what level of resolution could be achieved in the system design. It was determined that while characterizing the RSD of the entire spray from a single nozzle was possible, mapping out the trajectory of the individual particle sizes emitted from the nozzle (in the statistical sense) over the spray cone was not easily achieved. Second, disdrometer measurements were performed in stagnant air 19

110 and in steady wind to determine if the presence of wind caused the changes in the RSD. The differences were found to be inconsequential for this application. Based on this result, spray nozzles were tested in stagnant air conditions in the third stage. Twelve commercially available hydraulic spray nozzles were evaluated in a large test stand with the nozzle spraying perpendicular to the floor. RSD measurements were made along the radial extents of the area receiving spray and integrated using the method of circular disks to characterize the RSD for the entire spray. These results were used to select the best nozzle. Finally, validation tests were performed in the IBHS Research Center. The remainder of this chapter is organized into five sections. The first section discusses the design specifications for the rain simulator. The remaining sections present information about the four research stages. Design Specifications for the Rain Simulator in the IBHS Research Center IBHS specified that the rain deposition rate on the windward wall of the test building should be of 23 mm/hr in 58 m/s winds. The decision to use 23 mm/hr was based on duplicating current water penetration resistance test standards, including ASTM E331-, ASTM E547-, ASTM E115-5, and ASTM E The deposition rate is not the same as the wind-driven and horizontal rain intensities; therefore it is not easy to relate the specified value to an actual rain event. More importantly, the discharge rate of the nozzles can be directly inferred from deposition rate. This section discusses these issues in detail. As discussed in Chapter 2, rain intensity is commonly presented in several forms. The flux of rain falling vertically is defined by the horizontal rainfall intensity ( ) and has units of LT-1. It is equal to the volume of water collected over a specified duration 11

111 divided by the horizontal area of the collection chamber. WDR intensity ( ) is the flux of rain passing through a vertical plane. scales with wind speed (Eq. 2-22), therefore it is significantly larger than in strong winds. The third term is the rain deposition rate ( ), which is the specified wetting rate on the building façade (23 mm/hr). It differs from because of the flow-structure interaction. Only a fraction of the wind-driven rain wets the wall. Table 5-1 presents values for, and from multiple field studies and the value used in ASTM water penetration resistance test standards. Table 5-1. Rainfall Intensities. All values shown as mm/hr. RWDR was computed assuming the wind speed U = 6 m/s. Italicized values are estimates. Source R H R WDR R F = W R WDR Hurricane Ike Mean (FCMP) Typical Cat 3-5 Peak (Lonfat et al., 24) Tropical Cyclones (Tokay et al., 28) NOAA TP4-1 year 6 hr Event (NWS, 1961) ASTM Test Standards Supercell T-storm peak (Smith et al., 2) The non-italicized values in the table are the values reported in the literature. The italicized values are estimates. and were converted using the Lacy (1967; Eq. 2-24) equation. and were converted following BS EN ISO :29 (Eq. 2-26), assuming open exposure terrain, flat topography and no obstructions immediately upwind. The wall factor ( ) was selected from Table 4 in the standard, which contains values for a wide range of building shapes. was selected based on its frequency as a windward wall coefficient. Of the three intensity parameters, only can be directly modulated in the IBHS Research Center. can be determined rationally by dividing the discharge rate of a 111

112 single nozzle (L 3 T -1 ) by the grid cell area (L 2 ), which was nominally 66 mm x 61 mm. Prior research (e.g. Choi, 1994; Blocken and Carmeliet, 22) has shown that does not vary significantly with wind speed. Therefore, the major challenge in this research was to select a spray nozzle that would best replicate a naturally occurring RSD, therefore resulting in a value approximately equal to.4. A nozzle with too many small drops would cause a decrease in the parameter, and vice versa. Spray Uniformity and the Effect of Wind Velocity on the Raindrop Size Distribution Manufacturer-supplied specifications for hydraulic spray nozzles typically include the discharge rate, spray angle and spray pattern (circular or square). The RSD is not given for off-the-shelf products, such as the nozzles considered in this study. Therefore it was necessary to devise a test protocol to characterize the RSD of each nozzle. Given the absence of information about the RSD, several questions needed to be answered before this protocol could be developed: 1. Are the wetting rate and RSD uniform along the radial extent of the spray pattern? Would one measurement suffice (and thereby save significant time and expense of testing) or are multiple measurements required to develop a composite RSD for the entire spray region? 2. How does the presence of wind change the RSD? Can the RSD characterization procedure be performed in still air, eliminating the need for expensive and timeconsuming tests? To answer question 1, a simple experiment was performed to quantify the variability of the spray using the apparatus shown in Figure 5-1a. A single nozzle was aligned perpendicularly to the center of a vertically oriented collection chamber with 12 receivers. Each partition drained into its own graduated cylinder. The test consisted of (1) covering the chamber, (2) setting the flow rate of the nozzle using a pressure regulator, (3) removing the cover to allow water to enter the receivers, (4) covering the 112

113 chamber after five minutes, (5) shutting off the nozzle and (6) reading the graduated cylinder and recording the accumulated water volume. Ten nozzles were evaluated in this manner, and the results of all tests clearly demonstrated a non-uniform wetting pattern. The most uniform spray pattern observed in test series is shown in Figure 5-1B. The wetting rates varied by almost a factor of three ( mm/hr) with a coefficient of variation, CoV, of.25. The implication is that multiple measurements inside the spray region would be required. This led to the radial profiling technique discussed in the next section. (a) Collection Chamber (b) Sample results for a BETE WL 1 1/2 nozzle Figure 5-1. Apparatus to measure the spray uniformity of a single nozzle (photo courtesy of author) To answer question 2, a series of tests were performed in stagnant air and in a steady jet generated by the wind generator at UF. The experimental configuration consisted of an OTT PARSIVEL and DMT PIP located 3 m downwind of a 3 m x 3 m uniform, open jet. The sensors were oriented horizontally such that the jet and laser planes were parallel. Measurements were taken for wind velocity U = 8.9 m/s, 17.9 m/s, and 35.8 m/s and compared to measurements taken in still air. The still air test was 113

114 performed with the nozzle facing downward and the PARSIVEL in an upright (standard) position and located at the center of the circular spray area. Three tests were performed for each case. The sampling duration was five minutes. Results are shown in Figure 5-2. The vertical axis is the number concentration, which is the number of particles divided by a sample volume and diameter (dependent on wind speed, Equation 2-28). The horizontal axis is the raindrop size diameter. The wind speed and wind-driven rain intensity are shown in the legend. The figure demonstrates that regardless of wind speed, the RSDs in the < 1 mm regime match the stagnant air case for both instruments. Concentrations for drop diameters greater than 1 mm were greater than the stagnant air case; however, this behavior was expected and attributed to the narrowing of the spray cone in the presence of wind. As the following section will explain, all of the nozzles evaluated in stagnant air, ejected large droplets ( ) towards the outer edge of the spray area. Thus, as the spray cone narrowed, a higher concentration of larger drops was expected. The variation of the wind-driven rain intensities was attributed to the small sample volumes resulting from five minute tests ). Mueller and Sims (1966) determined that a minimum sample volume of is necessary to get reliable estimates of the rainfall intensity and reflectivity (within 1% at a 95% confidence interval). The required test time to achieve this sample volume (employing PARSIVEL and PIP disdrometers) is a minimum of 15 minutes; however, this test duration was time and cost prohibitive given the fuel, water, and labor resources necessary to operate the wind generator. 114

115 Concentration (mm -1 m -3 ) Concentration (mm -1 m -3 ) The results from these tests indicate that the RSD behavior for the majority of the drops (i.e., less than 1 mm) was unaffected by wind and the RSD of larger drops behaved as expected. Therefore, an investigation of the characteristic nozzle RSD in stagnant air was acceptable U = m/s R WDR = 59 mm/hr U = 8.9 m/s, R WDR = 172 mm/hr U = 17.9 m/s, R WDR = 155 mm/hr U = 35.8 m/s, R WDR = 36 mm/hr U = m/s R WDR = 59 mm/hr U = 8.9 m/s, R WDR = 977 mm/hr U = 17.9 m/s, R WDR = 12 mm/hr U = 35.8 m/s, R WDR = 15 mm/hr Diameter (mm) Figure 5-2. Comparison of raindrop size distributions measured in stagnant air and in a steady wind by the PARSIVEL (left) and PIP (right, BETE WL 1 1/2 at 138 kpa) Characterization of the Raindrop Size Distribution of a Spay Nozzles in Stagnant Air: A Proxy for Full-Scale Testing Specimen Matrix The 12 full cone hydraulic nozzles shown in Table 5-2 were selected for evaluation based on their discharge rates and cost (> 45 nozzles are used in the IBHS Research Center Full-Scale Test Facility) Diameter (mm) Table 5-2. Spray nozzles evaluated in this study BETE Lechler Steinen Spray Systems WL 1 ½ BE SM 151W 1/8GG-8W WL BE SM 33W 3/8GG-17W WL BE TF 8 TF 1 115

116 Experimental Configuration The experimental setup consisted of a 4.9 m x 4.9 m x 4.9 m chamber with enclosed sides to reduce the effect of ambient wind. The nozzle was suspended 3.1 m (1 m) above the ground in the center of the chamber facing downward. Four radial extents were marked on the floor in.3 m increments, as shown in Figure 5-3. The PARISIVELs were relocated to each position for five minutes of data capture. 3.5 m m x x x x x x x x x x x x x x x x Figure 5-3. PARSIVEL test locations for nozzle characterization Analysis The PARSIVEL outputs drop counts in a 32 x 32 matrix. Each cell corresponds to a specific combination of drop diameter and fall velocity (see Chapter 2 for a detailed explanation). Values were summed for all velocities for each diameter bin, and then averaged over the four radial extents, yielding a 32 x 1 array of drop counts per diameter along the radial extent. Results for one nozzle (BETE WL-3) are shown in 116

117 Figure 5-4 for D =.31 mm to D = 3.8 m. The black lines correspond to the number of drops as a function of radial distance from the centerline of the nozzle. The dashed lines correspond to the maximum theoretical distance the particles should travel based on the spray angle of the nozzle and the initial velocity. The governing equations for the trajectory of a smooth rigid sphere acted upon by gravity and wind are Reynolds (Re) number dependent. For Stokes flow conditions (Re < 1), the force acting on the raindrop is equal to: (5-1) For 1<Re< 8, Schiller and Nauman (1933) define the drag coefficient and force as: (5-2) (5-3) For 8<Re<15, Clift and Gauvin (197) define the drag coefficient and force as: (5-4) (5-5) where is the drag coefficient, is the force vector, is the Reynolds number, is air density, is water density, is dynamic viscosity of air, is the drop mass, is gravitational acceleration, is the cross-sectional area of each particle, is the wind velocity vector, and is the velocity vector of the particles. The acceleration ( ), velocity( ), and position ( ) are iteratively solved for each time step as follows: 117

118 (5-6) (5-7) (5-8) The initial velocities, i.e., were determined using a Phantom V9.1 high speed camera recording 1152 x 72 pixel images at 1 fps and passed through an edge filter (Figure 5-5). It is evident from Figure 5-4 that the smaller particles tend to fall closer to the centerline of the nozzle. The outermost fall locations match the theoretical estimates for D =.68 mm to 1.68 mm. The particles that fell outside of this region exceeded the limit due to the temporary walls not completely preventing air currents from forming inside of the test chamber and water drops generating turbulence during their descent. For the smaller drops, wind velocities as low as.1 m/s are sufficient to cause the deviation observed in the data. The larger drops did not reach the theoretical limit and tended to fall further from the center of the spray area. This behavior was observed visually in all of the nozzles and is hypothesized to be a result of the higher momentum of the larger drops. The helical ducting in the nozzles (all of the nozzles had similar designs) introduces an angular velocity component into the flow; consequently, larger drops (higher mass) have a higher tangential momentum and are ejected farther from the center. Plots of the other nozzles (see appendix) demonstrate that the spray pattern for all nozzles was similar. 118

119 3 x 14 D =.31 mm 6 x 14 D =.44 mm 6 x 14 D =.56 mm 4 x 14 D =.69 mm x 14 D =.81 mm D =.94 mm D = 1.1 mm D = 1.2 mm D = 1.4 mm D = 1.6 mm D = 1.9 mm D = 2.1 mm D = 2.4 mm D = 2.8 mm D = 3.3 mm D = 3.8 mm Figure 5-4. Count of drops radially outward from nozzle centerline

120 Figure 5-5. Determining initial velocity using high speed footage (photo courtesy of author) To compute the number of drops per diameter for the entire spray region from the data averaged over the radial extent, each array was integrated using the method of circular discs: (5-9) where is the total number of drops of diameter, is the radial distance from the center to the measurement location, is the number of observed diameter droplets at location and is the sample area of the PARSIVEL. continuity: The nozzle flow rate was also computed from the PARISVEL data to verify mass (5-1) where is the flow rate, and is the sample time. The estimated values agreed very well with the known discharge values. Errors were on the order of 15% or less. The RSD of each nozzle was calculated from: 12

121 Concentration, N D (mm -1 m -3 ) (5-11) where is the circular spray area and is the terminal velocity of a diameter drop. Figure 5-6 shows the calculated RSD for all of the nozzles (RSDs of other nozzles are independently listed in the appendix). As the figure shows, the major difference in the RSD of the nozzles was the number concentration of large particles. The concentration of smaller particles was virtually identical for raindrop diameters <= 1. mm. Therefore the selection of the BETE WL3 was made based on achieving the largest number of large drops in the flow BETE WL3 Other Nozzles Best (195) Diameter (mm) Figure 5-6. Raindrop size distribution of BETE WL3 in stagnant air conditions 121

122 Validation of the Wind-Driven Rain Simulation System at the IBHS Research Facility Upon selection of the nozzle, the wind-driven rain simulation system was constructed. The system consists of three nozzle grids for each of the three horizontal fan cells. The flow rate and pressure of each grid is actively controlled by automated gate valves. Horizontal nozzle spacing was limited to either 132 mm or 66 mm due to the 132 mm spacing of the air foils. It was observed from testing at UF that the shorter horizontal spacing would yield a more uniform spray, particularly in high wind speeds; thus, 66 mm was selected. Similarly, shorter vertical spacing would yield more uniform spray patterns; however, the closer the spacing, the lower the required pressure and flow rate per nozzle. The lowest pressure recommended by the manufacturers was 34.5 kpa; thus, the minimum vertical spacing required was 61 mm. To validate the design recommendations, a PARSIVEL and PIP were used to take RSD measurements at multiple locations in the stream. The instruments were placed on vertical steel lattices at 1 m and 3 m from the air foils (Figure 5-7). Flow rates were determined to be approximately 3.8 L/min per nozzle (using inline digital flow gauges, corresponds to approximately 34.5 kpa). Five minute measurements of the RSD were taken at 2. m, 3. m, 4. m, 5.2 m, and 6. m for each wind speed. The measured RSDs matched the RSD predicted by the Best (195) model at an intensity of 59 mm/hr, confirming the choice of nozzle and spacing (Figure 5-8). The mean of all the RSDs also agreed with the Best (195) model (Figure 5-9). This observation implies that for full scale simulations, the wetting rate reaching the building façade will be approximately 23 mm/hr and the RSD will be representative of natural conditions. 122

123 Figure 5-7. Instrument arrangement at the Insurance Institute for Business & Home Safety Research Center (photo courtesy of author) 123

124 Z = 6. m Z = 5.2 m Z = 3.7 m Z = 2.7 m Z = 1.8 m U = 8.9 m/s U = 17.9 m/s U = 26.8 m/s Diameter (mm) Diameter (mm) Diameter (mm) Best (195) Model Parsivel Measurement PIP Measurement Based on 59 mm/hr Figure 5-8. Measured raindrop size distributions at multiple heights and wind velocities 124

125 Concentration (mm -1 m -3 ) Parsivel Mean PIP Mean Best (195) Model for R = 59 mm/hr Diameter (mm) Figure 5-9. Comparison of measured raindrop size distributions and Best (195) model Summary This chapter presented the design of a realistic wind-driven rain simulation system that can reproduce the rainfall intensity and drop size distributions obtained from the field measurement activities (Chapter 4). The simulation system was implemented at the Insurance Institute for Business & Home Safety Research Facility for the water penetration resistance evaluation of low-rise buildings. The following chapter will summarize contributions and conclusions of the research described in this document and present recommendations for future studies. 125

126 CHAPTER 6 SUMMARY, CONCLUSIONS,AND RECOMMENDATIONS This chapter summarizes the efforts taken to advance the knowledge base by characterizing extreme wind-driven rain (WDR) events, creating and evaluating a WDR measurement technique, and the design of a full scale WDR simulation system. Characterization of Extreme Wind-Driven Rain Events The VORTEX2 and FCMP field campaigns yielded 17 thunderstorm and two tropical cyclone datasets. For these activities, a novel approach was taken to minimize errors associated with strong winds. The instrumentation platforms continuously align the disdrometers towards the mean rain vector using continuous wind velocity and direction feedback. The first instrument platform was designed to be mounted on one of the ruggedized FCMP meteorological measurement stations to record rain data at 3m and wind data at 5m; however, the employed disdrometer was cost prohibitive when considering a multiple instrument array. Thus, as part of this project, a low cost, easy to handle, robust instrument platform was designed and constructed.to achieve a low cost solution (2% of the cost of the high performance disdrometer) OTT PARSIVELs were employed; however, the OTT PARSIVELs have a lower resolution and sampling rate than the DMT PIP. Moreover, the disdrometer is not intended to be used in strong winds. Thus, three laboratory experiments were conducted to investigate the errors exhibited by the PARSIVEL associated with strong winds. Proof of Concept The first two experiments were measurements of the raindrop size distribution (RSD) emitted from a nozzle in stagnant air conditions. One test compared the measured RSD of the PARSIVEL to the RSD determined by a morphological image 126

127 processing algorithm that was created to count the number of droplets captured in an oil medium. Results demonstrated that for practical purposes, when the PARSIVEL is oriented towards the mean rain direction, the measured RSDs compared favorably to those measured by the image processing algorithm. The second test evaluated the effect of oblique trajectory angles on the measured diameter and velocities. High tolerance steel bearings, of multiple diameters, were dropped through the laser band of the PARSIVEL and different angles. The different angles replicated the effect of wind on droplets passing through the measurement area of a stationary instrument (i.e. laser band continuously parallel to the ground). These tests revealed that at oblique trajectory angles, diameters of drops are measured with high fidelity; however, the measured velocities are unrealistically reduced. The consequence is unrealistic large number concentrations ( ). The third laboratory experiment compared measurements from the PARSIVEL, oriented at different angles, and a DMT PIP mounted on a gantry system exposed to multiple wind speeds and rainfall intensities. Results from these tests confirmed the findings from the stagnant air tests; the PARSIVELs exhibited erroneous measurements resulting from incorrect orientation which are magnified by higher wind velocities. These observations validated the concept taken in the design of the articulating instrumentation platforms. The articulating instrumentation platforms continuously aligned the disdrometers such that the laser plane was perpendicular to the mean rain vector. Field data from collocated stationary and articulating instrumentation platforms confirm that in moderately high wind speeds (> 1 m/s), data from the stationary 127

128 instrumentation becomes compromised; while the data from the articulating instrumentation exhibited no wind induced error. Conclusions from Field Data Results The simultaneous wind and rain data gathered by the articulating instruments, to the author s knowledge, has been the first attempt at characterizing ground level freestream RSDs in extreme wind events. Thus, as part of the research project the following five topics were investigated:1) the effect of wind on the drop diameter, 2) the effect of wind on the RSD, 3) comparison of RSD Models to RSDs measured in multiple wind velocities, 4) calculation of the peak to mean ratios of, and 5) comparison of rain data measured in extreme events to WSR-88D data. Effect of wind velocity and turbulence intensity on raindrop diameter The effect of wind velocity and turbulence intensity on raindrop size was investigated using data from both field campaigns. Two main trends were observed: 1) mean drop diameter tends to increase with increased wind velocity and 2) mean drop diameter tends to decrease in the presence of high turbulence intensities. The cause of these trends is postulated to be attributable to one or both of the following possibilities: 1) It is possible that in increased wind velocities the high drag forces acting on the droplets overcome the surface tension holding the shape together and breakup occurs or 2) high turbulence intensities cause the probability of coalescence to increase. These conclusions are intended to motivate further study. This is a limited dataset and more data is necessary for a comprehensive study to be performed and definite conclusions to be made. 128

129 wind velocity and turbulence intensity dependency of the raindrop size distribution Observations of drop diameter changing in the presence of multiple wind scenarios led to investigate the effect of wind on the RSD. For this investigation, wind velocity, turbulence intensities, and the gamma parameters for each one minute segment of VORTEX2 and FCMP data were calculated. These data (Figure 4-11) suggest that,, and remain constant with increasing longitudinal velocity, and longitudinal and lateral turbulence intensities; however, the variance of each of the variables decreased with increasing longitudinal velocity. Thus, it was concluded that that the RSD does not significantly change with increased wind speed, or longitudinal and lateral turbulence intensities. When investigating the relationship between and, (Figure 5-12) it was observed that the measured relationship differs from the empirical relationship established by Zhang (23, Equation 5-12). A possible source for the difference is that there are too few one minute measurements. If the observed relationship is valid, the implication could ultimately mean incorrect interpretation of RSD parameters from reflectivity measurements. Presently, the reason for the dissimilarity is unclear and more data is necessary to make an assessment. Comparison of Measured Data to Raindrop Size Distribution Models RSD models are used by the wind engineering community as a simple technique to acquire an adequate RSD for WDR modeling. Thus, an evaluation of the applicability of current RSD models to data gathered in strong winds was performed. Field measured data was compared to the Marshall and Palmer (1948), Best (195), and Willis and Tattleman (1989) dependent models and the gamma model using the mean 129

130 parameters from the measured data. The results indicate that the difference in the performance of the models below or above 15 m/s, below or above.2, or below or above.2 (Figures ) was insignificant. The data also shows that the Best (195) model yields the least error and is thus the most accurate. Comparison of Measured Data to WSR-88D Data FCMP Hurricane Ike data were compared with remotely sensed estimates from the National Weather Service WSR-88D Doppler Radar KHGX. Generally, there was good agreement between disdrometer estimated and radar measured reflectivities. The data shows that there is also relatively good agreement between the ground level measured rainfall intensity and the radar estimated rainfall intensity below 1 mm/hr. Above 1 mm/hr the default Z-R relationship, employed at KHGX, underestimated the observed rainfall intensity. Thus, other commonly used empirical Z-R relationships (Table 5.3, as recommended by the WSR-88D Operational Support Facility) were compared. Of the recommended relationships, the Rosenfeld tropical relationship more closely resembled the observed data; however, all of the Z-R relationships underestimated the observed rainfall. Peak to Mean Ratio of Rainfall Intensities Peak to mean ratios of rainfall intensities were calculated for their potential use in design practice. Currently, the widely accepted standard (BSI EN ISO ) calculates the rain deposition rate from hourly and data. The research demonstrates that designing for a shorter duration peak can significantly increase the design wind driven rainfall intensity ( ) and subsequently, the deposition rate ( ). For instance, using 1 minute FCMP Hurricane Ike data, the design free-stream wind driven rain intensity is approximately three times larger than using hourly data. For 13

131 design, the applicability of a peak to mean ratio would depend on site specific probabilities of type of precipitation and wind speed. The presented research shows the potential value of peak to mean ratios of rainfall intensities in design practice; however, more data should be collected to assess the validity of the peak to mean ratio curves presented herein. Design and Implementation of a Full Scale Wind-Driven Rain System The Insurance Institute for Business & Home Safety tasked researchers at the University of Florida with the design of a WDR simulation system for the IBHS Research Center Full Scale Test Facility. The design was based on field measured RSD data and intended to reproduce the rain deposition rate specified in current test standards. The methodology consisted of characterizing the nozzles and RSD, selecting the most appropriate nozzle, and validating the full-scale WDR simulation system. Nozzle Characterization The OTT PARSIVEL was utilized to investigate the characteristics of nozzles whose manufacturers specifications indicate the approximate necessary flow rate. Measurements in the presence of wind revealed that there was negligible change in the RSD indicating that the particles accelerated with the flow with little interaction. Thus, it was deemed acceptable to characterize the RSD of individual nozzles in stagnant air. Of the tested nozzles, the BETE WL-3 exhibited an RSD that closely resembled the Best (195) RSD model (the model that best fit field measured data). Nozzle spacing was set to the minimum possible spacing given space and flow rate limitations. Full Scale Implementation Upon determining the spray system characteristics, the rain simulation system was constructed at the IBHS Research Center. Free-stream WDR measurements were 131

132 taken at the facility with the PARSIVEL and the PIP at multiple locations in the stream. The measurements indicate that the RSD closely resembles the RSD predicted by the Best (195) model. Thus, future full scale simulations will be representative of natural conditions. Recommendations for Future Research Recommendations for Instrumentation Construction of more instrument platforms will facilitate the investigation of the spatial correlation of WDR. The instruments can also be easily adapted to make tower mounted meteorological observations. Thus, the vertical and horizontal evolution of WDR can be investigated. Currently the design of the articulating instrumentation platforms allows for the upgrade of real-time data streaming. The availability of real-time data streaming may aid agencies such as meteorological institutions in updating forecasts, emergency management agencies in assignment of resources during and after extreme events, and FCMP teams responding to errors by instrumentation during data acquisition. Future research, utilizing the articulating instrument platforms would benefit from the implementation of GPS and compass instrumentation. These digital measurements would enable real time processing of cardinal wind direction and global position. Currently, the algorithm that aligns the disdrometers to the mean rain vector assumes a mean drop diameter of 1.2 mm, a valid assumption for extreme rainfall scenarios. A new tracking algorithm that aligns the disdrometer to the measured mean drop size may expand the use of the instrumentation platforms to other applications and decrease the occurrence of, otherwise unknown, incorrectly measured data. 132

133 PARSIVEL validation results from this research project indicate that the error introduced by oblique trajectory angles manifest as incorrect velocities, while droplet diameters are measured correctly. A new data processing algorithm may be created to utilize real time wind data for the calculation of droplet velocities rather than residence time in the measurement field. This new measurement technique may provide a way of increasing the maximum acceptable wind speed when using stationary instrumentation. Recommendations for Full Scale and Numerical Models The implementation of a full scale WDR simulation system that accurately simulates naturally occurring RSDs may be used in the validation of numerical WDR models by implementing similar structures in the models and the IBHS Research Facility. The full scale simulation system will also prove useful in the evaluation of building component performance. The data gathered from field measurements can be used in time varying full-scale simulations of extreme WDR scenarios. Thus, future experiments could compare the performance of building components subjected to current testing protocols and simulations based on field measured data to establish the efficacy current standardized test methods. Current numerical WDR models make a Stoke s flow assumption (Re << 1) to determine the driving forces of particles moving through space. Implementing a wind field determined by a CFD software package to the developed trajectory model will result in a new Reynold s number dependent WDR model. Such a model may yield better results and warrants further research. 133

134 Recommendations for the Morphological Image Processing Algorithm The design of a robust graphical user interface for the morphological image processing algorithm would serve as an economical solution to RSD measurement. When compared to disdrometric instrumentation, it is a labor intensive process; however, it may be a useful tool for applications where the drop size distribution is known to remain constant or for instrumentation validation. If the morphologic image processing algorithm and Reynold s number dependent trajectory model are made available to the public, they may be used by nozzle manufacturers and designers for the characterization of nozzles and for the design of custom water delivery applications. To the author s knowledge, no such design tool is currently available. 134

135 APPENDIX Theoretical Proof of Greater Accuracy from an Articulating Instrument This proof follows the work of Griffiths (1974) in which he demonstrated that the sample volume for an articulating instrument is different than that of a stationary instrument. Ultimately, it is shown that the articulating sensor has a greater accuracy due to the greater sample volume. Case 1 represents a stationary instrument oriented such that the sample area is oriented parallel to the ground, Case 2 and 3 represent an articulating instrument platform such that the sample area is tilted to angle (determined by the trajectory of the assumed mean droplet diameter) and other droplets are crossing the sample area at angle depending whether the droplet is smaller than assumed mean (Case 2) or larger (Case 3). Case 1 Case 2 Case 3 S = Sample area (m 2 ) V TD = Terminal velocity of droplets of diameter D (m/s) U = Longitudinal wind velocity (m/s) V RD = Resultant velocity of droplets of diameter D (m/s) C D = Number density of droplets of diameter D (m -1 ) n DΦ = Number of droplets of diameter D having trajectory Φ collected per sec (sec -1 ) V S = Sample volume (m 3 ) M DΦ = Total number of droplets having trajectory Φ collected over time T N D = Number concentration of droplets of diameter D 135

136 Stationary instrument (Case 1): If we collect data for Time T and say total number of droplets = MDΦ: We know: Thus: Solve for concentration of droplets: Given: Thus we find that the sample volume for a stationary instrument is: 136

137 Articulating instrumentation (Case 2 & 3) Using the following identities: We get the following for both Case 2 and 3: We know: Substitution gives us: Say : Check for Φ = 9 (Case where U = ): Checks If we collect data for Time T and say total number of droplets = MD,Φ: Solve for concentration of droplets: Thus we find that the sample volume for an articulating instrument is: 137

138 Compare Instrumentation platforms: Given we know: for stationary instrumentation for articulating instrumentation K can vary from U to VT,D. If the tracking algorithm is accurate, in high winds K approaches U and VT,D in no wind for stationary instrumentation for articulating instrumentation in no wind for articulating instrumentation in high winds Therefore as the wind speed increases the sample volume of the articulating instrument platform increases, making it more accurate. Example: Assuming that the mean drop diameter is 1.2 mm and U = 1 m/s: Ф as the tracking algorithm calculates it for stationary instrumentation for articulating instrumentation Thus the sample volume of articulating instrument is greater than twice as large at U = 1 m/s. Similarly the ratio of the sample volumes can be calculated for multiple wind velocities: U V T,(1.2 m m ) φ K V S ratio

139 Nozzle Selection Nozzles are used in a wide range of applications, including evaporative cooling, gas conditioning, fire suppression, spray drying, and agriculture. Nozzles generate droplets through the process of atomization, in which a fluid with a potential energy (water pressure) is released through an opening (nozzle end) and emerge as ligaments that evolve into droplets (Schick 1997). The resultant drop size distribution is a function of the nozzle type, spray type, spray angle, and the pressure and flow rate of the fluid. Figure A-1. Droplet formation process 139

140 Figure A-2. Types of nozzles and drop size relationship The effect of each variable is as follows: Nozzle types, which can be hydraulic or air assisted. Air assisted nozzles generally produce droplets in a smaller size range than hydraulic nozzles. Spray types are flat, solid stream, square, hollow cone, and full cone sprays. Full cone and square spray nozzles produce the largest drop size distribution followed by flat spray and hollow cone spray nozzles (Fig 4-2). Spray angle the angle through which the spray is effective is inversely proportional to drop size. Nozzle pressure is inversely proportional to drop size and is controlled by the pumping system. Flow rate is directly proportional to the drop size and controlled by an actively controlled valve system. 14

141 Measured Diameter Wind Relationships Hurricane Ike Data 141

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144 Hurricane Irene (Beaumont) Data 144

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147 Hurricane Irene (Deal) Data 147

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150 Florida Coastal Monitoring Program Hurricane Ike Data 15

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