AUTOMATED ICE MONITORING SYSTEM FOR THE VETERANS GLASS CITY SKYWAY AT TOLEDO

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2 AUTOMATED ICE MONITORING SYSTEM FOR THE VETERANS GLASS CITY SKYWAY AT TOLEDO A Thesis submitted to the School of Electronics and Computing Systems Of the University of Cincinnati In partial fulfillment of the Requirements for the degree of MASTER OF SCIENCE (M.S.) 2011 By SHEKHAR AGRAWAL B.E., Birla Institute of Technology, India, 2007 Thesis Advisor and Committee Chair: Arthur J. Helmicki, Ph.D.

3 Abstract The Veteran s Glass City Skyway (VGCS) is a large cable - stayed bridge in Toledo, Ohio owned by the Ohio Department of Transportation (ODOT). During certain weather conditions, ice gets accumulated on the bridge stays, which can even make up to 19mm (3/4 ) - 75mm in thickness. As the stays warm, ice shed up to two hundred and fifty feet to the roadway and the pieces of ice can be blown across several lanes of traffic on the bridge deck causing difficulty for motorists to commute and may cause potential traffic accidents. Currently manual checks are performed on the bridges to avoid such scenario, but it does not discard the possibility of hazardous ice shedding events being missed. The chances of such ice event occurrence are complimented by unique weather conditions at VGCS and its sheath structure, which is evident from the fact that four times in the last three years, ODOT closed the lanes due to ice accumulation. Since no existing ice anti/deicing technology was found to be practical for VGCS, to deal with this situation in an effectual way a novel solution involving algorithmic modeling of meteorological data for ice accumulation and ice shedding is implemented. It aims at automated monitoring of the icing status using the meteorological variables: atmospheric temperature, type of precipitation, and sky cover. Dashboard is developed as a part of the solution that makes information accessible and viable. The tasks that have been completed are: realized criterions causing ice accumulation and ice shedding, developed algorithm to determine the ice events, a background study that included weather conditions for the past icing incidents and designed a one-click user friendly interface to monitor the icing status at VGCS. This model was implemented at the site in winter 2011 and since then it has successfully predicted all the ice accumulation occurrences since the advent of VGCS. The algorithm also produced decent results when its performance is tested for the past icing events. Perhaps the algorithm needs few modifications to predict ice-shedding events more effectively. Though, the developed system use only previously installed sensors and does not require new instruments, but for future it is recommended to install additional sensors. Thus the system is a very good first step towards a long-term solution for icing/deicing issue at VGCS. ii

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5 Acknowledgement I take this opportunity to express my sincere thanks and gratitude towards my advisor Dr. Arthur J Helmicki for his guidance, encouragement and support during my research. I thank Dr. Victor Hunt for his constant support and directions during my research. I thank Dr. Nims for the time spent to review my thesis, being my research committee and for their valuable suggestions and comments. I thank ODOT for financial support and constant feedback on my research. I thank Kathleen Jones, CRREL, for her help in gaining insight about the problem statement. I thank ECE IT department for the technical assistance and the University of Cincinnati libraries for all the facilities. I thank my family for their constant support and encouragement without which none of this could be possible. I would like to thank my lab members, friends and colleagues in the University of Cincinnati. iv

6 Table of Contents CHAPTER 1 INTRODUCTION 1 SECTION 1.1 VGCS BRIDGE: AN INTRODUCTION 1 SECTION 1.2 ICING BACKGROUND 1 SECTION CURRENT SYSTEM 3 SECTION PREVIOUS RESEARCH 4 SECTION PROPOSED SYSTEM 7 SECTION 1.3 OBJECTIVES 8 SECTION 1.4 PROCESS FLOW DIAGRAM 9 CHAPTER 2 WEATHER DATA 12 SECTION 2.1 ICE EVENTS CRITERIA 12 SECTION 2.2 DATA SOURCES 13 SECTION 2.3 DATA CLASSIFICATION 17 SECTION 2.4 DATA COLLECTION AND STORAGE 21 CHAPTER 3 ICE DETERMINATION ALGORITHM 24 SECTION 3.1 DATA UPDATE TIME 24 SECTION 3.2 ICE ACCUMULATION ALGORITHM 24 SECTION 3.3 STATION INDIVIDUAL WEIGHTS 25 SECTION 3.4 THRESHOLD WEIGHTS 27 SECTION 3.5 ICE SHEDDING 28 CHAPTER 4 ICE PERSISTENCE ALGORITHM 31 SECTION 4.1 ICE STATES 31 SECTION 4.2 ICE ACCUMULATION PERSISTENCE ALGORITHM 32 SECTION TRANSITIONS FROM STATE CLEAR 33 v

7 SECTION TRANSITIONS FROM STATE YELLOW 1 33 SECTION TRANSITIONS FROM STATE YELLOW 2 34 SECTION 4.3 ICE PRESENCE CONFIRMATION 36 SECTION 4.4 ICE SHEDDING PERSISTENCE ALGORITHM 37 SECTION TRANSITIONS FROM STATE ALERT 38 SECTION TRANSITIONS FROM STATE RED 1 39 SECTION TRANSITIONS FROM STATE RED 2 40 CHAPTER 5 MONITOR WEBSITE 43 SECTION 5.1 DASHBOARD MAIN PANEL 43 SECTION 5.2 WEATHER MAP 46 SECTION 5.3 HISTORY 55 SECTION 5.4 IMPLEMENTATION TOOLS 58 CHAPTER 6 PERFORMANCE TESTING 59 SECTION 6.1 SYSTEM RELIABILITY TEST 59 SECTION WEATHER STATIONS DEPENDABILITY 60 SECTION 6.2 GROUND TRUTH 64 SECTION STUDY OF FEB 2011 ICE EVENTS 64 SECTION COMMENTS 77 SECTION STUDY OF PAST ICE EVENTS 78 CHAPTER 7 CONCLUSIONS AND FUTURE WORK 86 SECTION 7.1 LEARNING 87 SECTION 7.2 RECOMMENDATION 87 SECTION IMPROVEMENTS IN ALGORITHM 88 SECTION IMPROVEMENTS IN THE LOGISTICS 89 vi

8 APPENDIX A FREEZING RAIN 91 APPENDIX B DATA COLLECTION PROGRAM 92 APPENDIX C FLOW CHART 93 APPENDIX D ALGORITHM PROGRAM 94 APPENDIX E PERFORMANCE TESTING PROGRAM 95 BIBLIOGRAPHY 96 vii

9 List of Figures FIGURE 1 VETERANS' GLASS CITY SKYWAY 1 FIGURE 2 ATMOSPHERIC PRECIPITATION EXAMPLES 2 FIGURE 3 PROCESS FLOW DIAGRAM 10 FIGURE 4 SAMPLE RWIS MEASUREMENT 13 FIGURE 5 SAMPLE METAR MEASUREMENT 15 FIGURE 6 MAP SHOWING DISTANCES OF WEATHER STATIONS FROM VGCS 17 FIGURE 7 DATA COLLECTION FROM INTERNET 21 FIGURE 8 ICE DETERMINATION ALGORITHM 25 FIGURE 9 MAP SHOWING WEIGHTS OF WEATHER STATIONS 26 FIGURE 10 DATABASE SCREENSHOT WITH NO ICE 28 FIGURE 11 DATABASE SCREENSHOT WITH ICE ACCUMULATION 29 FIGURE 12 DATABASE SCREENSHOT WITH ICE SHEDDING CONDITIONS 29 FIGURE 13 ICE ACCUMULATION FLOWCHART 32 FIGURE 14 STATE TRANSITIONS POSSIBLE FROM CLEAR 33 FIGURE 15 STATE TRANSITIONS POSSIBLE FROM YELLOW LEVEL 1 34 FIGURE 16 STATE TRANSITIONS POSSIBLE FROM YELLOW LEVEL 2 35 FIGURE 17 SAMPLE ICE ACCUMULATION MESSAGE ALERT 35 FIGURE 18 DASHBOARD WITH ICE ACCUMULATION ALERT 36 FIGURE 19 ICE PRESENCE FLOWCHART 36 FIGURE 20 STATE TRANSITIONS POSSIBLE FROM YELLOW LEVEL 3 37 FIGURE 21 ICE SHEDDING FLOWCHART 38 FIGURE 22 STATE TRANSITIONS POSSIBLE FROM ALERT 39 FIGURE 23 STATE TRANSITIONS POSSIBLE FROM RED LEVEL 1 39 viii

10 FIGURE 24 STATE TRANSITIONS POSSIBLE FROM RED LEVEL 2 40 FIGURE 25 SAMPLE ICE SHEDDING MESSAGE ALERT 41 FIGURE 26 DASHBOARD WITH ICE SHEDDING ALERT 41 FIGURE 27 STATE TRANSITIONS POSSIBLE FROM RED LEVEL 3 42 FIGURE 28 DASHBOARD MAIN PANEL 44 FIGURE 29 DASHBOARD FOR ICE ACCUMULATION ALERT 45 FIGURE 30 DASHBOARD FOR ICE SHEDDING ALERT 45 FIGURE 31 MAP SHOWING WEATHER STATIONS 46 FIGURE 32 RWIS MEASUREMENT FROM DASHBOARD 48 FIGURE 33 RWIS MEASUREMENT FROM BUCKEYE TRAFFIC 49 FIGURE 34 AIR TEMPERATURE GRAPH 49 FIGURE 35 DASHBOARD METAR MEASUREMENT 50 FIGURE 36 DASHBOARD LOCAL STATION MEASUREMENT 52 FIGURE 37 INSTALLED CAMERAS AT VGCS 54 FIGURE 38 DASHBOARD HISTORY PANEL 55 FIGURE 39 DASHBOARD HISTORY PANEL SHOWING ICE ACCUMULATION 55 FIGURE 40 DASHBOARD HISTORY PANEL SHOWING RESPONSES FROM ICING TEAM 57 FIGURE 41 DASHBOARD SUMMARY SECTION 57 FIGURE 42 WEATHER SUMMARY ON FEB FIGURE 43 SCREENSHOT SHOWING ICE ACCRETION ON VGCS 67 FIGURE 44 WEATHER SUMMARY ON FEB FIGURE 45 WEATHER SUMMARY ON FEB FIGURE 46 ICE ACCUMULATION ON STAYS ON FEB FIGURE 47 WEATHER SUMMARY ON FEB ix

11 FIGURE 48 WEATHER SUMMARY ON FEB FIGURE 49 SCREENSHOT SHOWING ICE FALLING FROM VGCS ON FEB FIGURE 50 WEATHER SUMMARY ON FEB FIGURE 51 FEB ALGORITHM PERFORMANCE GRAPH 75 FIGURE 52 CONTRIBUTION OF THE ICING CRITERIA AND WEATHER STATIONS TOWARDS FEB ICING EVENT 77 FIGURE 53 SOLAR RADIATION VARIATION ON FEB FIGURE 54 FEATURES OF FOUR PAST ICING EVENTS 80 FIGURE 55 DEC ALGORITHM PERFORMANCE GRAPH 81 FIGURE 56 MAR ALGORITHM PERFORMANCE GRAPH 82 FIGURE 57 DEC ALGORITHM PERFORMANCE GRAPH 83 FIGURE 58 JAN ALGORITHM PERFORMANCE GRAPH 84 FIGURE 59 FLOW CHART 93 x

12 List of Tables TABLE 1 SUMMARY OF ICE ACCUMULATION AND ICE SHEDDING PAST EVENTS AT VGCS 3 TABLE 2 SHEET TO MAINTAIN ICE EVENTS RECORDS 3 TABLE 3 VGCS PAST ICING EVENTS AND REASONS 11 TABLE 4 SENSOR SYSTEMS AT RWIS STATIONS 14 TABLE 5 AIRPORTS INFORMATION 15 TABLE 6 DISTANCES OF WEATHER STATIONS FROM VGCS 16 TABLE 7 METAR AND RWIS PRECIPITATION MEASUREMENTS FOR ICE ACCUMULATION 18 TABLE 8 ICE ACCUMULATION CRITERIA 19 TABLE 9 METAR AND RWIS PRECIPITATION MEASUREMENTS FOR ICE SHEDDING 19 TABLE 10 ICE SHEDDING CRITERIA 20 TABLE 11 FINAL ICE ACCUMULATION/SHEDDING CRITERIA 21 TABLE 12 DATA UPDATE TIME 24 TABLE 13 WEATHER STATION WEIGHTS 25 TABLE 14 STATION WEIGHT CASES 27 TABLE 15 ICE ACCUMULATION/SHEDDING LOOK-UP TABLE 30 TABLE 16 DIAL STATES EXPLANATION 31 TABLE 17 TOOLS USED TO DESIGN DASHBOARD 58 TABLE 18 STATIONS CHOSEN FOR PERFORMANCE TESTING 59 TABLE 19 DATES FOR WHICH PAST ICE EVENTS TESTED 60 TABLE 20 WEATHER STATISTICS FOR DEC ICE EVENT 60 TABLE 21 WEATHER STATISTICS FOR MAR ICE EVENT 61 TABLE 22 WEATHER STATISTICS FOR DEC ICE EVENT 62 TABLE 23 WEATHER STATISTICS FOR JAN ICE EVENT 63 xi

13 TABLE 24 SUMMARY OF EVENTS WHEN ICE ACCUMULATION OCCURRED IN TABLE 25 INTERSTICE TEMPERATURE ON FEB TABLE 26 OVER ALL PERFORMANCE OF DASHBOARD ON PAST ICING EVENTS 84 xii

14 Chapter 1 Introduction Section 1.1 VGCS Bridge: An Introduction Located in Northern Ohio, near the Lake Erie, Toledo is the fourth most populous city in the state of Ohio. At the heart of Toledo city, over the Maumee River, is built Veterans Glass City Skyway (VGCS) at Latitude and Longitude The construction of VGCS across the Maumee River began in 2001 and the bridge was opened to traffic in June The main span is a cable-stayed type bridge with a single pylon and two spans 612'-6" on each side of the pylon. The main span approaches are approximately 4,000 feet north of the river and 3,350 feet south of the Maumee. It carries three lanes of traffic in each direction and is located on Interstate 280 in Toledo. The stays of the bridge are covered with stainless steel sheathing, which offers aesthetic and life cycle cost advantage over other materials. [Figure 1] shows a snapshot of the VGCS Bridge. Figure 1 Veterans' Glass City Skyway During winter season VGCS is exposed to the issues where, under certain weather conditions, ice gets accumulated on the sheaths. This causes fragments of ice falling into traffic, which presents safety issues for the motorists traveling below. To provide aesthetics to the bridge, stays are made of stainless steel of larger diameter but this unique feature also contributes to the icing problem. When this occurs the Ohio Department of Transportation (ODOT) must close lanes in each direction until the ice is gone. Due to randomness in the occurrence of ice accumulation/shedding processes, determining when lanes should be closed is extremely difficult. The icing problem depends on the weather data that is stochastic in nature. Also the solution should not affect the aesthetics of the bridge. Therefore, a novel solution is required along with an in-depth understanding of the microclimate on the bridge and conditions 1

15 of the stays. The work done in this project aims at understanding the microclimate of the bridge, predicting/sensing ice accumulation and ice fall and to design a system to monitor the icing issue considering the local situations at VGCS. Section 1.2 Icing Background There are two mainly two kinds of icing namely precipitation icing and in-cloud icing. Both of them cause severe damage to the infrastructures like bridges, power lines, and aircraft turbines. The icing problem along with power lines and aircraft turbines affects infrastructure like bridges and cause damage to man and money.[figure 2] provides screenshots of precipitation icing at VGCS, photo credits given to the research team in University of Toledo. Figure 2 Atmospheric Precipitation Examples Figure 2a: Icing precipitation on Bridge Stays Figure 2b: Icing precipitation on Bridge Stays At VGCS, under some winter conditions, ice forms on the stay cables. Ice accumulations can be up to approximately 3/4 thick and the ice conforms to the cylindrical shape of the stay sheath. As the stays warm, they shed the ice in curved sheets that fall up to two hundred and fifty feet to the roadway and can be blown across several lanes of the bridge deck. The falling ice sheets require lane closures and could present a potential hazard to the traveling public. This kind of event is known as an icing event and is mainly divided into two stages: Ice accumulation Process where ice gets accumulated on the stays of the bridge, as shown in [Figure 2a] Ice shedding Process where accumulated ice sublimes and falls in large pieces to the roadway, as shown in [Figure 2b] 2

16 Based on the work done by Kathleen Jones at the U.S. Army Cold Regions Research and Engineering Laboratory (CRREL) in [2], four icing events of these kinds have occurred on the VGCS Bridge for the period between Dec 2007 and Jan 2009 [Table 1] where ice shedding either occurred or was considered close to occurring. The table provides the ice events dates and the causes behind ice accumulation and ice shedding. This study can be set the basis to design a system that will aid ODOT to take preventive measures. Table 1 Summary of Ice Accumulation and Ice Shedding Past Events at VGCS Ice Incidents Precipitation event Ice shedding weather causing ice accumulation Dec-07 Freezing rain and fog Rain with temperature above freezing Mar-08 Snow, rain and fog Sun with temperature above freezing Dec-08 Snow and fog; Rain, gusty winds and Freezing rain and fog temperatures above freezing Jan-09 Freezing rain and fog Gusty winds, temperature above freezing Feb-2011 Freezing Rain Solar Radiation, temperature above freezing [Table 1] shows that out of the four icing events, three were caused due to freezing rain. Jones in [2] showed that since there were one freezing rain event in the winter of and two freezing rain events in the winter of , winters at VGCS with no ice events are not likely to occur often. Since VGCS is an area that is prone to icing events, it is important to find a permanent solution that keeps vigilance on ice accumulation/ice shedding events and informs ODOT so that they can take preventive measures on time. Section Current System To tackle with the icing problem, currently ODOT does not have any automatic monitoring system. They check VGCS manually whenever weather conditions are pertinent to ice accumulation. Upon finding sufficient ice on stays, two or three lanes are closed. They keep checking the stays for ice shedding upon occurrence of which bridge is closed, and when most of the ice gets shed off, they bring back the normal operation again. These manual checks pose a possibility of missing the ice shedding event that may, in turn, allow leeway to the chances of an accident. A working sheet is provided in [Table 2], which ODOT uses to maintain the record of icing events. Table 2 Sheet to maintain ice events records Ice on Cables No. Lanes Closed Ice Fell (Y/N) Ice off Cables 3 No. Days No. Days

17 Date NB SB NB SB Date with Closure with Ice Comments Section Previous Research Over the past decades, icing models have been developed in order to create reliable tool for predicting the type, amount, or shape of ice accumulation on cylinders, plates, and other objects. The developed systems categorize analytical models, time dependent numerical models, statistical models, and machine learning techniques models. Here is a brief description on few models that researchers across the globe developed to model atmospheric icing. Savadjiev in [1] performed statistical analysis of meteorological data for icing and ice shedding on overhead power-line conductors. The authors studied 57 icing events occurred between Feb 1998 and Jan 2000 in Quebec, Canada. The paper drew correlations between the icing rate on overhead line conductors and some standard meteorological variables. The relationship obtained is then applied as a statistical base for developing an empirical, probabilistic icing model and it covered the whole evolution in time of an ice event ice accumulation, persistence, and ice shedding. To measure ice, authors used a cylindrical metallic probe excited to vibrate at its natural frequency and during an icing event, mass of ice observing to the probe causes proportional shift of its frequency. The count and the frequency of this probe are used to measure the intensity of ice formation in the surrounding atmosphere. The same method was not used to predict ice shedding because the cumulative probe counter will give the same readings during no ice and after ice events, thus Ice shedding is predicted by force sensor. After the readings were obtained from the probe, it is used as one of the parameters to establish the correlation between icing rate and the accompanying meteorological random variables. The estimation is done through multiple linear regression curve fitting and expected value of icing rate is calculated for given random variables by additive model. Let s suppose X be the icing rate and A, B and C be the three meteorological parameters, then E (X A, B, C) = f (A) + f (B) + f(c). The curve thus obtained was smoothed by back fitting algorithm. Different meteorological parameters used in the study were temperature, probe reading, and precipitation rate, mean and maximum wind speed, wind direction, relative humidity of air. The notable thing in this paper was that the analysis was done for both kinds of icing i.e., in-cloud icing and freezing precipitation icing. The author concluded by comparing the effect of different meteorological parameters for ice accumulation, ice persistence and ice shedding processes. Notable results were: Maximum accumulation at -2.5 o C, no significant influence of wind speed in the interval from 0 to 50 km/h for ice accumulation. Ice persisted for temperature less than -0.5 o C and 4

18 shedding occurred for temperatures greater than -1.5 o C.Thus the authors concluded that only the temperature, and wind direction is dependent on hourly icing rate, rests of the parameters were independent. The problems in this kind of model are multifold: First of all, it won t work if the correlation is nonlinear. Secondly, the model is not suited well for continuous input. Finally, authors are using instrumentations to measure and determine icing, which could be costly and might not be applied generically to all the sites where icing needs to be determined because the power lines and other infrastructures have different microenvironments. In his improved version of the previous paper Savadjiev in [3] used a similar model but the analysis was limited to precipitation ice events. Also emission of Icing Rate Meter (IRM) signals is used as a criterion to distinguish the accumulation phase of an ice event, from persistence and shedding, characterized by no emission of IRM signals. The author, unlike his previous paper, included freezing rain and wet snow as the climactic factors for ice accumulation. This paper differs from the previous paper in a way that the main purpose of this paper is the creation of a numerical model for indirect evaluation of precipitation icing accumulation, persistence, and shedding on actual energized overhead lines using field meteorological measurements and IRM signals. Methodology used by authors was same as the previous paper, but there have been a considerable change in the results as different meteorological parameters were used this time. The analysis shows that, on average, icing accumulation rates during periods with precipitation (P>0), are significantly greater than those during periods with dry conditions (P=0). Also, the same was found for periods with wind blowing from the parallel sectors, than for periods with wind from the perpendicular sectors. Thus the authors developed a numerical model for precipitation ice events for calculating the hourly icing rate as a function of the number of IRM signals, ambient temperature, wind speed and direction, and precipitation rate. Still the results were not satisfactory due to the large variability of the icing data, rarity of occurrence of ice events, lack of sufficiently long time series records, and abundance of breakdowns of the principal data-supplying instrumentation. But the work done by authors provided the readers a better platform to understand ice accumulation, and ice shedding processes numerically. The above two papers does provide a method to measure atmospheric icing but it involves some cost for implementation. Also authors used microenvironment details and thus the analysis might be restricted only to power lines conductors. However, few results from these papers could be used for further analysis, two of the most important results were: a. Icing and deicing are not affected by wind speed much b. More icing occurs on wet surfaces than on dry Back in 1982, Pohlman in [4] performed a literature review and explained the seriousness of ice accumulation on the power lines. His work is excellent for those who are new to the icing world. According to him, the physics of the storm and the 5

19 topographical features of the location control nature, amount and shape of the ice formed. The author explained almost all the models used to analyze atmospheric ice namely mathematical modeling and its extrapolation; Empirical observation of natural icing phenomena after the fact; Experimental observation of icing before, during, and after the fact, both in the laboratory and field; Probabilistic mapping; and Deicing mechanisms. He concluded that there is practically no data available from the National Weather Service or other sources on frequency of occurrence, nature or magnitude of ice accretion. He also told that mathematical models for predicting ice accretion on wires have been developed and verified within the constraints of laboratory conditions. Because of the lack of field data, the models cannot be used to predict icing with any degree of confidence. Certain microenvironments consist of characteristics: availability of moisture-laden air, rapidly changing elevations, topographical features that channel wind -that intensify the likelihood and severity of ice build-up. Since the above work was just a literature review on different models already built to model atmospheric icing, it does not provide a novel solution for icing. There have been a series of work towards monitoring of ice in the last couple of years. To compliment them Geng in [5] provided a novel solution to monitor atmospheric ice on-line through image processing techniques. The author first studied the types and shapes of ice that are formed due to bad weather. Then a Closed Circuit camera is installed at the experiment site, which takes photographs of transmission line. A DSP chip is used to analyze the image to determine the thickness of ice. Also micrometeorological sensors were used to measure the environmental parameters for calculation of the ice density. The output of the CCD camera and other sensors are then sampled by DSP chip through video decoding chip and A/D converter. The images are compressed and then sent to the monitor site through GPRS wireless network. The work done by the author is an excellent attempt to monitor ice on the power lines and the method could be used in ice determination for any form of infrastructure like bridges, skyscrapers, etc. However, the negativity of the method resides in the cost of extra sensors, ice determining and other monitoring instruments. Also maintenance cost of such a system is always higher since the instruments need to be calibrated and maintained throughout the year. The models to monitor icing comprise climatologically data method, image method, load cell method, etc. Unlike these models, a novel on-line monitoring system based on Fiber Bragg Grating (FBG) sensing techniques was implemented by Guo-ming in [6]. Author studied the modulation of the reflection wavelength of FBGs in response to temperature and strain, and thus FBG was used as direct sensing elements for strain and temperature. The author developed a FBG tension & tilt sensor to measure the tension and tilt of the insulator string that connects the conductor and the electric power. Demodulating FBG s wavelength shift and transmitting to wireless module through RS232 follow the FBG measurement step. Finally ice-monitoring system was developed and alarms were generated if the load becomes greater than the threshold load. Luming Li in [7] performed a similar study in the year 2010 and presented his 6

20 work in the second IITA Intl Conference on Geosciences and Remote Sensing. The difference in the method between [6] and [7] is that in [7] authors performed extensive simulation for calculating ice thickness during windless and windy environment. All these methods involve additional sensors or instruments to determine the ice accretion, which incur installation and maintenance cost. After then several papers were presented in international journals but in none of them is described a generic model to monitor ice. Makkonen in [9] used airport data of 23 years to construct ice load risk maps for rime ice and freezing precipitation. He classified freezing precipitation (freezing rain, and freezing drizzle) as 18% contributor for icing issue. He concluded that variations of ice loads are not closely related to geographical locations or elevation from the sea level. The main problem with all the mentioned monitor/predictive systems is the cost incurred in the modeling. Most of the models aren t generalized or flexible. Since none of the method can be used to model VGCS icing problem a cheap, and generalized method need to be developed so that it can be used as a part of a long-term icing solution to any kind of infrastructures, which is explained in brief in the next section of this chapter. Section Proposed System Icing is a difficult problem to deal with due to large variability of the icing data, rarity of occurrence of ice events, and stochastic nature of weather data. On the top of it, VGCS stays are made of stainless steel for maintenance, lifecycle and aesthetic reasons. The stainless steel sheaths are anticipated to require less maintenance throughout their lives than comparable conventional HDPE sheaths. Therefore, the solution must preserve the aesthetics and utility of the VGCS sheaths. Factors kept in mind while designing the solution towards icing problem at VGCS are: Installation Cost: Should be less costly Maintenance: Should be less costly and require minimum effort Keep intact the integrity of VGCS: Should not disturb the aesthetics and utility of VGCS Long term solution: Should contribute towards long term solution of VGCS icing/deicing As seen in the previous section Joshua [23] in his thesis suggested that no existing ice prevention or removal technology appears to be practical and/or economical for the ice protection at the VGCS. This means the solution may require a modification to or combination of existing approaches or an entirely new technology. Therefore, an indepth understanding of the microclimate on the bridge, the state of the art and the state of the practice must be acquired and used to assess ice protection technologies that can be applied in the near term. 7

21 Keeping all these factors in mind a novel solution is designed, implemented and executed which is a first step in finding a long-term solution. To realize the solution, a Dashboard is developed that helps in monitoring ice events and other related parameters at VGCS for continuous flow of weather data. It also helps in getting actionable information in the hands of those who must anticipate and respond to an icing event. The working of the dashboard is based on the newly designed algorithm, working of which is discussed in next few chapters. Here is discussed the key features of dashboard: Dashboard is using only already installed sensors, thus there is no cost of instrumentation or installation. Dashboard works on an algorithm, which is the result of extensive study of icing causes and patterns, thus it require almost zero maintenance. The solution used in the dashboard is very flexible and can be modified as per user s requirements. The solution can be used for any location/site and not just restricted to VGCS This document explains the working, algorithm, user interface and performance of the dashboard in detail. It can also be seen that since designing and implementing a perfect solution will take several winters, the current solution needs ODOT attention at several parts of the solution. The next section explains the objectives completed in the project. Section 1.3 Objectives The primary objective of this phase of a larger project was to leverage existing weather data from sources available on the web in order to develop a virtual instrument. This virtual instrument allows weather researchers, infrastructure researchers, and transportation personnel all to monitor for potential icing events from any Internet connected device. Listed below is the list of tasks for this phase of the project: Add weather data to existing VGCS web interface and database for possible use in algorithms below. Add new stay-mounted camera views to existing VGCS weather interface and database and for possible use in algorithms below. Develop a check engine light (e.g., green (no ice), yellow (ice, but no shedding), red (ice with possibility of shedding) that responds to the algorithm and sends out corresponding alerts to a list of ODOT s choosing. Develop a reporting function that will allow ODOT to: o Verify that alerts are responded to o Declare an icing event o Capture time stamp and observation notes/comments Develop database of ground truth field data collected during actual icing events to compare against Dashboard performance. Develop export function for historical data archived on the VGCS weather website. 8

22 Run calibration studies based on historical/archived/ground truth data and characterize probabilities of false alarms and missed detections (i.e., false positives and false negatives). To accomplish this, a dashboard was developed which included the virtual instrument to deliver the right information based on the task list mentioned above. The dashboard also provides a rich toolset for more detailed monitoring and assessment based on regular collection and storage of weather data from multiple sources. In addition the dashboard provides a way to interact with all data collected by location on a map and plotting the different types of measurements over time. A complete section on the features of the dashboard is provided in the chapter 5. The next section explains the process flow of the entire project, about how from the raw idea of icing the novel solution was developed. Section 1.4 Process Flow Diagram Icing is a complex problem and no perfect solution has been designed to solve it, therefore to design an online monitoring system of continuous weather data, divide and conquer approach is followed. This approach is explained in [Figure 3] by a process flow diagram. Following are the major steps followed: 1. Icing Experts: First step is to get necessary information from icing experts that includes researchers in Cold Region Research and Engineering laboratory (CRREL) and ODOT. The information is then analyzed to determine the icing criteria for all the three stages in icing namely ice accumulation, ice persistence, and ice shedding. 2. Data Collection: This is a crucial phase and involves three steps: Realizing reliable weather sources Choosing appropriate weather parameters that need to be considered in the algorithm design, since different sensor system measure different weather parameters. Collecting weather data from different sources and make it available to use 3. Data Processing: This phase involves the main analysis and design; it is explained through a flowchart in Chapter 3 and 4.The main steps are: Ice accumulation checks for the last one-hour, get the results and store in the database Ice accumulation check for the last few hours to determine ice persistence In case ice is detected, manual check should be done followed by reporting to dashboard Ice shedding checks for the last one-hour, get the results and store in the database Ice shedding check for the last few hours to determine ice persistence 9

23 4. User Interface: The final step is the design of a simple user interface that lets the user to check icing status at just one-click. It also provides lot of useful data to researchers pertaining to ice events. [Figure 3] shows the diagram of the entire process flow diagram explained above. Figure 3 Process Flow Diagram Overview of this document: Chapter 2 explains different criteria for ice accumulation and ice shedding. It also explains about different types of weather stations and the parameters that are important for ice event determination. Chapter 3 explains the algorithm used to determine ice accumulation and ice shedding for one-hour time. Chapter 4 explains the algorithm for calculating likelihood of icing using the results from chapter 3. 10

24 Chapter 5 explains the features and design of the dashboard. Chapter 6 deals with the performance testing of dashboard for the past and recent icing events. 11

25 Chapter 2 Weather Data To accomplish the objectives mentioned in the chapter above, the first step is to gather information regarding weather conditions pertinent to icing. Based on statistical analysis of meteorological data, Savadjiev and Farzaneh in [1], there are various meteorological variables that affect the process of ice accretion and ice shedding. Extensive analysis was done on 57 icing events, which occurred in the period between February 1998 and January 2000 at the Mont Bélair in Quebec, Canada. Savadjiev also declared that freezing rain is the most important reason behind ice accretion. K.F.Jones in [2] performed a pervasive analysis of the icing events occurred in Toledo between 2000 and 2010, and found some of the common properties that was persistent during each ice events. Some of the results taken from Jones work are listed below: Ice accumulation occurred in both freezing rain and snow, both accompanied by fog. Ice shedding occurs when the air temperature warms to above freezing, which may be accompanied by rain, sunshine, or gusty winds. Freezing rain was associated with ice accumulation on the Skyway stays in three of the four ice events. [Table 3] lists the reason behind the past four ice events at Veterans, analyzed by Jones [2]. Table 3 VGCS Past Icing Events and Reasons Event Dates Ice Accumulation Ice Shedding Dec-07 Freezing rain and Rain with temperature fog above freezing Mar-08 Snow, rain, and fog Sun with temperature above freezing Dec-08 Snow and fog, freezing rain and fog Jan-09 Freezing rain and fog Rain, gusty winds and temperatures above freezing Gusty winds, temperature above freezing Section 2.1 Ice Events Criteria The results from Savadjiev [1] and Jones [2] led to the development of criteria to use when evaluating for ice events conditions: 12

26 Criteria that would likely cause Ice Accumulation: 1. Freezing Rain: Precipitation with air temperature below 32 o F. 2. Freezing Fog: Fog with air temperature below 32 o F. 3. Wet Snow: Snow with air temperature above 32 o F. Similarly, Criteria that would likely cause Ice Fall are as follows: 1. Warm Air: Air Temperature above 32 o F. 2. Solar Radiation: Clear sky during daylight. Since freezing rain is the reason behind most of the ice events occurring worldwide, it gives sufficient reason for its perusal. A detailed explanation about freezing rain is given in Appendix A. The next section of the chapter explains the different sources from which meteorological data are taken for the study. Section 2.2 Data Sources To capture the icing criterions mentioned above, meteorological data are taken from different weather sources. They are: (1) Road Weather Information System (RWIS): RWIS can be defined as a combination of technologies that uses historic and current climatologically data to develop road and weather information (for example, now casts and forecasts) to aid in roadway-related decision making. The three main elements of RWIS are: Environmental sensor system (ESS) technology to collect data. Models and other advanced processing systems to develop forecasts and tailor the information into an easily understood format. Dissemination platforms on which to display the tailored information. A typical RWIS contain data for the air temperature, dew point temperature, surface temperature, relative humidity, wind speed and direction, and precipitation type. A sample RWIS data screenshot taken from the second link is shown below: Figure 4 Sample RWIS Measurement 13

27 The reading is taken from buckeye traffic website and it shows one of the readings for the RWIS station at Veterans Bridge. The station name, temperature and precipitation readings are labeled. Here s listed the links to the RWIS data including the second sources for redundancy and reliability: ftp://ftp.dot.state.oh.us/pub/doit/ssi_rwis/ To determine the icing events at Veterans Glass Skyway Bridge, weather data from four RWIS stations are considered. The sensor systems of the four RWIS stations are tabulated below: Table 4 Sensor Systems at RWIS Stations Site # ID Site Description US-23 Split - Lucas co. I-475 Split - SLM 4.9 Lucas VGCS Libbey Road Distance from VGCS 6.4 miles 11.2 miles 0 miles 10.2 miles NLF ID SLUCIR00475** C SLUCIR00075**C SLUCIR00280**C SWOOSR00420* *C Latitude Degree Longitude Degree Atmospheric Sensor WIVIS Hawkeye WIVIS Generic Precip Wind Sensor RM Young RM Young RM Young RM Young R/H Temp Theis Theis Theis Theis Sensor Pavement Sensor 9 GH / 2 Repeaters 2 FP2000/ 2 GH / 2 Repeaters 6 GH 1 FP2000 (2) Meteorological Terminal Aviation Routine (METAR): METAR is a format for reporting weather information. A METAR weather report is predominantly used by pilots in fulfillment of a part of a pre-flight weather briefing, and by meteorologists, who use aggregated METAR information to assist in weather forecasting. METAR typically 14

28 come from airports or permanent weather observation stations. Reports are generated once an hour, but if conditions change significantly, a report known as a SPECI may be issued several times in an hour. A typical METAR contain data for the temperature, dew point, wind speed and direction, cloud cover and heights, visibility, barometric pressure, precipitation amount, lightning, and other information. A sample METAR data is shown below: Figure 5 Sample METAR Measurement The reading is taken from wunderground website and it shows one of the readings for the Toledo express airport METAR measurements. Temperature, events (precipitation type) and conditions (detailed precipitation type) readings are labeled. Here s listed the links to the METAR data including the second sources for redundancy and reliability: To determine the icing events at Veterans Glass Skyway Bridge, weather data from two Airports are considered. The sensor systems of the two airports are tabulated below: Table 5 Airports Information Site # Toledo Express Airport Metcalf Field Airport ID KTOL KTDZ Latitude Degree Longitude Degree Dist. from VGCS 12.2 (air distance) 10.3 miles (air distance) Observing Program LAND SURFACE COOP AB ASOS ASOS-NWS LAND SURFACE ASOS ASOS-FAA Source of the above data is: 15

29 The observing system in both the airports are of the type Automated Surface Observing Systems (ASOS). It is a joint effort of the National Weather Service (NWS), the Federal Aviation Administration (FAA), and the Department of Defense (DOD). The ASOS system serves as the nation's primary surface weather observing network and is designed to support weather forecast activities and aviation operations and, at the same time, support the needs of the meteorological, hydrological, and climatologically research communities. The basic weather elements measured by ASOS observing systems are: Sky condition: cloud height and amount (clear, scattered, broken, overcast) up to 12,000 feet Visibility (to at least 10 statute miles) Basic present weather information: type and intensity for rain, snow, and freezing rain Obstructions to vision: fog, haze Pressure: sea-level pressure, altimeter setting Ambient temperature, dew point temperature Wind: direction, speed and character (gusts, squalls) Precipitation accumulation Source of the above data is: As explained above the implemented ice monitoring system for the Veterans Bridge uses a total of six weather stations which is tabulated below: Table 6 Distances of Weather Stations from VGCS Weather Station Station Type Arial Distance from Veterans 140-IR US 23 Split RWIS 6.4 miles 141-IR SLM Split RWIS 11.2 miles VGCS RWIS 0 miles Libbey Road RWIS 10.2 miles Toledo Express Airport Airport 12.2 miles Metcalf Field Airport Airport 10.3 miles There is couple of reasons behind choosing the above six weather stations for pooling weather data for the ice monitoring system, some of them are listed below: (1) Distance from the site: The distances of the weather stations from the Veterans are shown in the table above. It shows that all the chosen stations are within 12 miles radius of the experimental site. A map showing the weather stations is attached below. 16

30 Figure 6 Map Showing Distances of Weather Stations from VGCS (2) Reliability: All the six weather stations are operated by National Weather Service (NWS), which is the most dependable weather service of the country. Section 2.3 Data Classification The weather data collected consisted of RWIS measurements and METAR data as explained above. Since the two weather stations measure a variety of weather parameters, it s important to filter out the pertinent ones that match the ice criterion need. As we know that Ice events can be classified in two main stages namely: Ice Accumulation: The criterion for ice accumulation was given in above sections. To revise: Criteria 1: -Freezing Rain: Precipitation with air temperature below 32 o F. Criteria 2: -Freezing Fog: Fog with air temperature below 32 o F. Criteria 3: -Wet Snow: Snow with air temperature above 32 o F. We can see in all three criterions that temperature is a common parameter, whose absolute value can be received by any weather station but the precipitation type can have different values. [Table 7] lists all the precipitation types values for RWIS and METAR measurements. Then we will try to classify the precipitation types that can be used for the ice accumulation determination. 17

31 Table 7 METAR and RWIS Precipitation Measurements for Ice Accumulation METAR Precipitation Types RWIS precipitation Types Used For CRITERIA 1 Used for CRITERIA 1 Mist Rain Rain Light Rain Heavy Rain Thunderstorm Light Freezing Rain Light freezing Drizzle Heavy Thunderstorms Light Thunderstorms Thunderstorms and Rain Used for CRITERIA 2 Fog Light Freezing Fog Common to CRITERIA 1 and 2 Ice Pellets Light Ice Pellets Used for CRITERIA 3 Snow Light Snow Heavy Snow Blowing Snow Ice Pellets Light Ice Pellets Light Freezing Fog Light Freezing Rain Light Freezing Drizzle Unused types Clear Haze Partly Cloudy Scattered Clouds Used for CRITERIA 3 Snow 18 Unused types Other None Unknown Overcast Mostly Cloudy It can be seen that there are 30+ precipitation types reported by METAR data and couple of them can be used to determine criteria 1, 2 and 3 as shown in [Table 7]. On the contrary, RWIS measures only Rain and Snow for criterions 1 and 3 respectively.

32 Taking these criteria and the data being collected into account, a specific set of criteria was developed for Ice Accumulation shown in [Table 8]. Table 8 Ice Accumulation Criteria Source Condition Description RWIS Freezing Rain Atmospheric Temp. <= 32 o F & Precipitation type is Rain RWIS Wet Snow Atmospheric Temp. > 32 o F & Precipitation type is Snow METAR Freezing Rain (Atmospheric Temp. <= 32 o F & Precipitation type is Rain) OR (All precipitation types listed under criteria 1 in METAR Freezing Fog METAR Wet Snow the above table) (Atmospheric Temp. <= 32 o F & Precipitation type is Fog) OR (All precipitation types listed under criteria 2 in the above table) (Atmospheric Temp. > 32 o F & Precipitation type is Snow) OR (All precipitation types listed under criteria 3 in the above table) Similar to the ice accumulation, data classification can be done for ice shedding. Ice Shedding: The criterion for ice shedding was given in above sections. To revise: Criteria 1: - Warm Air: Air Temperature above 32 o F. Criteria 2: -Solar Radiation: Clear sky during daylight. We can see that one of the criteria needs temperature as a parameter, whose absolute value can be received by any weather station but the second criteria require precipitation type giving information for the sky cover, which can have different values. Let s first go through the complete list of the precipitation types that METAR and RWIS measure and then try to classify the precipitation types that can be used for the ice shedding determination. Table 9 METAR and RWIS Precipitation Measurements for Ice Shedding METAR Precipitation Types Fog Mist Rain Light Rain Heavy Rain RWIS precipitation Types Rain 19

33 Thunderstorm Heavy Thunderstorms Light Thunderstorms Thunderstorms and Rain Ice Pellets Light Ice Pellets Light Freezing Fog Light Freezing Rain Light Freezing Drizzle Snow Light Snow Heavy Snow Blowing Snow Unknown Overcast Mostly Cloudy Used for CRITERIA 2 Clear Haze Partly Cloudy Scattered Clouds Snow Other None Based on the two ice shedding criteria previously mentioned and the available precipitation types reported, the only metric for sky cover available was from the METAR data. This metric had several values, four of which are used to classify the sky cover as clear whereas all other values are evaluated as sky cover not clear. Taking these criteria and the data being collected into account, a specific set of criteria was developed for Ice shedding shown in [Table 10]. Table 10 Ice Shedding Criteria Source Condition Description RWIS Warm Air Atmospheric Temp. >= 32 o F METAR Warm Air Atmospheric Temp. >= 32 o F METAR Clear (Sky Condition type is Clear) OR (All sky cover or visibility types listed under criteria 2 in the above table) 20

34 [Table 11] summarizes different checks need to be done according to the algorithm and checks actually being doing in the dashboard. Table 11 Final Ice Accumulation/Shedding Criteria Type of Possibility of Ice accumulation check station RWIS Temperature less than 32 F and precipitation type: rain Wet snow with temperature greater than 32 F Fog with the temperature less than 32 F Airports Temperature less than 32 F and precipitation type: rain Wet snow with temperature greater than 32 F Fog with the temperature less than 32 F Possibility of Ice shedding check Temperature greater than or equal to 32 F Clear sky Temperature greater than or equal to 32 F Clear sky / Scattered Clouds / Partly Cloudy during day time (8am to 6pm) Legends - Conditions checked in dashboard - Conditions not checked in dashboard Section 2.4 Data Collection and Storage Once the relevant weather data from RWIS and METAR measurements has been identified, it s the need to collect them in the local database. Figure 7 Data Collection from Internet 21

35 Since METAR records are updated every 1-hour, the automated program runs every 1 hour for the data collection. METAR data for the two airports are collected from the following websites: RWIS measurements are updated every 10-minute, the automated program runs every 10 minute for the data collection. RWIS data for the four weather stations are collected from the following websites: Data collection: The automated program is written in the language Python. The program is shown in Appendix B. Data storage: Data being collected is stored in MySQL database in the UCII server. The tables in the database used in data storage are listed below: (a) METAR: Store METAR data. (b) RWISatmos: Store RWIS atmospheric measurements. (c) RWISsurface: Store RWIS surface measurements. (d) RWIStraffic: Store RWIS traffic measurements. Table (a) METAR Data: The fields in this table are as follows: Unixtime Time of record (in UNIX time) Temperature Atmospheric temperature reading (in o F) Events Precipitation type/sky cover in detail Conditions Precipitation type/sky cover in detail Airport Airport KTOL or KTDZ There are few other fields recorded in this table, which are not used in the algorithm. They are: Dewpoint, Humidity, Pressure, Visibility, wind_dir, wind_speed, gust_speed, and precipitation. Table (b) RWIS Atmospheric Measurements: The fields in this table are as follows: unixtime Time of the record (in Unix time) Sysid System-id 1 for the RWIS station. For the station Sysid is 582. Rpuid System-id 2 for the station. For the station , Rpuid is

36 ApAir_T Atmospheric temperature (in o F) Pc_Type Precipitation Type (1 Rain, 2 Snow, etc.) There are other fields recorded in this table, which are not used in the algorithm. They are: RcdType, ApAir_Dewpoint, ApAir_RH, ApW_SpdAvg, ApW_SpdGust, ApW_DirAvg, ApW_DirMax, ApPrs_Barometric, Pc_Intens, Pc_Rate Pc_Accum, Vis_Distance. *Tables (c) and (d) are stored only for future use and not used in the current algorithm. 23

37 Chapter 3 Ice Determination Algorithm Once the data sources and the criteria are decided, we need to use them to determine the possibility of Ice Accumulation or Ice Shedding occurrences. The determination of ice conditions at each of the weather stations can be done and used to further evaluate the likelihood of an icing event. Section 3.1 Data Update Time It must be noted that each of the weather stations has its own data collection time, which is shown below. Table 12 Data Update Time Source RWIS METAR Update Time 10 minutes 1 hour This has a considerable significance on the time between which the algorithm is run. Since METAR data is important in both the Ice accumulation and Ice Fall determination and its update time is 1 hour, the algorithm cannot run for less than onehour time difference to avoid checking same records for consecutive algorithm run. That s why the least count between two runs in this algorithm is one hour. Section 3.2 Ice Accumulation Algorithm Sensors in any environment can occasionally misread the actual measurement so for each of the six weather station (including RWIS and METAR), we evaluate all the records for the last hour but even if only 80% of the total records from the last hour meet any (or combination) of the three ice accumulation criteria, then the station has met the icing criteria as a whole for the last hour and is given a Boolean value 1, but if this condition is not satisfied by a weather station, the respective station is provided a Boolean value 0. This is then used to find the conditions favorable to ice accumulation by multiplying the condition of each weather station (0 for not met, 1 for met) by the station weight and summing each result. If the total weight calculated, as above, is greater than a set threshold, we consider that icing conditions has met for the last hour. The pictorial representation of the algorithm for determination of Ice accumulation is shown in Figure below: 24

38 Figure 8 Ice Determination Algorithm Below are the mathematical equations representing the algorithm shown above. ( ) Here, = Station condition (0 or 1), value is 1 if 80% of the records meet ice accumulation conditions in the last hour, otherwise 0. = Station weight n = 6 stations WL = Likelihood TH = Threshold, 0.3 in this case Section 3.3 Station Individual Weights As seen in the algorithm, is the weight/vote provided to eachweather station. This plays a very crucial role in determining the ice accretion or ice shedding. [Table 13] given below list the weights provided to each weather station. Table 13 Weather Station Weights Weather Station Station Type Weight 140-IR US 23 Split RWIS

39 141-IR SLM Split RWIS 0.1 VGCS RWIS 0.3 Libbey Road RWIS 0.1 Toledo Express Airport Airport 0.3 Metcalf Field Airport Airport 0.3 Threshold weight: 0.3 These station weights are based on two factors listed below: (1) Station s geographic location relative to the VGCS Bridge Since the ice conditions at VGCS Bridge is to be determined; it is obvious to provide stations near to VGCS high weights. As seen in the [Figure 9] below, the weather stations distances from the VGCS Bridge influences weight, so the weather station on the bridge has the highest weight. Figure 9 Map Showing Weights of Weather Stations (2) Amount of useful weather information that could be retrieved As it can be seen in section 3.3, the weather data from METAR measurements offers more precipitation type parameters for ice accumulation determination, and as a result all the three ice accumulation criterions can be determined by METAR measurements. 26

40 On the contrary RWIS offers a smaller platter of precipitation parameters useful for ice determination. Due to this reason Airports are given higher weights than RWIS. [Table 13] and [Figure 9] show how the weights provided to six weather stations depend on the two factors listed above. Section 3.4 Threshold weights Once the hourly weather data are utilized to determine whether ice accretion criteria is/are met for individual weather stations, the results from each station can be used to determine ice accretion as a whole. In this algorithm, [0.3] has been taken the threshold weight. The reason why this threshold weight is considered in the analysis is multifold. (a) To provide each of the weather stations their share of contribution towards ice accretion process. To prove let s consider different scenarios listed in the table below. Table 14 Station Weight Cases Stations where ice accretion criterion are satisfied Any one in RWIS stations 140, 141 or 150, but not in RWIS station 142 neither in airports Any two in RWIS stations 140, 141 or 150, but not in RWIS station 142 neither in airports All three RWIS stations 140, 141 or 150, but not in RWIS station 142 neither in airports Any one in RWIS station 142 or airports Total weight accumulated Ice accretion as a whole 27 Proof of Logic 0.1 Not satisfied Large distance and fewer weather information from distant RWIS stations is not sufficient to determine ice accretion as a whole 0.2 Not satisfied Even if two of the distant RWIS stations meet the icing criterions, due to their larger distances from VGCS and fewer weather information does not provide enough reason to determine ice accretion as a whole 0.3 Satisfied If all the three weather stations meet icing criterion, it s advisable to go with them even if RWIS at VGCS are (-) on ice accretion 0.3 Satisfied RWIS at VGCS, or any one of the two airports is sufficient to determine the

41 ice accretion as a whole (b) To provide flexibility to the algorithm. In future if we want to disregard one of the stations, we just need to make its weight zero without having any change in the algorithm. If we want to add a new weather station, we just need to add one more element in the weight array, which will suffice our need. Section 3.5 Ice Shedding Ice shedding is the process when accumulated ice melts and falls in pieces. Currently there is no equipment installed on the VGCS Bridge to measure this degree of ice accumulation, so physical observations are required for this determination. Once it ice has accumulated to the degree of concern, the ice shedding determination is then evaluated from the data collected at each weather station. The same check for 80% of the last hour of Weather data from RWIS and METAR is used to determine the station s overall condition. Since the ice shedding check is significantly more critical and occurs at a faster rate, any indication of clear sky or sunlight has a high weight. This makes the weight of the airport s METAR sky cover measure as high as the temperature reading of the weather station on the bridge. From here, just like the ice accumulation, each weather station s condition assessment result (0 or 1) is multiplied by the station weights and each result is summed. Again if the total is greater than the threshold, ice shedding is considered possible during at this point in time. Three screenshots are provided below to explain one-hour results calculated from the above processes, taken from the database. Screenshot (a) No Ice: Figure 10 Database Screenshot with no Ice The screenshot above is taken from one of the three tables used in the design and implementation of the algorithm. This shows the last one-hour results from the weather data. The second column is the ice accumulation status for the last hour, 0 means icing conditions not satisfied and 1 means icing conditions satisfied. The third column 28

42 shows the ice shedding results for the last hour, 0 means ice shedding conditions not satisfied and 1 means ice shedding conditions satisfied. The sixth column shows the current status of icing, in the screenshot shown above, it s G = Green, means no ice present. The ninth column shows the accumulation flag, i.e. last hour result from the six weather stations, = means the icing conditions are not satisfied in any weather station. The six digits correspond to the six weather stations and there are several codes assigned to determine the condition met for ice accumulation/shedding. * A complete list for the ice accumulation and ice shedding codes used in the design are given later in this chapter. Screenshot (b) Ice accumulation met: Figure 11 Database Screenshot with Ice Accumulation The screenshot above shows the last one-hour results from the weather data. The second column is the ice accumulation status for the last hour, 1 means icing conditions are satisfied. The sixth column shows the current status of icing, in the screenshot shown above, it s Y1 = Yellow level 1; means icing conditions were met. The ninth column shows the accumulation flag, i.e. last hour result from the six weather stations, = means the icing conditions are not satisfied in the first four weather station but the last two station showed up criteria 4 condition of icing. * A complete list for the ice accumulation and ice shedding codes used in the design are given later in this chapter. Screenshot (c) Ice shedding met: Figure 12 Database Screenshot with Ice Shedding Conditions 29

43 The screenshot above shows the last one-hour results from the weather data. The third column is the ice shedding status for the last hour, 1 means ice-shedding conditions are satisfied. The sixth column shows the current status of icing, in the screenshot shown above, it s G = Green; means no ice. The tenth column shows the shedding flag, i.e. last hour result from the six weather stations, = means the ice shedding conditions are not satisfied for the 2 nd, 3 rd and 4 th weather stations. A complete look up table for the ice accumulation and ice shedding codes used in the design, shown in the last two columns, is provided below: Table 15 Ice Accumulation/Shedding Look-up Table Process Code Explanation ACCU 0 No Events ACCU 1 Snow with Temp higher than 32F ACCU 2 Fog with Temp lower than 32F ACCU 3 Fog with Temp lower than 32F Snow with Temp higher than 32F ACCU 4 Freezing Rain ACCU 5 Freezing Rain + Snow with Temp higher than 32F ACCU 6 Freezing Rain + Fog with Temp lower than 32F ACCU 7 Freezing Rain + Fog with Temp lower than 32F + Snow with Temp higher than 32F ACCU 99 Unknown FALL 0 No Events FALL 1 Clear Sky during daytime FALL 2 Temp higher than 32F FALL 3 Clear Sky during daytime + Temp higher than 32F FALL 99 Unknown 30

44 Chapter 4 Ice Persistence Algorithm The determination of possible ice conditions ( yes =1 or no = 0) occurs for each data collection cycle as discussed in the previous chapter. However, ice event conditions that lead to dangerous ice shedding; have historically been supported by persistent ice accumulation over a period of hours. To determine if ice event conditions persist and to what extent required the development of a set of states and corresponding conditions for transition between states. The resulting states were also driven by the need to have a dial or speedometer style gauge on the dashboard where each state is a position on it with the current state marked by a pointer as in a car. Transitioning between states is limited by the maximum measurement rate of the available date sources to get all pertinent measurements necessary for decision making. Specifically in this case the METAR data from the airport is updated once per hour, which limits the rate of determination and state transition to once per hour. Section 4.1 Ice States To determine the level of ice accretion or ice shedding, eight different states are introduced in a speedometer style gauge shown below. Explanation of eight states: Table 16 Dial States Explanation Nomenclature Color Significance Clear Green No ice present on stays Y1 Yellow Ice accretion possible. Icing conditions met for at least 1 hour, monitoring continuing Y2 Yellow Ice accretion likely. Icing conditions met for past 8 hours, monitoring continuing. Y3 Yellow Ice accretion very likely. Icing conditions met for past 10 hours, visual verification required. 31

45 Alert Orange Ice presence on the stays confirmed R1 Red Ice shedding possible. Ice shedding conditions met for past 1 hour, monitoring continuing R2 Red Ice shedding likely. Ice shedding conditions met for past 2 hours, monitoring continuing. R3 Red Ice shedding very likely. Ice shedding conditions met for past 3 hours, visual verification required. Section 4.2 Ice Accumulation Persistence Algorithm An algorithm is designed to monitor icing on the stays, which uses five different states during ice accumulation process. They are: Clear, Y1, Y2, Y3, and A. [Figure 13] shows the flow chart of the persistence algorithm for ice accumulation. A full flowchart is shown in [Appendix C]. Also the python program for the algorithm design is shown in [Appendix D]. Figure 13 Ice Accumulation Flowchart 32

46 The pictorial representation of each state transition in the ice accumulation process is shown below. Section Transitions from state lear At start, the state is at C, i.e. no ice. From C, the states may go either to A (if ODOT reports presence of ice) or Y1 (if the icing criteria are satisfied for the last hour). These two actions are done priority wise, the priority of A being higher than that of Y1. Y1 signifies icing is possible but the time for which ice accumulation criteria are met is very small. The state transition diagram is shown in the Figure [14]. Figure 14 State Transitions possible from Clear Section Transitions from state Yellow 1 From Y1, the states may go either to A (if ODOT reports presence of ice) or Y2 (if the icing criteria are satisfied for at least 6 of last 8 hours) or C (if none of the above two conditions are satisfied and also icing criteria did not meet during the last hour). Y2 signifies icing is likely to happen. 33

47 Figure 15 State Transitions possible from Yellow Level 1 Section Transitions from state Yellow 2 From Y2, the states may go either to A (if ODOT reports presence of ice) or Y3 (if icing criteria are satisfied for at least 8 of last 10 hours) or Y1 (if none of the above two conditions are satisfied and also icing criteria did not satisfy for Y2 state). Y3 means icing is very likely to happen and now a visual check must be done to confirm the presence of ice. 34

48 Figure 16 State Transitions possible from Yellow Level 2 Once Y3 state has been reached, the algorithm will take the following actions: No further transition until ODOT response is received. Send mail and text message to icing officials. Will keep sending alerts (mails/text) to officials until a response is received. Officials are needed to sign onto the dashboard and provide response on actual icing condition at the bridge. A sample mail/text message is pasted below, which is used to notifying the icing officials about the ice accretion on the stays. Figure 17 Sample Ice Accumulation Message Alert 35

49 Also at state Y3, dashboard will display blinking Yellow signal to catch the attention of users. Figure 18 Dashboard with Ice Accumulation Alert After the Y3 state is reached, the algorithm will wait for a response for ice presence. This led the algorithm to move to the next stage of the icing event process. Section 4.3 Ice Presence Confirmation Different states used in the Ice presence process are C, Y1, and A. Figure below shows the flow chart of the algorithm explained above. A full flowchart is shown in [Appendix C]. Figure 19 Ice Presence Flowchart At Y3, further states are determined using the received response. If the response says there is ice present on the stays, the state will move to A, which means ice is 36

50 confirmed. If the response says there is no ice present on the stays, the state will move to Y1. Figure 20 State Transitions possible from Yellow Level 3 There are two reasons why upon receiving a negative response, the state will move to Y1 and not C. Ice may present on the stays but it might not be enough to cause ice fall hazard. In that case, the response received may be negative but a smarter move is to assign Y1 as the new state in action to the response. In case of a mistake in submitting the response, assigning Y1 will take lesser time than C to go to Y3 state, which is desirable. From here, we move to the last stage of the ice events, i.e. state transition algorithm for the Ice Fall process. Section 4.4 Ice Shedding Persistence Algorithm Different states used in the Ice Fall process are R1, R2, R3, A, and C. Figure below shows the flow chart of the algorithm explained above. A full flowchart is shown in [Appendix C]. 37

51 Figure 21 Ice Shedding Flowchart The pictorial representation of each state transition in the ice shedding process is shown below. Section Transitions from state Alert From A, the states may go either to C (if ODOT reports that there is no ice on the stays) or R1 (if the ice fall criteria are satisfied for the last hour). These two actions are done priority wise, the priority of C being higher than that of R1. R1 means ice fall is possible. Since the ice shedding process is faster and more critical than the ice accumulation process, an alerting system is implemented at every state change. Therefore, if the ice fall criteria are met during the last hour, the state will be changed from A to R1, and also s and text messages are sent to the ODOT officials notifying about the weather and the states. 38

52 Figure 22 State Transitions possible from Alert Section Transitions from state Red 1 From R1, the states may go either to C (if ODOT reports no presence of ice) or R2 (ice shedding criteria are satisfied for the last hour) or A (if none of the above two conditions are satisfied). R2 means ice fall is likely to happen. Also mail/text message is sent to icing officials notifying them about the change. Figure 23 State Transitions possible from Red Level 1 39

53 Section Transitions from state Red 2 From R2, the states may go either to C (if ODOT reports no presence of ice) or R3 (ice shedding criteria are satisfied for the last hour) or R1 (none of the above two conditions are satisfied). R3 means ice shedding is very likely to happen. Also mail/text message is sent to icing officials notifying them about the change. Figure 24 State Transitions possible from Red Level 2 Once R3 state has been reached, the algorithm will take the following actions: No further transition until ODOT response is received. Send mail and text message to icing officials. Will keep sending alerts (mails/text) to officials until a response is received. Officials are needed to sign onto the dashboard and provide response on actual icing condition at the bridge. A sample mail/text message is pasted below, which is used to notifying the icing officials about the ice accretion on the stays. 40

54 Figure 25 Sample Ice Shedding Message Alert Also at state R3, dashboard will display blinking Yellow signal to catch the attention of users. Figure 26 Dashboard with Ice Shedding Alert After the R3 state is reached, the algorithm will wait for a response. If the response received is no ice present on the stays, the state will change to C and if the response received is ice present on the stays, the state will change to A. 41

55 Figure 27 State Transitions possible from Red Level 3 This completes the complete cycle of all the states used in the algorithm. The process flow for ice accumulation, ice presence and ice fall combined together can be seen in the process state diagram shown in Figure below. & Text Msg 42

56 Chapter 5 Monitor Website The algorithm design and working is explained in above chapters. This chapter explains how the algorithm is implemented for front end user. The tool through which the algorithm results are made accessible to users is Dashboard. The implementation of the algorithm and the dashboard was divided into three parts in order to integrate data from and manage connectivity between separate systems. The first part was to automate the collection and data warehousing of weather measurements of the RWIS stations and airport METAR data. These processes needed to be stable and robust so we had a reliable set of weather measurements from all the necessary sources. This system was not required for development of the next part, but having the necessary data stored locally inherently increases performance, reliability and robustness. From the developed algorithm and all the necessary conditions, software was developed to carry out the automated evaluation process and scheduled to run once per hour as previously mentioned. Finally a set of web pages were developed which is called the dashboard. As defined above the data collection and the results of the algorithm is stored in the database running in UCII server. This information is made available to the user through dashboard. Below listed are few of the dashboard s important features: Provide user-friendly speedo style display of current icing status Collect and maintain database of weather conditions Automatically process incoming weather data and update icing status Generate alerts during icing events User-friendly display and navigation of weather data User-friendly display and navigation of icing event history Section 5.1Dashboard Main Panel The main panel of the dashboard contains the icing speedometer showing all the states including {G, Y1, Y2, Y3, O, R1, R2, and R3}. 43

57 Clear No Ice present Y sice accumulation likely (Three levels are introduced for different likelihoods) AlertIce presence confirmed R sice Shedding likely (Three levels are introduced for different likelihoods) The main panel also includes the reporting function for ODOT, which can be used to report icing status after visual inspection. Ice conditions of the last 48 hours are shown on the ticker on the main panel. The main panel also includes the links to all other pages of the dashboard. Below is a screen shot of the web site showing the dashboard. Figure 28 Dashboard Main Panel As shown above, the dashboard main panel provides a user-friendly dial that shows the icing status. The dashboard main panel is changed when icing reaches the level when an alert is necessary. For e.g. when the state is Y3 and R3, a blinker is shown with the dial as shown below. For Y3: 44

58 Figure 29 Dashboard for Ice Accumulation Alert For R3: Figure 30 Dashboard for Ice Shedding Alert 45

59 Section 5.2 Weather Map Dashboard includes an interactive map of the weather stations where current sensor readings are shown and where historical readings can be plotted on a timeline. There are also cameras installed on the bridge that can be seen from this interactive map. For this purpose Google Maps API is used. Google Maps is a free web mapping service provided by Google, which offers street level maps for pedestrians, cars, and public transportation. It has an extendable API (Application Programming Interface) that can be used to develop custom Google Maps based applications. The Google map on the dashboard is used as an interface to provide the details about the various sites being monitored for determining the icing conditions at VGCS. It also contains the location of various sites, their past/current weather conditions along with a few reference links. Basically the features included in the weather map are: Locating weather stations Showing the live camera installed on the site Weather readings for all stations Site information of all the stations This section is a walkthrough of the working of the Google map on the dashboard. Once the accordion labeled Map (Weather Data by Location) on the dashboard is clicked, a Google map will open which has several markers on it. Below is a screenshot of the weather map used in the project. Figure 31 Map Showing Weather Stations 46

60 There are two green markers written A on it. These are the two Airports namely KTOL (Toledo Express Airport) and KTDZ (Metcalf Field Airport) whose weather data are being monitored by the dashboard. There are four red markers written R on it. These are the four RWIS stations namely Site 140-IR US 23 Split, Site 141-IR SLM Split, Site Veterans Glass City Skyway, and Site Libbey Rd whose weather data are being monitored by the dashboard. There are three yellow markers written L on it. These are the local weather stations near to the VGCS Bridge. They are: East Toledo, Oregon. These are not considered in the algorithm. The pink marker is the link to the live cameras installed on VGCS. RWIS Stations Red markers on the map represent RWIS stations. On clicking, an information box will be popped up that contains the weather station information, station id, current weather conditions and last 48 hours graphs to atmospheric and surface weather readings. Attached is the screenshot for the same for Site

61 Figure 32 RWIS Measurement from Dashboard Clicking the Buckeye Traffic Readings link on the information window opens up a new window having the weather reading fromhttp:// 48

62 Figure 33 RWIS Measurement from Buckeye Traffic Clicking all other links on the information window opens up a new window having the last 48 hours weather readings. Below is an example for last 48 hours plot for the parameter Air Temperature. The Export button will export the Air Temperature data for the last 48 hours into the excel sheet. Figure 34 Air Temperature Graph There are 185 RWIS stations in Ohio, out of which four stations were chosen to determine the Icing conditions of VGCS Bridge as they were the nearest to the bridge. Site 140-IR US 23 Split This weather station is 11.3 miles from the Veteran Skyway Bridge. 49

63 Site 141-IR SLM Split This weather station is 5.2 miles from the Veteran Skyway Bridge. Site Veterans Glass City Skyway This weather station is 0 miles from the Veteran Skyway Bridge. Site Libbey Rd This weather station is 10.1 miles from the Veteran Skyway Bridge. Airports Green markers on the map represent nearby airports. On clicking them, an information box will be popped up that contains the current METAR report, plots of last 24 hours METAR reports, last 4 hours PIREP readings, plots of PIREP Icing information since Nov , and WIKI references to METAR & PIREP. The two nearest airports are chosen to determine the Icing conditions of VGCS Bridge: Toledo Express Airport This airport is 9.1 miles from the Veteran Skyway Bridge Metcalf Field Airport This airport is 6.2 miles from the Veteran Skyway Bridge Attached is the screenshot for the same for the airport KTOL. Figure 35 Dashboard METAR Measurement 50

64 Clicking the METAR link on the information window opens up a new window having the current METAR reading from: Clicking the PIREP link on the information window opens up a new window having the latest PIREP readings from: Local Weather Stations Yellow markers on the map represent nearby weather stations. On clicking them, an information box will be popped up that contains the weather information at the local station. Attached below is the screenshot for the local weather station: East Toledo. 51

65 Figure 36 Dashboard Local Station Measurement Clicking the Rapid Fire Panel link opens up a new window having the weather information from: Clicking the More information link opens up a new window having the weather information from: 52

66 Live Camera Pink markers on the map represent link to the four live cameras installed at the Veteran Skyway Bridge. Clicking on any will get the live view with the images getting refreshed every five seconds. Attached is the screenshot for the same. 53

67 Figure 37 Installed Cameras at VGCS The enlarged view of one of the cameras is shown below. 54

68 Section 5.3 History The History section of the Dashboard provides many additional features that are useful for the users for analyzing and reporting purposes. This section provides a detailed explanation of the same. Figure below shows the main panel of the history section of the dashboard. Figure 38 Dashboard History Panel The main functionalities of this section of the dashboard include: (a) List of events (states other than green) between dates selected by the user: 55

69 Figure 39 Dashboard History Panel Showing Ice Accumulation User can select two dates between which the states need to be displayed. As shown in the Figure above, two dates are selected i.e. 03/11/2011 and 05/28/2011, and a click on update button will display all the events occurred between these dates. Also clicking on any event will provide the cause of the event. (b) List of responses received by dashboard: All the responses received by the dashboard over the time are displayed in this section. This also includes the name of the person who posted the comment. Given below is a screenshot. 56

70 Figure 40 Dashboard History Panel Showing Responses from Icing Team (c) Summary: This section provides statistics of the weather parameters for all the six weather stations. User can select two dates between which the statistics needs to be produced and which is then showed up upon clicking the update button. Figure 41 Dashboard Summary Section 57

71 Section 5.4 Implementation Tools To implement and design dashboard functionalities, several languages/tools/software are used. The list for the same is provided below: Table 17 Tools Used to Design Dashboard Category Tool Used Purpose Programming Language Python Data Pulling Main Algorithm Scripting Language PHP Website design Database MySQL Data Storage Graphing Tools JPGraph Matplotlib Weather charts Test results Map Google Maps API Weather Map Visuals Microsoft Visio, JavaScript Flowchart 58

72 Chapter 6 Performance Testing The dashboard became fully functional in the month of January 2011 (Jan , 18:05:05) and since then it has been running on the University of Cincinnati Infrastructure Institute server, monitoring the weather conditions at Veterans Glass City Skyway. To test the performance of the dashboard, rigorous testing methods were implemented, which is the main agenda in this chapter. The various tests done on the dashboard can be divided into two main sections: 1. System reliability test a. Weather station dependability 2. Ground truth a. Study of Feb 2011 ice events b. Study of past ice events Section 6.1 System Reliability Test Checking the weather data obtained from various RWIS stations does the reliability test. For this purpose, all RWIS stations within 10 miles radius of the VGCS site is considered for the study. This could also be used as a cross check for the choice of weather stations and provides us a better insight on whether the stations needs to be changed/added for future ice analysis. A complete list of RWIS and Airports stations used for the study is given in the table below: Table 18 Stations Chosen for Performance Testing Station Name Type SR SLM 7.5 Lucas RWIS 139-I-80 & I-475 Lucas RWIS 140-IR US 23 Split RWIS 141-IR SLM Split RWIS VGCS RWIS I-475 Wood RWIS I-80 Wood County Libbey Road Toledo Express (KTOL) Metcalf Field (KTDZ) RWIS RWIS Airport Airport 59

73 Section Weather stations dependability Weather readings are checked for the above-mentioned stations during the period when ice events occurred in the last 3 years. A table is provided for the date/time for which weather readings are studied. Table 19 Dates for which Past Ice Events Tested Icing Events during last 3 years Dates for which the data is studied 12 Dec Dec :00PM - 23 Dec :00PM 28 Mar Mar :00PM 8 Apr :00PM 17 Dec Dec :00PM 28 Dec :00PM 3 Mar Dec :00PM 14 Jan :00PM The following parameters are checked for stations fidelity: Null readings: Number of readings which doesn t have any value Bad readings: Checked +/- 3 standard deviation for outliers These checks are done for all the four icing events and the results are tabulated below: Table 20 Weather Statistics for Dec Ice Event Dec Site 137 Site 139 Site 140 Site 141 Site 142 Site 146 Site 147 Site 150 Airport Airport KTOL KTDZ Total number of records Temperature Max Temperature (F) Min Temperature (F) Null count Precipitation Occurrences of 'None' Occurrences of 'Yes'

74 Occurrences of 'Rain' Occurrences of 'Snow' Occurrences of 'Fog' Null count RWIS sites 140, 141, 142, 150 are used in the algorithm and it can be seen from the table that the precipitation types reading from sites 141 and 150 do not read anything except Yes and None. Whereas the RWIS sites 146 shows a good dependability in terms of weather readings. A similar analysis is done for the icing event of Mar 28, 2008 as it was done for Dec 12, It can also be seen that there were couple of outliers for the sites 140 and 146, the number being 2 and 7 respectively. Table 21 Weather Statistics for Mar Ice Event Mar Site 137 Site 139 Site 140 Site 141 Site 142 Site 146 Site 147 Site 150 Airport Airport KTOL KTDZ Total number of records Temperature Max Temperature (F) Min Temperature (F) 72 (twice more than 90) 72 (7 times more than 100) Null count Precipitation Occurrences of 'None' Occurrences of 'Yes' Occurrences of 'Rain'

75 Occurrences of 'Snow' Occurrences of 'Fog' Null count Analysis done for the icing event of Dec 17, 2008 showed similar results and it has been tabulated below. Table 22 Weather Statistics for Dec Ice Event Dec Site 137 Site 139 Site 140 Site 141 Site 142 Site 146 Site 147 Site 150 Airport KTOL Airport KTDZ Total number of records Temperature Max Temperature (F) (11 times 66 more than 90) Min Temperature (F) Null count Precipitation Occurrences of 'None' Occurrences of 'Yes' Occurrences of 'Rain'

76 Occurrences of 'Snow' Occurrences of 'Fog' Null count/unidentified Analysis done for the icing event of Jan 3, 2009 showed similar results and it has been tabulated below. Again it can be seen that the number of outliers during the period for site 147 is 13 and that of for site 150 is 3. Table 23 Weather Statistics for Jan Ice Event Jan Site 137 Site 139 Site 140 Site 141 Site Site Site 147 Site 150 Airport KTOL Airport KTDZ Total number of records Temperature Max Temperature (F) (3 (13 time time s s more more than than 155) 80) Min Temperature (F) Null count Precipitation Occurrences of 'None' Occurrences of 'Yes' Occurrences of 'Rain' Occurrences of 'Snow'

77 Occurrences of 'Fog' Null count All this analysis leads to the conclusion: RWIS sites 137, 139, 141, 147, 150 have just Yes or None as types of precipitation, so they cannot be used for ice determination RWIS sites 141, 146, 150 categorizes all the precipitation types so they can be used for ice determination Null or bad readings for all the RWIS sites are 82 out of 182,222 records in total i.e %, which is a very small number and thus sites 141, 146 and 150 can be used in the ice determination algorithm without having too many outliers Section 6.2 Ground Truth After the tests performed on data reliability, the next phase is the testing of the actual algorithm. Since the inception of dashboard on Jan , there have been several occurrences of icing precipitation on the VGCS site. The study and analysis of all those and its comparison with the ground truth is done in the next section. Section Study of Feb 2011 ice events Ice Accretion detected by the Dashboard Table below provides a statistics on the algorithm results within the period between Jan 15 to June Table 24 Summary of Events when Ice Accumulation occurred in 2011 Ice Events Dates Maximum Level reached Reason Comments Feb Y1 Freezing Rain No action taken since ice accretion was minimal Feb Y1 Freezing Rain No action taken since ice accretion was minimal Feb Y1 Freezing Rain No action taken since ice accretion was minimal 64

78 Feb G-Y3-O-R3 Multiple Described in detail Feb Y3 Freezing Rain Due to bad precipitation data from VGCS RWIS station, the dial was stuck at Y3. So, it was manually reset Mar Y2 Wet Snow Ice accretion occurred but it was not enough to cause Ice fall hazard. No action taken Mar Y2 Wet Snow Ice accretion occurred but it was not enough to cause Ice fall hazard. No action taken Mar Y1 Wet Snow No action taken since ice accretion was minimal Apr Y1 Wet Snow No action taken since ice accretion was minimal As it can be seen that in the period from Jan 15 to June , there have been several occurrences when the ice accretion was detected by the dashboard out of which only one event had sufficient ice to pose ice shedding hazard. The event from Feb is described in detail below: Feb 20: The ice accretion process started at 15:00 because the RWIS station at the VGCS met freezing rain criteria and the dial in the dashboard became Y1. After then, the criteria for freezing rain constantly met for 8 hours in one or more stations including the two airports and the RWIS at VGCS. This caused the dashboard to display Y3 at 21:00. The ice accumulation was confirmed by the visual inspection done by UT at 22:00, thus the dial in the dashboard moved to Orange, i.e. alert. Now the algorithm is waiting for the ice shedding conditions to come. Local TV forecast: The forecast for that night was freezing rain, then a drop in temperature. On a local television station s weather website (Storm Tracker 11, 2011), the forecasters predicted snow changing to freezing rain. The update for the overnight was scattered rain or freezing rain with additional ice accumulation. With low temperatures and precipitation, the conditions were conducive to ice accumulation. 65

79 Figure 42 Weather Summary on Feb The figure shows the close up view of ice accretion on Feb 20,

80 Figure 43 Screenshot Showing Ice Accretion on VGCS Feb 21: For the entire day, the weather conditions were such that none of the ice fall criteria met, and the dashboard remained at orange, i.e. Alert. As reported by the research team, University of Toledo: The ice remained on the stays throughout the day Monday. The wintery mix and snow fell, accumulating on the stays. A layer of snow was between the ice already on the stay and the new accumulation in some areas. ODOT placed barrels out at the inside shoulder. This allows the barrels to be quickly reconfigured to close lanes. The research team stopped a total of four times, three times on main span and once on the back span. Main span near stay 6, inspected the east side from the median, they chipped a hole in the ice to measure the thickness. The ice on the east side was roughly 1 2 inch thick with closely spaced icicles on the bottom. Viewed near stay 14 from northbound side, were specifically looking for a variation along the length of the bridge, conditions appeared the same as near stay 6. Near stay 10 from inside the truck on from the south bound side, below the damper collar the ice appeared very thin or there may have been bare spots, above the damper collar ice appeared thicker with pronounced frozen rivulets. Stopped one time on the back span, viewed near stay 8 from the south bound side, conditions roughly the same as observed when viewing near stay 10 form the south bound side. The wind was from the east as it has been throughout the storm. Generally, on the east side, the ice appeared to be thicker than on the west side. The ODOT supervisor 67

81 felt the coating on the east side was thicker than he had seen before. On the west side, there were some spots that appeared to have a very thin coat below the damper collar. Above the damper collar, ice was thicker and the frozen rivulets appeared more pronounced than on the east side. Both the east and the west sides, the ice above the collar appeared uniform as high as it could be seen. However, it is impossible to discern anything more than gross icing further up than about mid-height. Figure 44 Weather Summary on Feb Feb 22: For the entire day, the weather conditions were such that none of the ice fall criteria met, and the dashboard remained at orange, i.e. Alert. 68

82 Figure 45 Weather Summary on Feb The figure shows the ice accumulation on the stays on Feb 22, Figure 46 Ice Accumulation on Stays on Feb

83 Feb 23: For the entire day, the weather conditions were such that none of the ice fall criteria met, and the dashboard remained at orange, i.e. Alert. But there is one thing that caught the attention. The research team from The University of Toledo measured the temperature between stay and ice with a K contact thermocouple. One noticing observation was that temperature between the ice and stays (interstice) and atmospheric temperature were different. It was observed that interstice temperature was greater than air temperature. At 1:00PM interstice temperature was recorded32 F whereas air temperature was27 F.This may be due to greenhouse effect occurring in between ice and stays. Below provided are the results from The University of Toledo. Table 25 Interstice Temperature on Feb Time Interstice Air Temperature Temperature Stay Note 8:15 AM 24 F 20B No visible liquid water 8:50 AM 24 F N/A No visible liquid water 9:20 AM 24 F 20B 9:30 AM 28 F 15B 9:45 AM 26 F 11B Liquid water under ice 12:15 PM 30 F 19B Liquid water under ice. Large pieces of ice broke free easily 1:00 PM 31 F 20B 1:00 PM 32 F 19B 1:15 PM 35 F 18B Liquid water under ice. Sheets break free easily 2:55 PM 32 F 20B Liquid water that had bled from under the ice refroze on the stay 3:57 PM 32 F 20B 5:23 PM 31 F 19B Liquid water under ice. Sheets break free easily 70

84 Figure 47 Weather Summary on Feb Feb 24: At 7:00 am, the Airport at Metcalf field reported temperature above 32F, thus for the first time the dashboard showed R1 to indicate possible ice shedding. It continued for two more hours and the dashboard moved to R3 at 9:00 am. The entire contribution goes to Metcalf Field Airport. Dashboard then generated alert signals and requested visual inspection. At 16:00, dashboard received a response from UT that little ice is still remaining, so the dashboard moved to orange. The comment for the same, taken from the dashboard, is pasted below: As per discussion with ODOT and Dr. Nims, most of the ice has shed due to rise in temperature but a little ice is still remaining on the stays. Again at 18:00, due to high temperature at each station, dashboard moved to R1 and in two more hours, it moved to R3. As programmed, it sent alert signals for visual inspection at 20:00. Now dashboard is at R3 and waiting for a reply. 71

85 Figure 48 Weather Summary on Feb Feb 25: At 00:59 am, a response was received that temperature is again dropping and Ice Accumulation is again possible. Comment from the dashboard is pasted below. Temps dropping with ice persisting. Second round of possible icing followed by shedding With this, dashboard moved to orange again. Finally at 7:59 am, a response received that ice has shed and the possibility of second round of icing has diluted. The response received at 7:59 am is pasted below: No ice apparent. Additional accumulation expected last evening did not materialize. ODOT has reopened all lanes. 72

86 The dashboard was reset to Green at 8:59 am. Figure 49 Screenshot Showing Ice Falling from VGCS on Feb The figure shows that 80-90% of ice has fallen. 73

87 Figure 50 Weather Summary on Feb Conclusion: The ice event on February 20th reached Y3 due to persistent freezing rain, which was confirmed by visual inspection, and a response was submitted resulting in a state transition to alert. The ice remained on the stays until February 24th and was monitored by officials for possible ice shedding. The ice, eventually, from this event did fall into traffic lanes and in pieces large enough that could have damaged vehicles. However, well before this point ODOT officials closed the bridge until the ice had sublimated and it was safe for traffic to return. The dashboard reset to Green (all clear) after everything became normal. Thus the performance of the overall system during the winter months of 2011 proved to be successful especially in determining ice accumulation and persistence. The overall algorithm can be simulated on a graph as shown below. The graph shows the actual weather data (temperature and precipitation types) of the six weather stations and the algorithm response on those data. The simulation is done for the time period Feb 21 to Mar Parameters shown in the graph: 1 Temperature versus time (for six weather stations) 74

88 2 Average temperature vs. time (aggregated for six weather stations) 3Ice accretion criteria satisfied 4 Ice shedding criteria satisfied Figure 51 Feb Algorithm Performance Graph The above graph is a two-axis graph, in which x-axis is the time in dates (Feb 21 to Mar ), left y-axis is the temperature in F, and right y-axis is the % of snow/rain per hour. The graph shows the temperature versus time graph for all the weather stations as well as the average temperature versus time. The rain occurrences (in % of record per hour) are shown in blue crosses whereas snow occurrence (in % of record per hour) is shown by red crosses. The yellow triangles are the hours when ice accumulation criteria are met (as per the algorithm described in above chapters). The red squares are the hours when ice-shedding criteria are met (as per the algorithm described in above chapters). The python program to generate this graph is provided in [Appendix E]. Weather Station Performance Evaluation The analysis of algorithm for the recent icing events has been done in the above section. This section explains the contribution of each icing criteria towards those icing events. Also it explains the contribution of all the six weather stations towards the icing events. This study is done as it provides a deep understanding about the role of each icing criteria and weather station so that in future the focus could be laid on the most important factors affecting the icing and de-icing. The table below summarizes the 75

89 overall weather conditions at all the six weather stations during the period between Jan to May (date of creation of this document). This report is generated from the History tab of the dashboard. Weather Parameters Weather Report for VGCS Bridge from Jan to June Temperature ( F) KTOL (Toledo Express Airport) METAR KTDZ (Metcalf Field Airport) METAR RWIS Site US-23 Split - Lucas Co.) RWIS Site I-475 Split - SLM 4.9 Lucas) RWIS Site (142-I- VGCS) Average RWIS Site (150-I- Libbey Road) Minimum Maximum Precipitation Clear Rain XXX 2309 XXX Snow XXX 1579 XXX Others The above table shows temperature and precipitation readings for the six weather stations. It can be seen that there is no Rain or Snow readings for RWIS site and site Henceforth, these two stations cannot contribute to the necessary criteria required for Ice accretion. To compliment this, Figures are drawn to show that the main contribution towards the ice accretion process is limited to few weather criterions and only few weather stations. During the entire ice event s 68 hours, each of the precipitation type s contribution and each weather station s contribution was counted and shown in the Figures. The first figure shows the three icing criterion on the x-axis and number of hours meeting icing criteria on the y-axis. It can be seen that freezing rain is the leading criteria out of the three. The second figure shows the six weather stations on the x-axis and the number of hours meeting icing criterion on the 76

90 Number of hours Number of hours y-axis. It can be seen that out of the six weather stations, the RWIS station at the VGCS Bridge and the two airports are the leading contributor. Thus it can be concluded that the RWIS station on the VGCS Bridge and the Freezing Rain had the most influence on the ice accumulation decision process. Figure 52 Contribution of the icing Criteria and Weather Stations Towards Feb Icing Event wet snow freezing rain freezing fog Icing Conditions VGCS Station Weather Stations Figure 52a Contribution by the three criteria Figure 52b Contribution by the six stations Section Comments Dashboard has done pretty well in detecting ice accumulation each time, but the analysis done on the algorithm results and University of Toledo s response from the visual observations concludes that additional information would be needed to increase the ice fall detection speed and accuracy. This would also increase performance of ice accumulation detection and improve the overall system s accuracy and reliability. For example, it was noted that after taking temperature readings on the stay and in the gap between the ice and the stay, that the atmospheric temperature readings could be below 32F but the stay and air gap could be above 32F. This is most likely due to solar radiation, which could heat up the stay well before the air temperature increases (for example in the early morning or first sight of clear sky coverage). Following up on this assumption, data was collected from a weather station approximately 3 miles away that measures and records solar radiation, which was plotted and compared with the ice fall visual observation timeline. This plot shows that the solar radiation at this weather station started to rise close to 7:00am, which is approximately the same time the first observation was made that ice was beginning to fall off of stays, resulted from the dashboard algorithm. 77

91 7:04 AM 8:16 AM 9:28 AM 10:40 AM 11:52 AM Watt / m^2 Figure 53 Solar Radiation Variation on Feb Solar Radiation 2/24/2011 Ice Temp Gauge Stay Sheath Figure 53a: Solar Radiation Measurements (KOHOREGO1 Weather Station in Oregon, OH) Figure 53b: Measuring sheath temp and ice gap. Thus, it is proposed that for better ice shedding detection, a thermocouple sensor should be installed at VGCS so that it can measure the exact temperature beneath ice. Also, installation of a sensor measuring solar radiation could result in better detection of ice shedding events. In addition, since Ice shedding events are faster and crucial for the entire project, measures could be taken to decrease the time between which algorithms is run, at least while detecting ice shedding. As it was seen that when the first time sky became clear (with ice on stays), ice started melting and in less than 2 hours, most of the ice got melted. Section Study of Past Ice Events VGCS Bridge was built in the year 2007 and since then four icing events have occurred on the VGCS. Research team member, Kathleen Jones has prepared a report that describes the icing events and the weather that preceded them (Jones [2]). The events and the basic features are listed in tables below. Ice accumulation occurred in rain, snow, and/or freezing rain accompanied by fog. If the historical weather trends reported from 1955 to 1999 continued, there were likely 6-10 freezing rainstorms in the three years the bridge has been open. Freezing rain occurred for three of the four icing events on the VGCS. Four events in three years are likely representative of the future and years with no icing events will be unusual, as stated in Jones [2]. 78