DOCSLIB.ORG

A Thesis Entitled a Sensor for Measuring Liquid Water Content of Wet Snow on Superstructures by Mehdi Sarayloo Submitted To

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Thesis Entitled a Sensor for Measuring Liquid Water Content of Wet Snow on Superstructures by Mehdi Sarayloo Submitted To A Thesis entitled A Sensor for Measuring Liquid Water Content of Wet Snow on Superstructures by Mehdi Sarayloo Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Master of Science Degree in Civil Engineering ___________________________________________ Dr. Douglas K. Nims, Committee Chair ___________________________________________ Dr. Hossein Sojoudi, Committee Member ___________________________________________ Dr. Ahmed Abdelaal, Committee Member ___________________________________________ Dr. Amanda C. Bryant-Friedrich, Dean College of Graduate Studies The University of Toledo December 2019 Copyright 2019 Mehdi Sarayloo This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author. An Abstract of A Sensor for Measuring Liquid Water Content of Wet Snow on Superstructures by Mehdi Sarayloo Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Master of Science Degree in Civil Engineering The University of Toledo December 2019 Abstract One of the phenomena that has a considerable impact on many aspects of today’s world is atmospheric icing. In many cold areas around the world, such as northern Europe, Japan, Canada, and the United States, atmospheric icing causes difficulties in operating of structures or severe dam ages to them. A variety of structures, such as power lines, bridges, telecommunication towers, wind turbines, and naval structures are vulnerable to atmospheric icing problems. Previous studies done by the University of Toledo icing research team on cable- stayed bridges suffering from ice/snow shedding showed that none of the tested active or passive methods are as reliable and economic as an administrative approach. Apart from the method chosen to mitigate this problem, some parameters of frozen precipitation, especially wet snow, are needed to efficiently run the system. The liquid water content and density are the main parameters of wet snow which significantly affect the mechanical bonding between snow and a surface. One of the results of previous studies by the UT icing research team was the UT icing sensor to detect the ice accumulation on bridge cables. The UT icing sensor was a iii thin-film resistance-based sensor which also utilizes temperature to work properly. Due to the satisfactory utilization of this sensor on the VGCS bridge, and its capability of developing new functionalities, several experiments were designed and conducted to find a correlation between the liquid water content and the electrical resistance of wet snow. Based on this correlation, a new operation was developed for the UT icing sensor to measure the real-time liquid water content of wet snow. Additionally, the function previously developed for the UT icing sensor was tested and improved by implementing an algorithm to differentiate between frozen water (ice or snow), liquid water, and air. Utilizing these two capabilities together enables the sensor to be used for indicating ice or snow accumulation and shedding on a structure. In terms of design, the sensor is light, flexible, small, and inexpensive, which makes it easy to be implemented on structures such as cable-stayed bridges. Snow density is another parameter which significantly impacts the snow accumulation and shedding. In another part of this thesis, correlating the snow density to the normal velocity of snowflakes was attempted. iv Acknowledgements I would first like to thank my thesis advisor, Professor Douglas Nims of the Civil & Environmental Engineering Department at the University of Toledo, whose expertise was invaluable in the formulating of the research topic. As my teacher and mentor, he has taught me more than I could ever give him credit for here. I would like to acknowledge my co-advisor, Dr. Hossein Sojoudi of the Mechanical & Industrial Manufacturing Engineering Department at the University of Toledo, for his great support and for all the opportunities I was given to conduct my research. He taught me a great deal about scientific research and academic projects. I would especially like to acknowledge Dr. Ahmed Abdelaal of the Department of the Engineering Technology and Industrial Distribution at Texas A & M University as the committee member of this thesis, and I am gratefully indebted to him for his valuable professional guidance and comments on this thesis. I am also grateful to all of those with whom I have had the pleasure to work during this and other related projects. Finally, I must express my profound gratitude to my parents and to my lovely wife, Sara, for providing me with unfailing support and continuous encouragement throughout my years of study and through the process of researching and writing this thesis. This accomplishment would not have been possible without them. Thank you. v Table of Contents Table of Contents Abstract .............................................................................................................................. iii Acknowledgements ............................................................................................................. v Table of Contents ............................................................................................................... vi List of Tables ...................................................................................................................... x List of Figures .................................................................................................................... xi List of Abbreviations ....................................................................................................... xiv List of Symbols ................................................................................................................. xv 1 Introduction ...................................................................................................................... 1 1.1 Background .......................................................................................................... 1 1.2 Problem Statement ............................................................................................... 4 1.3 Objectives ............................................................................................................ 5 1.4 Thesis Outline ...................................................................................................... 5 2 Literature Review............................................................................................................. 7 2.1 Introduction to the Icing Problem ........................................................................ 7 2.1.1 Precipitation Icing .................................................................................... 8 2.1.2 In-cloud Icing ........................................................................................... 9 vi 2.1.3 Hoar Frost .............................................................................................. 10 2.2 History of Precipitation Icing Caused Problems on Bridges ............................. 11 2.2.1 Alex Fraser Bridge (Vancouver, BC, Canada) ...................................... 11 2.2.2 Arthur Ravenel Jr. Bridge (Charleston, SC, USA) ................................ 11 2.2.3 Leonard P. Zakim Bunker Hill Memorial Bridge (Boston, MA, USA) 12 2.2.4 Mackinac Bridge (Mackinaw City, MI, USA)....................................... 12 2.2.5 Penobscot Narrows Bridge (Stockton Springs, ME, USA) ................... 12 2.2.6 Port Mann Bridge (Vancouver, BC, Canada) ........................................ 13 2.2.7 Severn Bridge (South West England / South East Wales) ..................... 13 2.2.8 Sidney Lanier Bridge (Brunswick, GA, USA) ...................................... 14 2.2.9 Veterans’ Glass City Skyway Bridge (Toledo, OH, USA) .................... 14 2.3 Anti-icing and De-icing Technologies ............................................................... 15 2.4 Electrical Properties of Snow............................................................................. 16 2.5 University of Toledo’s Testing Facilities .......................................................... 19 2.5.1 UT Icing Tunnel ..................................................................................... 19 2.5.2 UT Icing Station ..................................................................................... 21 2.6 Snow Making Science........................................................................................ 23 2.7 Similar Sensors to UTLWC Sensor ................................................................... 25 2.7.1 Detecting Accumulation State ............................................................... 26 2.7.2 Detecting Liquid Water Content of Wet Snow ...................................... 28 vii 3 Sensor Functions and Experiments Setup ...................................................................... 34 3.1 Introduction ........................................................................................................ 34 3.2 Wet Snow vs. Dry Snow .................................................................................... 35 3.3 Importance of the UTLWC Sensor .................................................................... 37 3.3.1 Ice/Snow Accumulation Detection ........................................................ 38 3.3.2 Liquid Water Content ...........................................................................
Load more

Recommended publications
  • Spatial Representativeness Analysis for Snow Depth Measurements of Meteorological Stations in Northeast China
    APRIL 2020 W A N G A N D Z H E N G 791 Spatial Representativeness Analysis for Snow Depth Measurements of Meteorological Stations in Northeast China YUANYUAN WANG AND ZHAOJUN ZHENG National Satellite Meteorological Center, China Meteorological Administration, Beijing, China (Manuscript received 14 June 2019, in final form 20 February 2020) ABSTRACT Triple collocation (TC) is a popular technique for determining the data quality of three products that estimate the same geophysical variable using mutually independent methods. When TC is applied to a triplet of one point-scale in situ and two coarse-scale datasets that have the similar spatial resolution, the TC-derived performance metric for the point-scale dataset can be used to assess its spatial representativeness. In this study, the spatial representativeness of in situ snow depth measurements from the meteorological stations in northeast China was assessed using an unbiased correlation metric r2 estimated with TC. Stations are t,X1 considered representative if r2 $ 0:5; that is, in situ measurements explain no less than 50% of the variations t,X1 in the ‘‘ground truth’’ of the snow depth averaged at the coarse scale (0.258). The results confirmed that TC can be used to reliably exploit existing sparse snow depth networks. The main findings are as follows. 1) Among all the 98 stations in the study region, 86 stations have valid r2 values, of which 57 stations are t,X1 representative for the entire snow season (October–December, January–April). 2) Seasonal variations in r2 t,X1 are large: 63 stations are representative during the snow accumulation period (December–February), whereas only 25 stations are representative during the snow ablation period (October–November, March–April).
    [Show full text]
  • World Meteorological Organization Global Cryosphere Watch
    WORLD METEOROLOGICAL ORGANIZATION GLOBAL CRYOSPHERE WATCH REPORT No. 20/ 2018 GLOBAL CRYOSPHERE WATCH STEERING GROUP TH 5 SESSION OSLO, NORWAY, 10-12 January, 2018 © World Meteorological Organization, 2018 The right of publication in print, electronic and any other form and in any language is reserved by WMO. Short extracts from WMO publications may be reproduced without authorization, provided that the complete source is clearly indicated. Editorial correspondence and requests to publish, reproduce or translate this publication in part or in whole should be addressed to: Chair, Publications Board World Meteorological Organization (WMO) 7 bis, avenue de la Paix Tel.: +41 (0) 22 730 8403 P.O. Box 2300 Fax: +41 (0) 22 730 8040 CH-1211 Geneva 2, Switzerland E-mail: [email protected] NOTE The designations employed in WMO publications and the presentation of material in this publication do not imply the expression of any opinion whatsoever on the part of WMO concerning the legal status of any country, territory, city or area, or of its authorities, or concerning the delimitation of its frontiers or boundaries. The mention of specific companies or products does not imply that they are endorsed or recommended by WMO in preference to others of a similar nature which are not mentioned or advertised. The findings, interpretations and conclusions expressed in WMO publications with named authors are those of the authors alone and do not necessarily reflect those of WMO or its Members. - 2 - GROUP PHOTO, 10 JANUARY 2018 - 3 - EXECUTIVE SUMMARY The 5th session of the Steering Group of the Global Cryosphere Watch (GSG-5) was hosted by Norwegian Meteorological Institute (Met Norway), in Oslo, Norway, from 10th to 12 January.
    [Show full text]
  • Measuring Snow Properties Relevant to Snowsports & Outdoor
    Measuring snow properties relevant to Mittuniversitetet snowsports & outdoor 10.06.2019 Development of measuring method to ana- lyze snow properties Measuring snow properties relevant to snowsports & outdoor Development of measuring method to analyze snow properties Sebastian Klein Självständigt arbete Huvudområde: Mechanical Engineering MA,Thesis Högskolepoäng: 30 hp Termin/år: ST 2019 Handledare: Mikael Bäckström Examinator: Andrey Koptyug Kurskod/registreringsnummer: H4X94 Utbildningsprogram: Sportteknologi Based on the Mid Sweden University template for technical reports, written by Magnus Eriksson, Kenneth Berg and Mårten Sjöstr öm. i Measuring snow properties relevant to Mittuniversitetet snowsports & outdoor 10.06.2019 Development of measuring method to ana- lyze snow properties Abstract Snow is a common surface on which a lot of sports competitions take place. We know a lot about our equipment, but there has been done very little research on the snow itself regarding the use in sports. The aim of this project is to create a measurement device to investigate the properties of different snow types. The snow compound on the ski slopes nowadays does not only exist of natural snow, a big part of it is machine-made snow and the most common one is produced with snow guns. There are differ- ent theories why skis glide on snow and that is why a lot of research has been done on the snow behavior. But the main goal in the ski industry is to improve the equipment. The measurement tool should be compact, so it is possible to carry it around on the ski slope, waterproof and should give electronic data, not like previous devices where you have to measure by hand.
    [Show full text]
  • High Concentrations of Biological Aerosol Particles and Ice Nuclei Open Access During and After Rain Biogeosciences Biogeosciences Discussions J
    EGU Journal Logos (RGB) Open Access Open Access Open Access Advances in Annales Nonlinear Processes Geosciences Geophysicae in Geophysics Open Access Open Access Natural Hazards Natural Hazards and Earth System and Earth System Sciences Sciences Discussions Open Access Open Access Atmos. Chem. Phys., 13, 6151–6164, 2013 Atmospheric Atmospheric www.atmos-chem-phys.net/13/6151/2013/ doi:10.5194/acp-13-6151-2013 Chemistry Chemistry © Author(s) 2013. CC Attribution 3.0 License. and Physics and Physics Discussions Open Access Open Access Atmospheric Atmospheric Measurement Measurement Techniques Techniques Discussions Open Access High concentrations of biological aerosol particles and ice nuclei Open Access during and after rain Biogeosciences Biogeosciences Discussions J. A. Huffman1,2, A. J. Prenni3, P. J. DeMott3, C. Pohlker¨ 2, R. H. Mason4, N. H. Robinson5, J. Frohlich-Nowoisky¨ 2, Y. Tobo3, V. R. Despres´ 6, E. Garcia3, D. J. Gochis7, E. Harris2, I. Muller-Germann¨ 2, C. Ruzene2, B. Schmer2, 2,8 9 2 9 5 3 4 Open Access B. Sinha , D. A. Day , M. O. Andreae , J. L. Jimenez , M. Gallagher , S. M. Kreidenweis , A. K. Bertram , and Open Access U. Poschl¨ 2 Climate 1 Climate Department of Chemistry and Biochemistry, University of Denver, 2190 E. Illif Ave., Denver, CO, 80208, USA of the Past 2Max Planck Institute for Chemistry, P.O. Box 3060, 55020, Mainz, Germany of the Past 3Department of Atmospheric Science, Colorado State University, 1371 Campus Delivery, Fort Collins, CO, 80523, USA Discussions 4Department of Chemistry, University of British Columbia, Room D223, 2036 Main Mall, Vancouver, BC, V6T1Z1, Canada Open Access 5Centre for Atmospheric Sciences, University of Manchester, Simon Building, Oxford Road, Manchester, M139PL, UK Open Access 6Institute for General Botany, Johannes Gutenberg University, Mullerweg¨ 6, 55099, Mainz,Earth Germany System Earth System 7National Center for Atmospheric Research, P.O.
    [Show full text]
  • Comparison of the Compact Dopplar Radar Rain Gauge and Optical
    Short Paper J. Agric. Meteorol. 67 (3): 199–204, 2011 Comparison of the compact dopplar radar rain gauge and optical disdrometer Ko NAKAYA†, and Yasushi TOYODA (Central Research Institute of Electric Power Industry, 1646 Abiko, Abiko, Chiba, 270–1194, Japan) Abstract The operations of a compact Doppler radar rain gauge (R2S; Rufft, FRG) and optical disdrometer (LPM; THIES, FRG) are based on raindrop size distribution (DSD) measurements. We checked the instrumental error of these sensors and compared each sensor with a reference tipping-bucket rain gauge. This is because both rain gauges can detect fine particles and so they can function as rain sensors. The R2S has a measuring bias of rainfall intensity when the drop size distribution differs from the assumed statistical DSD model. The instrumental error on the LPM is small; in fact, the LPM shows good agreement with the reference rain gauge. Where the atmospheric density differs remarkably from the standard elevation, as is the case in highland areas, the R2S requires calibration using a reference rain gauge. The resultant calibration coefficient of the R2S to convert the reading into a reference tip- ping-bucket rain-gauge equivalent was 0.51 in a forest at an elevation of 1380 m. Further gathering of calibration coefficients obtained at different elevations will improve the R2S’s applicability. Key words: Doppler radar rain gauge, Drop size distribution, Optical disdrometer, Tipping-bucket rain gauge. Although the accuracy and suggested errors of rainfall 1. Introduction observations using typical Doppler radar have been Rainfall properties, such as the intensity, amount, reviewed in many studies (Maki et al., 1998, for duration, and type, constitute important meteorological example), reviews for the R2S compared to a reference information that is useful for agriculture and forestation rain gauge are few.
    [Show full text]
  • The Science of Snowflakes
    The Science of Snowflakes Author: Paulette Clancy Date Created: 1999 Subject: Earth Science, Engineering Level: Middle School Standards: New York State- Intermediate Science (www.emsc.nysed.gov/ciai/) Standard 1- Analysis, Inquiry and Design Standard 4- The Physical Setting Standard 6- Interconnectedness: Common Themes Standard 7- Interdisciplinary Problem Solving Schedule: Five to six 40-minute class periods Objectives: Vocabulary: Learn about states of matter, Matter Volume classification, and properties of Atom Density crystals Crystal Ion Materials: Students will: For Each Student: Activity Sheet 1: Activity Sheet 8: Design a Mini-Hut Thinking About Snowflakes Box with a lid • Catch snowflakes and classify Activity Sheet 2: The Can of “Crystal Clear” them by shape and structure States of Matter spray • Grow a crystal in a jar Activity Sheet 3: Glass microscope • Design an experiment that will Temperature of slides String show if the growth of the crystal Substances Activity Sheet 4: Let’s Wide mouth jar changes if grown under Classify Snowflakes White pipe cleaners different conditions Activity Sheet 5: Blue food coloring • Design a “mini-hut” to preserve Properties of Crystals (optional) the crystal structure of ice Activity Sheet 6: Grow Boiling water* a Snowflake in a Jar Borax • Reflect on scientific process Activity Sheet 7: For Each Pair: and discuss concepts that were Experiment Template Microscope* learned *Provided by the teacher Safety: Blue food coloring can stain clothing. If it is used, use caution when handling it. Science Content: Snow Crystals: When cloud temperature is at freezing or below and the clouds are moisture filled, snow crystals form. The ice crystals form on dust particles as the water vapor condenses and partially melted crystals cling together to form snowflakes.
    [Show full text]
  • Measurement of Precipitation
    CHAPTER CONTENTS Page CHAPTER 6. MEASUREMENT OF PRECIPITATION ..................................... 186 6.1 General ................................................................... 186 6.1.1 Definitions ......................................................... 186 6.1.2 Units and scales ..................................................... 186 6.1.3 Meteorological and hydrological requirements .......................... 187 6.1.4 Measurement methods .............................................. 187 6.1.4.1 Instruments ................................................ 187 6.1.4.2 Reference gauges and intercomparisons ........................ 188 6.1.4.3 Documentation. 188 6.2 Siting and exposure ........................................................ 189 6.3 Non-recording precipitation gauges .......................................... 190 6.3.1 Ordinary gauges .................................................... 190 6.3.1.1 Instruments ................................................ 190 6.3.1.2 Operation. 192 6.3.1.3 Calibration and maintenance ................................. 192 6.3.2 Storage gauges ..................................................... 192 6.4 Precipitation gauge errors and corrections ..................................... 193 6.5 Recording precipitation gauges .............................................. 196 6.5.1 Weighing-recording gauge ........................................... 196 6.5.1.1 Instruments ................................................ 196 6.5.1.2 Errors and corrections. 197 6.5.1.3 Calibration
    [Show full text]
  • Changes in Snow Depth, Snow Cover Duration, and Potential Snowmaking Conditions in Austria, 1961–2020—A Model Based Approach
    atmosphere Article Changes in Snow Depth, Snow Cover Duration, and Potential Snowmaking Conditions in Austria, 1961–2020—A Model Based Approach Marc Olefs 1,* , Roland Koch 1, Wolfgang Schöner 2 and Thomas Marke 3 1 Climate Research Department, ZAMG—Zentralanstalt für Meteorologie und Geodynamik, Hohe Warte 38, 1190 Vienna, Austria; [email protected] 2 Department of Geography and Regional Science, University of Graz, 8010 Graz, Austria; [email protected] 3 Department of Geography, University of Innsbruck, 6020 Innsbruck, Austria; [email protected] * Correspondence: [email protected] Received: 2 October 2020; Accepted: 4 December 2020; Published: 8 December 2020 Abstract: We used the spatially distributed and physically based snow cover model SNOWGRID-CL to derive daily grids of natural snow conditions and snowmaking potential at a spatial resolution of 1 1 km for Austria for the period 1961–2020 validated against homogenized long-term snow × observations. Meteorological driving data consists of recently created gridded observation-based datasets of air temperature, precipitation, and evapotranspiration at the same resolution that takes into account the high variability of these variables in complex terrain. Calculated changes reveal a decrease in the mean seasonal (November–April) snow depth (HS), snow cover duration (SCD), and potential snowmaking hours (SP) of 0.15 m, 42 days, and 85 h (26%), respectively, on average over Austria over the period 1961/62–2019/20. Results indicate a clear altitude dependence of the relative reductions ( 75% to 5% (HS) and 55% to 0% (SCD)). Detected changes are induced by − − − major shifts of HS in the 1970s and late 1980s.
    [Show full text]
  • Iowa (SMAPVEX16-IA) Experiment Plan
    Soil Moisture Active Passive Validation Experiment 2016- Iowa (SMAPVEX16-IA) Experiment Plan Iowa Landscape July 2014 Ver. 5/20/16 Table of Contents 1. Introduction .........................................................................................................................8 1.1. Role of Field Campaigns in SMAP Cal/Val...................................................................8 1.2. Science Objectives for a Post-Launch SMAP Aircraft-Based Field Campaign ...............9 1.2.1. Investigate and resolve anomalous observations and products ...............................9 1.2.2. Improving up-scaling functions for CVS .............................................................. 10 1.2.3. Contribution to a broader scientific/application objective.................................... 10 1.2.4. Validate the L2SMAP algorithm process: pending new directions ....................... 13 2. SMAPVEX16 Aircraft Experiment Concept ...................................................................... 13 3. South Fork (SF), Iowa Study Area ..................................................................................... 13 3.1. General Description .................................................................................................... 13 3.2. Land Cover/Vegetation ............................................................................................... 14 3.3. Soils ............................................................................................................................ 15 3.4. Climate ......................................................................................................................
    [Show full text]
  • Turf Smart Solutions 2021 Product Catalog
    TURF SMART SOLUTIONS 2021 PRODUCT CATALOG EASY ORDERING - REQUEST A QUOTE At Spectrum Technologies, we pride Online www.specmeters.com ourselves on helping our valued customers transform plant measurements into more E-mail [email protected] profitable growing decisions. Since 1987, Phone 1 (800) 248-8873 (Toll Free U.S. & Canada) we have manufactured and distributed affordable, leading-edge plant measurement 1 (815) 436-4440 technology to agricultural, horticultural, Fax 1 (815) 436-4460 environmental, and turf markets throughout the world, serving more than 3600 Thayer Court 14,000 customers in over 80 countries. Aurora, IL 60504 USA We proudly work alongside researchers, growers, golf course superintendents, Order Total Shipping/Handling (USA Only) and industry suppliers to provide a broad $25.01 to $99.99 $18.00 line of innovative devices and software Standard $100.00 to $499.99 $24.00 for better environmental monitoring, Ground Delivery: $500.00 to $999.99 $30.00 nutrient management, soil moisture $1,000.00 to $1,999.99 $35.00 measurement, irrigation scheduling, and Express Delivery: $2,000.00 to $2,999.99 $44.00 pest management. (Contact for charges) $3,000.00 to $4,999.99 $56.00 Canadian Delivery: $5,000.00 to $9,999.99 $78.00 (Contact for charges) 10,000 or more Contact for pricing Our products will positively impact the way you grow your turf; therefore, we stand behind every product with our 100% ORDERING INFORMATION Satisfaction Guarantee. Our friendly and Purchase Orders: We accept purchase orders from accredited universities, governments, businesses, and nonprofit organizations. Completed credit application knowledgeable product support team is may be required.
    [Show full text]
  • Advanced Weather Monitoring for a Cable Stayed Bridge
    Advanced weather monitoring for a Cable stayed bridge Chandrasekar Venkatesh June 11, 2018 Bachelors in Electrical and Electronics Engineering Ph.D. in Electrical Engineering Department of Electrical Engineering and Computer Science, College of Engineering and Applied Science Committee Chair: Dr. Arthur Helmicki Committee Members: Dr.Victor Hunt Dr. Douglas Nims Dr. Ali Minai Dr. William Wee Abstract In the northern United States, Canada, and many northern European countries, snow and ice pose serious hazards to motorists. Potential traffic disruptions caused by ice and snow are challenges faced by transportation agencies. Successful winter maintenance involves the selection and application of the most optimum strategy, over optimum time intervals. The risk associated with operating the bridges during winter emergencies varies depending on the size of the structure, the material of the stays, volume of average daily traffic, geographical location, nature of terrain and surroundings etc. The ‘Dashboard’, a monitoring system designed to help the bridge maintenance and operation personnel was developed at University of Cincinnati Infrastructure Institute. This was implemented at the Veterans Glass City Skyway Bridge in Toledo, Ohio. This system was also extended to the Port Mann Bridge in Vancouver, Canada. The aim of this research is to come up with an advanced monitoring system which will help the bridge management team make control actions during winter emergencies on the VGCS and Port Mann bridges. The current monitoring system gives information on the status of ice accumulation/ snow accretion or shedding based on last one hour’s weather data. This dissertation focuses on adding intelligence to the existing system through addition of sensors, identifying patterns in events, adding cost-benefit analysis and incorporating forecast parameters, while also extending the system to other bridges and structures.
    [Show full text]
  • By: ESSA RAMADAN MOHAMMAD D F Superintendent of Stations Kuwait
    By: ESSA RAMADAN MOHAMMAD Superintendent O f Stations Kuwait Met. Departmentt Geoggpyraphy and climate Kuwait consists mostly of desert and little difference in elevation. It has nine islands, the largest of which is Bubiyan, which is linked to the mainland by a concrete bridge. Summers (April to October) are extremely hot and dry with temperatures exceeding 51 °C(124°F) in Kuwait City several times during the hottest months of June, July and August. April and October are more moderate with temperatures over 40 °C uncommon . Winters (November through February) are cool with some precipitation and average temperatures around 13 °C(56°F) with extremes from ‐2 °Cto27°C. The spring season (Marc h) iswarmand pltleasant with occasilional thund ers torms. Surface coastal water temperatures range from 15 °C(59°F) in February to 35 °C(95°F) in August. The driest months are June through September, while the wettest are January through March. Thunderstorms and hailstorms are common in November, March and April when warm and moist Arabian Gulf air collides with cold air masses from Europe. One such thunderstorm in November 1997 dumped more than ten inches of rain on Kuwait. Kuwait Meteorology Dep artment Meteorological Department Forecasting Supervision Clima tes SiiSupervision Stations & Upper Air Supervision Communications Supervision Maintenance Supervision Stations and Upper air Supervision 1‐ SfSurface manned Sta tions & AWOS 2‐ upper air Stations & Ozone Kuwait Int. Airport Station In December 1962 one manned synoptic, climate, agro stations started to report on 24 hour basis and sending data to WMO Kuwait Int. Airport Station Kuwait started to observe and report meteorological data in the early 1940 with Kuwait Britsh oil company but most of the report were very limited.
    [Show full text]