DOCSLIB.ORG

CREATE 1 GROW 1 SUSTAIN Leading by Example

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
CREATE 1 GROW 1 SUSTAIN Leading by Example CREATE 1 GROW 1 SUSTAIN Leading by Example 2015 REPORT Business Roundtable CEO members lead companies with $7.2 trillion in annual revenues and nearly 16 million employees. Business Roundtable member companies comprise more than a quarter of the total market capitalization of U.S. stock markets and invest $190 billion annually in research and development — equal to 70 percent of U.S. private R&D spending. Our companies pay more than $230 billion in dividends to shareholders and generate more than $470 billion in sales for small and medium-sized businesses annually. Business Roundtable companies also make more than $3 billion a year in charitable contributions. Please visit us at www.brt.org, check us out on Facebook and LinkedIn, and follow us on Twitter. Copyright © 2015 by Business Roundtable CREATE 1 GROW 1 SUSTAIN Leading by Example 2015 REPORT April 2015 DEAR BUSINESS LEADERS AND STAKEHOLDERS: On behalf of the members of Business Roundtable, I am proud to share with you our 2015 sustainability report — Create, Grow, Sustain: Leading by Example. Now in its eighth year, the report features narratives from 148 CEOs on how their companies have contributed to sustainable economic growth and a cleaner environment in the United States and around the world. Aided by the efforts of Business Roundtable member companies, the United States is a sustainability leader. For example, between 2002 and 2012, global greenhouse gas (GHG) emissions increased by approximately one-third while U.S. GHG emissions decreased by more than 9 percent. Business Roundtable companies helped drive this success by setting sustainability goals, improving energy efficiency, investing in renewable energy, and developing new technologies that are transforming the way Americans produce and use energy. In anticipation of the December 2015 meeting in Paris of the United Nations Framework Convention on Climate Change, this year’s report has a special focus on actions or activities by Business Roundtable member companies that help limit GHG emissions. I hope you will enjoy reading Create, Grow, Sustain: Leading by Example and learning more about how U.S. companies are providing solutions to our greatest sustainability and quality of life challenges. Sincerely, Nicholas K. Akins Chairman, President and Chief Executive Officer, American Electric Power Chair, Energy and Environment Committee, Business Roundtable Business Roundtable 1 iii TABLE OF CONTENTS 3M 1 A. O. Smith Corporation 2 ABB 3 Abbott 4 Accenture 5 ACE Group 6 AECOM 7 The AES Corporation 8 Aetna Inc. 9 AK Steel Corporation 10 Alcoa Inc. 11 Altec, Inc. 12 American Electric Power 13 Anadarko Petroleum Corporation 14 Aramark Corporation 15 Assurant, Inc. 16 AT&T Inc. 17 Ball Corporation 18 Barclays PLC 19 Bayer AG 20 Bechtel Corporation 21 BlackRock, Inc. 22 The Blackstone Group 23 BNSF 24 The Boeing Company 25 BorgWarner Inc. 26 Boston Consulting Group 27 CA Technologies 28 Caesars Entertainment Corporation 29 Campbell Soup Company 30 Cardinal Health, Inc. 31 Caterpillar Inc. 32 CBRE Group, Inc. 33 CF Industries 34 CH2M HILL, Inc. 35 Chevron Corporation 36 Cigna Corporation 37 Cisco Systems, Inc. 38 CNH Industrial 39 Cognizant Technology Solutions Corporation 40 Comcast Corporation 41 ConocoPhillips 42 iv 1 Create, Grow, Sustain: Leading by Example Convergys Corporation 43 Corning Incorporated 44 CSX Corporation 45 Cummins Inc. 46 CVS Health 47 DaVita HealthCare Partners Inc. 48 Day & Zimmermann 49 Deere & Company 50 Dell Inc. 51 Deutsche Bank 52 DIRECTV 53 Dominion Resources, Inc. 54 The Dow Chemical Company 55 Duke Energy Corporation 56 Eastman Chemical Company 57 Eaton 58 Edison International 59 Eli Lilly and Company 60 EMC Corporation 61 Exelis Inc. 62 Express Scripts, Inc. 63 EY 64 FedEx Corporation 65 First Solar, Inc. 66 Fluor Corporation 67 Freeport-McMoRan Inc. 68 Frontier Communications Corporation 69 GE 70 General Mills, Inc. 71 W.W. Grainger, Inc. 72 The Guardian Life Insurance Company of America 73 Harman International Industries, Inc. 74 Honeywell 75 Humana Inc. 76 IBM Corporation 77 Ingersoll-Rand 78 International Paper Company 79 The Interpublic Group of Companies, Inc. 80 ITC Holdings Corp. 81 ITT Corporation 82 Johnson & Johnson 83 Johnson Controls, Inc. 84 Business Roundtable 1 v JPMorgan Chase & Co. 85 Kindred Healthcare, Inc. 86 KPMG 87 Lockheed Martin Corporation 88 Macy's Inc. 89 Marathon Oil Corporation 90 Marathon Petroleum Corporation 91 MassMutual Financial Group 92 MasterCard 93 McGraw Hill Financial 94 McKesson Corporation 95 MDC Partners 96 Medtronic, Inc. 97 Motorola Solutions, Inc. 98 Navistar International Corporation 99 NextEra Energy, Inc. 100 Norfolk Southern Corporation 101 Northrop Grumman Corporation 102 Oracle Corporation 103 Owens Corning 104 Peabody Energy Corporation 105 PepsiCo, Inc. 106 Pfizer Inc 107 PG&E Corporation 108 Pitney Bowes Inc. 109 PricewaterhouseCoopers International Limited 110 Principal Financial Group, Inc. 111 The Procter & Gamble Company 112 Qualcomm Incorporated 113 Realogy Holdings Corp. 114 Rockwell Automation, Inc. 115 RR Donnelley 116 SAS 117 Sempra Energy 118 Siemens AG 119 SIRVA, Inc. 120 Southern Company 121 Stanley Black & Decker, Inc. 122 Starr Companies 123 State Farm Insurance Companies 124 Steelcase Inc. 125 Suffolk Construction Company, Inc. 126 vi 1 Create, Grow, Sustain: Leading by Example SunGard Data Systems Inc. 127 Tenet Healthcare Corporation 128 Tenneco Inc. 129 Texas Instruments Incorporated 130 Thermo Fisher Scientific Inc. 131 Tishman Speyer 132 TransCanada Corporation 133 Tyco International 134 United Technologies Corporation 135 UPS 136 Verizon Communications 137 Visa Inc. 138 Voya Financial 139 Walmart 140 WESCO International, Inc. 141 Western & Southern Financial Group 142 Whirlpool Corporation 143 The Williams Companies, Inc. 144 WPP 145 Wyndham Worldwide Corporation 146 Xerox Corporation 147 Xylem Inc. 148 Business Roundtable 1 vii With sales in more than 200 countries of more than 60,000 products, 3M is a global innovation company that never stops inventing. Over the years, our innovations in industries ranging from consumer products to health care, electronics to energy, and industrial to safety and graphics, have led to the development of products that improve life for hundreds of millions of people worldwide. Not only have we made www.3M.com/sustainability driving at night easier, buildings safer, and consumer electronics lighter and less energy intensive, but we’ve also helped reduce infection rates and improved patient care. Our company’s vision guides our business decisions: ◗◗ 3M Technology Advancing Every Company ◗◗ 3M Products Enhancing Every Home ◗◗ 3M Innovation Improving Every Life Put simply, we are committed Put simply, we are committed to improving our business, our planet and every life. to improving our business, Striving to improve every life means putting 3M’s innovative solutions to work to help address our planet and every life. global challenges including energy availability and security, raw material scarcity, water stress, human health, safety, education, and development. It all starts with anticipating the needs of our customers. We’re investing heavily in the development of sustainable materials and the launch of many exciting new products with sustainability advantages, including 3M™ Novec™ Engineered Fluids, Scotch-Brite® Greener Clean Heavy Duty Scrub Sponge and Scour Pad, 3M Littmann® Classic III™ Stethoscope, 3M Petrifilm™ Rapid Yeast and Mold Plate, expanded 3M™ Air and Vapor Barrier portfolio, hard hat- attachable 3M Speedglas™ Welding Shield, 3M™ Floor Pads with Recycled Content, and more. We also focus on how our products are made. Now in its 40th year, our Pollution Prevention Pays program, has saved 3M 1.9 million metric tons of waste and $1.8 billion since 1975. Indexed to net sales, we’ve reduced global water usage by 42 percent (2005–14) and improved energy efficiency by 50 percent (2000–14), as we’ve voluntarily achieved a 57 percent absolute reduction in greenhouse gas emissions (2002–13). Improving every life also means empowering individuals and being intricately linked to the health of our communities. Accordingly, our support of education programs reaches more than 7.6 million young people each year, and we have provided more than $20 million in cash; volunteerism; and in-kind donations to nonprofit partners for disaster preparedness, relief and recovery since 2005. Moving forward, our emphasis on innovation and invention will continue to grow, as will our emphasis on collaboration — with customers, partners and communities. Inge G. Thulin Chairman of the Board, President and Chief Executive Officer Business Roundtable 1 1 As a global water technology company, A. O. Smith Corporation is in a unique position to address the issue of sustainability. Over the last several years, we have placed our emphasis on products, plants and people as we work to develop sustainable solutions for our customers and our employees. www.aosmith.com ◗◗ Products: We developed our first high-efficiency residential water heater in 1976, and today our company offers the most extensive line of high-efficiency and renewable-energy residential and commercial water heaters and boilers on the market. Over the last five years, another focus has been on water treatment technology to meet the growing need for fresh, clean water around the world. Our engineers created a patented technology that increases the output of fresh water from reverse osmosis residential water treatment products. We market
Load more

Recommended publications
  • Innovations Across the Grid Partnerships Transforming the Power Sector
    VOLUME II INNOVATIONS ACROSS THE GRID PARTNERSHIPS TRANSFORMING THE POWER SECTOR The Edison Foundation INSTITUTE for ELECTRIC INNOVATION The Edison Foundation INSTITUTE for ELECTRIC INNOVATION VOLUME II INNOVATIONS ACROSS THE GRID PARTNERSHIPS TRANSFORMING THE POWER SECTOR December 2014 © 2014 by the Institute for Electric Innovation All rights reserved. Published 2014. Printed in the United States of America. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage or retrieval system or method, now known or hereinafter invented or adopted, without the express prior written permission of the Institute for Electric Innovation. Attribution Notice and Disclaimer This work was prepared by the Edison Foundation Institute for Electric Innovation (IEI). When used as a reference, attribution to IEI is requested. IEI, any member of IEI, and any person acting on IEI's behalf (a) does not make any warranty, express or implied, with respect to the accuracy, completeness or usefulness of the information, advice, or recommendations contained in this work, and (b) does not assume and expressly disclaims any liability with respect to the use of, or for damages resulting from the use of any information, advice, or recommendations contained in this work. The views and opinions expressed in this work do not necessarily reflect those of IEI or any member of IEI. This material and its production, reproduction, and distribution by IEI does
    [Show full text]
  • Management Console Reference Guide
    Secure Web Gateway Management Console Reference Guide Release 10.0 • Manual Version 1.01 M86 SECURITY SETUP AND CONFIGURATION GUIDE © 2010 M86 Security All rights reserved. 828 W. Taft Ave., Orange, CA 92865, USA Version 1.01, published November 2010 for SWG software release 10.0 This document may not, in whole or in part, be copied, photo- copied, reproduced, translated, or reduced to any electronic medium or machine readable form without prior written con- sent from M86 Security. Every effort has been made to ensure the accuracy of this document. However, M86 Security makes no warranties with respect to this documentation and disclaims any implied war- ranties of merchantability and fitness for a particular purpose. M86 Security shall not be liable for any error or for incidental or consequential damages in connection with the furnishing, performance, or use of this manual or the examples herein. Due to future enhancements and modifications of this product, the information described in this documentation is subject to change without notice. Trademarks Other product names mentioned in this manual may be trade- marks or registered trademarks of their respective companies and are the sole property of their respective manufacturers. II M86 SECURITY, Management Console Reference Guide CONTENT INTRODUCTION TO THE SECURE WEB GATEWAY MANAGEMENT CONSOLE .................................................................... 1 WORKING WITH THE MANAGEMENT CONSOLE................ 3 Management Console . 3 Main Menu . 4 Using the Management Console . 6 Management Wizard . 10 User Groups Wizard . 11 Log Entry Wizard . 28 DASHBOARD............................................................... 33 Dashboard Console . 33 Functionality. 34 Device Gauges . 35 Performance Graphs . 38 Messages (SNMP). 40 Device Utilization Graphs. 41 USERS ......................................................................
    [Show full text]
  • Accurate Weather Forecasting Through Locality Based Collaborative Computing
    Accurate Weather Forecasting Through Locality Based Collaborative Computing Bard˚ Fjukstad John Markus Bjørndalen Otto Anshus Department of Computer Science Department of Computer Science Department of Computer Science Faculty of Science and Technology Faculty of Science and Technology Faculty of Science and Technology University of Tromsø, Norway University of Tromsø, Norway University of Tromsø, Norway Email: [email protected] Email: [email protected] and the Norwegian Meteorological Institute Forecasting Division for Northern Norway Email: [email protected] Abstract—The Collaborative Symbiotic Weather Forecasting of collaboration is when users use forecasts from the national (CSWF) system lets a user compute a short time, high-resolution weather services to produce short-term, small area, and higher forecast for a small region around the user, in a few minutes, resolution forecasts. There is no feedback of these forecasts on-demand, on a PC. A collaborated forecast giving better uncertainty estimation is then created using forecasts from other from the users to the weather services. These forecasts take users in the same general region. A collaborated forecast can be minutes to compute on a single multi-core PC. The third is visualized on a range of devices and in a range of styles, typically a symbiotic collaboration where users share on-demand their as a composite of the individual forecasts. CSWF assumes locality locally produced forecasts with each other. between forecasts, regions, and PCs. Forecasts for a region are In a complex terrain like the fjords and mountains of Nor- computed by and stored on PCs located within the region. To locate forecasts, CSWF simply scans specific ports on public IP way, the topography have a significant impact on the weather addresses in the local area.
    [Show full text]
  • Bankovní Institut Vysoká Škola Praha
    Bankovní institut vysoká škola Praha Katedra informačních technologií a elektronického obchodování Moderní servery a technologie Servery a superservery, moderní technologie a využití v praxi Bakalářská práce Autor: Jan Suchý Informační technologie a management Vedoucí práce: Ing. Vladimír Beneš Praha Duben, 2007 Prohlášení: Prohlašuji, ţe jsem bakalářskou práci zpracoval samostatně a s pouţitím uvedené literatury. podpis autora V Praze dne 14.4.2007 Jan Suchý 2 Anotace práce: Obsahem této práce je popis moderní technologie v oblasti procesorového vývoje a jeho nasazení do provozu v oblasti superpočítačových systémů. Vědeckotechnický pokrok je dnes významně urychlován právě specializovanými prototypy superpočítačů určenými pro nejnáročnější úkoly v oblastech jako jsou genetika, jaderná fyzika, termodynamika, farmacie, geologie, meteorologie a mnoho dalších. Tato bakalářská práce se v krátkosti zmiňuje o prvopočátku vzniku mikroprocesoru, víceprocesorové architektury aţ po zajímavé projekty, jako jsou IBM DeepBlue, Deep Thunder nebo fenomenální IBM BlueGene/L, který je v současné době nejvýkonnějším superpočítačem na světě. Trend zvyšování výkonu a důraz na náklady související s provozováním IT, vedl zároveň k poţadavku na efektivnější vyuţívání IT systémů. Technologie virtualizaci a Autonomic Computingu se tak staly fenoménem dnešní doby. 3 Obsah 1 ÚVOD 6 2 PROCESORY 7 2.1 HISTORIE A VÝVOJ 7 2.2 NOVÉ TECHNOLOGIE 8 2.2.1 Silicon Germanium - SiGe (1989) 8 2.2.2 První měděný procesor (1997) 8 2.2.3 Silicon on Insulator - SOI (1998) 9 2.2.4
    [Show full text]
  • Modeling and Distributed Computing of Snow Transport And
    Modeling and distributed computing of snow transport and delivery on meso-scale in a complex orography Modelitzaci´oi computaci´odistribu¨ıda de fen`omens de transport i dip`osit de neu a meso-escala en una orografia complexa Thesis dissertation submitted in fulfillment of the requirements for the degree of Doctor of Philosophy Programa de Doctorat en Societat de la Informaci´oi el Coneixement Alan Ward Koeck Advisor: Dr. Josep Jorba Esteve Distributed, Parallel and Collaborative Systems Research Group (DPCS) Universitat Oberta de Catalunya (UOC) Rambla del Poblenou 156, 08018 Barcelona – Barcelona, 2015 – c Alan Ward Koeck, 2015 Unless otherwise stated, all artwork including digital images, sketches and line drawings are original creations by the author. Permission is granted to copy, distribute and/or modify this document under the terms of the Creative Commons BY-SA License, version 4.0 or ulterior at the choice of the reader/user. At the time of writing, the license code was available at: https://creativecommons.org/licenses/by-sa/4.0/legalcode Es permet la lliure c`opia, distribuci´oi/o modificaci´od’aquest document segons els termes de la Lic`encia Creative Commons BY-SA, versi´o4.0 o posterior, a l’escollida del lector o usuari. En el moment de la redacci´o d’aquest text, es podia accedir al text de la llic`encia a l’adre¸ca: https://creativecommons.org/licenses/by-sa/4.0/legalcode 2 In memoriam Alan Ward, MA Oxon, PhD Dublin 1937-2014 3 4 Acknowledgements A long-term commitment such as this thesis could not prosper on my own merits alone.
    [Show full text]
  • Internet of Things and Big Data Analytics Toward Next-Generation Intelligence Studies in Big Data
    Studies in Big Data 30 Nilanjan Dey Aboul Ella Hassanien Chintan Bhatt Amira S. Ashour Suresh Chandra Satapathy Editors Internet of Things and Big Data Analytics Toward Next-Generation Intelligence Studies in Big Data Volume 30 Series editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland e-mail: [email protected] About this Series The series “Studies in Big Data” (SBD) publishes new developments and advances in the various areas of Big Data- quickly and with a high quality. The intent is to cover the theory, research, development, and applications of Big Data, as embedded in the fields of engineering, computer science, physics, economics and life sciences. The books of the series refer to the analysis and understanding of large, complex, and/or distributed data sets generated from recent digital sources coming from sensors or other physical instruments as well as simulations, crowd sourcing, social networks or other internet transactions, such as emails or video click streams and other. The series contains monographs, lecture notes and edited volumes in Big Data spanning the areas of computational intelligence incl. neural networks, evolutionary computation, soft computing, fuzzy systems, as well as artificial intelligence, data mining, modern statistics and Operations research, as well as self-organizing systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output. More information about this series at http://www.springer.com/series/11970 Nilanjan Dey • Aboul Ella Hassanien Chintan Bhatt • Amira S. Ashour Suresh Chandra Satapathy Editors Internet of Things and Big Data Analytics Toward Next-Generation Intelligence 123 Editors Nilanjan Dey Amira S.
    [Show full text]
  • SPERI Paper No. 13 Climate Risk, Big Data and the Weather Market
    SPERI Paper No. 13 Climate Risk, Big Data and the Weather Market. Jo Bates About the author Jo Bates Jo Bates is a Lecturer in Information Politics and Policy in the Information School, University of Sheffield. She researches the political economy of data, particularly data sets that are produced by public bodies and which are often referred to as Public Sector Information. Her research focuses on understanding the socio- cultural and political economic factors shaping developments in this field, including the ideas, practices and policies shaping the production and distribution of public data sets and their re-use by third parties including citizens and businesses. Her research has explored the development of the UK’s Open Government Data initiative. Her current project is focused on the distribution and re-use of weather data. ISSN 2052-000X Published in May 2014 SPERI Paper No. 13 – Climate Risk, Big Data and the Weather Market 1 Introduction In a recent SPERI paper, Colin Hay and Tony Payne (2013) develop the concept of “The Great Uncertainty” to describe our current era. They point to “three major processes of structural change” which underlie this uncertainty – financial crisis, shifting economic power and environmental threat – arguing that, despite their differing historical timespans, these deep structural changes are all taking place in the present and “arguably will come to a head at broadly the same time” (Hay & Payne 2013, p. 3). This paper draws together two core elements of “The Great Uncertainty” – financial crisis and environmental threat – with Sandra Braman’s (2006) concept of “informational power” to explore the relationship between this uncertain terrain and developments in (‘big’) data analytics.
    [Show full text]
  • Intelligent Drug Supply Chain Creating Value from AI About the Deloitte Centre for Health Solutions
    Intelligent drug supply chain Creating value from AI About the Deloitte Centre for Health Solutions The Deloitte Centre for Health Solutions (CfHS) is the research arm of Deloitte’s Life Sciences and Health Care practices. We combine creative thinking, robust research and our industry experience to develop evidence-based perspectives on some of the biggest and most challenging issues to help our clients transform themselves and, importantly, benefit the patient. At a pivotal and challenging time for the industry, we use our research to encourage collaboration across all stakeholders, from pharmaceuticals and medical innovation, health care management and reform, to the patient and health care consumer. Connect To learn more about the CfHS and our research, please visit www.deloitte.co.uk/centreforhealthsolutions Subscribe To receive upcoming thought leadership publications, events and blogs from the UK Centre, please visit https://www.deloitte.co.uk/aem/centre-for-health-solutions.cfm To subscribe to our blog, please visit https://blogs.deloitte.co.uk/health/ Life sciences companies continue to respond to a changing global landscape and strive to pursue innovative solutions to address today’s challenges. Deloitte understands the complexity of these challenges and works with clients worldwide to drive progress and bring discoveries to life. Contents The rationale for transforming the biopharma supply chain 2 How AI can augment supply chain transformation 6 AI’s role in helping supply chains respond, recover and thrive after COVID-19 16 A roadmap for implementing an intelligent supply chain 21 Endnotes 32 Intelligent drug supply chain The rationale for transforming the biopharma supply chain The Intelligent biopharma series explores the ways artifi cial intelligence (AI) can impact the biopharma value chain.
    [Show full text]
  • An Ensemble of Deep Learning Models for Weather Nowcasting Based on Radar Products’ Values Prediction
    applied sciences Article NowDeepN: An Ensemble of Deep Learning Models for Weather Nowcasting Based on Radar Products’ Values Prediction Gabriela Czibula 1,*,† , Andrei Mihai 1,† and Eugen Mihule¸t 2 1 Department of Computer Science, Babe¸s-BolyaiUniversity, 400084 Cluj-Napoca, Romania; [email protected] 2 Romanian National Meteorological Administration, 013686 Bucharest, Romania; [email protected] * Correspondence: [email protected]; Tel.: +40-264-405327 † These authors contributed equally to this work. Abstract: One of the hottest topics in today’s meteorological research is weather nowcasting, which is the weather forecast for a short time period such as one to six hours. Radar is an important data source used by operational meteorologists for issuing nowcasting warnings. With the main goal of helping meteorologists in analysing radar data for issuing nowcasting warnings, we propose NowDeepN, a supervised learning based regression model which uses an ensemble of deep artificial neural networks for predicting the values for radar products at a certain time moment. The values predicted by NowDeepN may be used by meteorologists in estimating the future development of potential severe phenomena and would replace the time consuming process of extrapolating the radar echoes. NowDeepN is intended to be a proof of concept for the effectiveness of learning from radar data relevant patterns that would be useful for predicting future values for radar products based on their historical values. For assessing the performance of NowDeepN, a set of experiments on real radar data provided by the Romanian National Meteorological Administration is conducted. The impact of a data cleaning step introduced for correcting the erroneous radar products’ values is investigated both from the computational and meteorological perspectives.
    [Show full text]
  • State of New York Public Service Commission
    STATE OF NEW YORK PUBLIC SERVICE COMMISSION __________________________________________ : In the Matter of Department of Public Service : Staff Investigation into the Utilities’ : Preparation for and Response to August 2020 : Case 20-E-0586 Tropical Storm Isaias and Resulting Electric : Power Outages : ___________________________________________ AFFIDAVIT OF BRIAN CERRUTI ON BEHALF OF CONSOLIDATED EDISON COMPANY OF NEW YORK, INC. I, Brian Cerruti, being duly sworn, depose and say: 1. My name is Brian Cerruti. My business address is 4 Irving Place, New York, New York 10003. My official title is Project Specialist, but I perform the functions of a meteorologist. I have been employed by Consolidated Edison Company of New York, Inc., (Con Edison or the Company) for seven years. 2. My responsibilities include creating custom weather forecasts for the Company, leading weather discussions on storm preparation conference calls, responding to questions from operating personnel before and during a storm, overseeing contracts with weather information vendors, developing, calibrating, verifying, and implementing outage prediction models for Con Edison and Orange and Rockland Utilities, Inc. (O&R), and providing subject matter expertise to Con Edison and O&R as needed. I am also the lead on the Company’s Probabilistic Load Forecasting Project, which is a tool co-developed with a vendor, TESLA, that quantifies weather uncertainty in the Company’s electric and steam load forecasts. 3. I earned a Bachelor of Science degree in Meteorology from Rutgers University’s George H. Cook School of Environmental and Biological Sciences and a Master of Science 1 degree in Atmospheric Science from Rutgers University’s Graduate School of Atmospheric Science.
    [Show full text]
  • Accurate Weather Forecasting Through Locality Based Collaborative Computing
    WK,(((,QWHUQDWLRQDO&RQIHUHQFHRQ&ROODERUDWLYH&RPSXWLQJ1HWZRUNLQJ$SSOLFDWLRQVDQG:RUNVKDULQJ &ROODERUDWH&RP Accurate Weather Forecasting Through Locality Based Collaborative Computing Bard˚ Fjukstad John Markus Bjørndalen Otto Anshus Department of Computer Science Department of Computer Science Department of Computer Science Faculty of Science and Technology Faculty of Science and Technology Faculty of Science and Technology University of Tromsø, Norway University of Tromsø, Norway University of Tromsø, Norway Email: [email protected] Email: [email protected] and the Norwegian Meteorological Institute Forecasting Division for Northern Norway Email: [email protected] Abstract—The Collaborative Symbiotic Weather Forecasting of collaboration is when users use forecasts from the national (CSWF) system lets a user compute a short time, high-resolution weather services to produce short-term, small area, and higher forecast for a small region around the user, in a few minutes, resolution forecasts. There is no feedback of these forecasts on-demand, on a PC. A collaborated forecast giving better uncertainty estimation is then created using forecasts from other from the users to the weather services. These forecasts take users in the same general region. A collaborated forecast can be minutes to compute on a single multi-core PC. The third is visualized on a range of devices and in a range of styles, typically a symbiotic collaboration where users share on-demand their as a composite of the individual forecasts. CSWF assumes locality locally produced forecasts with each other. between forecasts, regions, and PCs. Forecasts for a region are In a complex terrain like the fjords and mountains of Nor- computed by and stored on PCs located within the region.
    [Show full text]
  • Agenda Northeast Regional Operational Workshop XVI Albany, New York Wednesday, November 4, 2015 Session A
    Agenda Northeast Regional Operational Workshop XVI Albany, New York Wednesday, November 4, 2015 8:30 am Welcoming Remarks Raymond G. O’Keefe, Meteorologist In Charge Warren R. Snyder, Science & Operations Officer National Weather Service, Albany, New York Session A – Cold Season Topics 8:40 am A Multi-scale Analysis of the 26-27 November 2014 Pre-Thanksgiving Snowstorm Thomas A. Wasula NOAA/NWS Weather Forecast Office, Albany, New York 9:01 am Update to Gridded Snowfall Verification: Computing Seasonal Bias Maps Joseph P. Villani NOAA/NWS Weather Forecast Office, Albany, New York 9:22 am Cool-season extreme precipitation events in the Central and Eastern United States Benjamin J. Moore Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, New York 9:43 am A Case Study of the 18 January 2015 High-Impact Light Freezing Rain Event Across the Northern Mid-Atlantic Region Heather Sheffield NOAA/NWS Weather Forecast Office, Sterling, Virginia 10:04 am The November 26, 2014 banded snowfall case in southern New York Michael Evans NOAA / NWS Weather Forecast Office, Binghamton, New York 10:25 am Break 10:55 am An analysis of Chesapeake Bay effect snow events from 1999 to 2013 David F. Hamrick NOAA/NWS Weather Prediction Center, College Park, Maryland 11:16 am Changes in the Winter Weather Desk Operations at the Weather Prediction Center (WPC), and New Experimental Forecasts Dan Petersen NOAA/NWS/NCEP Weather Prediction Center, College Park, Maryland 11:37 am Applying Fuzzy Clustering Analysis to Assess Uncertainty and Ensemble System Performance for Cool Season High-Impact Weather Brian A.
    [Show full text]