DOCSLIB.ORG

Data Management System for Energy Analytics and Its Application to Forecasting

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Data Management System for Energy Analytics and Its Application to Forecasting Data Management System for Energy Analytics and its Application to Forecasting Francesco Fusco Ulrike Fischer Vincent Lonij IBM Research Ireland IBM Research Ireland IBM Research Ireland [email protected] ufi[email protected] [email protected] Pascal Pompey Jean-Baptiste Fiot Bei Chen IBM Research Ireland IBM Research Ireland IBM Research Ireland [email protected] jean- [email protected] baptiste.fi[email protected] Yiannis Gkoufas Mathieu Sinn IBM Research Ireland IBM Research Ireland [email protected] [email protected] ABSTRACT forecasting, are essential operations for the optimization of The effective management of a power grid with an increas- the power flow in a smart grid. For the successful applica- ing share of (distributed) renewables and more and more tion of such operations the reliable processing and manage- available data, e.g., coming from smart meters, heavily re- ment of the data collected by a smart grid is a key factor. lies on advanced data analytics such as demand and supply Due to the increasing number of diverse devices consider- forecasting. In this context, data management is one ma- able amount of data is produced by a smart grid, leading to jor challenge in electric grids. Large amount of data from a trend of emerging big data architectures and the discussion multiple heterogeneous sources require transformations, e.g., of technical challenges as well as potential use cases [1, 2]. spatio-temporal alignment or anomaly detection, to serve Data management is one major challenge in electric grids data analytics tasks and are often applied on different views as data is incomplete in nature, heterogeneous, difficult to of the data, e.g., on state, substation or feeder level. merge and arrives at different rates [3]. Energy data ar- In this paper, the progress on the development of an en- rives from various distributed sources, e.g., supervisory con- ergy data management systems for the electricity grid is pre- trol and data acquisition (SCADA) systems, smart meters, sented. The design of the system was inspired by the real- renewable generation systems, and differs significantly in world use case of forecasting short-term energy demand in terms of format, resolution and quality. Moreover, data Vermont, using data from a combination of SCADA, smart might represent different views of a power system, e.g., load meters and weather forecasting services. A general data at feeder level or over a whole substation. Analytics tasks, model addressing the aforementioned challenges and aimed such as demand forecasting, also require contextualisation at supporting advanced data analytics is introduced. The with additional data sets, such as calendar information and proposed data model views a time series as an abstract con- weather forecasts. The latter might come from different cept that might represent raw measurements or arbitrary weather services, again arriving at different rates and in dif- operations. The benefits of the system is demonstrated for ferent time and location resolutions. The system needs to the design and live update energy demand forecasts. be able to consolidate all these diverse data sets and pro- vide a common view. Analytics are then rarely performed on raw data, but require transformations of the data such as the computation of weighted averages or anomaly detec- 1. INTRODUCTION tion. The system has to support, store and continuously re-compute such transformation so that analytics can be The smart grid is the next-generation power system char- continuously applied. acterized by the inclusion of highly-distributed intelligent In this paper, we explicitly address the challenge of data devices and communication technologies that manage elec- management in a smart grid, proposing a data management tricity demand in a sustainable, reliable and economic man- architecture for the smart grid that is, on the one side, able ner. Advanced data analytics, such as load classification or to manage such diverse data sets and, on the other side, sup- ports operations, such as forecasting, that perform various transformation on the available data. To achieve this, we introduce a generic data model for the energy domain and show how this data model can be applied for the use case of c 2016, Copyright is with the authors. Published in the Workshop Pro- short term energy demand forecasting. ceedings of the EDBT/ICDT 2016 Joint Conference (March 15, 2016, Bor- There have been other efforts in the design of smart grid deaux, France) on CEUR-WS.org (ISSN 1613-0073). Distribution of this architectures. For example, Yang et. al. [4] give a high-level paper is permitted under the terms of the Creative Commons license CC- overview for a smart grid big data management system, in- by-nc-nd 4.0 troducing a simple data model, but mainly discussing data EnDM ’16 Bordeaux, France Data ingestion Modeling Runtime env. Data Feeds (off-line, runtime) (off-line) interval energy data from MV-90. Where required, energy demand or distributed generation is obtained by aggregating Data curation FeatureFeature selection selection Data curation ModelModel scoring scoring Spatio-temporal ModelModel training training Weather Spatio-temporal On-lineOn-line learning learning alignment RefinementsRefinements data alignment AnomalyAnomaly flags flags data from AMI up to the desired level of the grid (feeder, FilteringFiltering MetricsMetrics & & KPIs KPIs External substation). IBM Deep Thunder [12] was used to obtain applications Smart weather predictions up to 72 hours ahead. Deep Thunder Meters WebWeb Portal Portal InterfaceInterface APIsAPIs DataData produces weather forecasts on a grid with 1 km resolution ModelModel RESTREST APIs APIs SQLSQL Spark,Spark, HBASEHBASE and 10 minute time steps. Forecasts are updated twice per SCADA, ICCP,MV90 MarketMarket biddingbidding day and each run produces about 300 gigabytes of data. In- ...... ternally, the data are stored in a combination of relational Metadata, HDFS NetCDF DB2 Asset data - smart meters - Gridded database management systems (RDBMS) for electrical asset - Weather feat. weather data - Power data - Metadata data and SCADA/MV-90, Hadoop distributed file system - Models Data storage (HDFS) for the AMI data, network common data format (NetCDF) for the weather gridded data. Figure 1: Architecture diagram. As shown in Fig. 1, the data management system supports the tasks of: data ingestion, curation and spatio-temporal alignment of energy and weather data; training forecasting distribution and load balancing. Using cloud computing as models, which requires retrieving raw data and designing a platform for smart grid data management [5] and pro- input features (covariates); retrieving data of covariates and viding time series analytics as a service [6] poses another scoring forecasting models at runtime; interfaces to client trend in this area. A cloud-based demand response plat- applications (e.g. web portal, market bidding services). form is introduced in [7], which provides a workflow for data ingestion and scalable demand forecasting on Hadoop. To handle large amount of data and enable real-time reactions data streaming techniques have also been applied for the 3. THE DATA MODEL smart grid context [8]. A real-time data management sys- In order to manage highly heterogeneous sets of data and tem was developed within the MIRABEL project with focus support a variety of analytical operations typical of energy on storing and processing special energy planning objects for and utilities, as the ones detailed in section 2, a data model demand-response [9]. In the specific context of modeling en- was developed. The main objective of the data model was ergy data, the representation of time series and flex-offers providing a transparent, high-level interface to the users and within the context of distributed energy markets has been client applications, as well maintaining consistency, integrity studied in [10], while an ontology for big data in the smart and traceability between the various data sources. grids has been considered in [11], with particular focus on Figure 2 shows a conceptual structure of the proposed offshore wind farms. The proposed data model specifically data model. All dynamic data in an electricity grid can be addresses the management of dynamic time series data, as represented as a time series. Analogue, operations on the well as the operations and analytics applied to them. It can data applied by analytics also produce time series, such as therefore be seen as complementary to the reviewed research lags or rolling-window forecasts. Consequently, at the core of efforts and might serve as a basis for other existing designs the proposed data model is the representation of time series, of smart grid architectures. including timestamp/value pairs and abstract operations, as In the remainder of the paper a general system architec- described in section 3.1. The data model also contextualises ture supporting data management and analytics tasks for the time series with respect to a physical quantity (e.g. en- energy utilities is introduced in section 2. The paper then ergy demand, power, temperature) and entity (e.g. substa- focuses on the data model and query operations at the core tion, service territory), through the concept of signals, as of the proposed system in section 3. To demonstrate the us- discussed in section 3.2. Finally, the representation of ana- ability of our system, in section 4, the use case of short-term lytical models, which links the output time series of complex energy demand forecasting is discussed and concrete exam- operations, such as energy forecasts, to one or more several ples on the stored data and query operations are given. Final input time series, is then detailed in section 3.3. conclusions and future possibilities are discussed in section 5. 3.1 Time series At the core of the data model is the abstract concept of 2. SYSTEM ARCHITECTURE a TimeSeries, which in general can represent materialized A data management system was developed, conceptually data or abstract operations.
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]