DOCSLIB.ORG

Panel 4 Presentations – Enhanced Technologies for Weather Emergencies

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Panel 4 Presentations – Enhanced Technologies for Weather Emergencies ITS-NY 2012 SPRING FORUM April 12, 2012 Managing Weather-Related Events with ITS Technologies PANEL 4 PRESENTATIONS “Enhanced Technologies for Weather Emergencies” Panel Moderator: “The Realities of Disasters: What New “Integrating Weather and Dr. Camille Kamga, UTRC Decision Support Systems Must Transportation Information,” Consider,” Prof. Reza Khanbilvardi, CCNY Dr. Jose Holguin-Veras, Rensselaer Polytechnic Institute “Precision Weather Modeling, Analytics and “Application of High Resolution Weather Visualization for Emergency Management,” Modeling and Damage Prediction at Con Anthony Praino, IBM Edison Emergency Management,” Carlos D. Torres, Con Edison Photos by Matt Ficarra, ITS-NY Board Member and Photographer Extraordinaire 1 The Realities of Disasters: What New Decision Support Systems Must Consider José Holguín-Veras, William H. Hart Professor, Director of the Center for Infrastructure, Transportation, and the Environment Acknowledgments Other contributors: Miguel Jaller, Noel Pérez, Lisa Destro, Tricia Wachtendorf Research was supported by NSF: NSF-RAPID CMMI-1034635 “Investigation on the Comparative Performance of Alternative Humanitarian Logistic Structures” CMMI-0624083 “DRU: Contending with Materiel Convergence: Optimal Control, Coordination, and Delivery of Critical Supplies to the Site of Extreme Events” CMS-SGER 0554949 “Characterization of the Supply Chains in the Aftermath of an Extreme Event: The Gulf Coast Experience” "RAPID: Field Investigation on Post-Disaster Humanitarian Logistic Practices under Cascading Disasters and a Persistent Threat: The Tohoku Earthquake Disasters" 2 Humanitarian Logistics Research Group The group has pioneered the multidisciplinary study of post-disaster humanitarian logistics: Identified lessons learned from the largest disasters of recent times; Translated these lessons into policy recommendations; Shared these suggestions with disaster response agencies; Developed new paradigms of humanitarian logistic models that account for material convergence, deprivation costs and other unique features of post-disaster operations; Conducted detailed analyses of: Hurricane Katrina, the Port-au-Prince earthquake, the tornadoes in Joplin and Alabama, Hurricane Irene, the Tohoku disasters in Japan, among others. 3 Competing paradigms Relatively new field, difficult to characterize Lacks an accepted cannon, very different than normal, tough places to visit, isolated operations, few and far between (disasters/catastrophes), small professional community, lack of published accounts, transient and dynamic operations Complex behaviors Wide range of operations: Regular HL: Distribution of aid Post-Disaster HL: Relief distribution after a disaster Wide range of extreme events: Disasters Catastrophes A qualitatively different response Differential scalability in response functions? 4 Multidimensionally Complex Phenomena Technical Logisticians A really complex Engineers socio-technical process, with internal dynamics that cannot Post- be disentangled along Disaster disciplinary lines HL Social Regular HL Disaster Catastrophe Scientists 5 Social Three essential components A social network of individuals orchestrate the logistical operations A set technical activities are performed by the social network These social and technical activities are performed over a set of supporting systems (e.g., transportation, communication) Weight 6 Our main focus (emphasis Two different environments on catastrophes) Wide Spectrum of Operations Characte- Regular Humanitarian Post-Disaster Commercial Logistics ristic Logistics Humanitarian Logistics Objective Minimization of private Minimization of social Minimization of social pursued (logistic) costs costs (logistic+deprivation) costs (logistic+deprivation) Origination of Impacted by material Self-contained Mostly self-contained cargo flows convergence Knowledge of Known with some Unknown/dynamic, lack of Uncertain demand certainty information/access to site Decision ma- Structured interactions Structured interactions Non-structured interac- king structure controlled by few DMs controlled by few DMs tions, thousands of DMs Periodicity / Repetitive, relative steady Repetitive, relative steady One in a lifetime events, volume flows, "large" volumes flows, "large" volumes large pulse Supporting Stable, though not always Impacted and dynamically Stable and functional 7systems functional changing Disaster: Joplin, Missouri (50,000 residents) 8 Disaster: Joplin, Missouri (160 deaths) 9 Catastrophe: Minami Sanriku (19,170 residents) 10 Catastrophe: Minami Sanriku (10,000 missing) 11 ITS-Related Decision Support Systems 12 ITS that could help Global Positioning Systems: ITS Japan shared GPS tracks to allow agencies to infer which roads were passable Agreements with companies to get GPS data Robust communication systems Very tricky In 1995 Kobe earthquake only cell phones worked well; in 2001 WTC only Blackberry worked well; in 2012 Tohoku disasters, only 4G Ipads worked well Systems to support coordination Emerging coordination Use of the social media to improve coordination 13 Analysis capabilities Knowledge of demand /supply: Development of models to estimate immediate resource requirements (agent- and response-generated demands) Use of remote sensing to assess state of transportation networks, and locations of groups of survivors Systems to influence behavior: Panic/precautionary buying, control strategies Response to persistent threats Emergent social practices and response structures 14 Theory, Modeling Appropriate decision support tools: Routing models, Inventory allocation models based on DCs Dynamic control models to allocate resources to manage and physically control material convergence Planning of Points of Distribution Use of quasi-real time gathering of information about emerging donation drives to both quantify donation flows, and modify donation behavior Reverse logistics Supply pre-positioning accounting for DCs 15 Thanks 16 Monitoring Weather Impacts on Transportation Dr. Reza Khanbilvardi NOAA-CREST / City University of New York; [email protected] ITS-NY 2012 SPRING FORUM Hudson Valley Transportation Management Center, Hawthorne, New York Managing Weather-Related Events with ITS Technologies April 12, 2012 OUTLINE: How Do Weather Events Impact Roads? Weather Phenomena and Impacts Rain Impacts – Flood and Flash Flood How Remote Sensing Helps Data Acquisition Sources NOAA-CREST Case Study: The flood event in Iowa How Do Weather Impact Roads? Weather acts through visibility impairments, precipitation, high winds, and temperature extremes to affect driver capabilities, vehicle performance (i.e., traction, stability and maneuverability), pavement friction, roadway infrastructure, crash risk, traffic flow, and agency productivity (www.dot.gov). Three different categories of weather impacts: 1. Roadways (Visibility distance , friction) 2. Traffic flow (Traffic speed , Accident risk) 3. Operational decisions (Traffic signal timing, Road treatment strategy) Weather Phenomena & Impacts: Major weather phenomena have certain negative impacts such as lane obstruction, …. Road Weather Traffic Flow Roadway Impacts Operational Impacts Variables Impacts Air temperature and Road treatment strategy N/A N/A humidity. (e.g., snow and ice control) Lane obstruction (due Evacuation decision Wind speed to wind-blown snow, Accident risk support debris) Fog Visibility distance Traffic speed Driver capabilities/behavior Pavement temperature Infrastructure damage N/A Road treatment strategy Pavement condition Pavement friction Traffic speed Traffic signal timing Evacuation decision Water level Lane submersion Travel time delay support Precipitation Pavement friction Traffic speed Road treatment strategy (type, rate, start/end times) Visibility distance Accident risk Evacuation decision support source: www.dot.gov Rainfall & Road Safety: In term of weather and road safety most weather-related crashes happen on wet pavement and during rainfall: 75% on wet pavement. 47% during rainfall. 15% during snow or sleet. Weather-Related Crash Annual Rates (Approximately) Statistics 707,000 crashes 11% of vehicle crashes 47% of weather-related crashes 52% of weather-related crash 330,200 persons injured 11% of crash injuries Rain injuries 46% of weather-related crash 3,300 persons killed 8% of crash fatalities fatalities source: www.dot.gov Rainfall Impacts (Flood & Flash Flood) A flash flood is a rapid rise of water along a stream or low-lying (e.g. urban area) . Flash flood damage and most fatalities tend to occur in areas immediately adjacent to a stream or arroyo, due to a combination of heavy rain, rapid snowmelt. Heavy rain falling on steep terrain can weaken soil and cause debris flow, damaging roads, and property. Flash floods can be produced when slow moving or multiple thunderstorms occur over the same area. Flash Flood Risk in Roads: • Almost half of all flash flood fatalities occur in vehicles. • Water can erode the road bed, creating unsafe driving conditions. • Underpasses can fill rapidly with water, while the adjacent roadway remains clear. Courtesy: www.noaa.gov Data Acquisition Sources: In Situ Data Collection (usually direct Observation) Remote Sensing Data Collection: - Aerial Photography - Satellite imagery & Observations - Radar (NEXTAD) Measurements Model Output Products Multi-Source Data Acquisition TRMM NASA NOAA-M MW, IR EOS TMI, PR, VIRS VIS, NIR, IR POES-M MODIS VIS, NIR, IR SOUNDING ASTER, CERES AMSU- Precipitable Water Wind Speed
Load more

Recommended publications
  • An Evaluation of Snow Initializations in NCEP Global and Regional Forecasting Models
    JUNE 2016 D A W S O N E T A L . 1885 An Evaluation of Snow Initializations in NCEP Global and Regional Forecasting Models NICHOLAS DAWSON,PATRICK BROXTON,XUBIN ZENG, AND MICHAEL LEUTHOLD Department of Atmospheric Sciences, The University of Arizona, Tucson, Arizona MICHAEL BARLAGE Research Applications Laboratory, Boulder, Colorado PAT HOLBROOK Idaho Power Company, Boise, Idaho (Manuscript received 13 November 2015, in final form 10 April 2016) ABSTRACT Snow plays a major role in land–atmosphere interactions, but strong spatial heterogeneity in snow depth (SD) and snow water equivalent (SWE) makes it challenging to evaluate gridded snow quantities using in situ measurements. First, a new method is developed to upscale point measurements into gridded datasets that is superior to other tested methods. It is then utilized to generate daily SD and SWE datasets for water years 2012–14 using measurements from two networks (COOP and SNOTEL) in the United States. These datasets are used to evaluate daily SD and SWE initializations in NCEP global forecasting models (GFS and CFSv2, both on 0.5830.58 grids) and regional models (NAM on 12 km 3 12 km grids and RAP on 13 km 3 13 km grids) across eight 28328 boxes. Initialized SD from three models (GFS, CFSv2, and NAM) that utilize Air Force Weather Agency (AFWA) SD data for initialization is 77% below the area-averaged values, on av- erage. RAP initializations, which cycle snow instead of using the AFWA SD, underestimate SD to a lesser degree. Compared with SD errors, SWE errors from GFS, CFSv2, and NAM are larger because of the ap- plication of unrealistically low and globally constant snow densities.
    [Show full text]
  • This Is a Digital Document from the Collections of the Wyoming Water Resources Data System (WRDS) Library
    This is a digital document from the collections of the Wyoming Water Resources Data System (WRDS) Library. For additional information about this document and the document conversion process, please contact WRDS at [email protected] and include the phrase “Digital Documents” in your subject heading. To view other documents please visit the WRDS Library online at: http://library.wrds.uwyo.edu Mailing Address: Water Resources Data System University of Wyoming, Dept 3943 1000 E University Avenue Laramie, WY 82071 Physical Address: Wyoming Hall, Room 249 University of Wyoming Laramie, WY 82071 Phone: (307) 766-6651 Fax: (307) 766-3785 Funding for WRDS and the creation of this electronic document was provided by the Wyoming Water Development Commission (http://wwdc.state.wy.us) THE UNIVERSITY OF WYOMING WATER RESOURCES RESEARCH INSTITUTE P. 0. BOX 3038, UNIVERSITY STATION TELEPHONE: 766·2143 LARAMIE, WYOMING 82070 AREA CODE: 307 Water Resources Series No. 24 PRECIPITATION AND ITS MEASUREMENT A STATE OF THE ART Lee W. Larson June 1971 Abstract A comprehensive review of the literature since 1966 for studies and articles dealing with precipitation and its measurement. Topics discussed include gages, gage comparisons, gage shields, errors in measurements, precipitation data, data analysis, networks, and electronic measurements for rain and snow. A bibliogr~phy is included. KEY WORDS: Climatic data/ equipment/ precipitation data/ statistics/ weather data/ wind/ rainfall/ rain gages/ rain/ snow/ snowfall/ snow gages/ snow surveys NOTE: References numbered 1 through 331 in the Partially Annotated Bibliography are listed in alphabetical order. Those numbered 332 and above were obtained after the first list had been prepared for publication and form a second alphabetical listing.
    [Show full text]
  • Download Gate.Html (Accessed on 20 April 2021)
    remote sensing Article Assessment of Snowfall Accumulation from Satellite and Reanalysis Products Using SNOTEL Observations in Alaska Yang Song 1, Patrick D. Broxton 2, Mohammad Reza Ehsani 1 and Ali Behrangi 1,* 1 Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, AZ 85721, USA; [email protected] (Y.S.); [email protected] (M.R.E.) 2 School of Natural Resources and the Environment, The University of Arizona, Tucson, AZ 85721, USA; [email protected] * Correspondence: [email protected] Abstract: The combination of snowfall, snow water equivalent (SWE), and precipitation rate mea- surements from 39 snow telemetry (SNOTEL) sites in Alaska were used to assess the performance of various precipitation products from satellites, reanalysis, and rain gauges. Observation of precipita- tion from two water years (2018–2019) of a high-resolution radar/rain gauge data (Stage IV) product was also utilized to give insights into the scaling differences between various products. The outcomes were used to assess two popular methods for rain gauge undercatch correction. It was found that SWE and precipitation measurements at SNOTELs, as well as precipitation estimates based on Stage IV data, are generally consistent and can provide a range within which other products can be assessed. The time-series of snowfall and SWE accumulation suggests that most of the products can capture snowfall events; however, differences exist in their accumulation. Reanalysis products tended to overestimate snow accumulation in the study area, while the current combined passive microwave Citation: Song, Y.; Broxton, P.D.; remote sensing products (i.e., IMERG-HQ) underestimate snowfall accumulation.
    [Show full text]
  • 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]
  • Downloaded 09/25/21 08:21 AM UTC 1708 JOURNAL of HYDROMETEOROLOGY VOLUME 18
    JUNE 2017 W E N E T A L . 1707 Evaluation of MRMS Snowfall Products over the Western United States a,b c,d d c b YIXIN WEN, PIERRE KIRSTETTER, J. J. GOURLEY, YANG HONG, ALI BEHRANGI, a,e AND ZACHARY FLAMIG a Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma b Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California c School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, Oklahoma d NOAA/National Severe Storms Laboratory, Norman, Oklahoma e School of Meteorology, University of Oklahoma, Norman, Oklahoma (Manuscript received 18 November 2016, in final form 21 February 2017) ABSTRACT Snow is important to water resources and is of critical importance to society. Ground-weather-radar-based snowfall observations have been highly desirable for large-scale weather monitoring and water resources applications. This study conducts an evaluation of the Multi-Radar Multi-Sensor (MRMS) quantitative es- timates of snow rate using the Snowpack Telemetry (SNOTEL) daily snow water equivalent (SWE) datasets. A detectability evaluation shows that MRMS is limited in detecting very light snow (daily snow accumulation ,5 mm) because of the quality control module in MRMS filtering out weak signals (,5dBZ). For daily snow accumulation greater than 10 mm, MRMS has good detectability. The quantitative compar- isons reveal a bias of 277.37% between MRMS and SNOTEL. A majority of the underestimation bias occurs in relatively warm conditions with surface temperatures ranging from 2108 to 08C. A constant reflectivity– SWE intensity relationship does not capture the snow mass flux increase associated with denser snow particles at these relatively warm temperatures.
    [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]
  • A Lysimetric Snow Pillow Station at Kùhtai/Tyrol R. KIRNBAUER & G. BLÔSCHL Institut Fur Hydraulik, Gewâsserkunde U. Wasse
    Hydrology in Mountainous Regions. J - Hydrological Measurements; the Water Cycle (Proceedings of two Lausanne Symposia, August 1990). IAHS Publ. no. 193, 1990. A lysimetric snow pillow station at Kùhtai/Tyrol R. KIRNBAUER & G. BLÔSCHL Institut fur Hydraulik, Gewâsserkunde u. Wasserwirtschaft, Technische Universitât Wien, Karlsplatz 13, 1040 Vienna, Austria ABSTRACT For properly forecasting snowmeIt-runoff the understanding of processes associated with a melting snow cover may be of primary importance. For this purpose a snow monitoring station was installed at Kuhtai/Tyrol at an elevation of 1930 m a.s.l. In order to study individual physical processes typical snow cover situations are examined. These situations include cold and wet snow under varying weather conditions. Based on a few examples the diversity of phenomena occuring at the snow surface and within the snow cover is demonstrated. INTRODUCTION Within a short-term flood-forecasting system a snowmelt model should be capable of representing extreme conditions. As Leavesley (1989) points out, a more physically based understanding of the processes involved will improve forecast capabilities. Subjective watching of phenomena together with measuring adequate data of sufficient accuracy and time resolution may form the foundations of process understanding. Most field studies performed so far concentrated on investigating the energy input to snow, particularly under melting conditions (see e.g. Kuusisto, 1986). Differences in the relative importance of processes during contrasting weather conditions have been reported by numerous authors (e.g. Lang, 1986). Considering these differences some of the authors (e.g. Anderson, 1973) distinguished between advection and radiation melt situations in their models. In this study meteorological data and snowpack observations from an alpine experimental plot are presented.
    [Show full text]
  • Estimating Snow Water Equivalent on Glacierized High Elevation Areas (Forni Glacier, Italy)
    1 Estimating the snow water equivalent on glacierized high elevation 2 areas (Forni Glacier, Italy) 3 4 Senese Antonella1, Maugeri Maurizio1, Meraldi Eraldo2, Verza Gian Pietro3, Azzoni Roberto Sergio1, 5 Compostella Chiara4, Diolaiuti Guglielmina1 6 7 1 Department of Environmental Science and Policy, Università degli Studi di Milano, Milan, Italy. 8 2 ARPA Lombardia, Centro Nivometeorologico di Bormio, Bormio, Italy. 9 3 Ev-K2-CNR - Pakistan, Italian K2 Museum Skardu Gilgit Baltistan, Islamabad, Pakistan. 10 4 Department of Earth Sciences, Università degli Studi di Milano, Milan, Italy. 11 12 Correspondence to: Antonella Senese ([email protected]) 13 14 Abstract. 15 We present and compare 11 years of snow data (snow depth and snow water equivalent, SWE) measured by an Automatic 16 Weather Station corroborated by data resulting from field campaigns on the Forni Glacier in Italy. The aim of the analyses is 17 to estimate the SWE of new snowfall and the annual peak of SWE based on the average density of the new snow at the site 18 (corresponding to the snowfall during the standard observation period of 24 hours) and automated depth measurements, as 19 well as to find the most appropriate method for evaluating SWE at this measuring site. 20 The results indicate that the daily SR50 sonic ranger measures allow a rather good estimation of the SWE (RMSE of 45 mm 21 w.e. if compared with snow pillow measurements), and the available snow pit data can be used to define the mean new snow 22 density value at the site. For the Forni Glacier measuring site, this value was found to be 149 ± 6 kg m-3.
    [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]
  • GOES Data Collection System : User Programs / Merle L
    0F .«*? /°* / ft NOAA Technical Memorandum NESS 110 vV^I^.<; GOES DATA COLLECTION SYSTEM - USER PROGRAMS Washington, D.C August 1980 U.S. DEPARTMENT OF National Oceanic and National Environmental COMMERCE / Atmospheric Administration / Satellite Service , NOAA TECHNICAL MEMORANDUMS National Environmental Satellite Service Series The National Environmental Satellite Service (NESS) is responsible for the establishment and oper- ation of NOAA's environmental satellite systems. NOAA Technical Memorandums facilitate rapid distribution of material that may be preliminary in nature and so may be published formally elsewhere at a later date. Publications 1 to 20 and 22 to 25 are in the earlier ESSA National Environmental Satellite Center Technical Memorandum (NESCTM) series. The current NOAA Technical Memorandum NESS series includes 21, 26, and subsequent issuances. Publications listed below are available (also in microfiche form) from the National Technical Informa- tion Service, U.S. Department of Commerce, Sills Bldg. , 5285 Port Royal Road, Springfield, VA 22161. Prices on request. Order by accession number (given in parentheses). Information on memorandums not listed below can be obtained from Environmental Data and Information Service (D822), 6009 Executive Boulevard, Rockville, MD 20852. NESS 66 A Summary of the Radiometric Technology Model of the Ocean Surface in the Microwave Region. John C. Alishouse, March 1975, 24 pp. (COM-75-10849/AS) NESS 67 Data Collection System Geostationary Operational Environmental Satellite: Preliminary Report. Merle L. Nelson, March 1975, 48 pp. (COM-75-10679/AS) NESS 68 Atlantic Tropical Cyclone Classifications for 1974. Donald C. Gaby, Donald R. Cochran, James B. Lushine, Samuel C. Pearce, Arthur C. Pike, and Kenneth 0. Poteat, April 1975, 6 pp.
    [Show full text]