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Snow Nowcasting Using a Real-Time Correlation of Radar Reflectivity
20 JOURNAL OF APPLIED METEOROLOGY VOLUME 42 Snow Nowcasting Using a Real-Time Correlation of Radar Re¯ectivity with Snow Gauge Accumulation ROY RASMUSSEN AND MICHAEL DIXON National Center for Atmospheric Research, Boulder, Colorado STEVE VASILOFF National Severe Storms Laboratory, Norman, Oklahoma FRANK HAGE,SHELLY KNIGHT,J.VIVEKANANDAN, AND MEI XU National Center for Atmospheric Research, Boulder, Colorado (Manuscript received 21 November 2001, in ®nal form 13 June 2002) ABSTRACT This paper describes and evaluates an algorithm for nowcasting snow water equivalent (SWE) at a point on the surface based on a real-time correlation of equivalent radar re¯ectivity (Ze) with snow gauge rate (S). It is shown from both theory and previous results that Ze±S relationships vary signi®cantly during a storm and from storm to storm, requiring a real-time correlation of Ze and S. A key element of the algorithm is taking into account snow drift and distance of the radar volume from the snow gauge. The algorithm was applied to a number of New York City snowstorms and was shown to have skill in nowcasting SWE out to at least 1 h when compared with persistence. The algorithm is currently being used in a real-time winter weather nowcasting system, called Weather Support to Deicing Decision Making (WSDDM), to improve decision making regarding the deicing of aircraft and runway clearing. The algorithm can also be used to provide a real-time Z±S relationship for Weather Surveillance Radar-1988 Doppler (WSR-88D) if a well-shielded snow gauge is available to measure real-time SWE rate and appropriate range corrections are made. -
A Study on Snow Density Variations at Different Elevations
Brian Miretzky 1 A Study on Snow Density Variations at Different Elevations and the Related Consequences; Especially to Forecasting. Brian J. Miretzky Senior Undergrad at the University of Wisconsin- Madison ABSTRACT With observations becoming more common and forecasting becoming more accurate snow density research has increased. These new observations can be correlated with current knowledge to develop reasons for snow density variations. Currently there is still no specific consensus on what are the important characteristics in snow density variations. There are many points of general agreement such as that temperature and relative humidity are key factors. How to incorporate these into snowfall prediction is the key. Once there is more substantiated knowledge, more accurate forecasts can be made. Finally, the last in the chain of events is that society can plan accordingly to this new data. Better prediction means less work, less time, and less money spent. This study attempts to determine if elevation is an important characteristic in snow density variations. It also looks at computer models and how snow density is used in conjunction with these models to make snowfall projections. The results seem to show elevation is not an important characteristic. The results also show that model accuracy is more important than snow density variations when using models to make snowfall projections. 1. Introduction volume of the sample. Snow densities are typically on an order of 70 to 150 kg The density of snow is a m-3, but can be lower or higher in some very important topic in winter weather. cases. This is because the density of snow The fact that snow densities can helps determine characteristics of a vary makes them one of the most given snowfall. -
Weather and Snow Observations for Avalanche Forcasting: an Evaluation of Errors in Measurement and Interpretation
143 WEATHER AND SNOW OBSERVATIONS FOR AVALANCHE FORCASTING: AN EVALUATION OF ERRORS IN MEASUREMENT AND INTERPRETATION R.T. Marriottl and M.B. Moorel Abstract.--Measurements of weather and snow parameters for snow stability forecasting may frequently contain false or misleading information. Such error~ can be attributed primarily to poor selection of the measuring sites and to inconsistent response of the sensors to changing weather conditions. These problems are examined in detail and some remedies are suggested. INTRODUCTION SOURCES OF ERROR A basic premise of snow stability analysis for Errors which arise in instrumented snow and avalanche forecasting is that point measurements of weather measurements can be broken into two, if snow and weather parameters can be used to infer the somewhat overlapping, parts: those associated with snow and weather conditions over a large area. Due the representativeness of the site where the to the complexity of this process in the mountain measurements are to be taken, and those associated environment, this "extrapolation" of data has with the response of the instrument to its largely been accomplished subjectively by an environment. individual experienced with the area in question. This experience was usually gained by visiting the The first source of error is associated with areas of concern, during many differing types of the site chosen for measurements. The topography of conditions, allowing a qualitative correlation mountains results in dramatic variations in between the measured point data and variations in conditions over short distances and often times the snow and weather conditions over the area. these variations are not easily predictable. For example, temperature, which may often be In many instances today, the forecast area has extrapolated to other elevations using approximate expanded, largely due to increased putlic use of lapse rates, may on some occasions be complicated by avalanche-prone terrain (e.g. -
Evaluation of the Hotplate Snow Gauge
Evaluation of the Hotplate Snow Gauge http://aurora-program.org Aurora Project 2004-01 Final Report July 2005 Technical Report Documentation Page 1. Report No. 2. Government Accession No. 3. Recipient’s Catalog No. Aurora Project 2004-01 4. Title and Subtitle 5. Report Date Evaluation of the Hotplate Snow Gauge July 2005 6. Performing Organization Code 7. Author(s) 8. Performing Organization Report No. Jack Stickel, Bill Maloney, Curt Pape, Dennis Burkheimer 9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) Center for Transportation Research and Education Iowa State University 11. Contract or Grant No. 2711 South Loop Drive, Suite 4700 Ames, IA 50010-8664 12. Sponsoring Organization Name and Address 13. Type of Report and Period Covered Aurora Program Iowa State University 14. Sponsoring Agency Code 2711 South Loop Drive, Suite 4700 Ames, IA 50010-8664 15. Supplementary Notes Visit www.ctre.iastate.edu for color PDF files of this and other research reports. 16. Abstract Winter precipitation (e.g., snow, ice, freezing rain) is poorly measured by current National Weather Service (NWS), Federal Aviation Administration (FAA), and State Departments of Transportation (SDOT) automated weather observation systems. The lack of accurate winter precipitation measurements, particularly snow, negatively impacts the ability of winter maintenance personnel to conduct snow and ice control operations. The inability to accurately measure winter precipitation is an ongoing problem that is well recognized by the meteorological community as well as organizations and industries dependent on accurate quantitative precipitation information. The FAA recognized this limitation and its impact on the ability to conduct aircraft deicing operations, and began a research program in the 1990s to improve decision support for aircraft deicing. -
Measurement of Precipitation
CHAPTER CONTENTS Page CHAPTER 6. MEASUREMENT OF PRECIPITATION ..................................... 186 6.1 General ................................................................... 186 6.1.1 Definitions ......................................................... 186 6.1.2 Units and scales ..................................................... 186 6.1.3 Meteorological and hydrological requirements .......................... 187 6.1.4 Measurement methods .............................................. 187 6.1.4.1 Instruments ................................................ 187 6.1.4.2 Reference gauges and intercomparisons ........................ 188 6.1.4.3 Documentation. 188 6.2 Siting and exposure ........................................................ 189 6.3 Non-recording precipitation gauges .......................................... 190 6.3.1 Ordinary gauges .................................................... 190 6.3.1.1 Instruments ................................................ 190 6.3.1.2 Operation. 192 6.3.1.3 Calibration and maintenance ................................. 192 6.3.2 Storage gauges ..................................................... 192 6.4 Precipitation gauge errors and corrections ..................................... 193 6.5 Recording precipitation gauges .............................................. 196 6.5.1 Weighing-recording gauge ........................................... 196 6.5.1.1 Instruments ................................................ 196 6.5.1.2 Errors and corrections. 197 6.5.1.3 Calibration -
Snow Accumulation Algorithm for the Wsr-88D Radar: Supplemental Report
R-99-11 SNOW ACCUMULATION ALGORITHM FOR THE WSR-88D RADAR: SUPPLEMENTAL REPORT November 1999 U.S. DEPARTMENT OF THE INTERIOR Bureau of Reclamation Technical Service Center Civil Engineering Services Materials Engineering and Research Laboratory Denver, Colorado R-99-11 SNOW ACCUMULATION ALGORITHM FOR THE WSR-88D RADAR: SUPPLEMENTAL REPORT by Edmond W. Holroyd, III Technical Service Center Civil Engineering Services Materials Engineering and Research Laboratory Denver, Colorado November 1999 UNITED STATES DEPARTMENT OF THE INTERIOR ò BUREAU OF RECLAMATION ACKNOWLEDGMENTS This work extends previous efforts that were supported primarily by the WSR-88D (Weather Surveillance Radar - 1988 Doppler) OSF (Operational Support Facility) and the NEXRAD (Next Generation Weather Radar) Program. Significant additional support was provided by the Bureau of Reclamation’s Research and Technology Transfer Program, directed by Dr. Stanley Ponce, and by the NOAA (National Oceanic and Atmospheric Administration) Office of Global Programs GEWEX (Global Energy and Water Cycle Experiment) GCIP (Continental-Scale International Project ), directed by Dr. Rick Lawford. Most of the work for this supplemental report was performed and coordinated by Dr. Arlin B. Super, since retired. Programming and data support was provided by Ra Aman, Linda Rogers, and Anne Reynolds. In additional, we had useful feedback from several NWS (National Weather Service) personnel. Reviewer comments by Curt Hartzell and Mark Fresch were very helpful. U.S. Department of the Interior Mission Statement The Mission of the Department of the Interior is to protect and provide access to our Nation’s natural and cultural heritage and honor our trust responsibilities to tribes. Bureau of Reclamation Mission Statement The mission of the Bureau of Reclamation is to manage, develop, and protect water and related resources in an environmentally and economically sound manner in the interest of the American public. -
Vol.37 No.6 D'océanographie
ISSN 1195-8898 . CMOS Canadian Meteorological BULLETIN and Oceanographic Society SCMO La Société canadienne de météorologie et December / décembre 2009 Vol.37 No.6 d'océanographie Le réseau des stations automatiques pour les Olympiques ....from the President’s Desk Volume 37 No.6 December 2009 — décembre 2009 Friends and colleagues: Inside / En Bref In late October I presented the CMOS from the President’s desk Brief to the House of Allocution du président Commons Standing by/par Bill Crawford page 177 Committee on Finance at its public hearing in Cover page description Winnipeg. (The full text Description de la page couverture page 178 of this brief was published in our Highlights of Recent CMOS Meetings page 179 October Bulletin). Ron Correspondence / Correspondance page 179 Stewart accompanied me in this presentation. Articles He is a past president of CMOS and Head of the The Notoriously Unpredictable Monsoon Department of by Madhav Khandekar page 181 Environment and Geography at the The Future Role of the TV Weather Bill Crawford Presenter by Claire Martin page 182 CMOS President University of Manitoba. In the five minutes for Président de la SCMO Ocean Acidification by James Christian page 183 our talk we presented three requests for the federal government to consider in its The Interacting Scale of Ocean Dynamics next budget: Les échelles d’interaction de la dynamique océanique by/par D. Gilbert & P. Cummins page 185 1) Introduce measures to rapidly reduce greenhouse gas emissions; Interview with Wendy Watson-Wright 2) Invest funds in the provision of science-based climate by Gordon McBean page 187 information; 3) Renew financial support for research into meteorology, On the future of operational forecasting oceanography, climate and ice science, especially in tools by Pierre Dubreuil page 189 Canada’s North, through independent, peer-reviewed projects managed by agencies such as CFCAS and Weather Services for the 2010 Winter NSERC. -
260-2510 Standard Rain and Snow Gauge
Precipitation 260-2510 Standard Rain and Snow Gauge The 260-2510 Standard Rain and Snow Gauge is a National Weather Service type all-aluminum rain gauge with a total capacity of 20" of rainfall. The gauge includes a funnel, measuring tube, overflow can and measuring stick with English and metric markings. The tripod support is sold separately. The upper portion of the funnel is cylindrical in shape and is turned to a fine edge. Rainwater falling into the funnel is delivered into a measuring tube. The cross- section area of the tube is one-tenth the cross-section area of the funnel. Therefore, when 1 inch of rain falls into the funnel, it fills the measuring tube to a depth of 10 inches. The scale on the measuring stick is expanded 10 times, and since the scale is graduated to hundredths of an inch, the correct rainfall depth of water in the tube is read directly to hundredths from the stick. The capacity of the measuring tube is 2" of rainfall. Any excess overflows into the outer chamber. The overflow water must be transferred to the empty measuring tube for direct measurement with the stick. In winter, the funnel and measuring tube are removed so that rain/sleet/snow/hail are collected by the outer chamber. The amount of precipitation is measured by melting the ice and then pouring the water into the measuring tube. 260-2510 Rain Gauge Features with 260-2510S Tripod National Weather Service Type Rain and Snow Gauge Total capacity 20 inches (500 mm) English / metric measuring stick included Optional tripod support stand Specifications -
The Measurement Factors in Estimating Snowfall Derived from Snow Cover Surfaces Using Acoustic Snow Depth Sensors
MARCH 2011 F I S C H E R 681 The Measurement Factors in Estimating Snowfall Derived from Snow Cover Surfaces Using Acoustic Snow Depth Sensors ALEXANDRE P. FISCHER Observing Systems and Engineering, Weather and Environmental Monitoring, Environment Canada, Toronto, Ontario, Canada (Manuscript received 8 October 2009, in final form 14 October 2010) ABSTRACT At three Canadian test locations during the cold seasons of 2006 and 2007/08, snowfall measurements are derived from changes in the total depth of snow on the ground using multiple Campbell Scientific, Inc., SR50 ultrasonic ranging sensors over very short (minute–hour) time scales. Data analysis reveals that, because of the interplay of numerous essential factors that influence snow cover levels, the measurements exhibit a strong dependence on the time interval between consecutive measurements used to generate the snowfall value. This finding brings into question the reasonable accuracy of snowfall measurements that are derived from the snow cover surface using automated methods over very short time scales. In this study, two math- ematical methods are developed to assist in quantifying the magnitude of the snowfall measurement error. From time-series analysis, the suggested characteristics of the snowdrift signal in the snow depth time series is shown by using measurements taken by FlowCapt Snowdrift acoustic sensors. Furthermore, the use of three collocated SR50s shows that repeated snow depth measurements represent three pairwise essentially dif- ferent time series. These results question the reasonable accuracy of snowfall measurements derived using only a single ultrasonic ranging sensor, especially in cases in which the snow cover is redistributed by the wind and in which snow depth spatial variability is prominent. -
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DECEMBER 2007 C H E R R Y E T A L . 1243 Development of the Pan-Arctic Snowfall Reconstruction: New Land-Based Solid Precipitation Estimates for 1940–99 J. E. CHERRY International Arctic Research Center, and Arctic Region Supercomputing Center, University of Alaska Fairbanks, Fairbanks, Alaska L.-B. TREMBLAY Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Quebec, Canada M. STIEGLITZ School of Civil and Environmental Engineering, and School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia G. GONG Department of Earth and Environmental Engineering, Columbia University, New York, New York S. J. DÉRY Environmental Science and Engineering Program, University of Northern British Columbia, Prince George, British Columbia, Canada (Manuscript received 24 April 2006, in final form 5 January 2007) ABSTRACT A new product, the Pan-Arctic Snowfall Reconstruction (PASR), is developed to address the problem of cold season precipitation gauge biases for the 1940–99 period. The method used to create the PASR is different from methods used in other large-scale precipitation data products and has not previously been employed for estimating pan-arctic snowfall. The NASA Interannual-to-Seasonal Prediction Project Catch- ment Land Surface Model is used to reconstruct solid precipitation from observed snow depth and surface air temperatures. The method is tested at four stations in the United States and Canada where results are examined in depth. Reconstructed snowfall at Dease Lake, British Columbia, and Barrow, Alaska, is higher than gauge observations. Reconstructed snowfall at Regina, Saskatchewan, and Minot, North Dakota, is lower than gauge observations, probably because snow is transported by wind out of the Prairie region and enters the hydrometeorological cycle elsewhere. -
JO 7900.5D Chg.1
U.S. DEPARTMENT OF TRANSPORTATION JO 7900.SD CHANGE CHG 1 FEDERAL AVIATION ADMINISTRATION National Policy Effective Date: 11/29/2017 SUBJ: JO 7900.SD Surface Weather Observing 1. Purpose. This change amends practices and procedures in Surface Weather Observing and also defines the FAA Weather Observation Quality Control Program. 2. Audience. This order applies to all FAA and FAA-contract personnel, Limited Aviation Weather Reporting Stations (LAWRS) personnel, Non-Federal Observation (NF-OBS) Program personnel, as well as United States Coast Guard (USCG) personnel, as a component ofthe Department ofHomeland Security and engaged in taking and reporting aviation surface observations. 3. Where I can find this order. This order is available on the FAA Web site at http://faa.gov/air traffic/publications and on the MyFAA employee website at http://employees.faa.gov/tools resources/orders notices/. 4. Explanation of Changes. This change adds references to the new JO 7210.77, Non Federal Weather Observation Program Operation and Administration order and removes the old NF-OBS program from Appendix B. Backup procedures for manual and digital ATIS locations are prescribed. The FAA is now the certification authority for all FAA sponsored aviation weather observers. Notification procedures for the National Enterprise Management Center (NEMC) are added. Appendix B, Continuity of Service is added. Appendix L, Aviation Weather Observation Quality Control Program is also added. PAGE CHANGE CONTROL CHART RemovePa es Dated Insert Pa es Dated ii thru xi 12/20/16 ii thru xi 11/15/17 2 12/20/16 2 11/15/17 5 12/20/17 5 11/15/17 7 12/20/16 7 11/15/17 12 12/20/16 12 11/15/17 15 12/20/16 15 11/15/17 19 12/20/16 19 11/15/17 34 12/20/16 34 11/15/17 43 thru 45 12/20/16 43 thru 45 11/15/17 138 12/20/16 138 11/15/17 148 12/20/16 148 11/15/17 152 thru 153 12/20/16 152 thru 153 11/15/17 AppendixL 11/15/17 Distribution: Electronic 1 Initiated By: AJT-2 11/29/2017 JO 7900.5D Chg.1 5. -
Snow, Weather, and Avalanches
SNOW, WEATHER, AND AVALANCHES: Observation Guidelines for Avalanche Programs in the United States SNOW, WEATHER, AND AVALANCHES: Observation Guidelines for Avalanche Programs in the United States 3rd Edition 3rd Edition Revised by the American Avalanche Association Observation Standards Committee: Ethan Greene, Colorado Avalanche Information Center Karl Birkeland, USDA Forest Service National Avalanche Center Kelly Elder, USDA Forest Service Rocky Mountain Research Station Ian McCammon, Snowpit Technologies Mark Staples, USDA Forest Service Utah Avalanche Center Don Sharaf, Valdez Heli-Ski Guides/American Avalanche Institute Editor — Douglas Krause — Animas Avalanche Consulting Graphic Design — McKenzie Long — Cardinal Innovative © American Avalanche Association, 2016 ISBN-13: 978-0-9760118-1-1 American Avalanche Association P.O. Box 248 Victor, ID. 83455 [email protected] www. americanavalancheassociation.org Citation: American Avalanche Association, 2016. Snow, Weather and Avalanches: Observation Guidelines for Avalanche Programs in the United States (3rd ed). Victor, ID. FRONT COVER PHOTO: courtesy Flathead Avalanche Center BACK COVER PHOTO: Chris Marshall 2 PREFACE It has now been 12 years since the American Avalanche Association, in cooperation with the USDA Forest Service National Ava- lanche Center, published the inaugural edition of Snow, Weather and Avalanches: Observational Guidelines for Avalanche Programs in the United States. As those of us involved in that first edition grow greyer and more wrinkled, a whole new generation of avalanche professionals is growing up not ever realizing that there was a time when no such guidelines existed. Of course, back then the group was smaller and the reference of the day was the 1978 edition of Perla and Martinelli’s Avalanche Handbook.