Measurement of Precipitation
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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. -
High Concentrations of Biological Aerosol Particles and Ice Nuclei Open Access During and After Rain Biogeosciences Biogeosciences Discussions J
EGU Journal Logos (RGB) Open Access Open Access Open Access Advances in Annales Nonlinear Processes Geosciences Geophysicae in Geophysics Open Access Open Access Natural Hazards Natural Hazards and Earth System and Earth System Sciences Sciences Discussions Open Access Open Access Atmos. Chem. Phys., 13, 6151–6164, 2013 Atmospheric Atmospheric www.atmos-chem-phys.net/13/6151/2013/ doi:10.5194/acp-13-6151-2013 Chemistry Chemistry © Author(s) 2013. CC Attribution 3.0 License. and Physics and Physics Discussions Open Access Open Access Atmospheric Atmospheric Measurement Measurement Techniques Techniques Discussions Open Access High concentrations of biological aerosol particles and ice nuclei Open Access during and after rain Biogeosciences Biogeosciences Discussions J. A. Huffman1,2, A. J. Prenni3, P. J. DeMott3, C. Pohlker¨ 2, R. H. Mason4, N. H. Robinson5, J. Frohlich-Nowoisky¨ 2, Y. Tobo3, V. R. Despres´ 6, E. Garcia3, D. J. Gochis7, E. Harris2, I. Muller-Germann¨ 2, C. Ruzene2, B. Schmer2, 2,8 9 2 9 5 3 4 Open Access B. Sinha , D. A. Day , M. O. Andreae , J. L. Jimenez , M. Gallagher , S. M. Kreidenweis , A. K. Bertram , and Open Access U. Poschl¨ 2 Climate 1 Climate Department of Chemistry and Biochemistry, University of Denver, 2190 E. Illif Ave., Denver, CO, 80208, USA of the Past 2Max Planck Institute for Chemistry, P.O. Box 3060, 55020, Mainz, Germany of the Past 3Department of Atmospheric Science, Colorado State University, 1371 Campus Delivery, Fort Collins, CO, 80523, USA Discussions 4Department of Chemistry, University of British Columbia, Room D223, 2036 Main Mall, Vancouver, BC, V6T1Z1, Canada Open Access 5Centre for Atmospheric Sciences, University of Manchester, Simon Building, Oxford Road, Manchester, M139PL, UK Open Access 6Institute for General Botany, Johannes Gutenberg University, Mullerweg¨ 6, 55099, Mainz,Earth Germany System Earth System 7National Center for Atmospheric Research, P.O. -
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. -
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. -
Comparison of the Compact Dopplar Radar Rain Gauge and Optical
Short Paper J. Agric. Meteorol. 67 (3): 199–204, 2011 Comparison of the compact dopplar radar rain gauge and optical disdrometer Ko NAKAYA†, and Yasushi TOYODA (Central Research Institute of Electric Power Industry, 1646 Abiko, Abiko, Chiba, 270–1194, Japan) Abstract The operations of a compact Doppler radar rain gauge (R2S; Rufft, FRG) and optical disdrometer (LPM; THIES, FRG) are based on raindrop size distribution (DSD) measurements. We checked the instrumental error of these sensors and compared each sensor with a reference tipping-bucket rain gauge. This is because both rain gauges can detect fine particles and so they can function as rain sensors. The R2S has a measuring bias of rainfall intensity when the drop size distribution differs from the assumed statistical DSD model. The instrumental error on the LPM is small; in fact, the LPM shows good agreement with the reference rain gauge. Where the atmospheric density differs remarkably from the standard elevation, as is the case in highland areas, the R2S requires calibration using a reference rain gauge. The resultant calibration coefficient of the R2S to convert the reading into a reference tip- ping-bucket rain-gauge equivalent was 0.51 in a forest at an elevation of 1380 m. Further gathering of calibration coefficients obtained at different elevations will improve the R2S’s applicability. Key words: Doppler radar rain gauge, Drop size distribution, Optical disdrometer, Tipping-bucket rain gauge. Although the accuracy and suggested errors of rainfall 1. Introduction observations using typical Doppler radar have been Rainfall properties, such as the intensity, amount, reviewed in many studies (Maki et al., 1998, for duration, and type, constitute important meteorological example), reviews for the R2S compared to a reference information that is useful for agriculture and forestation rain gauge are few. -
Iowa (SMAPVEX16-IA) Experiment Plan
Soil Moisture Active Passive Validation Experiment 2016- Iowa (SMAPVEX16-IA) Experiment Plan Iowa Landscape July 2014 Ver. 5/20/16 Table of Contents 1. Introduction .........................................................................................................................8 1.1. Role of Field Campaigns in SMAP Cal/Val...................................................................8 1.2. Science Objectives for a Post-Launch SMAP Aircraft-Based Field Campaign ...............9 1.2.1. Investigate and resolve anomalous observations and products ...............................9 1.2.2. Improving up-scaling functions for CVS .............................................................. 10 1.2.3. Contribution to a broader scientific/application objective.................................... 10 1.2.4. Validate the L2SMAP algorithm process: pending new directions ....................... 13 2. SMAPVEX16 Aircraft Experiment Concept ...................................................................... 13 3. South Fork (SF), Iowa Study Area ..................................................................................... 13 3.1. General Description .................................................................................................... 13 3.2. Land Cover/Vegetation ............................................................................................... 14 3.3. Soils ............................................................................................................................ 15 3.4. Climate ...................................................................................................................... -
Turf Smart Solutions 2021 Product Catalog
TURF SMART SOLUTIONS 2021 PRODUCT CATALOG EASY ORDERING - REQUEST A QUOTE At Spectrum Technologies, we pride Online www.specmeters.com ourselves on helping our valued customers transform plant measurements into more E-mail [email protected] profitable growing decisions. Since 1987, Phone 1 (800) 248-8873 (Toll Free U.S. & Canada) we have manufactured and distributed affordable, leading-edge plant measurement 1 (815) 436-4440 technology to agricultural, horticultural, Fax 1 (815) 436-4460 environmental, and turf markets throughout the world, serving more than 3600 Thayer Court 14,000 customers in over 80 countries. Aurora, IL 60504 USA We proudly work alongside researchers, growers, golf course superintendents, Order Total Shipping/Handling (USA Only) and industry suppliers to provide a broad $25.01 to $99.99 $18.00 line of innovative devices and software Standard $100.00 to $499.99 $24.00 for better environmental monitoring, Ground Delivery: $500.00 to $999.99 $30.00 nutrient management, soil moisture $1,000.00 to $1,999.99 $35.00 measurement, irrigation scheduling, and Express Delivery: $2,000.00 to $2,999.99 $44.00 pest management. (Contact for charges) $3,000.00 to $4,999.99 $56.00 Canadian Delivery: $5,000.00 to $9,999.99 $78.00 (Contact for charges) 10,000 or more Contact for pricing Our products will positively impact the way you grow your turf; therefore, we stand behind every product with our 100% ORDERING INFORMATION Satisfaction Guarantee. Our friendly and Purchase Orders: We accept purchase orders from accredited universities, governments, businesses, and nonprofit organizations. Completed credit application knowledgeable product support team is may be required. -
Advanced Weather Monitoring for a Cable Stayed Bridge
Advanced weather monitoring for a Cable stayed bridge Chandrasekar Venkatesh June 11, 2018 Bachelors in Electrical and Electronics Engineering Ph.D. in Electrical Engineering Department of Electrical Engineering and Computer Science, College of Engineering and Applied Science Committee Chair: Dr. Arthur Helmicki Committee Members: Dr.Victor Hunt Dr. Douglas Nims Dr. Ali Minai Dr. William Wee Abstract In the northern United States, Canada, and many northern European countries, snow and ice pose serious hazards to motorists. Potential traffic disruptions caused by ice and snow are challenges faced by transportation agencies. Successful winter maintenance involves the selection and application of the most optimum strategy, over optimum time intervals. The risk associated with operating the bridges during winter emergencies varies depending on the size of the structure, the material of the stays, volume of average daily traffic, geographical location, nature of terrain and surroundings etc. The ‘Dashboard’, a monitoring system designed to help the bridge maintenance and operation personnel was developed at University of Cincinnati Infrastructure Institute. This was implemented at the Veterans Glass City Skyway Bridge in Toledo, Ohio. This system was also extended to the Port Mann Bridge in Vancouver, Canada. The aim of this research is to come up with an advanced monitoring system which will help the bridge management team make control actions during winter emergencies on the VGCS and Port Mann bridges. The current monitoring system gives information on the status of ice accumulation/ snow accretion or shedding based on last one hour’s weather data. This dissertation focuses on adding intelligence to the existing system through addition of sensors, identifying patterns in events, adding cost-benefit analysis and incorporating forecast parameters, while also extending the system to other bridges and structures. -
By: ESSA RAMADAN MOHAMMAD D F Superintendent of Stations Kuwait
By: ESSA RAMADAN MOHAMMAD Superintendent O f Stations Kuwait Met. Departmentt Geoggpyraphy and climate Kuwait consists mostly of desert and little difference in elevation. It has nine islands, the largest of which is Bubiyan, which is linked to the mainland by a concrete bridge. Summers (April to October) are extremely hot and dry with temperatures exceeding 51 °C(124°F) in Kuwait City several times during the hottest months of June, July and August. April and October are more moderate with temperatures over 40 °C uncommon . Winters (November through February) are cool with some precipitation and average temperatures around 13 °C(56°F) with extremes from ‐2 °Cto27°C. The spring season (Marc h) iswarmand pltleasant with occasilional thund ers torms. Surface coastal water temperatures range from 15 °C(59°F) in February to 35 °C(95°F) in August. The driest months are June through September, while the wettest are January through March. Thunderstorms and hailstorms are common in November, March and April when warm and moist Arabian Gulf air collides with cold air masses from Europe. One such thunderstorm in November 1997 dumped more than ten inches of rain on Kuwait. Kuwait Meteorology Dep artment Meteorological Department Forecasting Supervision Clima tes SiiSupervision Stations & Upper Air Supervision Communications Supervision Maintenance Supervision Stations and Upper air Supervision 1‐ SfSurface manned Sta tions & AWOS 2‐ upper air Stations & Ozone Kuwait Int. Airport Station In December 1962 one manned synoptic, climate, agro stations started to report on 24 hour basis and sending data to WMO Kuwait Int. Airport Station Kuwait started to observe and report meteorological data in the early 1940 with Kuwait Britsh oil company but most of the report were very limited. -
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.