What Is the Appropriate RAWS Network?
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University of Oklahoma
UNIVERSITY OF OKLAHOMA GRADUATE COLLEGE INVESTIGATION OF POLARIMETRIC MEASUREMENTS OF RAINFALL AT CLOSE AND DISTANT RANGES A DISSERTATION SUBMITTED TO THE GRADUATE FACULTY in partial fulfillment of the requirements for the degree of Doctor of Philosophy By SCOTT EDWARD GIANGRANDE Norman, Oklahoma 2007 UMI Number: 3291249 UMI Microform 3291249 Copyright 2008 by ProQuest Information and Learning Company. All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code. ProQuest Information and Learning Company 300 North Zeeb Road P.O. Box 1346 Ann Arbor, MI 48106-1346 INVESTIGATION OF POLARIMETRIC MEASUREMENTS OF RAINFALL AT CLOSE AND DISTANT RANGES A DISSERTATION APPROVED FOR THE SCHOOL OF METEOROLOGY BY ____________________________________ Dr. Michael Biggerstaff ____________________________________ Dr. Alexander Ryzhkov ____________________________________ Dr. Jerry Straka ____________________________________ Dr. Guifu Zhang ____________________________________ Dr. Mark Yeary © Copyright by SCOTT EDWARD GIANGRANDE 2007 All Rights Reserved. ACKNOWLEDGEMENTS I would like to extend my sincerest thanks to the numerous individuals who have helped me complete this work. To begin, this work would not have been possible without the guidance of my primary research advisor, Dr. Alexander Ryzhkov. His leadership and patience were instrumental throughout this process. I would also like to extend my gratitude to the other members of my committee: Drs. Michael Biggerstaff (Co-Chair and primary OU School of Meteorology advisor), Guifu Zhang, Jerry Straka, and Mark Yeary. The reviews performed by these individuals strengthened this work. I also thank the members of the Radar Research and Development Division (RRDD) at the National Severe Storms Laboratory (NSSL), which includes Drs. Douglas Forsyth, Dusan Zrnic, Dick Doviak, Allen Zahrai, Terry Schuur, Pam Heinselman, Valery Melnikov, Sebastian Torres, Pengfei Zhang and Svetlana Bachmann. -
Kentucky Mesonet Weather Station Moves to Ephram White Park
Home / News https://www.bgdailynews.com/news/kentucky-mesonet-weather-station-moves-to-ephram-white- park/article_5c142169-496b-5905-90c0-fb3ac31cfab0.html Kentucky Mesonet weather station moves to Ephram White Park By AARON MUDD [email protected] 9 min ago A Kentucky Mesonet weather station was recently relocated to Warren County’s Ephram White Park from its previous location near the General Motors Bowling Green Assembly Plant. Submitted photo courtesy of WKU Visitors to Ephram White Park might notice a new feature – a Kentucky Mesonet weather station has been relocated to the park from its previous location near the General Motors Corvette plant. Stuart Foster, director of the Kentucky Mesonet and the Kentucky Climate Center, said the move comes after the station was damaged during a lightning strike. He said the new location shouldn’t afect forecasting in the area. On the contrary, the real- time weather information the station ofers could become even more valuable in its new location at the park at 885 Mount Olivet Road. “With the growing population, nearby schools and industrial park, we felt it was important to keep coverage in that area so we worked with Chris Kummer and Warren County Parks and Recreation and partnered with them on the site at Ephram White Park,” Foster said in a news release. Part of a statewide network of 71 stations in 69 counties, the Mesonet station collects real- time data on temperature, precipitation, humidity, barometric pressure, solar radiation, soil moisture, soil temperature, wind speed and direction. That data is transmitted to the Kentucky Climate Center at WKU every fve minutes, 24 hours a day throughout the year, and is available online at kymesonet.org. -
Weather Station Handbook
PART 2A. MANUAL WEATHER STATIONS: MEASUREMENTS; INSTRUMENTS This portion of the handbook focuses on manual 5. Mechanical wind counte r equipped with a timer wea ther station instru ments and thei r operational fea (Forester IO-Minute Wind Counter), or other suitable tures. The individual chapters first define and describe read out device, such as the Forester (Haytronics) Total the weather elements or parameters that are measured izing Wind Counter. The counter enables measurement at fire-weather an d other stations. of average windspeed in miles per hour. The generator Observers with a basic understanding of weather in an emometers mentione-d above have a n electronic accu struments, and wha t the measurements re present, are mulator or odomete r that can give a digital readout of better prepared toward obtaining accurate wea ther data. IO-minute average windspeed. They will be better able to recognize an erroneous rea ding 6. Wind direction system-including wind vane and or defective instrumen t. Similarly, persona assigned the remote readout device. Again, no particular model has task will be more likely to properly install and maintai n been adopted as the standard. the instrume nts. 7. Nonrecording ("stick") rai n gauge, with support; In addition. a n increased understandin g of the instru gauge has 8-inch diameter and may be either large capac ments may bring greater satisfaction to what might other ity type or Fores t Service type. Measuri ng stick gives wise become a routine, mechan ical task. An understand rainfall in hundred ths of an inch. ing of the weather elements and processe s may further 8. -
National Data Buoy Center Command Briefing for Marine Technology Society Oceans in Action
National Data Buoy Center Command Briefing For Marine Technology Society Oceans in Action August 21, 2014 Helmut H. Portmann, Director National Data Buoy Center To provide• a real-time, end-to-end capability beginning with the collection of marine atmospheric and oceanographic data and ending with its transmission, quality control and distribution. NDBC Weather Forecast Offices/ IOOS Partners Tsunami Warning & other NOAA River Forecast Centers Platforms Centers observations MADIS NWS Global NDBC Telecommunication Mission Control System (GTS) Center NWS/NCEP Emergency Managers Oil & Gas Platforms HF Radars Public NOAA NESDIS (NCDC, NODC, NGDC) DATA COLLECTION DATA DELIVERY NDBC Organization National Weather Service Office of Operational Systems NDBC Director SRQA Office 40 Full-time Civilians (NWS) Mission Control Operations Engineering Support Services Mission Mission Information Field Production Technology Logistics and Business Control Support Technology Operations Engineering Development Facilities Services Center Engineering 1 NOAA Corps Officer U.S. Coast Guard Liaison Office – 1 Lt & 4 CWO Bos’ns NDBC Technical Support Contract –90 Contractors Pacific Architects and Engineers (PAE) National Data Buoy Center NDBC is a cradle to grave operation - It begins with requirements and engineering design, then continues through purchasing, fabrication, integration, testing, logistics, deployment and maintenance, and then with observations ingest, processing, analysis, distribution in real time NDBC’s Ocean Observing Networks Wx DART Weather -
UMAP Lidars (UMBC Measurements of Atmospheric Pollution)
UMAP Lidars (UMBC Measurements of Atmospheric Pollution) Raymond Hoff Ruben Delgado, Tim Berkoff, Jamie Compton, Kevin McCann, Patricia Sawamura, Daniel Orozco, UMBC October 5 , 2010 DISCOVR-AQ Engel-Cox, J. et.al. 2004. Atmospheric Environment. Baltimore, MD Summer 2004 100 1.6 90 Old Town TEOMPM2.5 MODIS 1.4 80 MODIS AOD July 21 ) 1.2 3 70 Mixed down July 9 smoke 1 g/m 60 High altitude µ ( smoke 50 0.8 (g ) AOD 2.5 40 0.6 PM 30 0.4 20 0.2 MODISAerosol Optical Depth 10 0 0 07/01/04 07/08/04 07/15/04 07/22/04 07/29/04 08/05/04 08/12/04 08/19/04 08/26/04 Courtesy EPA/UWisconsin Conceptually simple idea Η α Extinction Profile and PBL H 100 1.6 Smoke mixing in Hourly PM2.5 90 Daily Average PM2.5 1.4 Maryland 80 MODIS AOD Lidar OD Total Column 20-22 July 2004 Lidar OD Below Boundary Layer 1.2 ) 70 3 60 1 (ug/m 50 0.8 2.5 40 0.6 PM 30 Depth Optical 0.4 20 10 0.2 0 0 07/19/04 07/20/04 07/21/04 07/22/04 07/23/04 Before upgrading: 2008 Existing ALEX - UV N2, H2O Raman lidar - 355, 389, 407 nm ELF - 532(⊥,), 1064 elastic lidar GALION needs QA/QC site for lidars in US Have all the wavelengths that people might use routinely Ability to cross-calibrate instruments Need to add MPL and LEOSPHERE Micropulse Lidar Leosphere UMBC Monitoring of Atmospheric Pollution (UMAP) (NASA Radiation Sciences Supported Project) Weblinks to instruments http://alg.umbc.edu/UMAP Data available 355 nm Leosphere retrieval (new) New instruments BAM and TEOM (PM2.5 continuously) TSI 3683 (3λ nephelometer) CIMEL (8λ AOD, size distribution) LICEL (new detector package for ALEX) MPL Shareware Concept Lidar and radiosonde measurements were also used to evaluate nearby ACARS PBLH estimates at RFK and BWI airports. -
The Impacts on Flow by Hydrological Model with NEXRAD Data: a Case Study on a Small Watershed in Texas, USA Taesoo Lee*
Journal of the Korean Geographical Society, Vol. 46, No. 2, 2011(168~180) The Impacts on Flow by Hydrological Model with NEXRAD Data: A Case Study on a small Watershed in Texas, USA Taesoo Lee* 레이더 강수량 데이터가 수문모델링에서 수량에 미치는 영향 -미국 텍사스의 한 유역을 사례로- 이태수* Abstract:The accuracy of rainfall data for a hydrological modeling study is important. NEXRAD (Next Generation Radar) rainfall data estimated by WRS-88D (Weather Surveillance Radar - 1988 Doppler) radar system has advantages of its finer spatial and temporal resolution. In this study, NEXRAD rainfall data was tested and compared with conventional weather station data using the previously calibrated SWAT (Soil and Water Assessment Tool) model to identify local storms and to analyze the impacts on hydrology. The previous study used NEXRAD data from the year of 2000 and the NEXRAD data was substituted with weather station data in the model simulation in this study. In a selected watershed and a selected year (2006), rainfall data between two datasets showed discrepancies mainly due to the distance between weather station and study area. The largest difference between two datasets was 94.5 mm (NEXRAD was larger) and 71.6 mm (weather station was larger) respectively. The differences indicate that either recorded rainfalls were occurred mostly out of the study area or local storms only in the study area. The flow output from the study area was also compared with observed data, and modeled flow agreed much better when the simulation used NEXRAD data. Key Words : Local storm, NEXRAD, Radar, Rainfall, SWAT 요약:강수량 데이터의 정확성은 수리모델링에서 중요하다. -
Analyzing Surface Weather Conditions on the Mesoscale
Analyzing Surface Weather Conditions on the Mesoscale John Horel Department of Meteorology University of Utah [email protected] • Acknowledgements – Dan Tyndall & Xia Dong (Univ. of Utah) – Manuel Pondeca (NCEP) • References – Kalnay, E., 2003: Atmospheric Modeling, Data Assimilation and Predictability. Cambridge – Myrick, D., and J. Horel, 2006: Verification over the Western United States of surface temperature forecasts from the National Digital Forecast Database. Wea. Forecasting, 21, 869-892. – Benjamin, S., J. M. Brown, G. Manikin, and G. Mann, 2007: The RTMA background – hourly downscaling of RUC data to 5-km detail. Preprints, 22nd Conf. on WAF/18th Conf. on NWP, Park City, UT, Amer. Meteor. Soc., 4A.6. – De Pondeca, M., and Coauthors, 2007: The status of the Real Time Mesoscale Analysis at NCEP. Preprints, 22nd Conf. on WAF/18th Conf. on NWP, Park City, UT, Amer. Meteor. Soc., 4A.5. – Horel, J., and B. Colman, 2005: Real-time and retrospective mesoscale objective analyses. Bull. Amer. Meteor. Soc., 86, 1477-1480. – Manikin, G. and M. Pondeca, 2009: Challenges with the Real Time Mesoscale Analysis (RTMA). 23WAF19NWP. June 2009. – Pondeca, M., G. Manikin, 2009: Recent improvements to the Real-Time Mesoscale Analysis (RTMA). 23WAF19NWP. June 2009. – Tyndall, D., J. Horel, M. Pondeca, 2009: Sensitivity of surface temperature analyses to background and observation errors. Submitted to Wea. Forecasting Class Discussion Points • Why are analyses needed? – Application driven: data assimilation for NWP (forecasting) vs. objective analysis -
2020 Infra Surface Weather Observations
Surface Weather Observations Comparison of Various Observing Systems Scott Landolt & Matthias Steiner National Center for Atmospheric Research [email protected] USHST Infrastructure Summit 12 – 13 March 2020 in Washington, DC © 2020 University Corporation for Atmospheric Research 1 Surface Stations & Reporting Frequency Station Type Frequency of Reports Automated Surface 5 minutes Observing System (ASOS) (limited access to 1 minute data) Automated Weather 20 minutes Observing System (AWOS) 15 minutes (standard), can be Road Weather Information more frequent but varies state to System (RWIS) state and even site to site 5 – 15 minutes, can vary from Mesonet station to station Iowa station network © 2020 University Corporation for Atmospheric Research 2 Reporting Variables Weather Variable ASOS AWOS RWIS Mesonet Temperature X X X X Relative X X X X Humidity/Dewpoint Wind Speed/Direction X X X X Barometric Pressure X X X X Ceiling Height X X X X Visibility X X X X Present Weather X X X X Precipitation X X X X Accumulation Road Condition X X X X X – All Stations Report X – Some Stations Report X – No Stations Report © 2020 University Corporation for Atmospheric Research 3 Station Siting Requirements Station Type Siting Areal Representativeness Automated Surface Miles (varies depending on Airport grounds, unobstructed Observing System (ASOS) local conditions & weather) Automated Weather Miles (varies depending on Airport grounds, unobstructed Observing System (AWOS) local conditions & weather) Next to roadways, can be in canyons, valleys, mountain -
Chris Gibson, National Weather Service, Salt Lake City, UT 84116
2.6 FARSITE WEATHER STREAMS FROM THE NWS IFPS SYSTEM Chris Gibson * National Weather Service, Salt lake City, Utah Carl Gorski Deputy Chief, Meteorological Services Division National Weather Service, Western Region Headquarters, Salt lake City, Utah 1. Introduction quality and flash flood potential forecasts (Gibson 2000). Products can be derived from the DFD in text, The National Oceanic and Atmospheric grib, graphical or interactive web based formats. This Administration’s National Weather Service (NWS) paper describes a demonstration project that creates provides a wide range of fire weather products and a tabular text product specifically designed as input services. Over the last several years, NWS Weather for the Fire Area Simulation Model (FARSITE). Forecast Offices (WFO) have begun to derive these services from a locally created Digital Forecast 2. FARSITE - Fire Area Simulation System Database (DFD) as part of the Interactive Forecast Process System (IFPS). The local DFD in turn is FARSITE was developed by Dr. Mark Finney used to populate the National Digital Forecast of the Department of Agriculture’s Missoula Fire Lab, Database. Fire Behavior Project. The Fire Area Simulator is a two-dimensional model of fire growth which The DFD allows the flexibility to generate incorporates existing fire behavior models of surface national, regional and local products tailored to the fire spread, crown fire, spotting, point source fire customer or partner who requests it. Products from acceleration and fuel moisture. The fire is modeled the DFD can be easily formatted in text, grib, assuming the fire front is a row of independent small graphical or interactive web based formats. -
Relative Forecast Impact from Aircraft, Profiler, Rawinsonde, VAD, GPS-PW, METAR and Mesonet Observations for Hourly Assimilation in the RUC
16.2 Relative forecast impact from aircraft, profiler, rawinsonde, VAD, GPS-PW, METAR and mesonet observations for hourly assimilation in the RUC Stan Benjamin, Brian D. Jamison, William R. Moninger, Barry Schwartz, and Thomas W. Schlatter NOAA Earth System Research Laboratory, Boulder, CO 1. Introduction A series of experiments was conducted using the Rapid Update Cycle (RUC) model/assimilation system in which various data sources were denied to assess the relative importance of the different data types for short-range (3h-12h duration) wind, temperature, and relative humidity forecasts at different vertical levels. This assessment of the value of 7 different observation data types (aircraft (AMDAR and TAMDAR), profiler, rawinsonde, VAD (velocity azimuth display) winds, GPS precipitable water, METAR, and mesonet) on short-range numerical forecasts was carried out for a 10-day period from November- December 2006. 2. Background Observation system experiments (OSEs) have been found very useful to determine the impact of particular observation types on operational NWP systems (e.g., Graham et al. 2000, Bouttier 2001, Zapotocny et al. 2002). This new study is unique in considering the effects of most of the currently assimilated high-frequency observing systems in a 1-h assimilation cycle. The previous observation impact experiments reported in Benjamin et al. (2004a) were primarily for wind profiler and only for effects on wind forecasts. This new impact study is much broader than that the previous study, now for more observation types, and for three forecast fields: wind, temperature, and moisture. Here, a set of observational sensitivity experiments (Table 1) were carried out for a recent winter period using 2007 versions of the Rapid Update Cycle assimilation system and forecast model. -
Online Fruit Pest Management Tool (Utah Traps) in Montana What Is
MSU Western Ag Research Center 580 Quast Lane, Corvallis, MT 59870 http://agresearch.montana.edu/warc/index.html Online fruit pest management tool (Utah TRAPs) in Montana What is TRAPs? TRAPs is an online or app-based pest management tool. TRAPs uses degree days to predict insect emergence and life stages (phenology), providing site-specific information for monitoring and controlling certain pests. MSU Western Agricultural Research Center is collaborating with Utah State University to use TRAPs to assist with tree fruit pest management, including codling moth and fire blight. Codling moth is an insect pest; fire blight is a bacterial plant disease. Both affect apple and certain related species of fruit. Features • View pest emergence rates or predicted disease risk, and treatment information for: o Codling moth o Fire blight o Western cherry fruit fly o Spotted wing drosophila (SWD) o Other fruit tree pests • Get pest recommendations for up to 3 weeks in the future. • View graphs that display 24 hours of temperature, dew point, wind, and precipitation data. Available Online https://climate.usu.edu/traps/ The current Montana TRAPs sites are Corvallis (COVM), Stevensville (STVM8), Flathead Lake (FLB05), Helena (E5549), Bozeman (BOZM), Columbus (C9321), and Miles City (KMLS). You can find them by either navigating on the map to their location or selecting them from the drop-down menu underneath “others courtesy of USU MesoWest”. Download the App Search for Utah TRAPs Available for Android or iPhone Select from the Montana weather stations: Corvallis (COVM), Stevensville (STVM8), Flathead Lake (FLB05), Helena (E5549), Bozeman (BOZM), Columbus (C9321), or Miles City (KMLS). -
Standard Methods for the Examination of Water and Wastewater
Standard Methods for the Examination of Water and Wastewater Part 1000 INTRODUCTION 1010 INTRODUCTION 1010 A. Scope and Application of Methods The procedures described in these standards are intended for the examination of waters of a wide range of quality, including water suitable for domestic or industrial supplies, surface water, ground water, cooling or circulating water, boiler water, boiler feed water, treated and untreated municipal or industrial wastewater, and saline water. The unity of the fields of water supply, receiving water quality, and wastewater treatment and disposal is recognized by presenting methods of analysis for each constituent in a single section for all types of waters. An effort has been made to present methods that apply generally. Where alternative methods are necessary for samples of different composition, the basis for selecting the most appropriate method is presented as clearly as possible. However, samples with extreme concentrations or otherwise unusual compositions or characteristics may present difficulties that preclude the direct use of these methods. Hence, some modification of a procedure may be necessary in specific instances. Whenever a procedure is modified, the analyst should state plainly the nature of modification in the report of results. Certain procedures are intended for use with sludges and sediments. Here again, the effort has been to present methods of the widest possible application, but when chemical sludges or slurries or other samples of highly unusual composition are encountered, the methods of this manual may require modification or may be inappropriate. Most of the methods included here have been endorsed by regulatory agencies. Procedural modification without formal approval may be unacceptable to a regulatory body.