Mesonet Programs – Needs and Best Practices
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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. -
The Error Is the Feature: How to Forecast Lightning Using a Model Prediction Error [Applied Data Science Track, Category Evidential]
The Error is the Feature: How to Forecast Lightning using a Model Prediction Error [Applied Data Science Track, Category Evidential] Christian Schön Jens Dittrich Richard Müller Saarland Informatics Campus Saarland Informatics Campus German Meteorological Service Big Data Analytics Group Big Data Analytics Group Offenbach, Germany ABSTRACT ACM Reference Format: Despite the progress within the last decades, weather forecasting Christian Schön, Jens Dittrich, and Richard Müller. 2019. The Error is the is still a challenging and computationally expensive task. Current Feature: How to Forecast Lightning using a Model Prediction Error: [Ap- plied Data Science Track, Category Evidential]. In Proceedings of 25th ACM satellite-based approaches to predict thunderstorms are usually SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’19). based on the analysis of the observed brightness temperatures in ACM, New York, NY, USA, 10 pages. different spectral channels and emit a warning if a critical threshold is reached. Recent progress in data science however demonstrates 1 INTRODUCTION that machine learning can be successfully applied to many research fields in science, especially in areas dealing with large datasets. Weather forecasting is a very complex and challenging task requir- We therefore present a new approach to the problem of predicting ing extremely complex models running on large supercomputers. thunderstorms based on machine learning. The core idea of our Besides delivering forecasts for variables such as the temperature, work is to use the error of two-dimensional optical flow algorithms one key task for meteorological services is the detection and pre- applied to images of meteorological satellites as a feature for ma- diction of severe weather conditions. -
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. -
6.4 the Warning Time for Cloud-To-Ground Lightning in Isolated, Ordinary Thunderstorms Over Houston, Texas
6.4 THE WARNING TIME FOR CLOUD-TO-GROUND LIGHTNING IN ISOLATED, ORDINARY THUNDERSTORMS OVER HOUSTON, TEXAS Nathan C. Clements and Richard E. Orville Texas A&M University, College Station, Texas 1. INTRODUCTION shear and buoyancy, with small magnitudes of vertical wind shear likely to produce ordinary, convective Lightning is a natural but destructive phenomenon storms. Weisman and Klemp (1986) describe the short- that affects various locations on the earth’s surface lived single cell storm as the most basic convective every year. Uman (1968) defines lightning as a self- storm. Therefore, this ordinary convective storm type will propagating atmospheric electrical discharge that results be the focus of this study. from the accumulation of positive and negative space charge, typically occurring within convective clouds. This 1.2 THUNDERSTORM CHARGE STRUCTURE electrical discharge can occur in two basic ways: cloud flashes and ground flashes (MacGorman and Rust The primary source of lightning is electric charge 1998, p. 83). The latter affects human activity, property separated in a cloud type known as a cumulonimbus and life. Curran et al. (2000) find that lightning is ranked (Cb) (Uman 1987). Interactions between different types second behind flash and river flooding as causing the of hydrometeors within a cloud are thought to carry or most deaths from any weather-related event in the transfer charges, thus creating net charge regions United States. From 1959 to 1994, there was an throughout a thunderstorm. Charge separation can average of 87 deaths per year from lightning. Curran et either come about by inductive mechanisms (i.e. al. also rank the state of Texas as third in the number of requires an electric field to induce charge on the surface fatalities from 1959 to 1994, behind Florida at number of the hydrometeor) or non-inductive mechanisms (i.e. -
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 -
Datasheet: Thunderstorm Local Lightning Sensor TSS928
Thunderstorm Local Lightning Sensor ™ TSS928 lightning Vaisala TSS928™ is a local-area lightning detection sensor that can be integrated with automated surface weather observations. Superior Performance in Local- area TSS928 detects: Lightning Tracking • Optical, magnetic, and electrostatic Lightning-sensitive operations rely on pulses from lightning events with zero Vaisala TSS928 sensors to provide critical false alarms local lightning information, both for • Cloud and cloud-to-ground lightning meteorological applications as well as within 30 nautical miles (56 km) threat data, to facilitate advance • Cloud-to-ground lightning classified warnings, initiate safety procedures, and into three range intervals: isolate equipment with full confidence. • 0 … 5 nmi (0 … 9 km) The patented lightning algorithms of TSS928 provide the most precise ranging • 5 … 10 nmi (9 … 19 km) of any stand-alone lightning sensor • 10 … 30 nmi (19 … 56 km) available in the world today. • Cloud-to-ground lightning classified The optical coincident requirement into directions: N, NE, E, SE, S, SW, W, Vaisala TSS928™ accurately reports the eliminates reporting of non-lightning and NW range and direction of cloud-to-ground events. The Vaisala Automated lightning and provides cloud lightning TSS928 can be used to integrate counts. Lightning Alert and Risk Management lightning reports with automated (ALARM) system software is used to weather observation programs such as visualize TSS928 sensor data. METAR. Technical Data Measurement Performance Support Services Detection range 30 nmi (56 km) radius from sensor Vaisala TSS928™ is fully supported by our Customer location Support Center, Technical Service Group, and Field Range resolution Three range groups: Service Engineering Team. Maintain optimal 0 … 5 nmi (0 … 9 km) performance by purchasing a service agreement 5 … 10 nmi (9 … 19 km) customized to your unique system requirements. -
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. -
Vaisala Jon Tarleton Road & Rail Marketing Manager [email protected] Twitter @Jontarleton
Vaisala Jon Tarleton Road & Rail Marketing Manager [email protected] twitter @jontarleton Things you might not know about us… Page 2 / 07-17-13/ Road Weather Management 2013/ ©Vaisala Vaisala Inc. the U.S. subsidiary employs over 330 people in offices located in Colorado, Massachusetts, At our world headquarters (Helsinki, Finland) Arizona, Missouri, and Minnesota. the sun rose today at 4:25am and will set at 10:26pm (loosing 4 min of daylight a day). Vaisala service personnel travel 30 miles via Snowcat in Wyoming to reach AWOS sites at mountain passes. Vaisala’s WMT700 is the only approved ultrasonic wind sensor for Federal AWOS systems. Our NLDN turned 30 years old last month! (National Lightning Detection Network) which detected 702,501,649 lightning flashes in 30 years. Vaisala Dropsonde is used by hurricane hunter aircraft to collect storm data.. A drop from 20,000 feet lasts 7 minutes. Page 3 / 07-17-13/ Road Weather Management 2013/ ©Vaisala Vaisala HMP155 sensor is located on the Mars Curiosity and has been recording humidity information for the past year. Page 4 / 07-17-13/ Road Weather Management 2013/ ©Vaisala Your Weather Technology Experts Non-intrusive Expert Sensors Consultation Open Architecture Mobile Weather Page 5 / 07-17-13/ Road Weather Management 2013/ ©Vaisala Winter Maintenance Operations Performance Measurement Mobile Weather Station Idaho Transportation Department worked with Vaisala West Virginia DOT, Idaho Trans. Dept., and the City of to develop multiple performance indexes to measure West Des Moines, Iowa, are a few examples of crew effectiveness and traffic flow. Index agencies that tested the Condition Patrol mobile measurements begin with using the quantitative system during the winter of 2012-13. -
METAR/SPECI Reporting Changes for Snow Pellets (GS) and Hail (GR)
U.S. DEPARTMENT OF TRANSPORTATION N JO 7900.11 NOTICE FEDERAL AVIATION ADMINISTRATION Effective Date: Air Traffic Organization Policy September 1, 2018 Cancellation Date: September 1, 2019 SUBJ: METAR/SPECI Reporting Changes for Snow Pellets (GS) and Hail (GR) 1. Purpose of this Notice. This Notice coincides with a revision to the Federal Meteorological Handbook (FMH-1) that was effective on November 30, 2017. The Office of the Federal Coordinator for Meteorological Services and Supporting Research (OFCM) approved the changes to the reporting requirements of small hail and snow pellets in weather observations (METAR/SPECI) to assist commercial operators in deicing operations. 2. Audience. This order applies to all FAA and FAA-contract weather observers, Limited Aviation Weather Reporting Stations (LAWRS) personnel, and Non-Federal Observation (NF- OBS) Program personnel. 3. Where can I Find This Notice? This order is available on the FAA Web site at http://faa.gov/air_traffic/publications and http://employees.faa.gov/tools_resources/orders_notices/. 4. Cancellation. This notice will be cancelled with the publication of the next available change to FAA Order 7900.5D. 5. Procedures/Responsibilities/Action. This Notice amends the following paragraphs and tables in FAA Order 7900.5. Table 3-2: Remarks Section of Observation Remarks Section of Observation Element Paragraph Brief Description METAR SPECI Volcanic eruptions must be reported whenever first noted. Pre-eruption activity must not be reported. (Use Volcanic Eruptions 14.20 X X PIREPs to report pre-eruption activity.) Encode volcanic eruptions as described in Chapter 14. Distribution: Electronic 1 Initiated By: AJT-2 09/01/2018 N JO 7900.11 Remarks Section of Observation Element Paragraph Brief Description METAR SPECI Whenever tornadoes, funnel clouds, or waterspouts begin, are in progress, end, or disappear from sight, the event should be described directly after the "RMK" element. -
Glossary of Fire Weather Terms
2021 Arkansas Fire Weather Operating Plan National Weather Service Forecast Office Little Rock, Arkansas 1 2021 Arkansas Fire Weather Operating Plan National Weather Service Forecast Office – Little Rock 8400 Remount Road North Little Rock, AR 72118 Joseph C Goudsward - Senior Forecaster Incident Meteorologist (IMET) 2 Table of Contents Chapter Page Introduction 4 Important Changes 6 General Information 7 Role of the National Weather Service 8 Red Flag Program 10 Basic vs. Special Services 11 Forecast Products 14 Spot Forecasts 31 NFDRS Forecasts 43 Update Policy 48 NWS Offices and Responsibilities 49 Appendices #1 Wildland Fire terminology 61 #2 Southern Region Contacts and IMETs 79 #3 Arkansas Fire Weather Zones 82 #4 National Fire Plan 83 #5 Fire weather Links 88 #6 NFDRS Sites 89 #7 Contact Information 90 #8 Forecast Products 97 3 Introduction This document is the latest Arkansas Fire Weather Operating Plan. It serves to consolidate the fire weather services provided by National Weather Service (NWS) offices covering the state. The purpose of this operating plan can be broken down into three distinct areas. The first is to consolidate all the fire weather services provided by the five NWS offices covering the state of Arkansas. The second purpose is to describe the services available to all land management agencies in Arkansas. The final purpose is to provide information and guidelines to the forecasting staff at the five NWS offices to ensure that consistent information is given to their customers. Customers of NWS fire weather products and services in Arkansas must understand that the products and policies contained within may differ slightly based on local policy and procedure. -
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 -
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.