Lightning Detection System in Korea Meteorological Administration Seung-Sook Shin, Jeong-Hee Kim, Ki-Ho Chang, Jong-Ho Lee, Duck-Mo Woo Observational Technology.

Slides:



Advertisements
Similar presentations
Sources of Weather Data How do we measure and predict the weather?
Advertisements

Space Weather in CMA Xiaonong Shen Deputy Administrator China Meteorological Administration 17 May 2011 WMO Cg-XVI Side Event Global Preparedness for Space.
Lightning Imager and its Level 2 products Jochen Grandell Remote Sensing and Products Division.
NOAA’s NWS and the USGS: Partnering to Meet America’s Water Information Needs Ernie Wells Hydrologic Services Division NOAA National Weather Service May.
Canadian Wildland Fire Information System Natural Resources Canada Canadian Forest Service Ressources naturelles Canada Service canadian des forêts.
Utilizing Social Media to Understand Human Interaction with Extreme Media Events - The Superstorm Sandy Beta Test Arthur G. Cosby Somya D. Mohanty National.
NOAA’s CENTER for OPERATIONAL OCEANOGRAPHIC PRODUCTS and SERVICES Improvements to the CO-OPS Storm QuickLook Product for Real-Time Storm Surge Monitoring.
March 13, 2006 Location Tracking System & Sensor Based Communications For Mining Response to RIN 1219-AB44.
Vaisala TLS200, Technological Advancements for VHF total lightning mapping / Nikki Hembury and Ron Holle - Vaisala / Southern Thunder Conference 2011.
The Influence of Basin Size on Effective Flash Flood Guidance
Model Flood Early Warning System on River Nzoia-Kenya
Unit 8.
Aeronautical Meteorology Service of China
Advances in Lightning Detection Kerry Anderson Canadian Forest Service Edmonton, Alberta Kerry Anderson Canadian Forest Service Edmonton, Alberta.
Correlation Between Lightning and the Joplin Missouri Tornado Ariel Powers 1, Brian West 1, Humberto Barbosa 2, Ivon Wilson 2 1 1Department of Earth Sciences,
Météorage Lightning data for flood monitoring Marc Bonnet V.P. International October, 2011 Météorage Lightning data for flood monitoring Marc Bonnet V.P.
An analysis of cloud-to-ground (CG) strokes in China during and the spatial distribution of CG with severe thunderstorm wind LAC S Laboratory.
12/03/041 Remote sensing in Weather applications We produce algorithms and prototypes of products for FMI forecasters and customers. Our tools include.
Chapter 24 Section 4 Handout
WMO / COST 718 Expert Meeting on Weather, Climate and Farmers November 2004 Geneva, Switzerland.
Elements of Weather Measuring Weather. What are the Basic Elements of Weather & Climate (go outside and list 6) Wind: speed and direction Temperature:
GLOBAL HAZARDS An introduction to hazards & disasters.
 Vision: Typhoon Committee is World’s best intergovernmental, regional organization for improving the quality of life of the Members’ populations through.
Proxy Data and VHF/Optical Comparisons Monte Bateman GLM Proxy Data Designer.
Real-Time GIC Simulator D. H. BOTELER, L. TRICHTCHENKO, J. PARMELEE, S. SOUKSALY Geomagnetic Laboratory, Natural Resources Canada R. PIRJOLA Space Research,
NOAA’s National Weather Service In Green Bay. The National Weather Service is responsible for issuing forecasts and warnings for the protection of life.
The Canadian Lightning Detection Network (CLDN). Novel Approaches for Performance Measurement & Network Management. Meteorological Service of Canada D.
Presented by Amira Ahmed El-Sharkawy Ibrahim.  There are six of eight turtle species in Ontario are listed as endangered, threatened or of special concern.
History, Detection Methods, and Purpose Matt Mahler – METR 2413.
Section 4: Forecasting the Weather
The Lightning Warning Product Fifth Meeting of the Science Advisory Committee November, 2009 Dennis Buechler Geoffrey Stano Richard Blakeslee transitioning.
Microcontroller-Based Wireless Sensor Networks
EUMETCAL NWP, 26 – 29 November 2007, DWD-BTZ Langen, Germany Man-machine mix and the forecaster's role Dr. Wilfried Jacobs Deutscher Wetterdienst Bildungs-
The Hydrometeorology Testbed Network. 2 An AR-focused long-term observing network is being installed in CA as part of a MOA between CA-DWR, NOAA and Scripps.
California Data Exchange Center Hydrologic Database System
USE AND EVALUATION OF TOTAL LIGHTNING DATA IN THE GOES-R PROVING GROUND AND EXPERIMENTAL WARNING PROGRAM Kristin Kuhlman (CIMMS/NSSL) Geoffrey Stano (NASA/SPORT)
Total Lightning Detection Your Name & Affiliation.
The IEM-KCCI-NWS Partnership: Working Together to Save Lives and Increase Weather Data Distribution.
The objective standard for Asian Dust determination using the instrument in the Republic of Korea SEONG-HEON KIM, HYUK-JE LEE, JAE-YOUNG KIM Observational.
Weather Predicting Weather forecasting is a prediction of what the weather will be like in an hour, tomorrow, or next week. Weather forecasting involves.
–thermometer –barometer –anemometer –hygrometer Objectives Recognize the importance of accurate weather data. Describe the technology used to collect.
U.S. Department of the Interior U.S. Geological Survey Bee Lake Water Quality Monitor Data Summary Period of record: to 2/19/07.
Drought related activities at NCC, CMA Division of Climate Application and Service National Climate Center (Beijing Climate Center) China Meteorological.
Utilizing your School Network John McLaughlin KCCI-TV, Des Moines, IA Daryl Herzmann Iowa State University.
Embedded Design Using ARM For Strong Room Security System
Geoffrey Stano – ENSCO / SPoRT David Hotz and Anthony Cavalluci– WFO Morristown, TN Tony Reavley – Director of Emergency Services & Homeland Security of.
Investigations of long and short term changes in Total Ozone at the Sonnblick Observatory (3106 m, Austria) S. Simic, P. Weihs and G. Rengarajan and W.
The Iowa Environmental Mesonet - KCCI SchoolNet - National Weather Service Partnership Working together to save lives and increase data distribution Contacts:
1 World Meteorological Organization WMO Information System (WIS) Managing & Moving Weather, Water and Climate Information in the 21 st Century WORLD METEOROLOGICAL.
HRV (70-100%)IR10.8 (Tb 35dBz HRV cloud (HRV,HRV,IR10.8) radar Zmax > 35dBz (CC+CG) 10-minute lightning data (CC+CG) Simultaneous.
Lightning Mapping Technology & NWS Warning Decision Making Don MacGorman, NOAA/NSSL.
The DEWETRA platform An advanced Early Warning System.
3-D rendering of jet stream with temperature on Earth’s surface ESIP Air Domain Overview The Air Domain encompasses a variety of topic areas, but its focus.
Forecasting the weather
1. Session Goals 2 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Understand use of the terms climatology and variability.
Make an information leaflet about what the sensors do in a Smart Phone for people over 65 years of age. You can use PowerPoint, Word or Publisher.
Holle | NWA Annual Meeting | October 06 Cloud Lightning from the National Lightning Detection Network (NLDN) Ronald L. Holle, Nicholas Demetriades, and.
Operational Use of Lightning Mapping Array Data Fifth Meeting of the Science Advisory Committee November, 2009 Geoffrey Stano, Dennis Buechler, and.
بسم الله الرحمن الرحيم In the Name of God In the Name of God
Outdoor alerting system
智能信息化和大众化的气象灾害预防研究 王 勃 郑州市气象局.
By KWITONDA Philippe Rwanda Natural Resources Authority
Weather Forecasting.
WHATAP 제가 와탭을 소개하겠습니다. November 14, 2018.
Hiding under a freeway overpass will protect me from a tornado.
Use of Lightning Data for Electricity Transmission Operations
Thunderstorms Features Cumulonimbus clouds Heavy rainfall Lightning
Status of Existing Observing Networks
Current progress in aviation nowcasting at SAWS: A Demo Project
(6-8 November 2018, Beijing, China)
Presentation transcript:

Lightning Detection System in Korea Meteorological Administration Seung-Sook Shin, Jeong-Hee Kim, Ki-Ho Chang, Jong-Ho Lee, Duck-Mo Woo Observational Technology and Management Division, Korea Meteorological Administration Lightning Network Instrument Lightning occurrence tendency  IMPACT ESP sensor  Since KMA introduced Lightning Location and Protection (LLP) in 1987, KMA is observing Lightning event occurred in Korea and using in forecaster.  Instrument observing lightning is changed into IMPACT ESP(IMProved Accuracy from Combined Technology Enhanced Sensitivity and Performance) in the early 2000, and used for observing lightning from  Recently, KMA installed LDAR II(Lightning Detection And Ranging System: Total lightning detection systems) sensor, is observing in-cloud discharge, and is doing the quality control of the data.  Network of lightning detection is composed of 7 IMPACT ESP sensors and 17 LDAR II sensors. (See Fig. 1)  LDAR II sensor  IMPACT ESP sensor detects cloud-to-ground discharge (C-G Lightning).  Detection efficiency and location accuracy is very high because of combining strongpoint of TOA (Time-Of-Arrival) technology and accuracy of MDF (Magnetic Direction Finding) technology.  Three IMPACT sensors are needed for exact location of the lightning.  LDAR II sensor uses TOA method.  Using VHF (Very High Frequency)  It can detect every discharge like in-cloud, cloud-to-atmosphere, and so on.  It needs minimum five sensors for three dimension observations and four sensors for two dimension observations. Display  The system collects the real-time data from lightning sensors and displays collected data every 10 minutes. Thereafter, user select data as lightning, satellite, and radar image and can display overlaid data at the monitor.  KMA can track severe weather from displaying accumulated lightning data and analysis the tendency of lightning occurrence through daily accumulated data.  KMA offer every people the real-time position information of lightning on the internet and intranet so that many people simultaneously can analysis the lighting data.  People can magnify the area of lighting occurrence and get the location information like latitude, longitude, and place name by online.  KMA supply the real-time lighting dangerous zone by using mobile warning system for lighting to minimize the damage concerned with natural disaster. Fig. 2 IMPACT ESP sensor Fig. 1 KMA network ( ● IMPACT, ◇ LDAR II ) Fig. 4 LDAR II sensor Fig. 5 Overlaid display of lightning, radar, and satellite image Fig. 6 The distribution of daily accumulated lightning Fig. 8 The display of lighting location Fig. 7 The distribution of lightning accumulated for 30 minutes to analyze the track Fig. 9 Mobile warning system for lighting Fig. 10 Annual number of lightning events for 2002~2007 Fig. 11 Monthly number of lightning events for 2002~2007 Fig. 14 Monthly mean intensity in 2007 Fig. 15 Monthly polarity frequency of lightning in 2007 Fig. 16 The number of lightning events in (a) spring, (b) summer, (c) fall, and (d) winter 2007 Fig. 13 The number distribution of lighting days in 2007 Fig. 12 The number distribution of lighting in 2007 Fig. 3 The range of lightning detection