Advances in Lightning Detection

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Advances in Lightning Detection. Kerry Anderson Canadian Forest Service Edmonton, Alberta. Introduction. The atmosphere is an electrical environment. The Earth has a natural net-negative charge that is countered by an equal and opposite charge in the atmosphere. - PowerPoint PPT Presentation

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Advances in Advances in Lightning DetectionLightning Detection

Kerry AndersonKerry AndersonCanadian Forest ServiceCanadian Forest Service

Edmonton, AlbertaEdmonton, Alberta

IntroductionIntroduction

The atmosphere is an electrical environment. The Earth has a natural net-negative charge that is countered by an equal and opposite charge in the atmosphere.

PointDischarge

300,000 Volts

50 Kilometres

Convective activity in the atmosphere collects and redistributes this charge within clouds.

IntroductionIntroduction

Lightning is a release of charge buildup that occurs within a cloud. This exchange of charge can occur within a cloud, between clouds, from a cloud to clear air, or between a cloud and ground.

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Lightning Detection

Lightning Detection

The current associated with each stroke within a lightning flash is thousands of amps in strength. This current sends out electromagnetic waves that can be detected and mapped with lightning detection systems.

There are two principle techniques of detecting lightning:

• Magnetic direction finding (MDF)

• Time of arrival (TOA)

Lightning DetectionLightning DetectionMagnetic direction finding (MDF) detects the electromagnetic signature of a cloud to ground lightning flash.

Detection by two or more antennae (a.k.a. direction finders) within a network are used to triangulate on the lightning flash location.

An antenna consisting of two orthogonal loops picks up the magnetic field associated with a flash. The relative strengths within each loop determines the direction to the flash.

Lightning DetectionLightning Detection

Time of Arrival (TOA) technique uses the difference in the time when the electromagnetic signature of a lightning flash is

detected by two or more sensors within a network.

Company Histories

Company HistoriesCompany Histories

Company HistoriesCompany Histories

The magnetic direction finding technique was pioneered in the 1970s by Dr. E. Philip Krider, Dr. Burt Pifer and Dr. Martin Uman, at the University of Arizona.

The first operational MDF system was developed for use in Alaska for the Bureau of Land Management (BLM) in 1976.

Lightning Location and Protection, Inc. (LLP) developed a commercial MDF product and made it widely available in the 1980s.

Company HistoriesCompany Histories

The Time of Arrival technique was first developed by Dr. Rodney Bent and Dr. Walter Lyons. A prototype system was designed and tested in 1982.

The Lightning Position and Tracking Systems (LPATS) was made commercially available by Atmospheric Research Systems, Inc. (ARSI).

Company HistoriesCompany HistoriesGlobal Atmospherics Inc. (GAI) was formed by the Sankosha Corporation of Japan. Sankosha purchased and reorganized three of the world’s leading companies LLP, ASRI and GeoMet Data Services.

The new company combined both MDF and TOA systems to run off the same detection device: the IMPACT sensor.

Global Atmospherics, Inc. was later bought out by Vaisalla Instruments.

Company HistoriesCompany HistoriesRecently, Time of Arrival Systems Inc. has developed the Advanced Lightning Positioning System (ALPS™) based on the time of arrival technique.

National Networks

US NetworksUS Networks

1984-1989: Three isolated networks were developed in the US.

1989: Regional networks agreed to share the data, creating the National Lightning Detection Network (NLDN).

1991: Real-time and

historic information becomes

commercially available

US NetworksUS Networks

Recently, TOA Systems has set up a similar national system, the United States Precision Lightning Network (USPLN).

Canadian NetworksCanadian Networks

In Canada, provincial forest protection agencies set up individual lightning detection networks in the early 1980s.

National NetworksNational Networks

In 1998, Environment Canada set up the Canadian Lightning Detection Network (CLDN), which runs in conjunction with the NLDN as the North American Lightning Detection Network (NALDN).

Network Performance

Network PerformanceNetwork Performance

The CDLN has a detection efficiency of 90% or more for

most of Canada.

The CDLN has a location accuracy of 500 metres of less for most of Canada.

Network PerformanceNetwork Performance

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The USLPN has a detection efficiency of 95% or more for most of the US and southern Canada.

The USLPN has a location accuracy of 150 metres or less

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Lightning Climatology

Lightning ClimatologiesLightning Climatologies

Data over several years can be used to build lightning climatologies for

Canada and for Alberta.

Days of observed Lightning within 20x20 cell

Lightning Forecasts

Environment Canada has recently begun producing lightning forecasts.

Based on the work of William Burrows, these forecasts predict the probability of lightning occurrence for Canada

at 24 km resolution in 3-hr intervals to 48 hours.

Lightning Forecasts

Lightning Forecasts

Lightning data was collected from the North American Lightning Detection Network (NALDN).

The CMC Global The CMC Global Environmental Model Environmental Model

(GEM) was used to (GEM) was used to provide predictors for provide predictors for

the model. the model.

Lightning Forecasts

Predictive models were built Predictive models were built using tree-structured using tree-structured

regression. regression.

Separate models were built Separate models were built for 5for 5o o x 5x 5oo cells for each cells for each

predictive period.predictive period.

Lightning Forecasts

Probability of lightning occurrence.Probability of lightning occurrence.

Lightning Forecasts

Most likely category of lightning occurrence.Most likely category of lightning occurrence.

Lightning-caused Fire Prediction

The process of lightning-caused fires can be broken into three distinct stages:

Lightning-caused Fire Prediction

The number of lightning-caused fires can be predicted by modelling the probabilities of each of these stages.

Ignition Survival Arrival

Probability maps can be produced from daily weather.

Lightning-caused Fire Prediction

Lightning can be layered upon the daily probability maps to predict lightning-caused fires.

Lightning-caused Fire Prediction

Thank You

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