The GLM: A New Generation of Lightning Detection

The GLM: A New Generation of Lightning Detection

April 6, 2019 Off By ThielWx

How do you catch something that happens in the blink of an eye? Very quickly of course! Lightning strikes typically happen on the order of milliseconds, making it slightly different from most observed weather phenomena. You can visualize rain via radar reflectivity, clouds via satellite imagery, and even tornadoes with radar velocities (usually), but how do we monitor lightning? And what makes the Geostationary Lightning Mapper (GLM) so special? This article will give a general overview of lightning detection, and show how the GLM is different than most lightning sensors.

From the Ground Up

Since 1988, the contiguous United States has had continuous monitoring of cloud-to-ground lightning from a group of 106 ground-based sensors called the National Lightning Detection Network (NLDN). This network was the first to achieve national coverage, and is just one of multiple group-based networks (WWLN, ENTLN, UBIMET, etc) that can be used to monitor lightning. These ground-based lightning sensors detect electromagnetic (EM) waves that are emitted from the lightning strike.

Sensor locations/types of NLDN (red points) and CLDN (blue points), along with their associated coverage area (gray). Source: Orville 2008
Sensor locations of the ENTLN. Source: Sloop et. al 2014

Under a typical CG lightning strike scenario, the initial branches propagate from the cloud in ~50m segments called leaders. These leaders ‘step’ their way outward, attempting to find a point of discharge. Once this happens, a massive surge of electrons rushes through the main channel called a return stroke, creating the lightning we commonly see (and hear!). Multiple return strokes can also move through the channel after the initial contact is made, creating ‘pulses’.

So why did I just break down how a CG strike works? Because 1) It’s really cool! and 2) Each strike, cloud-to-ground (CG) strikes and intra-cloud (IC) flashes, generate EM waves, but the waves they generate have different characteristics that can generally be measured by an EM wave sensor. Most networks only detect CG strikes because the return stroke(s) produce a stronger EM wave, meaning a sensor has a greater chance of detecting the strike, and that fewer sensors are needed to cover large regions. IC flashes do not have this return signature, yet still emit some EM radiation, so these waves are considerably harder to detect and therefore require more sensors with higher frequencies.

Annual CG lightning strike densities from Viasala. Source

By creating networks of sensors, the arrival times of the strikes can be put together to find the strikes location, charge, and strength with considerable accuracy. This information is of course great for forecasters, researchers, emergency managers, etc., but the GLM approaches lightning detection from a whole new perspective.

Your Friendly Neighborhood GLM

The GLM is a satellite-based sensor, and was launched onboard the GOES 16 satellite in 2016, with another one recently launched on GOES 17 in 2018. Scientifically, the instrument is designed to detect changes in the optical waveform due to cloud-top flashes, and objectively map them by latitude and longitude. In short, it’s a big high-speed camera that takes pictures of lightning!

A photo of the GLM prior to launch, coming it at 4 ft. 11 in. tall! Source

When a lightning flash happens, IC or CG, the light illuminates the outside of the cloud and is ‘seen’ by the GLM with its 2 millisecond frame rate. The individual lit pixels, called events, are the fundamental ‘units’ of the GLM and have resolutions from 8km to 14km, depending on viewing angle. Adjoining events in space and time called groups, which are then merged with a Lightning Cluster-Filter Algorithm (LCFA) to create flashes. From there a host of products can be created, including: Flash Extent Density, Group Extent Density, Event Density, Average Flash Area, Average Group Area, Average Flash Energy, and more. These products are then generated and updated every minute for forecasters in the National Weather Service.

Source: Goodman et. al 2013

This approach was initially used with the Lightning Imager Sensor (LIS) as part of the NASA Tropical Rainfall Measurement Mission. While the LIS was installed on a polar orbiting satellite (constantly orbiting the Earth), the GLM is on a geostationary satellite (fixed in one point relative to the Earth), making it the first to provide continuous lightning detection over a single region from space.

GLM collects information such as the frequency, location and extent of lightning discharges to identify intensifying thunderstorms and tropical cyclones… Used in combination with radar, satellite data, and surface observations, total lightning data from GLM has great potential to increase lead time for severe thunderstorm and tornado warnings and reduce false alarm rates.

NOAA/NASA GOES-R Products Page

Comparing the GLM to the ground-based lightning sensors discussed earlier is in someways like comparing WiFi to ethernet. They both serve the same purpose, but accomplish this task in different ways. One advantage of the GLM has is the ability to cover over both land and ocean with near uniform resolution, while ground-based sensors have increased detection and location errors over the ocean. While the GLM doesn’t have to worry about triangulation errors, location errors naturally will increase on the edge of the field of view due to what’s called the parallax effect. Thankfully with not one but TWO GLMs now in place, the entire Continental United States has a fairly uniform resolution when it comes to lightning detection.

The Next Steps, Watching and Learning

With a new operational sensor comes lots of questions, like how should forecasters use this for issuing warnings, does the GLM detect other things besides lightning (spoiler, the answer is meteors!), and things like what in the world does a femto-joule mean? These are all important questions that forecasters and researchers (myself included) will have to answer as data continues to be collected over the lifespan of the instrument.

A full-disk image of the GLM. Source

Want to learn even more about the GLM and see some of its data in real time? Check out the links below!

Header image source: GOES 16 image gallery