Creating demonstration data and products to train forecasters for GLM has presented unique challenges. Aside from the Optical Transient Detector (OTD) and the Lightning Imaging Sensor (LIS), no other space-based platform had similar capabilities to the GLM. Furthermore, the OTD and LIS were low-Earth orbiting instruments and would only view a small portion of the Earth for no more than a couple minutes at a time. This prevented their use as a demonstration data set as forecasters would need to see how total lightning (the combination of intra-cloud and cloud-to-ground) evolved with time. That put the focus on ground networks, which could observe the entire life cycle of a storm. The ground networks, unfortunately, lacked the ability to observe total lightning (or the capability was not yet available). The exception was the ground-based lightning mapping array.
The lightning mapping array (LMA) was developed by New Mexico Tech and evolved out of earlier systems, some of which were tested at Kennedy Space Center. By operating in the very high frequency end of the electromagnetic spectrum (~80 GHz), the LMAs could observe the entire lightning channel within a cloud. Primarily designed for lightning research, these would become instrumental in the training activities for the GOES-R Proving Ground and the GLM. This is because the LMAs were capable of observing total lightning. Their accuracy was extremely good and they have been used for ground verification for OTD and LIS and will do so again for GLM. Their primary disadvantage is a very short range; generally no more than 200 km from the center of the network. Figure 1 shows the physical relationship of total lightning to a storm updraft as well as the lightning jump concept.
The Melbourne, Florida forecast office was the first office to use total lightning data from a local lightning detection and ranging network (very similar to an LMA) in the late 1990s. This was a combined effort by the forecast office, Massachusetts Institute of Technology (MIT), MIT Lincoln Lab, and NASA Marshall Space Flight Center. The data became extremely popular with the office and the Lightning Imaging Sensor Demonstration and Display (LISDAD) system was instrumental in investigating the uses of total lightning in real-time.
In 2002, NASA’s Marshall Space Flight Center had installed the research oriented North Alabama Lightning Mapping Array (NALMA). By March 2003, the NASA SPoRT team, in collaboration with the Huntsville weather forecast office, had made NALMA data available in the National Weather Service display system; AWIPS. The Huntsville forecast office would then go on to issue its first warning using total lightning data that May. NASA SPoRT would extend collaborations with a handful of other forecast offices using NALMA as well as the NASA owned Washington D.C. LMA in the late 2000s.
By 2008, the GOES-R Proving Ground was accelerating its efforts with training and hands-on activities, such as the Hazardous Weather Testbed in Norman, Oklahoma. This required a demonstration product for GLM that could be used in real-time. The Marshall Space Flight Center had developed the GLM proxy that was derived from NALMA to test data processing algorithms for the GLM. The drawback was that it could not be run in real-time. However, in 2009 NASA SPoRT produced the pseudo-GLM (PGLM) product (Figure 2). It was not an exact replica of future GLM observations, but it represented a reasonable facsimile that allowed for hands-on training that let forecasters better learn about total lightning and its relation to storms intensity and severe weather.
NASA SPoRT, thanks to funding via the GOES-R visiting scientist program, was able to reach out to each of the other LMAs that were in operation across the country (and one in Canada!). By 2014, almost a dozen LMAs were collaborating. This allowed for the PGLM to be produced for numerous locations as well as expand partnerships to over a dozen forecast offices, three center weather service units, and the Aviation Weather Center. Figure 3 shows the approximate domain and collaborating organization for each available LMA. Combined, the collaborations between the forecasters, LMA owners, and the testbeds allowed for a wide variety of feedback discussing operational uses and visualization concepts. Much of this has directly supported my own efforts as the GLM satellite liaison for the ongoing work in the Satellite Foundational Course for GOES-R, preparing for operational applications training, and the 2017 summer assessment.
Next up in the “GLM is coming” series is a post describing the GLM instrument itself as we await in initial release of GLM data.