ESRL Director's Office Seminar

Amitabh Nag

Ph.D Scientist and Technical Product Manager

Ryan Said

Ph.D Research Scientist and Systems Engineer

Thursday, May 29, 2014, 1 pm
David Skaggs Research Center
Room GC402

Vaisala's Precision and Global Lightning Detection Capabilities

Abstract

Updates were made in 2013 to the U.S. National Lightning Detection Network (NLDN) that has led to performance improvements. Vaisala's LS7002 sensors have been deployed, replacing the older generation LS7001 and IMPACT sensors. The LS7002 takes advantage of the LS7001's digital sensor technology and provides improved embedded software with enhanced features. This improves the sensitivity of the sensor to low amplitude lightning-generated signals and leads to enhanced detection of cloud and cloud-to-ground lightning. Additionally, the central processor algorithms in the NLDN are being updated to include new techniques for classifying lightning using multiple waveform parameters, a "burst processing" algorithm for geolocation of multiple pulses in lightning pulse trains, and improved handling of electromagnetic wave propagation resulting in smaller arrival-time errors. The median location accuracy (given by the length of the semi-major axis of the 50% error ellipse) in the NLDN is expected to be about 200 m in the interior of the network. These performance characteristics continue to be validated by triggered lightning, tower strikes, network inter-comparison, and camera studies. We examine lightning discharges reported by the NLDN in early September to late November, 2013 within the interior of the contiguous United States. The large majority (87%) of flashes reported by the NLDN were ICs. Of the CG flashes reported by the NLDN during this period 81% were negative and 19% were positive. The average multiplicity (number of strokes were flash) for negative CG flashes was 2.7 and for positive CG flashes was 1.4. The negative and positive first stroke median peak current was -15 kA and 19 kA, respectively.

An algorithm change to the central processor of the network producing the GLD360 dataset is proposed to reduce the population of events with large (>5 km) location errors. In this study, we compare the data reprocessed using the proposed algorithm change to that from the realtime GLD360 dataset. The relative location accuracy using the National Lightning Detection Network as a reference is evaluated using data from July 20, 2013. With respect to the realtime dataset, the median location error of the reprocessed dataset decreased from 3.0 km to 1.7 km, and the 90th percentile decreased from 15 km to 6.5 km. The variation of the relative location error of the reprocessed dataset with local time is also investigated. The median location error is found to double near the local sunrise hours. This degradation in location accuracy is correlated with a drop in lightning event polarity estimation accuracy.

Biographies

Amitabh Nag received the M.S. and Ph.D. degrees in electrical and computer engineering in 2007 and 2010, respectively, from the University of Florida, Gainesville. From 2010 to 2011 he was employed as a postdoctoral research associate at the International Center for Lightning Research and Testing (ICLRT), University of Florida. He is currently working as a Scientist and Technical Product Manager at Vaisala Inc., Louisville, Colorado. His projects at Vaisala include those targeted toward improving sensor technology and performance of various lightning detection networks around the world. He is the author or coauthor of over 60 papers and technical reports on various aspects of lightning electromagnetics, with 20 papers being published in reviewed journals. His current research interests include electromagnetic wave propagation and detection, measurement, analysis, and modeling of electromagnetic fields, sensors and systems, and lightning protection. Dr. Nag is a member of IEC TC 81 WG 12 and WG 13 on "Lightning Locating Systems" and "Thunderstorm Warning Systems", respectively, the AMS Scientific and Technological Activities Commission on Atmospheric Electricity, the American Geophysical Union (AGU), the American Meteorological Society (AMS), and the Institute of Electrical and Electronic Engineers (IEEE).

Ryan Said is currently working as a research scientist and systems engineer at the Boulder, CO office of Vaisala, Inc. He received a B.S. degree in electrical engineering and computer science from the University of California at Berkeley in 2002 and M.S. and Ph.D. degrees in electrical engineering from Stanford University in 2004 and 2009, respectively. He was a postdoctoral fellow in the Very Low Frequency group at Stanford University from 2009-2010, and was a research associate in the same group in 2011. His research interests include the study of subionospheric propagation, long-range lightning detection and geo-location, and VLF/ELF signal processing. His dissertation work at Stanford led to the development of the long-range, global lightning detection network called GLD360, which is now fully operational and run as a commercial network by Vaisala, Inc.