Evaluation of ENTLN Performance Characteristics Based on the Ground Truth Natural and Rocket-Triggered Lightning Data Acquired in Florida

Autor: Martin A. Uman, W. R. Gamerota, Michael Stock, T. Ngin, R. A. Wilkes, C. Liu, J. A. Caicedo, J. T. Pilkey, Vladimir A. Rakov, D. M. Jordan, M. D. Tran, F. L. Carvalho, Stan Heckman, C. D. Sloop, Yanan Zhu, Brian Hare, D. A. Kotovsky
Rok vydání: 2017
Předmět:
Zdroj: Journal of Geophysical Research: Atmospheres. 122:9858-9866
ISSN: 2169-897X
DOI: 10.1002/2017jd027270
Popis: The performance characteristics of the Earth Networks Total Lightning Network (ENTLN) were evaluated by using as ground-truth natural cloud-to-ground (CG) lightning data acquired at the Lightning Observatory in Gainesville (LOG) and rocket-triggered lightning data obtained at Camp Blanding (CB), Florida, in 2014 and 2015. Two ENTLN processors (data processing algorithms) were evaluated. The old processor (P2014) was put into use in June 2014 and the new one (P2015) has been operational since August 2015. Based on the natural-CG-lightning dataset (219 flashes containing 608 strokes), the flash detection efficiency (DE), flash classification accuracy (CA), stroke DE, and stroke CA for the new processor were found to be 99%, 97%, 96%, and 91%, respectively, and the corresponding values for the old processor were 99%, 91%, 97%, and 68%. The stroke DE and stroke CA for first strokes are higher than those for subsequent strokes. Based on the rocket-triggered lightning dataset (36 CG flashes containing 175 strokes), the flash DE, flash CA, stroke DE, and stroke CA for the new processor were found to be 100%, 97%, 97%, and 86%, respectively, while the corresponding values for the old processor were 100%, 92%, 97%, and 42%. The median values of location error and absolute peak current estimation error were 215 m and 15% for the new processor, and 205 m and 15% for the old processor. For both natural and triggered CG lightning, strokes with higher peak currents were more likely to be both detected and correctly classified by the ENTLN.
Databáze: OpenAIRE