publication . Article . 2016

LMA FLASH DETECTION PERFORMANCE

Chmielewski, Vanna C.; Bruning, Eric C.;
  • Published: 01 Jul 2016
  • Publisher: American Geophysical Union (AGU)
Abstract
Abstract This study characterizes Lightning Mapping Array performance for networks that participated in the Deep Convective Clouds and Chemistry field program using new Monte Carlo and curvature matrix model simulations. These open‐source simulation tools are readily adapted to real‐time operations or detailed studies of performance. Each simulation accounted for receiver threshold and location, as well as a reference distribution of source powers and flash sizes based on thunderstorm observations and the mechanics of station triggering. Source and flash detection efficiency were combined with solution bias and variability to predict flash area distortion at lon...
Subjects
free text keywords: Geophysics, Convection, Lightning, Monte Carlo method, Curvature, Azimuth, Network performance, Thunderstorm, Remote sensing, Geology, Distortion, Aerosol and Clouds, Deep Convective Clouds and Chemistry 2012 Studies (DC3), Atmospheric Processes, Atmospheric Electricity, Monte Carlo technique, Instruments and Techniques, Natural Hazards, Remote Sensing and Disasters, Research Article, Research Articles, Lightning Mapping Array, detection efficiency, VHF source error, flash area distortion
Related Organizations
38 references, page 1 of 3

Albrecht, R. I., S. J.Goodman, D. E.Buechler, R. J.Blakeslee, and H. J.Christian (2016), Where are the lightning hotspots on Earth?, Bull. Am. Meteorol. Soc., doi:10.1175/bams‐d‐14‐00193.1, in press.

Barth, M., et al. (2015), The Deep Convective Clouds and Chemistry (DC3) field campaign, Bull. Am. Meteorol. Soc., 96, 1281–1309, doi:10.1175/BAMS‐D‐13‐00290.1.

Bruning, E. C., and D. R.MacGorman (2013), Theory and observations of controls on lightning flash size spectra, J. Atmos. Sci., 70(12), 4012–4029, doi:10.1175/JAS‐D‐12‐0289.1.

Bruning, E. C., and R. J.Thomas (2015), Lightning channel length and flash energy determined fr om moments of the flash area distribution, J. Geophys. Res. Atmos., 120, 8925–8940, doi:10.1002/2015JD023766.

Bruning, E. C., S. A.Weiss, and K. M.Calhoun (2014), Continuous variability in thunderstorm primary electrification and an evaluation of inverted‐polarity terminology, Atmos. Res., 135‐136, 274–284, doi:10.1016/j.atmosres.2012.10.009.

Calhoun, K. M. (2015), Forecaster use of total lightning data for short‐term forecasts and warnings in the Hazardous Weather Testbed, paper presented at Seventh Conference on the Meteorological Applications of Lightning Data, Paper 3.5, AMS Annual Meeting, Phoenix, Ariz., 4–8 Jan.

Calhoun, K. M., E. C.Bruning, D. M.Kingfield, S. D.Rudlosky, C. W.Siewert, T.Smith, G. T.Stano, and G. J.Stumpf (2013), Forecaster use and evaluation of pGLM data at the NOAA Hazardous Weather Testbed and GOES‐R Proving Ground, paper presented at Sixth Conference on the Meteorological Applications of Lightning Data, AMS Annual Meeting, Austin, Tex., 6–10 Jan.

Calhoun, K. M., G. T.Stano, E. C.Bruning, S. D.Rudlosky, and D. M.Kingfield (2014), The Hazardous Weather Testbed: Moving total lightning data from a research tool into forecast and warning operations, XV International Conference on Atmospheric Electricity, Paper O–04‐02, IUGG/IAMAS International Commission on Atmospheric Electricity, Norman, Okla., 15–20 June.

Carey, L. D., and K. M.Buffalo (2007), Environmental control of cloud‐to‐ground lightning polarity in severe storms, Mon. Weather Rev., 135(4), 1327–1353, doi:10.1175/MWR3361.1.

Cecil, D. J., D. E.Buechler, and R. J.Blakeslee (2014), Gridded lightning climatology from TRMM‐LIS and OTD: Dataset description, Atmos. Res., 135‐136, 404–414, doi:10.1016/j.atmosres.2012.06.028.

Christian, H. J., et al. (2003), Global frequency and distribution of lightning as observed from space by the Optical Transient Detector, J. Geophys. Res., 108, 4005, doi:10.1029/2002JD002347.

Cummings, K., et al. (2015), A WRF‐Chem analysis of flash rates, lightning‐NO x production and subsequent trace gas chemistry of the 29–30 May 2012 convective event in Oklahoma during DC3, paper presented at Seventh Conference on the Meteorological Applications of Lightning Data, Paper 9.4, AMS Annual Meeting, Phoenix, Ariz., 4–8 Jan.

Darden, C. B., D. J.Nadler, B. C.Carcione, R. J.Blakeslee, G. T.Stano, and D. E.Buechler (2010), Using total lightning information to diagnose convective trends, Bull. Am. Meteorol. Soc., 91(2), 167–175, doi:10.1175/2009BAMS2808.1.

Fuchs, B. A., S. A.Rutledge, E. C.Bruning, J.Pierce, T. J.Lang, D. R.MacGorman, P. R.Krehbiel, and W.Rison (2015), Environmental controls on storm intensity and charge structure in various regions of the United States, J. Geophys. Res. Atmos., 120, 6575–6596, doi:10.1002/2015JD023271.

Goodman, S. J., et al. (2012), The GOES‐R Proving Ground: Accelerating user readiness for the next‐generation geostationary environmental satellite system, Bull. Am. Meteor. Soc., 93(7), 1029–1040, doi:10.1175/bams‐d‐11‐00175.1.

38 references, page 1 of 3
Abstract
Abstract This study characterizes Lightning Mapping Array performance for networks that participated in the Deep Convective Clouds and Chemistry field program using new Monte Carlo and curvature matrix model simulations. These open‐source simulation tools are readily adapted to real‐time operations or detailed studies of performance. Each simulation accounted for receiver threshold and location, as well as a reference distribution of source powers and flash sizes based on thunderstorm observations and the mechanics of station triggering. Source and flash detection efficiency were combined with solution bias and variability to predict flash area distortion at lon...
Subjects
free text keywords: Geophysics, Convection, Lightning, Monte Carlo method, Curvature, Azimuth, Network performance, Thunderstorm, Remote sensing, Geology, Distortion, Aerosol and Clouds, Deep Convective Clouds and Chemistry 2012 Studies (DC3), Atmospheric Processes, Atmospheric Electricity, Monte Carlo technique, Instruments and Techniques, Natural Hazards, Remote Sensing and Disasters, Research Article, Research Articles, Lightning Mapping Array, detection efficiency, VHF source error, flash area distortion
Related Organizations
38 references, page 1 of 3

Albrecht, R. I., S. J.Goodman, D. E.Buechler, R. J.Blakeslee, and H. J.Christian (2016), Where are the lightning hotspots on Earth?, Bull. Am. Meteorol. Soc., doi:10.1175/bams‐d‐14‐00193.1, in press.

Barth, M., et al. (2015), The Deep Convective Clouds and Chemistry (DC3) field campaign, Bull. Am. Meteorol. Soc., 96, 1281–1309, doi:10.1175/BAMS‐D‐13‐00290.1.

Bruning, E. C., and D. R.MacGorman (2013), Theory and observations of controls on lightning flash size spectra, J. Atmos. Sci., 70(12), 4012–4029, doi:10.1175/JAS‐D‐12‐0289.1.

Bruning, E. C., and R. J.Thomas (2015), Lightning channel length and flash energy determined fr om moments of the flash area distribution, J. Geophys. Res. Atmos., 120, 8925–8940, doi:10.1002/2015JD023766.

Bruning, E. C., S. A.Weiss, and K. M.Calhoun (2014), Continuous variability in thunderstorm primary electrification and an evaluation of inverted‐polarity terminology, Atmos. Res., 135‐136, 274–284, doi:10.1016/j.atmosres.2012.10.009.

Calhoun, K. M. (2015), Forecaster use of total lightning data for short‐term forecasts and warnings in the Hazardous Weather Testbed, paper presented at Seventh Conference on the Meteorological Applications of Lightning Data, Paper 3.5, AMS Annual Meeting, Phoenix, Ariz., 4–8 Jan.

Calhoun, K. M., E. C.Bruning, D. M.Kingfield, S. D.Rudlosky, C. W.Siewert, T.Smith, G. T.Stano, and G. J.Stumpf (2013), Forecaster use and evaluation of pGLM data at the NOAA Hazardous Weather Testbed and GOES‐R Proving Ground, paper presented at Sixth Conference on the Meteorological Applications of Lightning Data, AMS Annual Meeting, Austin, Tex., 6–10 Jan.

Calhoun, K. M., G. T.Stano, E. C.Bruning, S. D.Rudlosky, and D. M.Kingfield (2014), The Hazardous Weather Testbed: Moving total lightning data from a research tool into forecast and warning operations, XV International Conference on Atmospheric Electricity, Paper O–04‐02, IUGG/IAMAS International Commission on Atmospheric Electricity, Norman, Okla., 15–20 June.

Carey, L. D., and K. M.Buffalo (2007), Environmental control of cloud‐to‐ground lightning polarity in severe storms, Mon. Weather Rev., 135(4), 1327–1353, doi:10.1175/MWR3361.1.

Cecil, D. J., D. E.Buechler, and R. J.Blakeslee (2014), Gridded lightning climatology from TRMM‐LIS and OTD: Dataset description, Atmos. Res., 135‐136, 404–414, doi:10.1016/j.atmosres.2012.06.028.

Christian, H. J., et al. (2003), Global frequency and distribution of lightning as observed from space by the Optical Transient Detector, J. Geophys. Res., 108, 4005, doi:10.1029/2002JD002347.

Cummings, K., et al. (2015), A WRF‐Chem analysis of flash rates, lightning‐NO x production and subsequent trace gas chemistry of the 29–30 May 2012 convective event in Oklahoma during DC3, paper presented at Seventh Conference on the Meteorological Applications of Lightning Data, Paper 9.4, AMS Annual Meeting, Phoenix, Ariz., 4–8 Jan.

Darden, C. B., D. J.Nadler, B. C.Carcione, R. J.Blakeslee, G. T.Stano, and D. E.Buechler (2010), Using total lightning information to diagnose convective trends, Bull. Am. Meteorol. Soc., 91(2), 167–175, doi:10.1175/2009BAMS2808.1.

Fuchs, B. A., S. A.Rutledge, E. C.Bruning, J.Pierce, T. J.Lang, D. R.MacGorman, P. R.Krehbiel, and W.Rison (2015), Environmental controls on storm intensity and charge structure in various regions of the United States, J. Geophys. Res. Atmos., 120, 6575–6596, doi:10.1002/2015JD023271.

Goodman, S. J., et al. (2012), The GOES‐R Proving Ground: Accelerating user readiness for the next‐generation geostationary environmental satellite system, Bull. Am. Meteor. Soc., 93(7), 1029–1040, doi:10.1175/bams‐d‐11‐00175.1.

38 references, page 1 of 3
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publication . Article . 2016

LMA FLASH DETECTION PERFORMANCE

Chmielewski, Vanna C.; Bruning, Eric C.;