Dataset Information

Lightning Mapping Array flash detection performance with variable receiver thresholds.

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 long ranges. Location errors and detection efficiency were highly dependent on the station configuration and thresholds, especially at longer ranges, such that performance varied more than expected across different networks and with azimuth within networks. Error characteristics matched prior studies, which led to an increase in flash distortion with range. Predicted flash detection efficiency exceeded 95% within 100 km of all networks.

SUBMITTER: Chmielewski VC

PROVIDER: S-EPMC5101853 | biostudies-other | 2016 Jul

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Lightning Mapping Array flash detection performance with variable receiver thresholds.

Chmielewski Vanna C VC Bruning Eric C EC

Journal of geophysical research. Atmospheres : JGR 20160728 14

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 th ...[more]

PMID: 27867785

Similar Datasets

Project description:This study assesses the impact of the lightning nitric oxide (LNO) production schemes in the Community Multiscale Air Quality (CMAQ) model on ground-level air quality as well as aloft atmospheric chemistry through detailed evaluation of model predictions of nitrogen oxides (NO x ) and ozone (O3) with corresponding observations for the US. For ground-level evaluations, hourly O3 and NO x values from the U.S. EPA Air Quality System (AQS) monitoring network are used to assess the impact of different LNO schemes on model prediction of these species in time and space. Vertical evaluations are performed using ozonesonde and P-3B aircraft measurements during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) campaign conducted in the Baltimore- Washington region during July 2011. The impact on wet deposition of nitrate is assessed using measurements from the National Atmospheric Deposition Program's National Trends Network (NADP NTN). Compared with the Base model (without LNO), the impact of LNO on surface O3 varies from region to region depending on the Base model conditions. Overall statistics suggest that for regions where surface O3 mixing ratios are already overestimated, the incorporation of additional NO from lightning generally increased model overestimation of mean daily maximum 8 h (DM8HR) O3 by 1-2 ppb. In regions where surface O3 is underestimated by the Base model, LNO can significantly reduce the underestimation and bring model predictions close to observations. Analysis of vertical profiles reveals that LNO can significantly improve the vertical structure of modeled O3 distributions by reducing underestimation aloft and to a lesser degree decreasing overestimation near the surface. Since the Base model underestimates the wet deposition of nitrate in most regions across the modeling domain with the exception of the Pacific Coast, the inclusion of LNO leads to reduction in biases and errors and an increase in correlation coefficients at almost all the NADP NTN sites. Among the three LNO schemes described in Kang et al. (2019), the hNLDN scheme, which is implemented using hourly observed lightning flash data from National Lightning Detection Network (NLDN), performs best for comparisons with ground-level values, vertical profiles, and wet deposition of nitrate; the mNLDN scheme (the monthly NLDN-based scheme) performed slightly better. However, when observed lightning flash data are not available, the linear regression-based parameterization scheme, pNLDN, provides an improved estimate for nitrate wet deposition compared to the base simulation that does not include LNO.

Dataset Information

Lightning Mapping Array flash detection performance with variable receiver thresholds.

Publications

Lightning Mapping Array flash detection performance with variable receiver thresholds.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets