A Mixed Exponential Distribution Model for Retrieving Ground Flash Fraction from Satellite Lightning Imager DataSource: Journal of Atmospheric and Oceanic Technology:;2011:;volume( 028 ):;issue: 004::page 475Author:Koshak, W. J.
DOI: 10.1175/2010JTECHA1438.1Publisher: American Meteorological Society
Abstract: Bayesian inversion method is introduced for retrieving the fraction of ground flashes in a set of flashes observed from a (low earth orbiting or geostationary) satellite lightning imager. The method employs a constrained mixed exponential distribution model to describe the lightning optical measurements. Because the method also retrieves certain population statistics of ground and cloud flash optical properties, the method can be applied to an arbitrary geographical region, including those regions where the lightning optical statistics either are not known or are difficult to obtain. The approach is tested by performing simulated retrievals, and retrieval error statistics are provided. A first-attempt retrieval of the global geographical distribution of ground flash fraction is obtained using the 5-yr Optical Transient Detector (OTD) dataset; the spatially averaged ground flash fraction over the global-scale domain studied was 0.151 with a standard deviation of 0.081. The ability to retrieve ground flash fraction has important benefits to the atmospheric chemistry community. For example, using the method to partition the existing OTD/Lightning Imaging Sensor (LIS) satellite global lightning climatology into separate ground and cloud flash climatologies would improve estimates of regional and global lightning nitrogen oxides (NOx) production; this, in turn, would improve both regional air quality and global chemistry/climate model predictions.
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contributor author | Koshak, W. J. | |
date accessioned | 2017-06-09T16:37:18Z | |
date available | 2017-06-09T16:37:18Z | |
date copyright | 2011/04/01 | |
date issued | 2011 | |
identifier issn | 0739-0572 | |
identifier other | ams-71099.pdf | |
identifier uri | http://onlinelibrary.yabesh.ir/handle/yetl/4212953 | |
description abstract | Bayesian inversion method is introduced for retrieving the fraction of ground flashes in a set of flashes observed from a (low earth orbiting or geostationary) satellite lightning imager. The method employs a constrained mixed exponential distribution model to describe the lightning optical measurements. Because the method also retrieves certain population statistics of ground and cloud flash optical properties, the method can be applied to an arbitrary geographical region, including those regions where the lightning optical statistics either are not known or are difficult to obtain. The approach is tested by performing simulated retrievals, and retrieval error statistics are provided. A first-attempt retrieval of the global geographical distribution of ground flash fraction is obtained using the 5-yr Optical Transient Detector (OTD) dataset; the spatially averaged ground flash fraction over the global-scale domain studied was 0.151 with a standard deviation of 0.081. The ability to retrieve ground flash fraction has important benefits to the atmospheric chemistry community. For example, using the method to partition the existing OTD/Lightning Imaging Sensor (LIS) satellite global lightning climatology into separate ground and cloud flash climatologies would improve estimates of regional and global lightning nitrogen oxides (NOx) production; this, in turn, would improve both regional air quality and global chemistry/climate model predictions. | |
publisher | American Meteorological Society | |
title | A Mixed Exponential Distribution Model for Retrieving Ground Flash Fraction from Satellite Lightning Imager Data | |
type | Journal Paper | |
journal volume | 28 | |
journal issue | 4 | |
journal title | Journal of Atmospheric and Oceanic Technology | |
identifier doi | 10.1175/2010JTECHA1438.1 | |
journal fristpage | 475 | |
journal lastpage | 492 | |
tree | Journal of Atmospheric and Oceanic Technology:;2011:;volume( 028 ):;issue: 004 | |
contenttype | Fulltext |