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Toward Development of Improved QPE in Complex Terrain Using Cloud-to-Ground Lightning Data: A Case Study for the 2005 Monsoon in Southern Arizona

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  • 1 Department of Atmospheric Sciences, The University of Arizona, Tucson, Arizona, and Departamento de Física, Universidad de Sonora, Hermosillo, Mexico
  • | 2 Department of Atmospheric Sciences, The University of Arizona, Tucson, Arizona
  • | 3 Department of Atmospheric Sciences, The University of Arizona, and Vaisala, Inc., Tucson, Arizona
  • | 4 Department of Atmospheric Sciences, The University of Arizona, Tucson, Arizona
  • | 5 Departamento de Matemáticas, Universidad de Sonora, Hermosillo, Mexico
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Abstract

The relationship between convective precipitation and cloud-to-ground (CG) lightning is examined over a study area in southwest Arizona and northwest Mexico. Using seasonal-to-daily and hourly time resolution, the National Climatic Data Center (NCDC) stage IV precipitation product and the U.S. National Lightning Detection Network lightning data have been analyzed with the aim of developing an improved understanding of the relationship between these variables. A Gaussian method of spatially smoothing discrete lightning counts is used to estimate convective rainfall and improve the quality and spatial coverage of radar-derived precipitation in areas of complex terrain. For testing the dependence of the relationship between CG lightning and precipitation, a precipitation “sensor coverage” analysis has been performed. If locations that have poor sensor coverage are excluded, R2 between lightning and precipitation improves by up to 15%. A complementary way to estimate convective precipitation is proposed based on 1-h lightning occurrence intervals, which is the maximum time resolution in this study. We find that ~67% of the seasonal 2005 precipitation over the analysis domain is associated with CG lightning. Daily precipitation estimates are improved by specifying a “diurnal day” based on the diurnal maxima and minima in precipitation and CG lightning within the domain. Our method for improving quantitative precipitation estimation (QPE) using lightning is able to track and estimate convective precipitation over regions that have poor sensor coverage, particularly in both air mass storms and large multicellular events, with R2 up to 70%.

Corresponding author address: Carlos Manuel Minjarez-Sosa, Department of Atmospheric Sciences, The University of Arizona, Physics and Atmospheric Sciences Bldg., Rm. 520, 1118 East Fourth Street, Tucson, AZ 85721-0081. E-mail: minjarez@atmo.arizona.edu

Abstract

The relationship between convective precipitation and cloud-to-ground (CG) lightning is examined over a study area in southwest Arizona and northwest Mexico. Using seasonal-to-daily and hourly time resolution, the National Climatic Data Center (NCDC) stage IV precipitation product and the U.S. National Lightning Detection Network lightning data have been analyzed with the aim of developing an improved understanding of the relationship between these variables. A Gaussian method of spatially smoothing discrete lightning counts is used to estimate convective rainfall and improve the quality and spatial coverage of radar-derived precipitation in areas of complex terrain. For testing the dependence of the relationship between CG lightning and precipitation, a precipitation “sensor coverage” analysis has been performed. If locations that have poor sensor coverage are excluded, R2 between lightning and precipitation improves by up to 15%. A complementary way to estimate convective precipitation is proposed based on 1-h lightning occurrence intervals, which is the maximum time resolution in this study. We find that ~67% of the seasonal 2005 precipitation over the analysis domain is associated with CG lightning. Daily precipitation estimates are improved by specifying a “diurnal day” based on the diurnal maxima and minima in precipitation and CG lightning within the domain. Our method for improving quantitative precipitation estimation (QPE) using lightning is able to track and estimate convective precipitation over regions that have poor sensor coverage, particularly in both air mass storms and large multicellular events, with R2 up to 70%.

Corresponding author address: Carlos Manuel Minjarez-Sosa, Department of Atmospheric Sciences, The University of Arizona, Physics and Atmospheric Sciences Bldg., Rm. 520, 1118 East Fourth Street, Tucson, AZ 85721-0081. E-mail: minjarez@atmo.arizona.edu
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