Refining the relationship between lightning and convective rainfall over the ocean. Issue 2 (30th January 2014)

• Record Type: Journal Article
• Authors: Stolz, Douglas C.; Businger, Steven; Terpstra, Annick
• Is Part Of: Journal of geophysical research. Volume 119:Issue 2(2014:Feb.)
• Page Start: 964
• Page End: 981
• Publication Date: 2014-01-30
• Subjects: Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2012JD018819
• Languages: English
• ISSNs: 2169-897X

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <p>The lightning stroke density observed by Vaisala's Global Lightning Dataset (GLD360) was compared to maximum reflectivity in the upper and lower troposphere observed by NASA's Tropical Rainfall Measuring Mission Precipitation Radar over remote oceanic regions. We found that GLD360 stroke density is strongly correlated with maximum reflectivity above 0°C and the height of the 30 dBZ isopleth in two ocean basins (best represented by a logarithmic least squares regression). The maximum reflectivity above 0°C increases ~8–10 dBZ and the maximum height of the 30 dBZ isopleth increases ~2.8–3.8 km across the domain of stroke density. Stroke density and the maximum near‐surface reflectivity are also correlated, but the range of the resulting logarithmic relationship is less than that found for maximum reflectivity above 0°C. There is high confidence in the logarithmic regressions as <italic>R</italic><sup>2</sup> values are generally above 0.9 during the study. A bootstrap resampling approach confirms that the lightning versus rainfall relationship is statistically significant. Vertical profiles of radar reflectivity show that progressively larger values of stroke density are associated with higher reflectivity at most altitudes (most pronounced near 6.5 km mean sea level) and less rapid decreases in reflectivity with increasing height above 0°C. The logarithmic relationships between lightning and rainfall defined in this study have significant utility in creating proxy data sets, such as pseudoreflectivity, for weather analysis and assimilation in numerical weather prediction.</p> </abstract>