Refining the relationship between lightning and convective rainfall over the ocean

Abstract

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 R 2 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.

A global lightning parameterization based on statistical relationships among environmental factors, aerosols, and convective clouds in the TRMM climatology

“…As documented in previous studies [e.g., Stolz et al ., , ], environmental variables such as lightning flash rate/flash rate density or CCN concentrations may be lognormally distributed, and thus, variable transformations are necessary in practice [e.g., Wilks , ]. An amenable form of the multiple‐linear regression model to handle logarithmic terms is then based on the assumption of direct (or inverse) proportionality between the predictand and an input variable or multiplicative relationships when two or more variables are included in the predictor set (see equations – in the appendix).…”

On the interaction of Tropical Cyclone Flossie and emissions from Hawaii's Kilauea volcano

“…Vaisala's GLD360 lightning detection network has been calibrated to detect lightning across the oceans of the Northern Hemisphere with 70% detection efficiency on average [Cummins and Murphy, 2009;Said et al, 2010;Stolz et al, 2014]. GLD360 data were overlaid on GOES IR images for this study.…”

Evaluation of Lightning Forecasting Based on One Lightning Parameterization Scheme and Two Diagnostic Methods

“…In the past decades, based on the microphysical mechanisms and dynamic variables in convective clouds, many researchers have tried to find a reliable relationship between lightning data and other meteorological variables to obtain a proper observation operator for lightning data assimilation. These variables are convective precipitation rate [7][8][9][10], convective available potential energy (CAPE) [11,12], maximum vertical velocity [13], proxy radar reflectivity [14][15][16][17], graupel mass [18], ice mass flux product [19,20], and updraft volume [21,22]. Then, researchers started to assimilate lightning data into NWP models.…”