Date of Award


Document Type


Degree Name

Master of Science (MS)

Legacy Department

Mechanical Engineering


Saylor, John R

Committee Member

Ulbrich , Carlton W

Committee Member

Miller , Richard S


The remote estimation of rainfall rate R is essential for the aviation industry, agriculture, and food warning. Radar, the current means of R estimation, is not available in much of the world. In addition, this measurement involves a level of inaccuracy. Using lightning to detect rain is a relatively inexpensive alternative to radar systems and can be done from existing satellites. Previous research has revealed correlations between lightning and rain, suggesting either that it is possible to estimate R using lightning, or that it is possible to use it to correct for a portion of the radar inaccuracies. These correlations are not only between the amount of lightning and the amount of rain, but also between other parameters, including statistics describing raindrop size.
Rain, lightning, and radar data were collected in Central Florida over a two month period in the summer of 2005. Rain data, including raindrop size statistics, were collected from a single point using a disdrometer. Lightning data were collected using the Los Alamos Sferic Array. Radar data were obtained from the WSR-88D radar network.
Rain rate R and the raindrop size statistics were compared to lightning statistics to determine which rain/lightning parameter pairs were most correlated. The degree of correlation between rain and lightning parameters was evaluated using the correlation coeffiecint r. Diffrent lightning types (Cloud-to-Ground, Intra-Cloud, Narrow-Bipolar-Event, Total) were considered, and various circular areas were used for lightning collection to optimize the strength of the correlations.
Four models using lightning and/or radar for the estimation of R were developed and then compared for accuracy. The rst model is based on the relationship between R and the radar reflectivity factor Z, as is the current practice. Two models using only lightning for the estimation of R were evaluated, and a final model used both radar and lightning data to estimate R. The performance of each model was evaluated using the RMS error.
The correlations between rain and lightning parameters were generally weak (r < 0.5), although some pairs clearly produced stronger correlations than others. Results show that the strongest correlations are between lightning density (strokes/km2/hr) and Lambda, a parameter of the raindrop size distribution. This correlation was strongest for Intra-Cloud (IC) lightning measured on a 75 km diameter.
Results from the R estimation models indicate that the use of lightning alone is a valid alternative to the use of radar for the conditions studied (R > 0.1 mm/hr, lightning present). The method combining radar and lightning parameters produces more accurate estimations of R than either type alone. Based on these results, lightning data can be used in addition to radar to provide greater accuracy to publicly available rain estimates, and it can be used to provide rain estimation capability to new locations, including greater food warning ability.