Extracting the Climatology of Thunderstorms

Abstract

The climatology of thunderstorms is an important weather forecasting tool and aids in improved predictability of thunderstorms (Schneider and Dean, A comprehensive 5-year severe storm environment climatology for the continental united states. In: 24th conference on severe local storms, Savannah. American Meteorological Society, p 16A.4, 2008). However, deriving such a climatology from observations of severe weather events is subject to demographic bias (Paruk and Blackwell, Natl Weather Dig 19(1):27–33, 1994), and this bias can be ameliorated by the use of remotely sensed observations to create climatologies (Cintineo et al., Weather Forecast 27:1235–1248, 2012). In this paper, we describe a fully automated method of identifying, tracking, and clustering thunderstorms to extract such a climatology and demonstrate it by deriving the climatology of thunderstorm initiations over the continental United States. The identification is based on the extended watershed algorithm of Lakshmanan et al. (J Atmos Ocean Technol 26(3):523–537, 2009), the tracking based on the greedy optimization method suggested in Lakshmanan and Smith (Weather Forecast 25(2):721–729, 2010), and the clustering is the Theil-Sen clustering method introduced in Lakshmanan et al. (J Appl Meteorol Clim 54:451–462, 2014). This method was employed on radar data collected across the conterminous United States for the year 2010 in order to determine the location of all thunderstorm initiations that year. Eighty-one percent of all thunderstorm initiation points occurred in the spring and summer months and were widely dispersed across all states. The remaining 19 % occurred in the fall and winter months, and a majority of these points were spatially dispersed across the southern half of the United States.