Creation and evaluation of a storm-tracking algorithm

Abstract

Thunderstorms can lead to flash floods, and can be accompanied by large hail, severe wind gusts and tornadoes, possibly having a high social impact. Accurate and detailed warnings for these storms are therefore desired. Because operational meteorologists, which monitor the weather and generate forecasts, are usually busy in situations with thunderstorms, they will benefit from algorithms that can automatically generate storm forecasts. These algorithms could determine the intensity of storms and their velocity, enabling it to produce a forecast. Such a forecast could provide a quick overview of storm intensities and velocities to operational meteorologists, or if accurate enough, might even be used to warn the public. The focus of this project is the creation and evaluation of a storm-tracking algorithm, which automatically produces storm motion estimates. Such a storm-tracking algorithm usually receives input from weather radars, which provide information about the precipitation intensity. The coupling (association) of precipitation patterns at consecutive times enables these algorithms to determine the motion. Several of these storm-tracking algorithms have been created in the past, but most of them suffer from storm interactions, e.g. the splitting of one storm into two, or the merging of two storms into one. This is because these algorithms perform one-to-one association of storms at consecutive times, which is not possible in the case of storm splits and mergers. Separate treatment of these processes is therefore required in these algorithms. The proposed new algorithm, named CBVELOCITY, uses a method that automatically treats storm interactions, in the same way as the usual translation of storms is treated. The key concept is the division of storm cells into subcells, which are subsequently associated between consecutive times, instead of the storm cells themselves. The velocity of the storm cells is then determined as the average of the velocities of the subcells in the storm. The subcell concept is based on the approach used in the storm-tracking algorithm of Matthews and Trostel (2010), but is optimized for determining velocity estimates instead of following thunderstorms through time. An evaluation of the performance of CBVELOCITY shows promising results compared to SCIT, the only existing algorithm to which it has been compared. It is further shown that the performance depends on the storm speed, with decreasing performance for decreasing storm speed.