Improving Quantitative Precipitation Estimation in Complex Terrain Using Cloud-to-Ground Lightning Data

Abstract

Thunderstorms that occur in areas of complex terrain are a major severe weather hazard in the intermountain western U.S. Short-term quantitative estimation (QPE) of precipitation in complex terrain is a pressing need to better forecast flash flooding. Currently available techniques for QPE, that utilize a combination of rain gauge and weather radar information, may underestimate precipitation in areas where gauges do not exist or there is radar beam blockage. These are typically very mountainous and remote areas, that are quite vulnerable to flash flooding because of the steep topography. Lightning has been one of the novel ways suggested by the scientific community as an alternative to estimate precipitation over regions that experience convective precipitation, especially those continental areas with complex topography where the precipitation sensor measurements are scarce. This dissertation investigates the relationship between cloud-to-ground lightning and precipitation associated with convection with the purpose of estimating precipitation- mainly over areas of complex terrain which have precipitation sensor coverage problems (e.g. Southern Arizona).The results of this research are presented in two papers. The first, entitled Toward Development of Improved QPE in Complex Terrain Using Cloud-to-Ground Lighting Data: A case Study for the 2005 Monsoon in Southern Arizona, was published in the Journal of Hydrometeorology in December 2012. This initial study explores the relationship between cloud-to-ground lightning occurrences and multi-sensor gridded precipitation over southern Arizona. QPE is performed using a least squares approach for several time resolutions (seasonal -June, July and August-, 24 hourly and hourly) and for a 8 km grid size. The paper also presents problems that arise when the time resolution is increased, such as the spatial misplacing of discrete lightning events with gridded precipitation and the need to define a "diurnal day" that is synchronized with the diurnal cycle of convection. The second manuscript (unpublished), entitled An Improved QPE Over Complex Terrain by Using Cloud-to-Ground Lightning Occurrences, provides a new method to retrieve lightning-derived precipitation at 5 minutes and 5 Km time and space resolutions. A stationary model that employs spatio-temporal neighboring (Space and Time Invariant model -STI) improves upon the least squares method in the first paper. By applying a Kalman filter to the STI model, lightning-precipitation is retrieved by a dynamic model that changes in time. The results for seasonal and 5 minutes time resolution show that the dynamic model improves the retrievals derived by the STI model.