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An Algorithm to Nowcast Lightning Initiation and Cessation in Real-time




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An Algorithm to Nowcast Lightning Initiation and Cessation in Real-time Valliappa Lakshmanan1,2∗, Travis Smith1,2 Abstract Cloud-to-ground lightning data from the National Lightning Data Network (NLDN), satellite visible and radar-derived products are used to train a light- ning prediction algorithm. The radar reflectivity values are clustered to iden- tify storm and real-time geometric, lagrangian and scalar attributes of those storms are computed. A lightning density field is ”precast” to form the target decision field to be predicted using the computed attributes. Several days of data from the continental United States were chosen to obtain a seasonally and geographically diverse dataset for training. The trained system is used to predict lightning density and the predicted lightning density field is advected to produce a 30-minute nowcast field. The skill of the resulting algorithm is evaluated against a steady-state prediction with motion correction. 1. Introduction Predicting the spatial and temporal location of cloud-to-ground lightning is a difficult prob- lem. Predicting lightning in time is tied to problems of determining convective initiation. Predicting the location of cloud-to-ground lightning within an electrified storm is subject to knowledge of how lightning travels withins the storm. Yet, predicting lighting accurately in both space and time is important because light- ning is a potent weather-related hazard. Thus, a short-term (0-1 hour) warning for in- tense cloud-to-ground lightning has the potential to become a very valuable U.S. National Weather Service product. ∗Corresponding author: V Lakshmanan, 120 David L. Boren Blvd, Norman OK 73072; laksh- [email protected] 1Cooperative Institutute of Mesoscale Meteorological Studies, University of Oklahoma; 2National Oceanic and Atmospheric Administration / National Severe Storms Laboratory 1 A variety of rules of thumb have been developed at various forecast offices to alert the public of the potential of “excessive lightning”. An application to predict lightning, from model forecasts, as an extension of convective activity have been developed, for example by Keller (2004). We, on the other hand, are interested in time frames of less than an hour. Therefore, we formulate the lightning prediction problem as a spatio-temporal predicition problem based on radar-observed inputs. At a particular location, we seek to estimate the probability that there will be a lightning strike at that position in the next 30 minutes. Since lightning is an almost instantaneous event, the probability of lightning is also estimated in a spatio-temporal sense: a particular location is said to have experienced lightning if there is a lightning strike within a given distance of that location within the past 15 minutes. This spatio-temporal defintion of lightning activity is represented by a lightning density grid. The lightning density grid is a two-dimensional grid that has a resolution of 0.01 de- grees in latitude and longitude (approximately 1km x 1km at midlatitudes). The remapping of lightning source data into lightning density grids is achieved using temporal averaging and spatial smoothing. All the source data that impacts a grid cell over a given time period are used to determine the lightning density at a grid cell. Spatially, we let each source impact not ...





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