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Using Lightning Mapping Array data to predict the onset of cloud to ground lightning
V. Lakshmanan, CIMMS/Univ. of Oklahoma/NOAA/NSSL, Norman, OK; and K. D. Hondl, D. R. MacGorman, and T. M. Smith
We are developing a multi-sensor application that uses iso-therm levels from the Rapid Update Cycle (RUC2) model, radar reflectivity data, Lightning Mapping Array (LMA) data and cloud-to-ground lightning data from the National Lightning Detection Network (NLDN) to predict the onset of cloud-to-ground lightning. The application uses a radial basis function (RBF) to form a relation between past observed reflectivity at various isotherm levels, and in-cloud lightning activity at those levels to current cloud-to-ground lightning activity. The RBF relationship matrix is constantly updated in real-time, and used to predict the onset of cloud-to-ground activity in the future based on current observations of radar reflectivity and LMA data at various isotherm levels. In this paper, we describe the performance and feasibility of such a prediction algorithm. We also explore whether such an algorithm can be trained offline on archived data, instead of actually doing its learning in real time.
Session 1, Technological advances in operations and warnings
Monday, 4 October 2004, 10:30 AM-12:00 PM
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