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Multivariate Statistical Inference of Lightning Occurrence, and Using Lightning ObservationsTwo classes of multivariate statistical inference using TRMM Lightning Imaging Sensor, Precipitation Radar, and Microwave Imager observation are studied, using nonlinear classification neural networks as inferential tools. The very large and globally representative data sample provided by TRMM allows both training and validation (without overfitting) of neural networks with many degrees of freedom. In the first study, the flashing / or flashing condition of storm complexes is diagnosed using radar, passive microwave and/or environmental observations as neural network inputs. The diagnostic skill of these simple lightning/no-lightning classifiers can be quite high, over land (above 80% Probability of Detection; below 20% False Alarm Rate). In the second, passive microwave and lightning observations are used to diagnose radar reflectivity vertical structure. A priori diagnosis of hydrometeor vertical structure is highly important for improved rainfall retrieval from either orbital radars (e.g., the future Global Precipitation Mission "mothership") or radiometers (e.g., operational SSM/I and future Global Precipitation Mission passive microwave constellation platforms), we explore the incremental benefit to such diagnosis provided by lightning observations.
Document ID
20040085918
Document Type
Conference Paper
Authors
Boccippio, Dennis (NASA Marshall Space Flight Center Huntsville, AL, United States)
Date Acquired
August 21, 2013
Publication Date
January 1, 2004
Subject Category
Meteorology and Climatology
Meeting Information
International Lightning Detection Conference(Helsinki)
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.