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Ousmane O. Sy, Simone Tanelli, Stephen L. Durden, Andrew Heymsfield, Aaron Bansemer, Kwo-Sen Kuo, Noppasin Niamsuwan, Robert M. Beauchamp, V. Chandrasekar, Manuel Vega, and Michael P. Johnson

, D m , S m ). In the same vein, big-data methods (e.g., machine learning) could be envisioned to better characterize the microphysics of hydrometeors using radars. Acknowledgments The authors would like to thank the editor and reviewers for their constructive remarks. The research described in this article was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Government sponsorship is

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Mircea Grecu, Lin Tian, Gerald M. Heymsfield, Ali Tokay, William S. Olson, Andrew J. Heymsfield, and Aaron Bansemer

Statistical Learning Data Mining, Inference, and Prediction . 2nd ed. Springer, 745 pp., http://www-stat.stanford.edu/~tibs/ElemStatLearn/ . Heymsfield , A. J. , A. Bansemer , P. R. Field , S. L. Durden , J. L. Stith , J. E. Dye , W. Hall , and C. A. Grainger , 2002 : Observations and parameterizations of particle size distributions in deep tropical cirrus and stratiform precipitating clouds: Results from in situ observations in TRMM field campaigns . J. Atmos. Sci. , 59 , 3457

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