John Williams

Project Scientist
Aviation Applications Program (AAP)

  • About

Job Duties

I co-manage the Turbulence Remote Sensing Project funded by the FAA's Aviation Weather Research program. Currently, our primary effort is to develop and implement a turbulence detection product based on data from NEXRAD radars. A web-based display tool providing real-time, 3-D depiction of in-cloud turbulence for a region around Chicago will be operationally demonstrated beginning in Summer 2005.

I am also a member of the American Meteorological Society's Committee on Artificial Intelligence Applications to Environmental Science.

Professional Interests

Remote sensing
Atmospheric turbulence
Optimization theory
Artificial intelligence


B.A., Physics, Swarthmore College, 1990
M.A., Mathematics, University of Colorado, 1996
M.S., Applied Mathematics, University of Colorado, 1998
Ph.D., Mathematics, University of Colorado, 2000

Selected Publications

Williams, J. K., L. Cornman, D. Gilbert, S. G. Carson, and J. Yee, 2004: Improved remote detection of turbulence using ground-based Doppler radars. AMS 11th Conference on Aviation, Range, and Aerospace Meteorology, CD-ROM, 4.5.

Williams, J. K., J. Vivekanandan and G. Zhang, 2003: Enhanced dual-wavelength technique for remote detection of cloud liquid water content. AMS 31st Conference on Radar Meteorology, 130-133.

Williams, J. K., J. Vivekanandan, and G. Zhang, 2002: Evaluation of remote icing detection techniques using X-, Ka-, and W-band radar and microwave radiometer observations. 10th Conference on Aviation, Range, and Aerospace Meteorology, 224-227.

Cornman, L. B., S. Gerding, G. Meymaris, and J. Williams, 2002: Evaluation of an airborne radar turb-ulence detection algorithm. 10th Conference on Aviation, Range, and Aerospace Meteorology, 237-240.

Cornman, L. B., J. K. Williams and R. K. Goodrich, 2000. The detection of convective turbulence using airborne Doppler radars. Ninth Conference on Aviation, Range, and Aerospace Meteorology.

Williams, J. K., 2000: On the Convergence of Model-free Policy Iteration Algorithms for Reinforcement Learning: Stochastic Approximation under Discontinuous Mean Dynamics. Doctoral Dissertation, University of Colorado.

Williams, J. K. and S. Singh, 1999: Experimental results on learning stochastic memoryless policies for partially observable Markov decision processes. In M. S. Kearns, S. A. Solla and D. A. Cohn, editors, Advances in Neural Information Processing Systems 11, MIT Press, 1073-1079.