David Lary
Research Professor

Departmental Affiliation:

Physics

JCET Research Group:

Atmospheric Chemistry and Dynamics

GSFC Code:

610.3

Mailing Address:

Software Integration and Visualization Office (SIVO)
Building 22, Room C185
NASA GSFC, Code 610.3
Greenbelt, MD 20771 USA

Phone:

(301) 614-8018

Fax:

(301) 614-xxxx

Email:

david.j.lary@nasa.gov

Most Recent Publication

Lary, D. J., L. A. Remer, D. MacNeill, B. Roscoe, and S. Paradise (2009), Machine Learning and Bias Correction of MODIS Aerosol Optical Depth, IEEE Trans. on Geoscience and Remote Sensing, accepted for publication.

Schoeberl, M. R., A. R. Douglass, P. A. Newman, L. R. Lait, J. Waters, N. Livesey, L. Froidevaux, A. Lambert, W. Read, M. J. Filipiak, and H. C. Pumphrey (2008), QBO and annual cycle variations in tropical lower stratosphere trace gases from HALOE and Aura MLS observations, JGR, 113, D05301, doi:10.1029/2007JD008678.

Research Interests:

Biography:

Dr. David J. Lary received a First Class Double Honors B.Sc. in Physics and Chemistry from King's College London (1987) with the Sambrooke Exhibition Prize in Natural Science, and a Ph.D. in Atmospheric Chemistry from the University of Cambridge, Churchill College (1991). His thesis described the first chemical scheme for the ECMWF numerical weather prediction model. He was awarded a Royal Society University Research Fellowship in 1996 at Cambridge University. From 1998 to 2000 Dr. Lary held a joint position at Cambridge and the University of Tel-Aviv as a senior lecturer and Alon fellow. In 2000 the chief scientific adviser to the British Prime Minister and Head of the British Office of Science and Technology, Professor Sir David King, recommended Dr. Lary to be appointed as a Cambridge University lecturer in Chemical Informatics. In 2001, he joined UMBC/GEST as the first distinguished Goddard fellow in earth science at the invitation of Richard Rood. Dr. Lary's automatic code generation software, AutoChem, has received five NASA awards. He is currently involved with NASA Aura validation using probability distribution functions and chemical data assimilation, neural networks for accelerating atmospheric models, the use of Earth Observing data for health and policy applications, and the optimal design of Earth Observing Systems. The thread running through all the research is atmospheric chemistry, and the use of observation and automation to facilitate scientific discovery.