Amy Braverman

Biographical Sketch

Dr. Amy Braverman is a Principal Statistician at the Jet Propulsion Laboratory in Pasadena, California. She received her doctorate in statistics from the University of California, Los Angeles (UCLA), a masters in Mathematics from UCLA, and a B.A. degree in economics from Swarthmore College, Swarthmore, PA, in 1982.

Her research interests include information-theoretic approaches for the analysis of massive data sets, data fusion methods for combining heterogeneous, spatial and spatio-temporal data, and statistical methods for the evaluation and diagnosis of climate models, particularly by comparison to observational data. Dr. Braverman focuses on the use of remote sensing data, and has designed and analyzed new Level 3 data products for MISR and other NASA missions.

Projects

• Spatio-temporal data fusion
• Climate model assessment
• MISR science team
• Atmospheric Infrared Sounder (AIRS) science development team

Code

Code and data archive to reproduce results reported in the paper, "Probabilistic Evaluation of Competing Climate Models," Amy Braverman, Snigdhansu Chatterjee, Megan Heyman, and Noel Cressie, Advances in Statistical Climatology, Meteorology and Oceanography (ASCMO), 2017. JPL CL#17-3774.

Selected Publications

Braverman, A., Hobbs, J., Teixeira, J., and Gunson, M. (2021). “Post hoc Uncertainty Quantification for Remote Sensing Observing Systems,” SIAM/ASA Jour. Uncert. Quantification. Accepted. Document (CL 21-0606).

Cawse-Nicholson, K., Braverman, A., Kang, E., Li, M., Johnson, M. and others (2020). “Sensitivity and uncertainty quantification for the ECOSTRESS evapotranspiration algorithm – DisALEXI,” International Journal of Applied Earth Observation and Geoinformation, 89, p. 102088. Document (link to DOI).

Ma, P., Kang, E.L., Braverman, A.J., and and Nguyen, H.M. (2019). “Spatial Statistical Downscaling for Constructing High-resolution Nature Runs in Global Observing System Simulation Experiments,” Technometrics, 61(3), pp. 322-340. Document.

Nguyen, Hai, Cressie, Noel, and Braverman, Amy (2017). “Multivariate Spatial Data Fusion for Very Large Remote Sensing Datasets,” Remote Sensing, 9, p. 142. Document (link to DOI).

Nguyen, Hai, Katzfuss, Matthias, Cressie, Noel, and Braverman, Amy (2014). “Spatio-Temporal Data Fusion for Very Large Remote Sensing Datasets,” Technometrics, 56(2), pp. 174-185. Document (link to DOI).

Nguyen, Hai, Cressie, Noel, and Braverman, Amy (2012). “Spatial statistical data fusion for remote sensing applications,” Jour. American Statistical Association, 107, pp. 1004-1018. Document.

Braverman, A. J., Cressie, N., and Teixeira, J. (2011). “A likelihood-based comparison of CMIP5 decadal experiment runs with observations from the Atmospheric Infrared Sounder,” AGU Fall Meeting Abstracts, pp. A773. Document.

Crichton, D. J., Mattmann, C. A., Braverman, A. J., and Cinquini, L. (2010). “A Distributed, Cross-Agency Software Architecture for Sharing Climate Models and Observational Data Sets (Invited),” AGU Fall Meeting Abstracts, pp. A2. Document.

Nguyen, H. M. and Braverman, A. J. (2010). “Components of uncertainty in spatial statistical modeling of geophysical processes (Invited),” AGU Fall Meeting Abstracts, pp. A2. Document.

Braverman, A. J., Cressie, N., and Teixeira, J. (2010). “A Bayesian Approach to Evaluating Consistency between Climate Model Output and Observations,” AGU Fall Meeting Abstracts, pp. C3. Document.

Nguyen, H. M., Cressie, N., Braverman, A. J., and Olsen, E. (2010). “Spatial interpolation of carbon dioxide using Fixed Rank Kriging,” AGU Fall Meeting Abstracts, pp. D3. Document.

Braverman, A. J. (2009). “The Role of Uncertainty in Spatial Statistical Modeling of Geophysical Processes (Invited),” AGU Fall Meeting Abstracts. Document.

Braverman, A. J., Cressie, N., Katzfuss, M., Michalak, A. M., Miller, C. E. and others (2009). “Geostatistical Data Fusion for Remote Sensing Applications,” AGU Fall Meeting Abstracts, pp. C1014. Document.

Mattmann, C. A., Williams, D., Braverman, A. J., and Crichton, D. J. (2009). “An Architecture and Analysis Environment for Model to Observational Data Intercomparisons,” AGU Fall Meeting Abstracts, pp. B1083. Document.