Jon Hobbs

Biographical Sketch

Jon Hobbs is a data scientist at the Jet Propulsion Laboratory, California Institute of Technology. As a member of the Uncertainty Quantification (UQ) and Statistical Analysis group, his research has included developing UQ methodology for atmospheric remote sensing retrievals, including simulation-based approaches for the Orbiting Carbon Observatory-2/3 (OCO-2/3) and Atmospheric Infrared Sounder (AIRS). He led a NASA AIST effort on UQ for remote sensing retrievals.

In addition, he has substantial experience in development of spatio-temporal statistical methods for geoscience applications, including hydrology, carbon cycle science, weather, and climate. He also has a background in Bayesian analysis of hierarchical statistical models for agricultural, environmental, and social science applications.

Jon completed a co-major PhD in statistics and meteorology at Iowa State University, where he had previously earned a BS in meteorology and statistics. His dissertation research combined physical and statistical models to address interannual variability and the diurnal cycle in the atmosphere.

Projects

• Uncertainty quantification, OCO-2 data analysis, carbon cycle science, hydrology, climate

Selected Publications

Hobbs, J., Katzfuss, M., Nguyen, H., Yadav, V., and Liu, J. (2024). “Functional analysis of variance (ANOVA) for carbon flux estimates from remote sensing data,” Geoscientific Model Development, 17(3), pp. 1133–1151. Document (link to DOI) (CL 23-7187).

Konomi, B. A., Kang, E. L., Almomani, A., and Hobbs, J. (2023). “Bayesian latent variable co-kriging model in remote sensing for quality flagged observations,” Journal of Agricultural, Biological and Environmental Statistics, 28(3), pp. 423–441. Document (link to DOI).

Ma, P., Mondal, A., Konomi, B. A., Hobbs, J., Song, J. J., and Kang, E. L. (2022). “Computer model emulation with high-dimensional functional output in large-scale observing system uncertainty experiments,” Technometrics, 64(1), pp. 65–79. Document (link to DOI).

Kalmus, P., Nguyen, H., Roman, J., Wang, T., Yue, Q. and others including Hobbs, J. (2022). “Data fusion of AIRS and CrIMSS near surface air temperature,” Earth and Space Science, 9(10), pp. e2022EA002282. Document (link to DOI).

Patil, P., Kuusela, M., and Hobbs, J. (2022). “Objective frequentist uncertainty quantification for atmospheric CO₂ retrievals,” SIAM/ASA Journal on Uncertainty Quantification, 10(3), pp. 827–859. Document (link to DOI).

Hobbs, J., Katzfuss, M., Zilber, D., Brynjarsdóttir, J., Mondal, A., and Berrocal, V. (2021). “Spatial retrievals of atmospheric carbon dioxide from satellite observations,” Remote Sensing, 13(4), p. 571. Document (link to DOI) (CL 21-0593).

Braverman, A., Hobbs, J., Teixeira, J., and Gunson, M. (2021). “Post hoc uncertainty quantification for remote sensing observing systems,” SIAM/ASA Journal on Uncertainty Quantification, 9(3), pp. 1064–1093. Document (link to DOI) (CL 21-0606).

Hobbs, J. M., Drouin, B. J., Oyafuso, F., Payne, V. H., Gunson, M. R. and others (2020). “Spectroscopic uncertainty impacts on OCO-2/3 retrievals of XCO2,” Journal of Quantitative Spectroscopy and Radiative Transfer, 257, p. 107360. Document (link to DOI) (CL 20-5165).

Nguyen, H. and Hobbs, J. (2020). “Intercomparison of remote sensing retrievals: An examination of prior-induced biases in averaging kernel corrections,” Remote Sensing, 12(19), p. 3239. Document (link to DOI).

Massoud, E., Turmon, M., Reager, J., Hobbs, J., Liu, Z., and David, C. H. (2020). “Cascading dynamics of the hydrologic cycle in California explored through observations and model simulations,” Geosciences, 10(2), p. 71. Document (link to DOI).

Emery, C. M., David, C. H., Andreadis, K. M., Turmon, M. J., Reager, J. T. and others including Hobbs, J. M. (2020). “Underlying fundamentals of Kalman filtering for river network modeling,” Journal of Hydrometeorology, 21(3), pp. 453–474. Document (link to DOI).

Lamminpää, O., Hobbs, J., Brynjarsdóttir, J., Laine, M., Braverman, A. and others (2019). “Accelerated MCMC for satellite-based measurements of atmospheric CO₂,” Remote Sensing, 11(17), p. 2061. Document (link to DOI).

David, C. H., Hobbs, J. M., Turmon, M. J., Emery, D. M., Reager, J. T., and Famiglietti, J. S. (2019). “Analytical propagation of runoff uncertainty into discharge uncertainty through a large river network,” Geophysical Research Letters, 46(14), pp. 8102–8113. Document (link to DOI).

Nguyen, H., Cressie, N., and Hobbs, J. (2019). “Sensitivity of optimal estimation satellite retrievals to misspecification of the prior mean and covariance, with application to OCO-2 retrievals,” Remote Sensing, 11(23), p. 2770. Document (link to DOI).

Lewis-Beck, C., Zhu, Z., Mondal, A., Song, J. J., Hobbs, J. and others (2019). “A parametric approach to unmixing remote sensing crop growth signatures,” Journal of Agricultural, Biological and Environmental Statistics, 24(3), pp. 502–516. Document (link to DOI).

Thompson, D. R., Babu, K. N., Braverman, A. J., Eastwood, M. L., Green, R. O. and others including Hobbs, J. M. (2019). “Optimal estimation of spectral surface reflectance in challenging atmospheres,” Remote Sensing of Environment, 232, p. 111258. Document (link to DOI).

Brynjarsdóttir, J., Hobbs, J., Braverman, A., and Mandrake, L. (2018). “Optimal estimation versus MCMC for CO₂ retrievals,” Journal of Agricultural, Biological and Environmental Statistics, 23(2), pp. 297–316. Document (link to DOI).

Hobbs, J., Braverman, A., Cressie, N., Granat, R., and Gunson, M. (2017). “Simulation-based uncertainty quantification for estimating atmospheric CO₂ from satellite data,” SIAM/ASA Journal on Uncertainty Quantification, 5(1), pp. 956–985. Document (link to DOI) (CL 17-2675).

Connor, B., Bösch, H., McDuffie, J., Taylor, T., Fu, D. and others including Hobbs, J. (2016). “Quantification of uncertainties in OCO-2 measurements of XCO2: simulations and linear error analysis,” Atmospheric Measurement Techniques, 9(10), pp. 5227–5238. Document (link to DOI).

Hobbs, J. M. (2015). “Uncertainty Quantification for Remote Sensing Retrievals: Monte Carlo Experiments for OCO-2,” (section seminar). Document (CL 15-5287).

Morton, L. W., Hobbs, J., Arbuckle, J. F., and Loy, A. (2015). “Upper Midwest climate variations: Farmer responses to excess water risks,” Journal of Environmental Quality, 44(3), pp. 810–822. Document (link to DOI).

Arbuckle, J. G., Morton, L. W., and Hobbs, J. (2015). “Understanding farmer perspectives on climate change adaptation and mitigation: The roles of trust in sources of climate information, climate change beliefs, and perceived risk,” Environment and Behavior, 47(2), pp. 205–234. Document (link to DOI).

Power, M. and Hobbs, J. M. (2015). “A comparative analysis of financial professionals' perception of the level of graduating business student retirement planning familiarity, motivation, and preparedness,” Risk Management and Insurance Review, 18(2), pp. 273–295. Document (link to DOI).

Arbuckle, J. G., Hobbs, J., Loy, A., Morton, L. W., Prokopy, L. S., and Tyndall, J. (2014). “Understanding Corn Belt farmer perspectives on climate change to inform engagement strategies for adaptation and mitigation,” Journal of Soil and Water Conservation, 69(6), pp. 505–516. Document (link to DOI).

Morton, L. W., Hobbs, J., and Arbuckle, J. G. (2013). “Shifts in farmer uncertainty over time about sustainable farming practices and modern farming's reliance on commercial fertilizers, insecticides, and herbicides,” Journal of Soil and Water Conservation, 68(1), pp. 1–12. Document (link to DOI).

Arbuckle, J. G., Morton, L. W., and Hobbs, J. (2013). “Farmer beliefs and concerns about climate change and attitudes toward adaptation and mitigation: Evidence from Iowa,” Climatic Change, 118(3-4), pp. 551–563. Document (link to DOI).

Fisel, B. J., Gutowski, W. J., Hobbs, J. M., and Cassano, J. J. (2011). “Multiregime states of Arctic atmospheric circulation,” Journal of Geophysical Research, 116(D20), pp. D20122. Document (link to DOI).

Hobbs, J., Wickham, H., Hofmann, H., and Cook, D. (2010). “Glaciers melt as mountains warm: a graphical case study,” Computational Statistics, 25(4), pp. 569–586. Document (link to DOI).