Simulation-Based Uncertainty Quantification for Estimating Atmospheric CO2 from Satellite Data. - Jonathan Hobbs; Amy Braverman; Noel Cressie; Robert Granat; Michael Gunson (SIAM/ASA J. Uncertain. Quantification, 2017)

Accelerated MCMC for Satellite-Based Measurements of Atmospheric CO2. - Otto Lamminpää; Jonathan Hobbs; Jenný Brynjarsdóttir; Marko Laine; Amy Braverman; Hannakaisa Lindqvist; Johanna Tamminen (Remote. Sens., 2019)

Sensitivity of Optimal Estimation Satellite Retrievals to Misspecification of the Prior Mean and Covariance, with Application to OCO-2 Retrievals. - Hai Nguyen 002; Noel Cressie; Jonathan Hobbs (Remote. Sens., 2019)

Intercomparison of Remote Sensing Retrievals: An Examination of Prior-Induced Biases in Averaging Kernel Corrections. - Hai Nguyen 002; Jonathan Hobbs (Remote. Sens., 2020)

Post hoc Uncertainty Quantification for Remote Sensing Observing Systems. - Amy Braverman; Jonathan Hobbs; Joaquim Teixeira; Michael Gunson (SIAM/ASA J. Uncertain. Quantification, 2021)

Spatial Retrievals of Atmospheric Carbon Dioxide from Satellite Observations. - Jonathan Hobbs; Matthias Katzfuss; Daniel Zilber; Jenný Brynjarsdóttir; Anirban Mondal; Veronica J. Berrocal (Remote. Sens., 2021)

Objective Frequentist Uncertainty Quantification for Atmospheric ( mathrm{CO}_2 ) Retrievals. - Pratik Patil; Mikael Kuusela; Jonathan Hobbs (SIAM/ASA J. Uncertain. Quantification, 2022)

Spatial Surface Reflectance Retrievals for Visible/Shortwave Infrared Remote Sensing via Gaussian Process Priors. - Daniel Zilber; David R. Thompson 001; Matthias Katzfuss; Vijay Natraj; Jonathan Hobbs; Amy Braverman (Remote. Sens., 2022)

Computer Model Emulation with High-Dimensional Functional Output in Large-Scale Observing System Uncertainty Experiments. - Pulong Ma; Anirban Mondal; Bledar A. Konomi; Jonathan Hobbs; Joon Jin Song; Emily Lei Kang (Technometrics, 2022)