Mission CO2ntrol: A Statistical Scientist's Role in Remote Sensing of Atmospheric Carbon Dioxide

DOI10.1080/01621459.2017.1419136zbMath1398.62356OpenAlexW2803608051WikidataQ104697021 ScholiaQ104697021MaRDI QIDQ4690936

Noel Cressie

Publication date: 23 October 2018

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/01621459.2017.1419136

zbMATH Keywords

climate changeKrigingpredictive distributionhierarchical statistical model\(\mathrm{CO}_2\) flux inversionorbiting carbon observatory-2

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Applications of statistics to environmental and related topics (62P12) Bayesian inference (62F15) General considerations in statistical decision theory (62C05)

Related Items (7)

Objective Frequentist Uncertainty Quantification for Atmospheric \(\mathrm{CO}_2\) Retrievals ⋮ A few statistical principles for data science ⋮ Beyond Matérn: On A Class of Interpretable Confluent Hypergeometric Covariance Functions ⋮ Measuring, mapping, and uncertainty quantification in the space-time cube ⋮ A conversation with Noel Cressie ⋮ Spatio-temporal expanding distance asymptotic framework for locally stationary processes ⋮ Post hoc Uncertainty Quantification for Remote Sensing Observing Systems

Uses Software

• FRK

Cites Work

• A comparison of spatial predictors when datasets could be very large
• Statistical decision theory and Bayesian analysis. 2nd ed
• A Bayesian tutorial for data assimilation
• Fixed Rank Kriging for Very Large Spatial Data Sets
• An Algorithm for Least-Squares Estimation of Nonlinear Parameters
• Simulation-Extrapolation Estimation in Parametric Measurement Error Models
• Simulation-Based Uncertainty Quantification for Estimating Atmospheric CO$_2$ from Satellite Data
• Spatial Statistical Data Fusion for Remote Sensing Applications
• A method for the solution of certain non-linear problems in least squares
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