Estimation of atmospheric CO2concentration using Kalman filtering
- 1 June 1986
- journal article
- research article
- Published by Taylor & Francis in International Journal of Systems Science
- Vol. 17 (6) , 897-909
- https://doi.org/10.1080/00207728608926855
Abstract
Using a dynamic state model for the observed upward trend and sinusoidal variation, a Kalman filter is constructed to estimate the atmospheric CO2 concentration, The process noise is assumed to be white with an unknown covariance, so an adaptive scheme is used to estimate the steady-state Kalman gain matrix. Several tests for optimality are performed on the adaptive filter. Measured data are then filtered using the Kalman algorithm. The filtering results are shown to reduce the variability of the airborne fraction of fossil-fuel-produced atmospheric CO2.This publication has 15 references indexed in Scilit:
- Convergence properties of the Riccati difference equation in optimal filtering of nonstabilizable systemsIEEE Transactions on Automatic Control, 1984
- Global Deforestation: Contribution to Atmospheric Carbon DioxideScience, 1983
- The Carbon Cycle and Its Perturbation by ManPublished by Springer Nature ,1980
- Dip in the atmospheric CO2 level during the mid‐1960'sJournal of Geophysical Research: Oceans, 1979
- Inferences drawn from atmospheric CO2dataJournal of Geophysical Research, 1979
- Practical state and bias estimation of process systems with initial information uncertaintyInternational Journal of Systems Science, 1977
- Adaptive state estimation for systems with unknown noise covariancesInternational Journal of Systems Science, 1977
- Modelling the global carbon cycle†International Journal of Systems Science, 1975
- On the identification of variances and adaptive Kalman filteringIEEE Transactions on Automatic Control, 1970
- An innovations approach to least-squares estimation--Part I: Linear filtering in additive white noiseIEEE Transactions on Automatic Control, 1968