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Title: Final report on "Carbon Data Assimilation with a Coupled Ensemble Kalman Filter"

Abstract

We proposed (and accomplished) the development of an Ensemble Kalman Filter (EnKF) approach for the estimation of surface carbon fluxes as if they were parameters, augmenting the model with them. Our system is quite different from previous approaches, such as carbon flux inversions, 4D-Var, and EnKF with approximate background error covariance (Peters et al., 2008). We showed (using observing system simulation experiments, OSSEs) that these differences lead to a more accurate estimation of the evolving surface carbon fluxes at model grid-scale resolution. The main properties of the LETKF-C are: a) The carbon cycle LETKF is coupled with the simultaneous assimilation of the standard atmospheric variables, so that the ensemble wind transport of the CO2 provides an estimation of the carbon transport uncertainty. b) The use of an assimilation window (6hr) much shorter than the months-long windows used in other methods. This avoids the inevitable “blurring” of the signal that takes place in long windows due to turbulent mixing since the CO2 does not have time to mix before the next window. In this development we introduced new, advanced techniques that have since been adopted by the EnKF community (Kang, 2009, Kang et al., 2011, Kang et al. 2012). These advancesmore » include “variable localization” that reduces sampling errors in the estimation of the forecast error covariance, more advanced adaptive multiplicative and additive inflations, and vertical localization based on the time scale of the processes. The main result has been obtained using the LETKF-C with all these advances, and assimilating simulated atmospheric CO2 observations from different observing systems (surface flask observations of CO2 but no surface carbon fluxes observations, total column CO2 from GoSAT/OCO-2, and upper troposphere AIRS retrievals). After a spin-up of about one month, the LETKF-C succeeded in reconstructing the true evolving surface fluxes of carbon at a model grid resolution. When applied to the CAM3.5 model, the LETKF gave very promising results as well, although only one month is available.« less

Authors:
; ;
Publication Date:
Research Org.:
The Regents of The University of California, Berkeley
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1144723
Report Number(s):
DOE-UCB-64436
DOE Contract Number:
FG02-07ER64436
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; 97 MATHEMATICS AND COMPUTING; LETKF; ensemble Kalman filter; CAM; assimilation; carbon fluxes

Citation Formats

Kalnay, Eugenia, Kang, Ji-Sun, and Fung, Inez. Final report on "Carbon Data Assimilation with a Coupled Ensemble Kalman Filter". United States: N. p., 2014. Web. doi:10.2172/1144723.
Kalnay, Eugenia, Kang, Ji-Sun, & Fung, Inez. Final report on "Carbon Data Assimilation with a Coupled Ensemble Kalman Filter". United States. doi:10.2172/1144723.
Kalnay, Eugenia, Kang, Ji-Sun, and Fung, Inez. Wed . "Final report on "Carbon Data Assimilation with a Coupled Ensemble Kalman Filter"". United States. doi:10.2172/1144723. https://www.osti.gov/servlets/purl/1144723.
@article{osti_1144723,
title = {Final report on "Carbon Data Assimilation with a Coupled Ensemble Kalman Filter"},
author = {Kalnay, Eugenia and Kang, Ji-Sun and Fung, Inez},
abstractNote = {We proposed (and accomplished) the development of an Ensemble Kalman Filter (EnKF) approach for the estimation of surface carbon fluxes as if they were parameters, augmenting the model with them. Our system is quite different from previous approaches, such as carbon flux inversions, 4D-Var, and EnKF with approximate background error covariance (Peters et al., 2008). We showed (using observing system simulation experiments, OSSEs) that these differences lead to a more accurate estimation of the evolving surface carbon fluxes at model grid-scale resolution. The main properties of the LETKF-C are: a) The carbon cycle LETKF is coupled with the simultaneous assimilation of the standard atmospheric variables, so that the ensemble wind transport of the CO2 provides an estimation of the carbon transport uncertainty. b) The use of an assimilation window (6hr) much shorter than the months-long windows used in other methods. This avoids the inevitable “blurring” of the signal that takes place in long windows due to turbulent mixing since the CO2 does not have time to mix before the next window. In this development we introduced new, advanced techniques that have since been adopted by the EnKF community (Kang, 2009, Kang et al., 2011, Kang et al. 2012). These advances include “variable localization” that reduces sampling errors in the estimation of the forecast error covariance, more advanced adaptive multiplicative and additive inflations, and vertical localization based on the time scale of the processes. The main result has been obtained using the LETKF-C with all these advances, and assimilating simulated atmospheric CO2 observations from different observing systems (surface flask observations of CO2 but no surface carbon fluxes observations, total column CO2 from GoSAT/OCO-2, and upper troposphere AIRS retrievals). After a spin-up of about one month, the LETKF-C succeeded in reconstructing the true evolving surface fluxes of carbon at a model grid resolution. When applied to the CAM3.5 model, the LETKF gave very promising results as well, although only one month is available.},
doi = {10.2172/1144723},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Jul 23 00:00:00 EDT 2014},
month = {Wed Jul 23 00:00:00 EDT 2014}
}

Technical Report:

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  • We proposed (and accomplished) the development of an Ensemble Kalman Filter (EnKF) approach for the estimation of surface carbon fluxes as if they were parameters, augmenting the model with them. Our system is quite different from previous approaches, such as carbon flux inversions, 4D-­Var, and EnKF with approximate background error covariance (Peters et al., 2008). We showed (using observing system simulation experiments, OSSEs) that these differences lead to a more accurate estimation of the evolving surface carbon fluxes at model grid-scale resolution. The main properties of the LETKF-­C are: a) The carbon cycle LETKF is coupled with the simultaneous assimilationmore » of the standard atmospheric variables, so that the ensemble wind transport of the CO2 provides an estimation of the carbon transport uncertainty. b) The use of an assimilation window (6hr) much shorter than the months-long windows used in other methods. This avoids the inevitable “blurring” of the signal that takes place in long windows due to turbulent mixing since the CO2 does not have time to mix before the next window. In this development we introduced new, advanced techniques that have since been adopted by the EnKF community (Kang, 2009, Kang et al., 2011, Kang et al. 2012). These advances include “variable localization” that reduces sampling errors in the estimation of the forecast error covariance, more advanced adaptive multiplicative and additive inflations, and vertical localization based on the time scale of the processes. The main result has been obtained using the LETKF-­C with all these advances, and assimilating simulated atmospheric CO2 observations from different observing systems (surface flask observations of CO2 but no surface carbon fluxes observations, total column CO2 from GoSAT/OCO-­2, and upper troposphere AIRS retrievals). After a spin-­up of about one month, the LETKF-­C succeeded in reconstructing the true evolving surface fluxes of carbon at a model grid resolution. When applied to the CAM3.5 model, the LETKF gave very promising results as well, although only one month is available.« less
  • A new approach is presented for data assimilation using the ensemble adjustment Kalman filter (EAKF) technique for surface measurements of carbon monoxide in a single tracer version of the community air quality model. An implementation of the EAKF known as the Data Assimilation Research Testbed at the National Center for Atmospheric Research was used for developing the model. Three different sets of numerical experiments were performed to test the effectiveness of the procedure and the range of key parameters used in implementing the procedure. The model domain includes much of the northeastern United States. The first two numerical experiments usemore » idealized measurements derived from defined model runs, and the last test uses measurements of carbon monoxide from approximately 220 Air Quality System monitoring sites over the northeastern United States, maintained by the U.S. Environmental Protection Agency. In each case, the proposed method provided better results than the method without data assimilation.« less
  • With growing importance of water resources in the world, remediations of anthropogenic contaminations due to reactive solute transport become even more important. A good understanding of reactive rate parameters such as kinetic parameters is the key to accurately predicting reactive solute transport processes and designing corresponding remediation schemes. For modeling reactive solute transport, it is very difficult to estimate chemical reaction rate parameters due to complex processes of chemical reactions and limited available data. To find a method to get the reactive rate parameters for the reactive urea hydrolysis transport modeling and obtain more accurate prediction for the chemical concentrations,more » we developed a data assimilation method based on an ensemble Kalman filter (EnKF) method to calibrate reactive rate parameters for modeling urea hydrolysis transport in a synthetic one-dimensional column at laboratory scale and to update modeling prediction. We applied a constrained EnKF method to pose constraints to the updated reactive rate parameters and the predicted solute concentrations based on their physical meanings after the data assimilation calibration. From the study results we concluded that we could efficiently improve the chemical reactive rate parameters with the data assimilation method via the EnKF, and at the same time we could improve solute concentration prediction. The more data we assimilated, the more accurate the reactive rate parameters and concentration prediction. The filter divergence problem was also solved in this study.« less
  • The Kalman-Filter technique is applied to time-series gamma-ray data from NaI(T1) detectors. A set of standard spectra that contribute significantly to the data in the least-squares sense are first determined using regression analysis. The Kalman-Filter computer program is then used to determine the time behavior for the intensities of the standards that contribute to the observed spectra. The program was written for the ND6620 computer in DEC RT-11 FORTRAN and is being transferred to the VAX 11/780.