Estimating surface carbon fluxes based on a local ensemble transform Kalman filter with a short assimilation window and a long observation window: an observing system simulation experiment test in GEOS-Chem 10.1
Abstract
We developed a carbon data assimilation system to estimate surface carbon fluxes using the local ensemble transform Kalman filter (LETKF) and atmospheric transport model GEOS-Chem driven by the MERRA-1 reanalysis of the meteorological field based on the Goddard Earth Observing System model,version 5 (GEOS-5). This assimilation system is inspired by the method of Kang et al. (2011, 2012), who estimated the surface carbon fluxes in an observing system simulation experiment (OSSE) as evolving parameters in the assimilation of the atmospheric CO2, using a short assimilation window of 6 h. They included the assimilation of the standard meteorological variables, so that the ensemble provided a measure of the uncertainty in the CO2 transport. After proposing new techniques such as “variablelocalization”, and increased observation weights near the surface, they obtained accurate surface carbon fluxes at grid-point resolution. We developed a new version of the local ensemble transform Kalman filter related to the “running-in-place”(RIP) method used to accelerate the spin-up of ensemble Kalman filter (EnKF) data assimilation(Kalnay and Yang, 2010; Wang et al., 2013; Yang et al., 2012). Like RIP, the new assimilation system uses the “no cost smoothing” algorithm for the LETKF (Kalnay et al., 2007b), which allows shifting the Kalman filter solutionmore »
- Authors:
-
- Univ. of Maryland, College Park, MD (United States); Texas A & M Univ., College Station, TX (United States)
- Univ. of Maryland, College Park, MD (United States)
- Joint Global Change Research Institute/PNNL, College Park, MD (United States)
- Univ. of East Anglia, Norwich (United Kingdom)
- Chinese Academy of Sciences (CAS), Beijing (China)
- Publication Date:
- Research Org.:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1548274
- Report Number(s):
- PNNL-SA-129693
Journal ID: ISSN 1991-9603
- Grant/Contract Number:
- AC05-76RL01830
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Geoscientific Model Development (Online)
- Additional Journal Information:
- Journal Name: Geoscientific Model Development (Online); Journal Volume: 12; Journal Issue: 7; Journal ID: ISSN 1991-9603
- Publisher:
- European Geosciences Union
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 58 GEOSCIENCES; Carbon Data Assimilation; Surface Carbon Flux; LETKF
Citation Formats
Liu, Yun, Kalnay, Eugenia, Zeng, Ning, Asrar, Ghassem, Chen, Zhaohui, and Jia, Binghao. Estimating surface carbon fluxes based on a local ensemble transform Kalman filter with a short assimilation window and a long observation window: an observing system simulation experiment test in GEOS-Chem 10.1. United States: N. p., 2019.
Web. doi:10.5194/gmd-12-2899-2019.
Liu, Yun, Kalnay, Eugenia, Zeng, Ning, Asrar, Ghassem, Chen, Zhaohui, & Jia, Binghao. Estimating surface carbon fluxes based on a local ensemble transform Kalman filter with a short assimilation window and a long observation window: an observing system simulation experiment test in GEOS-Chem 10.1. United States. https://doi.org/10.5194/gmd-12-2899-2019
Liu, Yun, Kalnay, Eugenia, Zeng, Ning, Asrar, Ghassem, Chen, Zhaohui, and Jia, Binghao. Fri .
"Estimating surface carbon fluxes based on a local ensemble transform Kalman filter with a short assimilation window and a long observation window: an observing system simulation experiment test in GEOS-Chem 10.1". United States. https://doi.org/10.5194/gmd-12-2899-2019. https://www.osti.gov/servlets/purl/1548274.
@article{osti_1548274,
title = {Estimating surface carbon fluxes based on a local ensemble transform Kalman filter with a short assimilation window and a long observation window: an observing system simulation experiment test in GEOS-Chem 10.1},
author = {Liu, Yun and Kalnay, Eugenia and Zeng, Ning and Asrar, Ghassem and Chen, Zhaohui and Jia, Binghao},
abstractNote = {We developed a carbon data assimilation system to estimate surface carbon fluxes using the local ensemble transform Kalman filter (LETKF) and atmospheric transport model GEOS-Chem driven by the MERRA-1 reanalysis of the meteorological field based on the Goddard Earth Observing System model,version 5 (GEOS-5). This assimilation system is inspired by the method of Kang et al. (2011, 2012), who estimated the surface carbon fluxes in an observing system simulation experiment (OSSE) as evolving parameters in the assimilation of the atmospheric CO2, using a short assimilation window of 6 h. They included the assimilation of the standard meteorological variables, so that the ensemble provided a measure of the uncertainty in the CO2 transport. After proposing new techniques such as “variablelocalization”, and increased observation weights near the surface, they obtained accurate surface carbon fluxes at grid-point resolution. We developed a new version of the local ensemble transform Kalman filter related to the “running-in-place”(RIP) method used to accelerate the spin-up of ensemble Kalman filter (EnKF) data assimilation(Kalnay and Yang, 2010; Wang et al., 2013; Yang et al., 2012). Like RIP, the new assimilation system uses the “no cost smoothing” algorithm for the LETKF (Kalnay et al., 2007b), which allows shifting the Kalman filter solution forward or backward within an assimilation window at no cost. In the new scheme a long “observation window” (e.g., 7 d or longer) is used to design a LETKF ensemble at 7 d. Then, the RIP smoother is used to obtain an accurate final analysis at 1 d. This new approach has the advantage of being based on a short assimilation window, which makes it more accurate,and of having been exposed to the future 7 d observations, which improves the analysis and accelerates the spin-up. The assimilation and observation windows are then shifted forward by 1 d, and the process is repeated.This reduces significantly the analysis error, suggesting that the newly developed assimilation method can be used with other Earth system models,especially in order to make greater use of observations in conjunction with models.},
doi = {10.5194/gmd-12-2899-2019},
journal = {Geoscientific Model Development (Online)},
number = 7,
volume = 12,
place = {United States},
year = {Fri Jul 12 00:00:00 EDT 2019},
month = {Fri Jul 12 00:00:00 EDT 2019}
}
Web of Science
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