Processing arctic eddy-flux data using a simple carbon-exchange model embedded in the ensemble Kalman filter
Author Posting. © Ecological Society of America, 2010. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Ecological Applications 20 (2010): 1285–1301, doi:10.1890/09-0876.1. Continuous time-ser...
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ftwhoas:oai:darchive.mblwhoilibrary.org:1912/4702 2023-05-15T14:58:08+02:00 Processing arctic eddy-flux data using a simple carbon-exchange model embedded in the ensemble Kalman filter Rastetter, Edward B. Williams, Mathew Griffin, Kevin L. Kwiatkowski, Bonnie L. Tomasky, Gabrielle Potosnak, Mark J. Stoy, Paul C. Shaver, Gaius R. Stieglitz, Marc Hobbie, John E. Kling, George W. 2010-07 application/pdf https://hdl.handle.net/1912/4702 en_US eng Ecological Society of America https://doi.org/10.1890/09-0876.1 Ecological Applications 20 (2010): 1285–1301 https://hdl.handle.net/1912/4702 doi:10.1890/09-0876.1 Ecological Applications 20 (2010): 1285–1301 doi:10.1890/09-0876.1 Alaska USA Data assimilation Ecosystem carbon balance Ecosystem models Eddy covariance Kalman filter Net ecosystem carbon exchange Article 2010 ftwhoas https://doi.org/10.1890/09-0876.1 2022-05-28T22:58:25Z Author Posting. © Ecological Society of America, 2010. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Ecological Applications 20 (2010): 1285–1301, doi:10.1890/09-0876.1. Continuous time-series estimates of net ecosystem carbon exchange (NEE) are routinely made using eddy covariance techniques. Identifying and compensating for errors in the NEE time series can be automated using a signal processing filter like the ensemble Kalman filter (EnKF). The EnKF compares each measurement in the time series to a model prediction and updates the NEE estimate by weighting the measurement and model prediction relative to a specified measurement error estimate and an estimate of the model-prediction error that is continuously updated based on model predictions of earlier measurements in the time series. Because of the covariance among model variables, the EnKF can also update estimates of variables for which there is no direct measurement. The resulting estimates evolve through time, enabling the EnKF to be used to estimate dynamic variables like changes in leaf phenology. The evolving estimates can also serve as a means to test the embedded model and reconcile persistent deviations between observations and model predictions. We embedded a simple arctic NEE model into the EnKF and filtered data from an eddy covariance tower located in tussock tundra on the northern foothills of the Brooks Range in northern Alaska, USA. The model predicts NEE based only on leaf area, irradiance, and temperature and has been well corroborated for all the major vegetation types in the Low Arctic using chamber-based data. This is the first application of the model to eddy covariance data. We modified the EnKF by adding an adaptive noise estimator that provides a feedback between persistent model data deviations and the noise added to the ensemble of Monte Carlo simulations in the EnKF. We also ran the EnKF with both a specified ... Article in Journal/Newspaper Arctic Brooks Range Tundra Alaska Woods Hole Scientific Community: WHOAS (Woods Hole Open Access Server) Arctic Northern Foothills ENVELOPE(163.917,163.917,-74.733,-74.733) Ecological Applications 20 5 1285 1301 |
institution |
Open Polar |
collection |
Woods Hole Scientific Community: WHOAS (Woods Hole Open Access Server) |
op_collection_id |
ftwhoas |
language |
English |
topic |
Alaska USA Data assimilation Ecosystem carbon balance Ecosystem models Eddy covariance Kalman filter Net ecosystem carbon exchange |
spellingShingle |
Alaska USA Data assimilation Ecosystem carbon balance Ecosystem models Eddy covariance Kalman filter Net ecosystem carbon exchange Rastetter, Edward B. Williams, Mathew Griffin, Kevin L. Kwiatkowski, Bonnie L. Tomasky, Gabrielle Potosnak, Mark J. Stoy, Paul C. Shaver, Gaius R. Stieglitz, Marc Hobbie, John E. Kling, George W. Processing arctic eddy-flux data using a simple carbon-exchange model embedded in the ensemble Kalman filter |
topic_facet |
Alaska USA Data assimilation Ecosystem carbon balance Ecosystem models Eddy covariance Kalman filter Net ecosystem carbon exchange |
description |
Author Posting. © Ecological Society of America, 2010. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Ecological Applications 20 (2010): 1285–1301, doi:10.1890/09-0876.1. Continuous time-series estimates of net ecosystem carbon exchange (NEE) are routinely made using eddy covariance techniques. Identifying and compensating for errors in the NEE time series can be automated using a signal processing filter like the ensemble Kalman filter (EnKF). The EnKF compares each measurement in the time series to a model prediction and updates the NEE estimate by weighting the measurement and model prediction relative to a specified measurement error estimate and an estimate of the model-prediction error that is continuously updated based on model predictions of earlier measurements in the time series. Because of the covariance among model variables, the EnKF can also update estimates of variables for which there is no direct measurement. The resulting estimates evolve through time, enabling the EnKF to be used to estimate dynamic variables like changes in leaf phenology. The evolving estimates can also serve as a means to test the embedded model and reconcile persistent deviations between observations and model predictions. We embedded a simple arctic NEE model into the EnKF and filtered data from an eddy covariance tower located in tussock tundra on the northern foothills of the Brooks Range in northern Alaska, USA. The model predicts NEE based only on leaf area, irradiance, and temperature and has been well corroborated for all the major vegetation types in the Low Arctic using chamber-based data. This is the first application of the model to eddy covariance data. We modified the EnKF by adding an adaptive noise estimator that provides a feedback between persistent model data deviations and the noise added to the ensemble of Monte Carlo simulations in the EnKF. We also ran the EnKF with both a specified ... |
format |
Article in Journal/Newspaper |
author |
Rastetter, Edward B. Williams, Mathew Griffin, Kevin L. Kwiatkowski, Bonnie L. Tomasky, Gabrielle Potosnak, Mark J. Stoy, Paul C. Shaver, Gaius R. Stieglitz, Marc Hobbie, John E. Kling, George W. |
author_facet |
Rastetter, Edward B. Williams, Mathew Griffin, Kevin L. Kwiatkowski, Bonnie L. Tomasky, Gabrielle Potosnak, Mark J. Stoy, Paul C. Shaver, Gaius R. Stieglitz, Marc Hobbie, John E. Kling, George W. |
author_sort |
Rastetter, Edward B. |
title |
Processing arctic eddy-flux data using a simple carbon-exchange model embedded in the ensemble Kalman filter |
title_short |
Processing arctic eddy-flux data using a simple carbon-exchange model embedded in the ensemble Kalman filter |
title_full |
Processing arctic eddy-flux data using a simple carbon-exchange model embedded in the ensemble Kalman filter |
title_fullStr |
Processing arctic eddy-flux data using a simple carbon-exchange model embedded in the ensemble Kalman filter |
title_full_unstemmed |
Processing arctic eddy-flux data using a simple carbon-exchange model embedded in the ensemble Kalman filter |
title_sort |
processing arctic eddy-flux data using a simple carbon-exchange model embedded in the ensemble kalman filter |
publisher |
Ecological Society of America |
publishDate |
2010 |
url |
https://hdl.handle.net/1912/4702 |
long_lat |
ENVELOPE(163.917,163.917,-74.733,-74.733) |
geographic |
Arctic Northern Foothills |
geographic_facet |
Arctic Northern Foothills |
genre |
Arctic Brooks Range Tundra Alaska |
genre_facet |
Arctic Brooks Range Tundra Alaska |
op_source |
Ecological Applications 20 (2010): 1285–1301 doi:10.1890/09-0876.1 |
op_relation |
https://doi.org/10.1890/09-0876.1 Ecological Applications 20 (2010): 1285–1301 https://hdl.handle.net/1912/4702 doi:10.1890/09-0876.1 |
op_doi |
https://doi.org/10.1890/09-0876.1 |
container_title |
Ecological Applications |
container_volume |
20 |
container_issue |
5 |
container_start_page |
1285 |
op_container_end_page |
1301 |
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1766330227108085760 |