scholarly journals Preliminary Evaluation of a Regional Atmospheric Chemical Data Assimilation System for Environmental Surveillance

2014 ◽  
Vol 11 (12) ◽  
pp. 12795-12816 ◽  
Author(s):  
Pius Lee ◽  
Yang Liu
Keyword(s):  
Data Assimilation ◽  
Chemical Data ◽  
Preliminary Evaluation ◽  
Environmental Surveillance ◽  
Data Assimilation System ◽  
Assimilation System

scholarly journals Evaluating model performance of an ensemble-based chemical data assimilation system during INTEX-B field mission

2007 ◽  
Vol 7 (4) ◽  
pp. 9717-9767
Author(s):  
◽  
K. Raeder ◽  
J. L. Anderson ◽  
P. G. Hess ◽  
L. K. Emmons ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Source Region ◽  
Model Performance ◽  
Chemical Data ◽  
Horizontal Wind ◽  
Model Errors ◽  
Atmosphere Model ◽  
Ne Pacific ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. We present a global chemical data assimilation system using a global atmosphere model, the Community Atmosphere Model (CAM3) with simplified chemistry and the Data Assimilation Research Testbed (DART) assimilation package. DART is a community software facility for assimilation studies using the ensemble Kalman filter approach. Here, we apply the assimilation system to constrain global tropospheric carbon monoxide (CO) by assimilating meteorological observations of temperature and horizontal wind velocity and satellite CO retrievals from the Measurement of Pollution in the Troposphere (MOPITT) satellite instrument. We verify the system performance using independent CO observations taken on board the NSF/NCAR C-130 and NASA DC-8 aircrafts during the April 2006 part of the Intercontinental Chemical Transport Experiment (INTEX-B). Our evaluations show that MOPITT data assimilation provides significant improvements in terms of capturing the observed CO variability relative to no MOPITT assimilation (i.e. the correlation improves from 0.62 to 0.71, significant at 99% confidence). The assimilation provides evidence of median CO loading of about 150 ppbv at 700 hPa over the NE Pacific during April 2006. This is marginally higher than the modeled CO with no MOPITT assimilation (~140 ppbv). Our ensemble-based estimates of model uncertainty also show model overprediction over the source region (i.e. China) and underprediction over the NE Pacific, suggesting model errors that cannot be readily explained by emissions alone. These results have important implications for improving regional chemical forecasts and for inverse modeling of CO sources and further demonstrates the utility of the assimilation system in comparing non-coincident measurements, e.g. comparing satellite retrievals of CO with in-situ aircraft measurements.

Evaluation of a Chemical Data Assimilation System

2014 ◽  
pp. 439-444
Author(s):  
Jeremy D. Silver ◽  
Jesper H. Christensen ◽  
Michael Kahnert ◽  
Lennart Robertson ◽  
Jørgen Brandt
Keyword(s):  
Data Assimilation ◽  
Chemical Data ◽  
Data Assimilation System ◽  
Assimilation System

scholarly journals Evaluating model performance of an ensemble-based chemical data assimilation system during INTEX-B field mission

2007 ◽  
Vol 7 (21) ◽  
pp. 5695-5710 ◽  
Author(s):  
◽  
K. Raeder ◽  
J. L. Anderson ◽  
P. G. Hess ◽  
L. K. Emmons ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Source Region ◽  
Model Performance ◽  
Chemical Data ◽  
Horizontal Wind ◽  
Model Errors ◽  
Atmosphere Model ◽  
Ne Pacific ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. We present a global chemical data assimilation system using a global atmosphere model, the Community Atmosphere Model (CAM3) with simplified chemistry and the Data Assimilation Research Testbed (DART) assimilation package. DART is a community software facility for assimilation studies using the ensemble Kalman filter approach. Here, we apply the assimilation system to constrain global tropospheric carbon monoxide (CO) by assimilating meteorological observations of temperature and horizontal wind velocity and satellite CO retrievals from the Measurement of Pollution in the Troposphere (MOPITT) satellite instrument. We verify the system performance using independent CO observations taken on board the NSF/NCAR C-130 and NASA DC-8 aircrafts during the April 2006 part of the Intercontinental Chemical Transport Experiment (INTEX-B). Our evaluations show that MOPITT data assimilation provides significant improvements in terms of capturing the observed CO variability relative to no MOPITT assimilation (i.e. the correlation improves from 0.62 to 0.71, significant at 99% confidence). The assimilation provides evidence of median CO loading of about 150 ppbv at 700 hPa over the NE Pacific during April 2006. This is marginally higher than the modeled CO with no MOPITT assimilation (~140 ppbv). Our ensemble-based estimates of model uncertainty also show model overprediction over the source region (i.e. China) and underprediction over the NE Pacific, suggesting model errors that cannot be readily explained by emissions alone. These results have important implications for improving regional chemical forecasts and for inverse modeling of CO sources and further demonstrate the utility of the assimilation system in comparing non-coincident measurements, e.g. comparing satellite retrievals of CO with in-situ aircraft measurements.

scholarly journals DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations

2021 ◽  
pp. 1-6
Author(s):  
Hao Luo ◽  
Qinghua Yang ◽  
Longjiang Mu ◽  
Xiangshan Tian-Kunze ◽  
Lars Nerger ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Southern Ocean ◽  
Sea Ice ◽  
Coupled Model ◽  
Sea Ice Concentration ◽  
Model Free ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
Data Assimilation System ◽  
Assimilation System

Abstract To improve Antarctic sea-ice simulations and estimations, an ensemble-based Data Assimilation System for the Southern Ocean (DASSO) was developed based on a regional sea ice–ocean coupled model, which assimilates sea-ice thickness (SIT) together with sea-ice concentration (SIC) derived from satellites. To validate the performance of DASSO, experiments were conducted from 15 April to 14 October 2016. Generally, assimilating SIC and SIT can suppress the overestimation of sea ice in the model-free run. Besides considering uncertainties in the operational atmospheric forcing data, a covariance inflation procedure in data assimilation further improves the simulation of Antarctic sea ice, especially SIT. The results demonstrate the effectiveness of assimilating sea-ice observations in reconstructing the state of Antarctic sea ice, but also highlight the necessity of more reasonable error estimation for the background as well as the observation.

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