scholarly journals High-Frequency Planetary Waves in the Polar Middle Atmosphere as Seen in a Data Assimilation System

2003 ◽  
Vol 60 (24) ◽  
pp. 2975-2992 ◽  
Author(s):  
L. Coy ◽  
I. Štajner ◽  
A. M. DaSilva ◽  
J. Joiner ◽  
R. B. Rood ◽  
...  
Keyword(s):  
Data Assimilation ◽  
High Frequency ◽  
Middle Atmosphere ◽  
Planetary Waves ◽  
Data Assimilation System ◽  
Assimilation System

scholarly journals Incremental Analysis Update Implementation into a Sequential Ocean Data Assimilation System

2006 ◽  
Vol 23 (12) ◽  
pp. 1729-1744 ◽  
Author(s):  
Y. Ourmières ◽  
J-M. Brankart ◽  
L. Berline ◽  
P. Brasseur ◽  
J. Verron
Keyword(s):  
Data Assimilation ◽  
High Frequency ◽  
General Circulation ◽  
Circulation Model ◽  
Low Rank ◽  
Incremental Analysis ◽  
High Frequency Oscillations ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

Abstract This study deals with the enhancement of a sequential assimilation method applied to an ocean general circulation model (OGCM). A major drawback of sequential assimilation methods is the time discontinuity of the solution resulting from intermittent corrections of the model state. The data analysis step can induce shocks in the model restart phase, causing spurious high-frequency oscillations and data rejection. A method called Incremental Analysis Update (IAU) is now recognized to efficiently tackle these problems. In the present work, an IAU-type method is implemented into an intermittent data assimilation system using a low-rank Kalman filter [Singular Evolutive Extended Kalman (SEEK)] in the case of an OGCM with a 1/3° North Atlantic grid. A 1-yr (1993) experiment has been conducted for different setups in order to evaluate the impact of the IAU scheme. Results from all of the different tests are compared with a specific interest in high-frequency output behaviors and solution consistency. The improvements brought up by the IAU implementation, such as the disappearance of spurious high-frequency oscillations and the time continuity of the solution, are shown. An overall assessment of the impact of this new approach on the assimilated runs is discussed. Advantages and drawbacks of the IAU method are pointed out.

scholarly journals Quantifying the constraint of biospheric process parameters by CO<sub>2</sub> concentration and flux measurement networks through a carbon cycle data assimilation system

2013 ◽  
Vol 13 (21) ◽  
pp. 10555-10572 ◽  
Author(s):  
E. N. Koffi ◽  
P. J. Rayner ◽  
M. Scholze ◽  
F. Chevallier ◽  
T. Kaminski
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Primary Productivity ◽  
High Frequency ◽  
Flux Measurement ◽  
Co2 Concentration ◽  
Flux Measurements ◽  
Net Flux ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. The sensitivity of the process parameters of the Biosphere Energy Transfer HYdrology (BETHY) model to choices of atmospheric concentration network, high frequency terrestrial fluxes, and the choice of flux measurement network is investigated by using a carbon cycle data assimilation system. We use BETHY-generated fluxes as a proxy of flux measurements. Results show that monthly mean or low-frequency observations of CO2 concentration provide strong constraints on parameters relevant for net flux (NEP) but only weak constraints for parameters controlling gross fluxes. The use of high-frequency CO2 concentration observations, which has led to great refinement of spatial scales in inversions of net flux, adds little to the observing system in the Carbon Cycle Data Assimilation System (CCDAS) case. This unexpected result is explained by the fact that the stations of the CO2 concentration network we use are not well placed to measure such high frequency signals. Indeed, CO2 concentration sensitivities relevant for such high frequency fluxes are found to be largely confined in the vicinity of the corresponding fluxes, and are therefore not well observed by background monitoring stations. In contrast, our results clearly show the potential of flux measurements to better constrain the model parameters relevant for gross primary productivity (GPP) and net primary productivity (NPP). Given uncertainties in the spatial description of ecosystem functions, we recommend a combined observing strategy.

scholarly journals Quantifying the constraint of biospheric process parameters by CO<sub>2</sub> concentration and flux measurement networks through a carbon cycle data assimilation system

2012 ◽  
Vol 12 (9) ◽  
pp. 24131-24172 ◽  
Author(s):  
E. Koffi ◽  
P. Rayner ◽  
M. Scholze ◽  
F. Chevallier ◽  
T. Kaminski
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Process Parameters ◽  
Primary Productivity ◽  
High Frequency ◽  
Flux Measurement ◽  
Co2 Concentration ◽  
Model Parameters ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. The sensitivity of the process parameters of the biosphere model BETHY (Biosphere Energy Transfer HYdrology) to choices of atmospheric concentration network, high frequency terrestrial fluxes, and the choice of flux measurement network is investigated by using a carbon cycle data assimilation system. Results show that monthly mean or low-frequency observations of CO2 concentration provide strong constraints on parameters relevant for net flux (NEP) but only weak constraints for parameters controlling gross fluxes. The use of high-frequency CO2 concentration observations, which has allowed a great refinement of spatial scales in direct inversions, adds little to the observing system in this case. This unexpected result is explained by the fact that the stations of the CO2 concentration network we are using are not well placed to measure such high frequency signals. Indeed, CO2 concentration sensitivities relevant for such high frequency fluxes are found to be largely confined in the vicinity of the corresponding fluxes, and are therefore not well observed by background monitoring stations. In contrast, our results clearly show the potential of flux measurements to better constrain the model parameters relevant for gross primary productivity (GPP) and net primary productivity (NPP). Given uncertainties in the spatial description of ecosystem functions we recommend a combined observing strategy.

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