An estimate of some analysis-error statistics using the Global Modeling and Assimilation Office observing-system simulation framework

2013 ◽  
Vol 140 (680) ◽  
pp. 1005-1012 ◽  
Author(s):  
Ronald M. Errico ◽  
Nikki C. Privé
Keyword(s):  
System Simulation ◽  
Simulation Framework ◽  
Error Statistics ◽  
Global Modeling ◽  
Analysis Error

scholarly journals Interpretation of Adaptive Observing Guidance for Atlantic Tropical Cyclones

2007 ◽  
Vol 135 (12) ◽  
pp. 4006-4029 ◽  
Author(s):  
C. A. Reynolds ◽  
M. S. Peng ◽  
S. J. Majumdar ◽  
S. D. Aberson ◽  
C. H. Bishop ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Total Energy ◽  
Tropical Cyclones ◽  
Error Variance ◽  
Naval Research ◽  
Error Statistics ◽  
Background Error ◽  
Analysis Error ◽  
The Impact ◽  
Perturbation Growth

Abstract Adaptive observing guidance products for Atlantic tropical cyclones are compared using composite techniques that allow one to quantitatively examine differences in the spatial structures of the guidance maps and relate these differences to the constraints and approximations of the respective techniques. The guidance maps are produced using the ensemble transform Kalman filter (ETKF) based on ensembles from the National Centers for Environmental Prediction and the European Centre for Medium-Range Weather Forecasts (ECMWF), and total-energy singular vectors (TESVs) produced by ECMWF and the Naval Research Laboratory. Systematic structural differences in the guidance products are linked to the fact that TESVs consider the dynamics of perturbation growth only, while the ETKF combines information on perturbation evolution with error statistics from an ensemble-based data assimilation scheme. The impact of constraining the SVs using different estimates of analysis error variance instead of a total-energy norm, in effect bringing the two methods closer together, is also assessed. When the targets are close to the storm, the TESV products are a maximum in an annulus around the storm, whereas the ETKF products are a maximum at the storm location itself. When the targets are remote from the storm, the TESVs almost always indicate targets northwest of the storm, whereas the ETKF targets are more scattered relative to the storm location and often occur over the northern North Atlantic. The ETKF guidance often coincides with locations in which the ensemble-based analysis error variance is large. As the TESV method is not designed to consider spatial differences in the likely analysis errors, it will produce targets over well-observed regions, such as the continental United States. Constraining the SV calculation using analysis error variance values from an operational 3D variational data assimilation system (with stationary, quasi-isotropic background error statistics) results in a modest modulation of the target areas away from the well-observed regions, and a modest reduction of perturbation growth. Constraining the SVs using the ETKF estimate of analysis error variance produces SV targets similar to ETKF targets and results in a significant reduction in perturbation growth, due to the highly localized nature of the analysis error variance estimates. These results illustrate the strong sensitivity of SVs to the norm (and to the analysis error variance estimate used to define it) and confirm that discrepancies between target areas computed using different methods reflect the mathematical and physical differences between the methods themselves.

scholarly journals Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part II: Results Using Optimal Error Statistics

2022 ◽  
Vol 14 (2) ◽  
pp. 375
Author(s):  
Sina Voshtani ◽  
Richard Ménard ◽  
Thomas W. Walker ◽  
Amir Hakami
Keyword(s):  
Kalman Filter ◽  
Cross Validation ◽  
Error Variance ◽  
Error Statistics ◽  
Optimal Error ◽  
Meaningful Improvement ◽  
Aircraft Observations ◽  
Variance Parameters ◽  
Analysis Error ◽  
Or Modelling

We applied the parametric variance Kalman filter (PvKF) data assimilation designed in Part I of this two-part paper to GOSAT methane observations with the hemispheric version of CMAQ to obtain the methane field (i.e., optimized analysis) with its error variance. Although the Kalman filter computes error covariances, the optimality depends on how these covariances reflect the true error statistics. To achieve more accurate representation, we optimize the global variance parameters, including correlation length scales and observation errors, based on a cross-validation cost function. The model and the initial error are then estimated according to the normalized variance matching diagnostic, also to maintain a stable analysis error variance over time. The assimilation results in April 2010 are validated against independent surface and aircraft observations. The statistics of the comparison of the model and analysis show a meaningful improvement against all four types of available observations. Having the advantage of continuous assimilation, we showed that the analysis also aims at pursuing the temporal variation of independent measurements, as opposed to the model. Finally, the performance of the PvKF assimilation in capturing the spatial structure of bias and uncertainty reduction across the Northern Hemisphere is examined, indicating the capability of analysis in addressing those biases originated, whether from inaccurate emissions or modelling error.

A System Simulation Framework for Modeling Space Modulation Techniques

2020 ◽  
Vol 14 (1) ◽  
pp. 1435-1446 ◽  
Author(s):  
Omar Hiari ◽  
Raed Mesleh ◽  
Abdullah Al-Khatib
Keyword(s):  
System Simulation ◽  
Simulation Framework ◽  
Modulation Techniques ◽  
Space Modulation

Diagnosis of observation, background and analysis-error statistics in observation space

2005 ◽  
Vol 131 (613) ◽  
pp. 3385-3396 ◽  
Author(s):  
G. Desroziers ◽  
L. Berre ◽  
B. Chapnik ◽  
P. Poli
Keyword(s):  
Error Statistics ◽  
Analysis Error ◽  
Observation Space

Development and validation of observing-system simulation experiments at NASA's Global Modeling and Assimilation Office

2012 ◽  
Vol 139 (674) ◽  
pp. 1162-1178 ◽  
Author(s):  
Ronald M. Errico ◽  
Runhua Yang ◽  
Nikki C. Privé ◽  
King-Sheng Tai ◽  
Ricardo Todling ◽  
...  
Keyword(s):  
System Simulation ◽  
Simulation Experiments ◽  
Global Modeling ◽  
Development And Validation

ON DESIGN OF A COUPLING COMPONENT FOR PARALLEL MULTIMODELING

2011 ◽  
Vol 02 (04) ◽  
pp. 445-458 ◽  
Author(s):  
DALI WANG ◽  
MICHAEL HARMON ◽  
MICHAEL BERRY ◽  
LOUIS GROSS
Keyword(s):  
Natural Resource Management ◽  
Dynamic Load ◽  
System Simulation ◽  
Data Communication ◽  
Ecosystem Restoration ◽  
Dynamic Load Balancing ◽  
Multiscale Simulations ◽  
Level System ◽  
Simulation Framework ◽  
On Demand

A component-based simulation framework is a favorable choice for parallel multiscale simulations, which require a dedicated coupling component to support flexible data communication/exchange, computational parallelism, and on-demand dynamic load balancing. In this paper, a coupling component (the Coupler) was designed to support integrated parallel multimodeling. An integrated ecological multimodeling, part of the across trophic level system simulation (ATLSS) for Everglades ecosystem restoration, has been used to demonstrate the advantage of the Coupler for natural resource management applications.

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