- Director's Message
- Table of Contents
- Strategic Goals: Science, Facilities & Technology
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Strategic Goal #1, Strategic Priority #1:
Exploring atmospheric, Earth System, and solar processes, variability, and change -
Strategic Goal #1, Strategic Priority #2:
Investigating the interaction of the atmosphere, the broader Earth system, and human society -
Strategic Goal #1, Strategic Priority #3:
Improving prediction of weather, climate, and other atmospheric phenomena -
Strategic Goal #1, Strategic Priority #4:
Developing community models -
Strategic Goal #5, Strategic Priority #1:
Enabling innovative field experiments and measurement campaigns -
Strategic Goal #5, Strategic Priority #2:
Developing new instrumentation
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Strategic Goal #1, Strategic Priority #1:
- Research Catalog
CGD's Dr. Joseph Tribbia
Wang, H., J.J. Tribbia, F. Baer, A. Fournier and M. A. Taylor, 2007: A Spectral Element Version of CAM2, Mon. Wea. Rev., To appear.
Duane, G.S.and J.J. Tribbia, 2007: Dynamical Synchronization of Truth and Model as an Approach to Data Assimilation, Parameter Estimation and Model Learning. To Appear in Advances in Nonlinear Dynamics in the Geosciences, Tsonis and J. Elsner, editors, Springer.
Chen, Q., J. Laminie, A. Rousseau, R. Temam, and J. Tribbia, 2007: A 2.5 Model for the Equations of the ocean and the atmosphere. Journal of Analysis and Applications, 5, 199-229.
Duane G.S., J.J. Tribbia, and J.B. Weiss, 2006: Synchronicity in predictive modelling: a new view of data assimilation. Nonlinear Processes in Geophysics, 13, 601-612.
Abstract
The problem of data assimilation can be viewed as one of synchronizing two dynamical systems, one representing "truth" and the other representing "model", with a unidirectional flow of information between the two. Synchronization of truth and model defines a general view of data assimilation, as machine perception, that is reminiscent of the Jung-Pauli notion of synchronicity between matter and mind. The dynamical systems paradigm of the synchronization of a pair of loosely coupled chaotic systems is expected to be useful because quasi-2D geophysical fluid models have been shown to synchronize when only medium-scale modes are coupled. The synchronization approach is equivalent to standard approaches based on least-squares optimization, including Kalman filtering, except in highly non-linear regions of state space where observational noise links regimes with qualitatively different dynamics. The synchronization approach is used to calculate covariance inflation factors from parameters describing the bimodality of a one-dimensional system. The factors agree in overall magnitude with those used in operational practice on an ad hoc basis. The calculation is robust against the introduction of stochastic model error arising from unresolved scales.
Baer F., H.J. Wang, J.J. Tribbia, et al., 2006: Climate modeling with spectral elements. Monthly Weather Review, 134, 3610-3624.
Abstract
As an effort toward improving climate model-component performance and accuracy. an atmospheric-component climate model has been developed, entitled the Spectral Element Atmospheric Climate Model and denoted as CAM SEM. CAM SEM includes a unique dynamical core coupled at this time to the physics component of the Community Atmosphere Model (CAM) as well as the Community Land Model. This model allows the inclusion of local mesh refinement to seamlessly study imbedded higher-resolution regional climate concurrently with the global climate. Additionally. the numerical structure of the model based on spectral elements allows for application of state-of-the-art computing hardware most effectively and economically to produce the best prediction/simulation results with minimal expenditure of computing resources. The model has been tested under various conditions beginning with the shallow water equations and ending with an Atmospheric Model Intercomparison Project (AMIP)-style run that uses initial conditions and physics comparable to the CAM2 (version 2 of the NCAR CAM climate model) experiments. For uniform resolution, the output of the model compares favorably with the published output from the CAM2 experiments. Further integrations with local mesh refinement included indicate that while greater detail in the prediction of mesh-refined regions-that is, regional climate-is observed, the remaining coarse-grid results are similar to results obtained from a uniform-grid integration of the model with identical conditions. It should be noted that in addition to spectral elements, other efficient schemes have lately been considered, in particular the finite-volume scheme. This scheme has not yet been incorporated into CAM_SEM. The two schemes-finite volume and spectral element-are quasi-independent and generally compatible. dealing with different aspects of the integration process. Their impact can be assessed separately and the omission of the finite-volume process herein will not detract from the evaluation of the results using the spectral-element method alone.
Leung L.R., Y.H. Kuo, J. Tribbia, 2006: Research needs and directions of regional climate modeling using WRF and CCSM. Bulletin of the American Meteorlogical Society, 87, 1747-1751.