2002 Annual

Wednesday, 16 January 2002
An overview of a mesoscale 4DVAR data assimilation research model: RAMDAS
Tomislava Vukicevic, Colorado State University, Ft. Collins, CO; and M. Zupanski, D. Zupanski, T. Greenwald, A. Jones, T. H. Vonder Haar, D. Ojima, and R. Pielke
Since today's data have multiple purposes such as: analysis of atmospheric processes via statistical and other methods, initialization and verification of numerical weather prediction models, and verification of climate models, requirements for both the accuracy and information content of the atmospheric and land surface data analysis are very high. To meet these stringent requirements, the data analysis has to be performed with as many independent observations as possible, but also in a way consistent with knowledge and understanding of the atmospheric system. The consistency of the data analysis with the atmospheric dynamics is neccessary because the observations are far fewer than number of degrees of freedom in the system.

A numerical data assimilation model is developed using the four dimensional variational (4DVAR) data assimilation approach applied to the Regional Atmospheric Modeling System (RAMS) to facilitate research studies that leverage the information content of diverse observations with a variety of spatial and temporal resolutions and to test various hypotheses about the physical processes involving both the land and atmosphere. The new data assimilation system is called the Regional Atmospheric Modeling and Data Assimilation System (RAMDAS). The specific 4DVAR technique applied in RAMDAS is similar to the 4DVAR used with the Eta model at NCEP which key characteristics are: small number of iterations due to preconditioning, inclusion of the forecast model and background errors and digital filter for the control of high frequency modes. An adjoint model associated with the RAMS includes a coupled atmosphere and land surface model (RAMS with LEAF-2). The atmospheric component of the adjoint includes prognostic explicit microphysics and several options for the atmospheric mixing. Because RAMSDAS has state of the art cloud physics in both the forward and adjoint models it facilitates state of the art research of assimilation of the satellite radiances measurements under all atmospheric conditions. The RAMDAS numerical algorithm is fully parallel.

This paper presents an overview of the characteristics of the system, and ongoing research activities at CIRA and NREL using RAMDAS. This includes data assimilation activities with the explicit cloud microphysics, land surface physics, and assessment of potential surface analysis improvements. Satellite remote sensing is employed heavily in several of the research topics and an overview of that activity will also be presented.

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