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Cloud Resolving Data Assimilation

Vukicevic, T.(a), Sengupta, M.(b), Evans, F.(c), and Vonder Haar, T.(d), CIRA/CSU (a), CIRA/CSU (b), PAOS/CU Boulder (c), CIRA/CSU (d)
Fourteenth Atmospheric Radiation Measurement (ARM) Science Team Meeting

State of the art cloud resolving modeling (CRM) shows considerable skill in realistic representation of microphysical and small scale dynamical processes. It is, however, rather difficult to systematically verify CRM skill by means of direct comparison with observations primarily because initial and boundary value problem that the CRMs are solving is underdetermined as consequence of gross shortage of observational constraint on the initial and boundary conditions. This implies that 4D CRM simulations cannot be effectively compared to actual cloud evolutions for the benefit of a) analysis of evolution dependent cloud properties or relationship between the microphysics and dynamics b) identification of model errors The CRM simulations for either of the two types of analysis could be improved considerably with aid of objective 4D data assimilation. The purpose of data assimilation, in general, is to use all available information to determine as accurately as possible state of the system under study (i.e. the Atmosphere with clouds). The available information includes both the observations and models which represent governing laws. We developed a 4D variational (4DVAR) data assimilation numerical algorithm for the Regional Atmospheric Modeling System (RAMS) with cloud resolving capability. This project supported by the Army Research Lab is intended to improve high resolution atmospheric state estimation with clouds in 4D. The current applications of the new 4DVAR system emphasize assimilation of cloudy satellite radiances from GOES in visible and IR wavelengths. The early results of experiments over the U.S. show skill and feasibility of cloud resolving data assimilation. The ARM central facility cloud measurements are used for verification for a case from March 2000 IOP. The relationship between the cloud evolution and mesoscale dynamics is analyzed.

Note: This is the poster abstract presented at the meeting; an extended version was not provided by the author(s).