Recovery of Three-Dimensional Wind and Temperature Fields from Simulated Single-Doppler Radar Data

Juanzhen Sun National Center for Atmospheric Research, Boulder, Colorado and University of Oklahoma, School of Meteorology, Norman, Oklahoma

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Dawn W. Flicker University of Oklahoma, School of Meteorology, Norman, Oklahoma

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Douglas K. Lilly University of Oklahoma, School of Meteorology, Norman, Oklahoma

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Abstract

A method for recovering the full three-dimensional wind and temperature fields from single-Doppler radar data is developed and demonstrated. This method uses a dynamic model, and attempts to determine the initial conditions of the unobserved flow and thermodynamic fields that have generated the time sequence of observed fields. A cost function is defined to measure the difference between the model solutions of the observed variables and the observations. A set of adjoint equations is constructed to determine the sensitivity of the cost function to initial state errors in those not observed. The initial state variables are then adjusted to minimize those errors.

Several experiments are conducted using simulated observations produced by a control run of a dry convection model. It is shown that the method is able to determine the spatial structures of the unobserved velocity components and temperature effectively; and the performance is enhanced by the use of a temporal smoothness constraint. The method is not sensitive to moderate amplitude random observational errors.

Abstract

A method for recovering the full three-dimensional wind and temperature fields from single-Doppler radar data is developed and demonstrated. This method uses a dynamic model, and attempts to determine the initial conditions of the unobserved flow and thermodynamic fields that have generated the time sequence of observed fields. A cost function is defined to measure the difference between the model solutions of the observed variables and the observations. A set of adjoint equations is constructed to determine the sensitivity of the cost function to initial state errors in those not observed. The initial state variables are then adjusted to minimize those errors.

Several experiments are conducted using simulated observations produced by a control run of a dry convection model. It is shown that the method is able to determine the spatial structures of the unobserved velocity components and temperature effectively; and the performance is enhanced by the use of a temporal smoothness constraint. The method is not sensitive to moderate amplitude random observational errors.

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