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Reduction of the Spinup Time for Evaporation and Precipitation in a Spectral Model

T. N. KrishnamurtiDepartment of Meteorology, Florida State University, Tallahassee, Florida

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H. S. BediDepartment of Meteorology, Florida State University, Tallahassee, Florida

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William HeckleyDepartment of Meteorology, Florida State University, Tallahassee, Florida

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Kevin InglesDepartment of Meteorology, Florida State University, Tallahassee, Florida

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Abstract

A dynamic relaxation technique is examined to update a spectral model. The technique consists of constraining selected time dependent model variables towards their predetermined space–time estimates, while the remaining variables evolve unconstrained. The scheme involves gradual assimilation of data and thus is essentially free from data insertion shocks generally associated with data assimilation schemes. The scheme can also be used to update the model variables consistent with the observed estimates of diabatic forcings. The spectral formulation is particularly suited to relax the current estimates of model variables towards their observed estimates scale-by-scale.

The scheme has been applied to initialize model variables by relaxing vorticity, divergence and total mass (surface pressure) fields through one to three observation periods using an 11-layer model with T-42 spectral resolution. In addition, the moisture field and diabatic heating rates have been relaxed consistent with the observed estimates of precipitation rates. The explicit two-day Newtonian relaxation of the streamfunction, velocity potential (consistent with rainfall estimates) and the surface pressure and an implicit treatment of the humidity (again consistent with rainfall estimates) results in a realistic initialization. Tropical rainfall, humidity analysis and the divergence field show considerable consistency and improvement. The study addresses the model initialization by this scheme and its impact on medium range forecasts using FGGE IIIb data. The reduction of the spinup time is accomplished by this procedure at the initial time. Globally averaged evaporation and precipitation exhibit an equilibration by this procedure.

A major result of this study is the ability to initialize an observed rainfall field from the use of a reverse Kuo algorithm, the Newtonian relaxation and the overall physical initialization within this model.

Abstract

A dynamic relaxation technique is examined to update a spectral model. The technique consists of constraining selected time dependent model variables towards their predetermined space–time estimates, while the remaining variables evolve unconstrained. The scheme involves gradual assimilation of data and thus is essentially free from data insertion shocks generally associated with data assimilation schemes. The scheme can also be used to update the model variables consistent with the observed estimates of diabatic forcings. The spectral formulation is particularly suited to relax the current estimates of model variables towards their observed estimates scale-by-scale.

The scheme has been applied to initialize model variables by relaxing vorticity, divergence and total mass (surface pressure) fields through one to three observation periods using an 11-layer model with T-42 spectral resolution. In addition, the moisture field and diabatic heating rates have been relaxed consistent with the observed estimates of precipitation rates. The explicit two-day Newtonian relaxation of the streamfunction, velocity potential (consistent with rainfall estimates) and the surface pressure and an implicit treatment of the humidity (again consistent with rainfall estimates) results in a realistic initialization. Tropical rainfall, humidity analysis and the divergence field show considerable consistency and improvement. The study addresses the model initialization by this scheme and its impact on medium range forecasts using FGGE IIIb data. The reduction of the spinup time is accomplished by this procedure at the initial time. Globally averaged evaporation and precipitation exhibit an equilibration by this procedure.

A major result of this study is the ability to initialize an observed rainfall field from the use of a reverse Kuo algorithm, the Newtonian relaxation and the overall physical initialization within this model.

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