Adaptation of Meteorological Variables Forced by Updating

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Abstract
Numerical experiments designed to investigate trade-offs among meteorological variables and between space and time in observing systems are conducted using the six-layer global circulation model of the National Center for Atmospheric Research (NCAR). The error growth characteristics of the NCAR model are first discussed in view of their effect on periodically updating historical data. The updating experiments are divided into two groups. In the first group, “observed” temperature data with and without errors are periodically inserted into the model to recover the wind field. The root mean square (rms) error of the wind field is reduced by updating temperature and it approaches an asymptotic level which depends on the magnitude of the random errors in the “observed” temperature field. In the second group, “observed” wind data with and without errors are periodically updated to recover the temperature field. The rms error of the temperature field is reduced by updating winds. The asymptotic level depends on the magnitude of errors in the “observed” wind field. The results of wind updating were found to be sensitive to a slight change in the prediction model. The scale and latitude dependence of the adaptation of meteorological variables forced by updating is also investigated. The wind is shown to adjust to temperature updating better at higher latitudes and for larger scales. The temperature adjusts to wind updating better for smaller scales, but not necessarily at lower latitudes.

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