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Ranjit M. Passi and Michael J. Carpenter


Prediction of meteorological phenomenon is an important problem in the Atmospheric Sciences. For this purpose the periodic components are usually identified first. Then, to apply well-known analytic tools, stationarity and ergodicity are often invoked. this tacitly implies fixed periodicities. However, we often come across instances where the data are nonstationary, having time-dependent periodicities. Further, some stationary noise component may also be superimposed on the data. The Quasi-Biennial Oscillation (QBD) is one such example. In such cases, only those data analysis techniques should be used which can handle both, stationary as well as nonstationary, data generating processes. the least mean square (LMS) algorithm is one such technique.

In this paper we explore the capabilities of the LMS algorithm for the prediction and frequency tacking of nonstationary processes. The technique is then applied to the QBD zonal winds to achieve a several month prediction and to highlight its “quasi” characteristic.

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Kimberly L. Elmore, F. Wesley Wilson Jr., and Michael J. Carpenter


On occasion, digital data gathered during field projects suffers damage due to hardware problems. If no more than half the data are damaged and if the damaged data are randomly distributed in space or time, there is a high probability that the damage can be isolated and repaired using the algorithm described in this paper. During subsequent analysis, some data from the NCAR CP4 Doppler radar were found to be damaged and initially seemed to be lost. Later, the nature of the problem was found and a general algorithm was developed that identifies outliers, which can then be corrected. This algorithm uses the fact that the second derivative of the damaged data with respect to (in this case) radial distance is relatively small. The algorithm can be applied to any similar data. Such data can be closely approximated by a first order, least-squares regression line if the regression line is not applied over too long an interval. This algorithm is especially robust because the length of the regression fit is adaptively chosen, determined by the residuals, such that the slope of the regression line approximates the first radial derivative. The outliers are then marked as candidates for correction, allowing data recovery. This method is not limited to radar data; it may be applied to any data with damage as outlined above. Examples of damaged and corrected data sets are shown and the limitations of this method are discussed as are general applications to other data.

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