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Viatcheslav V. Tatarskii, Maia S. Tatarskaia, and Ed R. Westwater

Abstract

A new method is presented of statistical retrieval of humidity profiles based on measurements of surface temperature ξ1, surface dewpoint ξ2, and integrated water vapor ξ3. In this method the retrieved values of humidity depend nonlinearly on predictors ξ1,2,3. A self-training algorithm was developed to obtain the values of parameters that enter into the retrieval algorithm. The data from two years of measurements in eight different locations were used for training. The method was applied to an independent dataset (including nonmonotonic profiles) of one month of surface measurements and integrated water vapor obtained from microwave radiometers. Three constraints were imposed: 1) the integrated retrieved humidity profiles had to be equal to the measured values ξ3, 2) the retrieved surface humidity had to coincide with the measured value, and 3) the retrieved humidity had to be positive. The rms deviations of restored humidity values from measured profiles were approximately two times less than natural variations. A limited comparison with conventional linear statistical inversion showed that the nonlinear method may improve the recovery of vertical structure.

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Maia S. Tatarskaia, Richard J. Lataitis, B. Boba Stankov, and Viatcheslav V. Tatarskii

Abstract

A numerical technique is described for synthesizing realistic atmospheric temperature and humidity profiles. The method uses an ensemble of radiosonde measurements collected at a site of interest. Erroneous profiles are removed by comparing their likelihood with prevailing meteorological conditions. The remaining profiles are decomposed using the method of empirical orthogonal functions. The corresponding eigenprofiles and the statistics of the expansion coefficients are used to numerically generate synthetic profiles that obey the same statistics (i.e., have the same mean, variability, and vertical correlation) as the initial dataset. The technique was applied to a set of approximately 1000 temperature and humidity soundings made in Denver, Colorado, during the winter months of 1991–95. This dataset was divided into four cloud classification categories and daytime and nighttime launches to better characterize typical profiles for the eight cases considered. It was found that 97% of the variance in the soundings could be accounted for by using only five eigenprofiles in the reconstructions. Ensembles of numerically generated profiles can be used to test the accuracy of various retrieval algorithms under controlled conditions not usually available in practice.

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