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A. A. Tsonis

Abstract

The spatial and spectral characteristics of the GOES visible and infrared images are examined. From that analysis, a scheme is developed which identifies and separates the following classes: clear skies/no snow cover, clear skies/snow cover and clouds. Clouds are then classified as high or low broken clouds and overcast. The scheme is tested for various weather situations. Comparison of the classification results with reports from ground synoptic stations and maps reflect an average accuracy of approximately 72%, and a higher accuracy (∼87%) when high or low broken clouds and overcast are considered as one class (i.e., clouds). The differentiation between clouds and snow, or no snow-covered ground has been found to be very satisfactory, even in cases of temperature inversions.

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A. A. Tsonis

Abstract

In this paper the evaluation of very simple approaches to delineate the rain area from satellite imagery is assessed in terms of single thresholding. Results and comparison with other more complicated techniques indicate that single thresholding may be quite adequate in delineating instantaneous rainfall areas from a single visible or a single infrared image. The implication of these findings for large scale (space/time) rainfall retrieval from satellites is also discussed.

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A. A. Tsonis

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This study employs principal component analysis to correct tropical precipitation estimates in the University of Wisconsin—Milwaukee (UWM)/Comprehensive Ocean–Atmosphere Dataset (COADS) data. The idea was to use a matrix made up of the other variables in the set, to reduce the dimensionality of the matrix by considering a small number of principal components, and then to regress precipitation to these principal components. The results indicate that, although some information on precipitation could be restored by this method, overall the resulting precipitation estimates are not reliable. This result is traced to the intrinsic complexity of precipitation and possibly to a newly discovered bias in the UWM/COADS data.

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A. A. Tsonis and G. A. Isaac

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Using satellite and weather radar data, a simple clustering analysis has been used in order to differentiate between raining and nonraining clouds. Based on these results, a scheme is proposed for instantaneous rain area delineation in the midlatitudes. Delineation of the rain areas will not require coextensive radar data which are only used to develop and evaluate the method. Warm season data during daylight hours were used to test the scheme. Results indicate that the proposed scheme has very good skills in delineating rain areas in the midlatitudes, resulting in an average probability of detection of about 66% and an average false alarm ratio of about 37%.

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J. B. Elsner and A. A. Tsonis
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Robert S. Schemenauer and A. A. Tsonis

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The aircraft measurements from the HIPLEX-1 weather modification experiment have been examined to determine if the nature of the change of liquid water content (LWC) in the supercooled portion of the clouds can be simply described, Three different data sets were created from the −8 and −5°C aircraft data base. Neither a simple linear nor a simple polynomial fit to the data are suitable for reasons discussed in the text. Two different forms of an exponential model were fit to two of the data sets. When a model for the decay of the maximum 1-km liquid water content (χ) of the form χ=χ0ebt was fit to data set number two, this yielded a cloud liquid water decay constant (τ) of 560 s (9.5 min), with a correlation coefficient r=0.47 and r2=0.22. This reduce the mean first pass χ value of 1.05 g m−3 to e−1 or 0.39 g m−3 in 9.5 min and to (2e)−1 or 0.14 g m−3 in 19 min. The best fit to the observations, however, comes from a calculation of an average rate of change of LWC at a constant altitude (−8°C) in the clouds. This is of the form χ=χ0+bt and gives a lifetime of 15 min for the maximum 1-km average LWC in the 20 HIPLEX-1 clouds. That is, the highest LWC regions in the upper part of the clouds would be expected to completely disappear in about 15 Min. Regions of lower LWC would disappear more quickly. This is a major limitation on both natural and artificial rain forming processes.

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A. A. Tsonis and J. B. Elsner

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In a recent paper Mohan et al. presented a reanalysis of climatic data using concepts from the theory of dynamical systems. The data is the oxygen isotope ratio 18O/16O record of the V28-238 deep sea care covering a period of a million years at a sampling time of 2 Kiloyears. This dataset was first analysed by Nicolis and Nicolis who reported that the dynamics of the records may be explained by a low-dimensional dynamical system. We take this opportunity to bring to the attention of the scientific community some major problems involved with the reanalysis of the data hoping that this comment will serve as a reference for other analyses of different datasets in the future.

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J. B. Elsner and A. A. Tsonis

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Recent studies have shown how concepts from information theory can be applied to climate models to better understand the problem of climate prediction. This paper describes how information theory, specifically the concept of entropy, can be used in the analysis of short-term precipitation records. The ideas are illustrated through analysis and comparisons of two long, hourly precipitation records. From the results it is concluded that the records are not periodic and are definitely more complex than records of random origin. This complexity, however, arises from underlying deterministic rules indicating the potential for predictability.

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A. A. Tsonis and J. B. Elsner
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