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Roland T. Chin, Jack Y. C. Jau, and James A. Weinman

Abstract

A modeling method for the quantitative description of remotely sensed cloud field images is presented. A two-dimensional texture modeling scheme based on one-dimensional time series procedures is adopted for this purpose. The time series procedure used is the seasonal autoregressive, moving average (ARMA) process in Box and Jenkins. Cloud field properties such as directionality, clustering and cloud coverage can be retrieved by this method. It has been demonstrated that a cloud field image can be quantitatively defined by a small set of parameters and synthesized surrogates can be reconstructed from these model parameters. This method enables cloud climatology to be studied quantitatively.

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Rongzhang Wu, James A. Weinman, and Roland T. Chin

Abstract

Radiances from clouds observed in visible and infrared images obtained from the SMS-2, GOES-2, and GOES-4 satellites have been used to estimate rainfall by means of a pattern recognition algorithm that was applied to single images. The algorithm classified rain into three classes: 0—no rain (0 ≤ R <0.5 mm h−1); 1—light rain (0.5 ≤ R <0.5 mm h−1); and 2—heavy rain (5.0 mm h−1R). The rainfall rates used in the training set and those used to test the algorithm were derived from a set of twenty-nine Plan Position Indicator (PPI) displays obtained from NOAA operational radars. Data were derived from summer storms, tropical storms and cyclones.

Rainfall from precipitating clouds was classified by a pattern recognition technique that used textural and radiance features in a hierarchic decision tree. The analysis was applied to regions 20 × 20 km in area that were measured in the visible spectral region with 1 × 1 km and 2 × 2 km resolution and in the infrared with 4 × 8 km resolution. The radiance features used in this analysis were the radiance maxima, minima, and the means. The textural features that were used included the edge strengths per unit area and the maxima and means of the mean, contrast, angular second moment, and entropy in four directions.

Of the arm sampled in this study, approximately one-third were in classes 0, one-half were in class 1 and one-sixth were in class 2. Case studies that employed data from both the visible and infrared sensors correctly identified rainfall classes 0 and (1 + 2) in about 96% of the cases and identification into classes 1 and 2 was correct in about 70% of the cases studied. The corresponding skill scores were ∼80 and 60% respectively. Data derived only from infrared images yielded correct identification of 0 and (1 + 2) classes in 85% of the cases and identification of classes 0, 1 and 2 was correct in 65% of the cases. The corresponding skill scores were ∼65% and 40% respectively.

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William S. Olson, Chia-Lung Yeh, James A. Weinman, and Roland T. Chin

Abstract

A restoration of the 37, 21, 18, 10.7 and 6.6 GHz satellite imagery from the Scanning Multichannel Microwave Radiometer (SMMR) aboard Nimbus-7 to 22.2 km resolution is attempted using a deconvolution method based upon nonlinear programming. The images are deconvolved with and without the aid of prescribed constraints, which form the processed image to abide by partial a priori knowledge of the high-resolution result. The restored microwave imagery may be utilized to examine the distribution of precipitating liquid water in maritime rain systems.

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