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- Author or Editor: Craig E. Motell x
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Abstract
Statistical models that estimate tropical Pacific rainfall from the National Oceanic and Atmospheric Administration's global archive of polar-orbiter satellite data have been derived and tested. These rainfall models are based on the assumptions that rainfall is linearly related to bright visible and cold infrared radiation (IR) satellite. The models were derived by using measured monthly rainfall from small, flat, tropical islands with elevations less than 30 m together with digital IR and visible satellite data.
Three models were derived: one used visible and nighttime IR data (NIRVISQ); the second used only visible data (VISQ), and the third used an average of daytime and nighttime IR data (AVEIR). These models were found to predict between 62% and 67% of the variance of 1051 station-months of hindcast rainfall data measured from June 1974 through mid-March 1978 (J74M78). However, rainfall was found to be underpredicted on relatively high mean rainfall islands and vice versa. Similar prediction accuracies were found when the rainfall models were used to estimate rainfall on new low-latitude island stations during the J74M78 period. All three models showed a decrease in predictive skill during time periods after J74M78.
Tropical Pacific annual rainfall maps, estimated using the rainfall models and satellite data from June 1974 through May 1977, showed that NIRVISQ and VISQ may greatly overpredict rainfall in regions where stratus clouds are common such as in the eastern Pacific Ocean, but AVEIR appeared to predict reasonable rainfall amounts throughout the tropical Pacific. The AVEIR is thus the preferred model for predicting tropical oceanic rainfall.
Abstract
Statistical models that estimate tropical Pacific rainfall from the National Oceanic and Atmospheric Administration's global archive of polar-orbiter satellite data have been derived and tested. These rainfall models are based on the assumptions that rainfall is linearly related to bright visible and cold infrared radiation (IR) satellite. The models were derived by using measured monthly rainfall from small, flat, tropical islands with elevations less than 30 m together with digital IR and visible satellite data.
Three models were derived: one used visible and nighttime IR data (NIRVISQ); the second used only visible data (VISQ), and the third used an average of daytime and nighttime IR data (AVEIR). These models were found to predict between 62% and 67% of the variance of 1051 station-months of hindcast rainfall data measured from June 1974 through mid-March 1978 (J74M78). However, rainfall was found to be underpredicted on relatively high mean rainfall islands and vice versa. Similar prediction accuracies were found when the rainfall models were used to estimate rainfall on new low-latitude island stations during the J74M78 period. All three models showed a decrease in predictive skill during time periods after J74M78.
Tropical Pacific annual rainfall maps, estimated using the rainfall models and satellite data from June 1974 through May 1977, showed that NIRVISQ and VISQ may greatly overpredict rainfall in regions where stratus clouds are common such as in the eastern Pacific Ocean, but AVEIR appeared to predict reasonable rainfall amounts throughout the tropical Pacific. The AVEIR is thus the preferred model for predicting tropical oceanic rainfall.
Abstract
An automated statistical pattern recognition technique is presented that uses visible and IR satellite imagery to estimate instantaneous surface rainfall rates. The technique uses both brightness and textural statistics to estimate rainfall in 10 × 10 pixel arrays of satellite data. Each array is centered over one of 137 Service A weather stations scattered over southeastern United States. Surface reports from these stations obtained during a 30 day period in August of 1979 are used to ground truth the technique. The technique classifies each 10 × 10 array into one of three categories: no rain, light rain, moderate/heavy rain. Cross-validation is used to estimate classification errors; results of these estimates yielded an overall error rate of 35% when both visible and IR data are used. When only visible or IR data are used the overall error rates are 39% and 42%, respectively. In addition to the three class problem, the two class problem of classifying rain / no rain is studied. Overall error rates of 18% are achieved using a technique with 16 image statistics and both visible and IR data. A simpler technique that uses only the mean and standard deviation statistics, derived from the visible and IR data, achieved an overall error rate of 20%. We conclude that the visible and IR pattern recognition technique could be used successfully to estimate instantaneous rainfall in three classes: no rain, light rain, moderate/heavy rain. During the night and during hours of low sun attitude, IR data could be used but with a slight decrease in accuracy. We also conclude that a simpler pattern recognition technique, based upon the mean and standard deviation statistics, could be used to distinguish between rain and no rain classes.
Abstract
An automated statistical pattern recognition technique is presented that uses visible and IR satellite imagery to estimate instantaneous surface rainfall rates. The technique uses both brightness and textural statistics to estimate rainfall in 10 × 10 pixel arrays of satellite data. Each array is centered over one of 137 Service A weather stations scattered over southeastern United States. Surface reports from these stations obtained during a 30 day period in August of 1979 are used to ground truth the technique. The technique classifies each 10 × 10 array into one of three categories: no rain, light rain, moderate/heavy rain. Cross-validation is used to estimate classification errors; results of these estimates yielded an overall error rate of 35% when both visible and IR data are used. When only visible or IR data are used the overall error rates are 39% and 42%, respectively. In addition to the three class problem, the two class problem of classifying rain / no rain is studied. Overall error rates of 18% are achieved using a technique with 16 image statistics and both visible and IR data. A simpler technique that uses only the mean and standard deviation statistics, derived from the visible and IR data, achieved an overall error rate of 20%. We conclude that the visible and IR pattern recognition technique could be used successfully to estimate instantaneous rainfall in three classes: no rain, light rain, moderate/heavy rain. During the night and during hours of low sun attitude, IR data could be used but with a slight decrease in accuracy. We also conclude that a simpler pattern recognition technique, based upon the mean and standard deviation statistics, could be used to distinguish between rain and no rain classes.
