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- Author or Editor: Richard Bankert x
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Abstract
Using Advanced Very High Resolution Radiometer data, 16 pixel × 16 pixel sample areas are classified into one of ten output classes using a probabilistic neural network (PNN). The ten classes are cirrus, cirrocumulus, cirrostratus, altostratus, nimbostratus, stratocumulus, stratus, cumulus, cumulonimbus, and clear. Over 200 features drawn from spectral, textural, and physical measures are computed from the pixel data for each sample area. The input patterns presented to the neural network are a subset of these features selected by a routine that indicates the discriminatory potential of each feature.
The training and testing input data used by the PNN are obtained from 95 expertly labeled images taken from seven maritime regions; these images provide 1633 sample areas. Theoretical accuracy of the PNN classifier is determined using two methods. In the hold-one-cut method, the network is trained on all data samples minus one and is tested on the, remaining sample. Using this technique, 79.8% of the samples are classified correctly. A bootstrap method of 100 randomly determined sample sets produces an average overall accuracy of 77.1%, with a standard deviation of 1.4%. In a more general classification using five classes (low clouds, altostratus, high clouds, precipitating clouds, and clear), 91.2% of the samples are accurately classified. A two-layer, four-network system that determines the general classification of a sample followed by a specific classification in another network is proposed. Testing of this system produces mixed results compared to the single ten-class PNN.
Abstract
Using Advanced Very High Resolution Radiometer data, 16 pixel × 16 pixel sample areas are classified into one of ten output classes using a probabilistic neural network (PNN). The ten classes are cirrus, cirrocumulus, cirrostratus, altostratus, nimbostratus, stratocumulus, stratus, cumulus, cumulonimbus, and clear. Over 200 features drawn from spectral, textural, and physical measures are computed from the pixel data for each sample area. The input patterns presented to the neural network are a subset of these features selected by a routine that indicates the discriminatory potential of each feature.
The training and testing input data used by the PNN are obtained from 95 expertly labeled images taken from seven maritime regions; these images provide 1633 sample areas. Theoretical accuracy of the PNN classifier is determined using two methods. In the hold-one-cut method, the network is trained on all data samples minus one and is tested on the, remaining sample. Using this technique, 79.8% of the samples are classified correctly. A bootstrap method of 100 randomly determined sample sets produces an average overall accuracy of 77.1%, with a standard deviation of 1.4%. In a more general classification using five classes (low clouds, altostratus, high clouds, precipitating clouds, and clear), 91.2% of the samples are accurately classified. A two-layer, four-network system that determines the general classification of a sample followed by a specific classification in another network is proposed. Testing of this system produces mixed results compared to the single ten-class PNN.
Abstract
Moderate Resolution Imaging Spectroradiometer (MODIS) data continue to provide a wealth of two-dimensional, cloud-top information and derived environmental products. In addition, the A-Train constellation of satellites presents an opportunity to combine MODIS data with coincident vertical-profile data collected from sensors on CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). Approximating the vertical structure of clouds in data-sparse regions can be accomplished through a two-step process that consists of cluster analysis of MODIS data and quantitative analysis of coincident vertical-profile data. Daytime data over the eastern North Pacific Ocean are used in this study for both the summer (June–August) and winter (December–February) seasons in separate cluster analyses. A-Train data from 2006 to 2009 are collected, and a K-means cluster analysis is applied to selected MODIS data that are coincident with single-layer clouds found in the CloudSat/CALIPSO (“GEOPROF-lidar”) data. The resultant clusters, 16 in both summer and winter, are quantified in terms of average cloud-base height, cloud-top height, and normalized cloud water content profile. A cluster and its quantified characteristics can then be assigned to a given pixel in near real-time MODIS data, regardless of its proximity to the observed vertical-profile data. When applied to a two-dimensional MODIS dataset, these assigned clusters can provide an approximate three-dimensional representation of the cloud scene.
Abstract
Moderate Resolution Imaging Spectroradiometer (MODIS) data continue to provide a wealth of two-dimensional, cloud-top information and derived environmental products. In addition, the A-Train constellation of satellites presents an opportunity to combine MODIS data with coincident vertical-profile data collected from sensors on CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). Approximating the vertical structure of clouds in data-sparse regions can be accomplished through a two-step process that consists of cluster analysis of MODIS data and quantitative analysis of coincident vertical-profile data. Daytime data over the eastern North Pacific Ocean are used in this study for both the summer (June–August) and winter (December–February) seasons in separate cluster analyses. A-Train data from 2006 to 2009 are collected, and a K-means cluster analysis is applied to selected MODIS data that are coincident with single-layer clouds found in the CloudSat/CALIPSO (“GEOPROF-lidar”) data. The resultant clusters, 16 in both summer and winter, are quantified in terms of average cloud-base height, cloud-top height, and normalized cloud water content profile. A cluster and its quantified characteristics can then be assigned to a given pixel in near real-time MODIS data, regardless of its proximity to the observed vertical-profile data. When applied to a two-dimensional MODIS dataset, these assigned clusters can provide an approximate three-dimensional representation of the cloud scene.
Abstract
An instance-based nearest-neighbor algorithm was developed for a Geostationary Operational Environmental Satellite (GOES) cloud classifier. Expert-labeled samples serve as the training sets for the various GOES image classification scenes. The initial implementation of the classifier using the complete set of available training samples has proven to be an inefficient method for real-time image classifications, requiring long computational run times and significant computer resources. A variety of training-set reduction methods were examined to find smaller training sets that provide quicker classifier run times with minimal reduction in classifier testing set accuracy. General differences within real-time image classifications as a result of using the various reduction methods were also analyzed. The fast condensed nearest-neighbor (FCNN) method reduced the size of the individual training sets by 68.3% (fourfold cross-validation testing average) while the average overall accuracy of the testing sets decreased by only 4.1%. Training sets resulting from these reduction methods were also applied within a real-time classifier using a one-nearest-neighbor subroutine. Using the FCNN-reduced set, the subroutine run time on a 30° latitude × 30° longitude image (GOES-10 daytime) with 11 289 600 total pixels decreased by an average of 60.7%.
Abstract
An instance-based nearest-neighbor algorithm was developed for a Geostationary Operational Environmental Satellite (GOES) cloud classifier. Expert-labeled samples serve as the training sets for the various GOES image classification scenes. The initial implementation of the classifier using the complete set of available training samples has proven to be an inefficient method for real-time image classifications, requiring long computational run times and significant computer resources. A variety of training-set reduction methods were examined to find smaller training sets that provide quicker classifier run times with minimal reduction in classifier testing set accuracy. General differences within real-time image classifications as a result of using the various reduction methods were also analyzed. The fast condensed nearest-neighbor (FCNN) method reduced the size of the individual training sets by 68.3% (fourfold cross-validation testing average) while the average overall accuracy of the testing sets decreased by only 4.1%. Training sets resulting from these reduction methods were also applied within a real-time classifier using a one-nearest-neighbor subroutine. Using the FCNN-reduced set, the subroutine run time on a 30° latitude × 30° longitude image (GOES-10 daytime) with 11 289 600 total pixels decreased by an average of 60.7%.
Abstract
Examination of various feature selection algorithms has led to an improvement in the performance of a probabilistic neural network (PNN) cloud classifier. Thee algorithms reduce the number of network inputs by eliminating redundant and/or irrelevant features (spectral, textural, and physical measurements). One such algorithm, selecting 11 of the 204 total features, provides a 7% increase in PNN overall accuracy compared to an earlier version using 15 features. This algorithm employs the same search procedure as before, but a different evaluation function than used previously, which provides a similar bias to that of the PNN classifier. Noticeable accuracy improvements were also evident in individual cloud-pipe classes.
Abstract
Examination of various feature selection algorithms has led to an improvement in the performance of a probabilistic neural network (PNN) cloud classifier. Thee algorithms reduce the number of network inputs by eliminating redundant and/or irrelevant features (spectral, textural, and physical measurements). One such algorithm, selecting 11 of the 204 total features, provides a 7% increase in PNN overall accuracy compared to an earlier version using 15 features. This algorithm employs the same search procedure as before, but a different evaluation function than used previously, which provides a similar bias to that of the PNN classifier. Noticeable accuracy improvements were also evident in individual cloud-pipe classes.
Abstract
An automated method to estimate tropical cyclone intensity using Special Sensor Microwave Imager (SSM/I) data is developed and tested. SSM/I images (512 km × 512 km) centered on a given tropical cyclone (TC), with a known best-track intensity, are collected for 142 different TCs (1988–98) from the North Pacific, Atlantic, and Indian Oceans. Over 100 characteristic features are computed from the 85-GHz (H-pol) imagery data and the derived rain-rate imagery data associated with each TC. Of the 1040 sample images, 942 are selected as training samples. These training samples are examined in a feature-selection algorithm to select an optimal subset of the characteristic features that could accurately estimate TC intensity on unknown samples in a K-nearest-neighbor (K-NN) algorithm. Using the 15 selected features as the representative vector and the best-track intensity as the ground truth, the 98 testing samples (taken from four TCs) are presented to the K-NN algorithm. A root-mean-square error (rmse) of 19.8 kt is produced. This “snapshot” approach is enhanced (rmse is 18.1 kt) when a TC intensity history feature is added to 71 of the 98 samples. Reconnaissance data are available for two recent (1999) Atlantic hurricanes, and a comparison is made in the rmse using those data as ground truth versus best track. For these two TCs (17 SSM/I images), an rmse of 15.6 kt is produced when best track is used and an rmse of 19.7 kt is produced when reconnaissance data are used as the ground truth.
Abstract
An automated method to estimate tropical cyclone intensity using Special Sensor Microwave Imager (SSM/I) data is developed and tested. SSM/I images (512 km × 512 km) centered on a given tropical cyclone (TC), with a known best-track intensity, are collected for 142 different TCs (1988–98) from the North Pacific, Atlantic, and Indian Oceans. Over 100 characteristic features are computed from the 85-GHz (H-pol) imagery data and the derived rain-rate imagery data associated with each TC. Of the 1040 sample images, 942 are selected as training samples. These training samples are examined in a feature-selection algorithm to select an optimal subset of the characteristic features that could accurately estimate TC intensity on unknown samples in a K-nearest-neighbor (K-NN) algorithm. Using the 15 selected features as the representative vector and the best-track intensity as the ground truth, the 98 testing samples (taken from four TCs) are presented to the K-NN algorithm. A root-mean-square error (rmse) of 19.8 kt is produced. This “snapshot” approach is enhanced (rmse is 18.1 kt) when a TC intensity history feature is added to 71 of the 98 samples. Reconnaissance data are available for two recent (1999) Atlantic hurricanes, and a comparison is made in the rmse using those data as ground truth versus best track. For these two TCs (17 SSM/I images), an rmse of 15.6 kt is produced when best track is used and an rmse of 19.7 kt is produced when reconnaissance data are used as the ground truth.
Abstract
Accurate cloud-ceiling-height forecasts derived from numerical weather prediction (NWP) model data are useful for aviation and other interests where low cloud ceilings have an impact on operations. A demonstration of the usefulness of data-mining methods in developing cloud-ceiling forecast algorithms from NWP model output is provided here. Rapid Update Cycle (RUC) 1-h forecast data were made available for nearly every hour in 2004. Various model variables were extracted from these data and stored in a database of hourly records for routine aviation weather report (METAR) station KJFK at John F. Kennedy International Airport along with other single-station locations. Using KJFK cloud-ceiling observations as ground truth, algorithms were derived for 1-, 3-, 6-, and 12-h forecasts through a data-mining process. Performance of these cloud-ceiling forecast algorithms, as evaluated through cross-validation testing, is compared with persistence and Global Forecast System (GFS) model output statistics (MOS) performance (6 and 12 h only) over the entire year. The 1-h algorithms were also compared with the RUC model cloud-ceiling (or cloud base) height translation algorithms. The cloud-ceiling algorithms developed through data mining outperformed these RUC model translation algorithms, showed slightly better skill and accuracy than persistence at 3 h, and outperformed persistence at 6 and 12 h. Comparisons to GFS MOS (which uses observations in addition to model data for algorithm derivation) at 6 h demonstrated similar performance between the two methods with the cloud-ceiling algorithm derived through data mining demonstrating more skill at 12 h.
Abstract
Accurate cloud-ceiling-height forecasts derived from numerical weather prediction (NWP) model data are useful for aviation and other interests where low cloud ceilings have an impact on operations. A demonstration of the usefulness of data-mining methods in developing cloud-ceiling forecast algorithms from NWP model output is provided here. Rapid Update Cycle (RUC) 1-h forecast data were made available for nearly every hour in 2004. Various model variables were extracted from these data and stored in a database of hourly records for routine aviation weather report (METAR) station KJFK at John F. Kennedy International Airport along with other single-station locations. Using KJFK cloud-ceiling observations as ground truth, algorithms were derived for 1-, 3-, 6-, and 12-h forecasts through a data-mining process. Performance of these cloud-ceiling forecast algorithms, as evaluated through cross-validation testing, is compared with persistence and Global Forecast System (GFS) model output statistics (MOS) performance (6 and 12 h only) over the entire year. The 1-h algorithms were also compared with the RUC model cloud-ceiling (or cloud base) height translation algorithms. The cloud-ceiling algorithms developed through data mining outperformed these RUC model translation algorithms, showed slightly better skill and accuracy than persistence at 3 h, and outperformed persistence at 6 and 12 h. Comparisons to GFS MOS (which uses observations in addition to model data for algorithm derivation) at 6 h demonstrated similar performance between the two methods with the cloud-ceiling algorithm derived through data mining demonstrating more skill at 12 h.
Abstract
Using imagery from NOAA’s Advanced Very High Resolution Radiometer (AVHRR) orbiting sensor, one of the authors (RLB) earlier developed a probabilistic neural network cloud classifier valid over the world’s maritime regions. Since then, the authors have created a database of nearly 8000 16 × 16 pixel cloud samples (from 13 Northern Hemispheric land regions) independently classified by three experts. From these samples, 1605 were of sufficient quality to represent 11 conventional cloud types (including clear). This database serves as the training and testing samples for developing a classifier valid over land. Approximately 200 features, calculated from a visible and an infrared channel, form the basis for the computer vision analysis. Using a 1–nearest neighbor classifier, meshed with a feature selection method using backward sequential selection, the authors select the fewest features that maximize classification accuracy. In a leave-one-out test, overall classification accuracies range from 86% to 78% for the water and land classifiers, with accuracies at 88% or greater for general height-dependent groupings. Details of the databases, feature selection method, and classifiers, as well as example simulations, are presented.
Abstract
Using imagery from NOAA’s Advanced Very High Resolution Radiometer (AVHRR) orbiting sensor, one of the authors (RLB) earlier developed a probabilistic neural network cloud classifier valid over the world’s maritime regions. Since then, the authors have created a database of nearly 8000 16 × 16 pixel cloud samples (from 13 Northern Hemispheric land regions) independently classified by three experts. From these samples, 1605 were of sufficient quality to represent 11 conventional cloud types (including clear). This database serves as the training and testing samples for developing a classifier valid over land. Approximately 200 features, calculated from a visible and an infrared channel, form the basis for the computer vision analysis. Using a 1–nearest neighbor classifier, meshed with a feature selection method using backward sequential selection, the authors select the fewest features that maximize classification accuracy. In a leave-one-out test, overall classification accuracies range from 86% to 78% for the water and land classifiers, with accuracies at 88% or greater for general height-dependent groupings. Details of the databases, feature selection method, and classifiers, as well as example simulations, are presented.
NASA A-Train vertical profilers provide detailed observations of atmospheric features not seen in traditional imagery from other weather satellite data. CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) profiles vividly depict the vertical dimension of otherwise two-dimensional features shown in mapped products. However, most forecasters have never seen these profiles and do not appreciate their capacity to convey fundamental information about cloud and precipitation systems. Here, these profiles are accompanied by weather satellite images and explained in the context of various meteorological regimes. Profile examples are shown over frontal systems, marine stratocumulus, orographic barriers, tropical cyclones, and a severe thunderstorm.
NASA A-Train vertical profilers provide detailed observations of atmospheric features not seen in traditional imagery from other weather satellite data. CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) profiles vividly depict the vertical dimension of otherwise two-dimensional features shown in mapped products. However, most forecasters have never seen these profiles and do not appreciate their capacity to convey fundamental information about cloud and precipitation systems. Here, these profiles are accompanied by weather satellite images and explained in the context of various meteorological regimes. Profile examples are shown over frontal systems, marine stratocumulus, orographic barriers, tropical cyclones, and a severe thunderstorm.
Abstract
An accurate precipitation climatology is presented for tropical depression (TD), tropical storm (TS), and tropical cyclone (TC) occurrences over oceans using recently released, consistent, and high-quality precipitation datasets from all passive microwave sensors covering 1998–2012 along with the Automated Rotational Center Hurricane Eye Retrieval (ARCHER)-based TC center positions. Impacts with respect to the direction of both TC movement and the 200–850-hPa wind shear on the spatial distributions of TC precipitation are analyzed. The TC eyewall contraction process during its intensification is noted by a decrease in the radius of maximum rain rate with an increase in TC intensity. For global TCs, the maximum rain rate with respect to the direction of TC movement is located in the down-motion quadrants for TD, TS, and category-1–3 TCs, and in a concentric pattern for category-4/5 TCs. A consistent maximum TC precipitation with respect to the direction of the 200–850-hPa wind shear is shown in the downshear left quadrant (DSLQ). With respect to direction of TC movement, spatial patterns of TC precipitation vary with basins and show different features for weak and strong storms. The maximum rain rate is always located in DSLQ for all TC categories and basins, except the Southern Hemisphere basin where it is in the downshear right quadrant. This study not only confirms previously published results on TC precipitation distributions relative to vertical wind shear direction, but also provides a detailed distribution for each TC category and TS, while TD storms display an enhanced rainfall rate ahead of the downshear quadrants.
Abstract
An accurate precipitation climatology is presented for tropical depression (TD), tropical storm (TS), and tropical cyclone (TC) occurrences over oceans using recently released, consistent, and high-quality precipitation datasets from all passive microwave sensors covering 1998–2012 along with the Automated Rotational Center Hurricane Eye Retrieval (ARCHER)-based TC center positions. Impacts with respect to the direction of both TC movement and the 200–850-hPa wind shear on the spatial distributions of TC precipitation are analyzed. The TC eyewall contraction process during its intensification is noted by a decrease in the radius of maximum rain rate with an increase in TC intensity. For global TCs, the maximum rain rate with respect to the direction of TC movement is located in the down-motion quadrants for TD, TS, and category-1–3 TCs, and in a concentric pattern for category-4/5 TCs. A consistent maximum TC precipitation with respect to the direction of the 200–850-hPa wind shear is shown in the downshear left quadrant (DSLQ). With respect to direction of TC movement, spatial patterns of TC precipitation vary with basins and show different features for weak and strong storms. The maximum rain rate is always located in DSLQ for all TC categories and basins, except the Southern Hemisphere basin where it is in the downshear right quadrant. This study not only confirms previously published results on TC precipitation distributions relative to vertical wind shear direction, but also provides a detailed distribution for each TC category and TS, while TD storms display an enhanced rainfall rate ahead of the downshear quadrants.
Abstract
Data-mining methods are applied to numerical weather prediction (NWP) output and satellite data to develop automated algorithms for the diagnosis of cloud ceiling height in regions where no local observations are available at analysis time. A database of hourly records that include Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) output, satellite data, and ground truth observations [aviation routine weather reports (METAR)] has been created. Data were collected over a 2.5-yr period for specific locations in California. Data-mining techniques have been applied to the database to determine relationships in the collected physical parameters that best estimate cloud ceiling conditions, with an emphasis on low ceiling heights. Algorithm development resulted in a three-step approach: 1) determine if a cloud ceiling exists, 2) if a cloud ceiling is determined to exist, determine if the ceiling is high or low (below 1 000 m), and 3) if the cloud ceiling is determined to be low, compute ceiling height. A sample of the performance evaluation indicates an average absolute height error of 120.6 m with a 0.76 correlation and a root-mean-square error of 168.0 m for the low-cloud-ceiling testing set. These results are a significant improvement over the ceiling-height estimations generated by an operational translation algorithm applied to COAMPS output.
Abstract
Data-mining methods are applied to numerical weather prediction (NWP) output and satellite data to develop automated algorithms for the diagnosis of cloud ceiling height in regions where no local observations are available at analysis time. A database of hourly records that include Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) output, satellite data, and ground truth observations [aviation routine weather reports (METAR)] has been created. Data were collected over a 2.5-yr period for specific locations in California. Data-mining techniques have been applied to the database to determine relationships in the collected physical parameters that best estimate cloud ceiling conditions, with an emphasis on low ceiling heights. Algorithm development resulted in a three-step approach: 1) determine if a cloud ceiling exists, 2) if a cloud ceiling is determined to exist, determine if the ceiling is high or low (below 1 000 m), and 3) if the cloud ceiling is determined to be low, compute ceiling height. A sample of the performance evaluation indicates an average absolute height error of 120.6 m with a 0.76 correlation and a root-mean-square error of 168.0 m for the low-cloud-ceiling testing set. These results are a significant improvement over the ceiling-height estimations generated by an operational translation algorithm applied to COAMPS output.
