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Richard L. Bankert

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.

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Richard L. Bankert
and
Jeremy E. Solbrig

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.

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Richard L. Bankert
and
David W. Aha

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.

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Richard L. Bankert
and
Paul M. Tag

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.

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Richard L. Bankert
and
Michael Hadjimichael

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.

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Paul M. Tag
,
Richard L. Bankert
, and
L. Robin Brody

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.

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Song Yang
,
Vincent Lao
,
Richard Bankert
,
Timothy R. Whitcomb
, and
Joshua Cossuth

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.

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Richard L. Bankert
,
Cristian Mitrescu
,
Steven D. Miller
, and
Robert H. Wade

Abstract

Cloud-type classification based on multispectral satellite imagery data has been widely researched and demonstrated to be useful for distinguishing a variety of classes using a wide range of methods. The research described here is a comparison of the classifier output from two very different algorithms applied to Geostationary Operational Environmental Satellite (GOES) data over the course of one year. The first algorithm employs spectral channel thresholding and additional physically based tests. The second algorithm was developed through a supervised learning method with characteristic features of expertly labeled image samples used as training data for a 1-nearest-neighbor classification. The latter’s ability to identify classes is also based in physics, but those relationships are embedded implicitly within the algorithm. A pixel-to-pixel comparison analysis was done for hourly daytime scenes within a region in the northeastern Pacific Ocean. Considerable agreement was found in this analysis, with many of the mismatches or disagreements providing insight to the strengths and limitations of each classifier. Depending upon user needs, a rule-based or other postprocessing system that combines the output from the two algorithms could provide the most reliable cloud-type classification.

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Richard L. Bankert
,
Jeremy E. Solbrig
,
Thomas F. Lee
, and
Steven D. Miller

Abstract

The Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) nighttime visible channel was designed to detect earth–atmosphere features under conditions of low illumination (e.g., near the solar terminator or via moonlight reflection). However, this sensor also detects visible light emissions from various terrestrial sources (both natural and anthropogenic), including lightning-illuminated thunderstorm tops. This research presents an automated technique for objectively identifying and enhancing the bright steaks associated with lightning flashes, even in the presence of lunar illumination, derived from OLS imagery. A line-directional filter is applied to the data in order to identify lightning strike features and an associated false color imagery product enhances this information while minimizing false alarms. Comparisons of this satellite product to U.S. National Lightning Detection Network (NLDN) data in one case as well as to a lightning mapping array (LMA) in another case demonstrate general consistency to within the expected limits of detection. This algorithm is potentially useful in either finding or confirming electrically active storms anywhere on the globe, particularly those occurring in remote areas where surface-based observations are not available. Additionally, the OLS nighttime visible sensor provides heritage data for examining the potential usefulness of the Visible-Infrared Imager-Radiometer Suite (VIIRS) Day/Night Band (DNB) on future satellites including the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP). The VIIRS DNB will offer several improvements to the legacy OLS nighttime visible channel, including full calibration and collocation with 21 narrowband spectral channels.

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Michael F. Donovan
,
Earle R. Williams
,
Cathy Kessinger
,
Gary Blackburn
,
Paul H. Herzegh
,
Richard L. Bankert
,
Steve Miller
, and
Frederick R. Mosher

Abstract

Three algorithms based on geostationary visible and infrared (IR) observations are used to identify convective cells that do (or may) present a hazard to aviation over the oceans. The performance of these algorithms in detecting potentially hazardous cells is determined through verification with Tropical Rainfall Measuring Mission (TRMM) satellite observations of lightning and radar reflectivity, which provide internal information about the convective cells. The probability of detection of hazardous cells using the satellite algorithms can exceed 90% when lightning is used as a criterion for hazard, but the false-alarm ratio with all three algorithms is consistently large (∼40%), thereby exaggerating the presence of hazardous conditions. This shortcoming results in part from the algorithms’ dependence upon visible and IR observations, and can be traced to the widespread prevalence of deep cumulonimbi with weak updrafts but without lightning over tropical oceans, whose origin is attributed to significant entrainment during ascent.

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