Engineering topics

Key, J. R.

Publications and source records attributed to Key, J. R..

8 records

NASA NTRS1997

Estimating Surface Radiation Fluxes in the Arctic from TOVS Brightness Temperatures

A new method for estimating downwelling shortwave and longwave radiation fluxes in the Arctic from TOVS brightness temperatures has been developed. The method employs a neural network to bypass computationally intensive inverse and for-ward radiative transfer calculations, Results from two drifting ice camps (CEAREX, LeadEx) and from one coastal station show that downwelling fluxes can be estimated with r.m.s. errors of 20 W/sq m for longwave radiation and 35 W/sq m for shortwave radiation. Mean errors are less than 4 W/sq m and are well within the bounds required for many climate process studies.

Schweiger, A. J.↗

NASA NTRS1995

Parameterization and scaling of arctic ice conditions in the context of ice-atmospheric processes

The goals of this project are to observe how the open water/thin ice fraction in a high-concentration ice pack responds to different short-period atmospheric forcings, and how this response is represented in different scales of observation. The objectives can be summarized as follows: determine the feasibility and accuracy of ice concentration and ice typing by ERS-1 SAR backscatter data, and whether SAR data might be used to calibrate concentration estimates from optical and massive-microwave sensors; investigate methods to integrate SAR data with other satellite data for turbulent heat flux parameterization at the ocean/atmosphere interface; determine how the development and evolution of open water/thin ice areas within the interior ice pack vary under different atmospheric synoptic regimes; compare how open-water/thin ice fractions estimated from large-area divergence measurements differ from fractions determined by summing localized openings in the pack; relate these questions of scale and process to methods of observation, modeling, and averaging over time and space.

Barry, R. G.↗

NASA NTRS1989

Cloud classification from satellite data using a fuzzy sets algorithm - A polar example

Where spatial boundaries between phenomena are diffuse, classification methods which construct mutually exclusive clusters seem inappropriate. The Fuzzy c-means (FCM) algorithm assigns each observation to all clusters, with membership values as a function of distance to the cluster center. The FCM algorithm is applied to AVHRR data for the purpose of classifying polar clouds and surfaces. Careful analysis of the fuzzy sets can provide information on which spectral channels are best suited to the classification of particular features, and can help determine like areas of misclassification. General agreement in the resulting classes and cloud fraction was found between the FCM algorithm, a manual classification, and an unsupervised maximum likelihood classifier.

Key, J. R.↗

NASA NTRS1989

Merging AVHRR and SMMR data for remote sensing of ice and cloud in polar regions

Multispectral data from the Advanced Very High Resolution Radiometer (AVHRR) were digitally processed and merged with Scanning Multichannel Microwave Radiometer (SMMR) imagery. Five channels of AVHRR data, four channels of SMRR brightness temperatures and SMMR-derived ice concentration and ice type were co-registered to a polar stereographic grid. The merged data sets are currently being used in combination with meteorological information for integrated studies of clouds and sea ice.

Maslanik, J. A.↗

NASA NTRS1988

Cloud cover determination in polar regions from satellite imagery

The principal objectives of this project are: (1) to develop suitable validation data sets to evaluate the effectiveness of the International Satellite Cloud Climatology Project (ISCCP) operational algorithm for cloud retrieval in polar regions and to validate model simulations of polar cloud cover; (2) to identify limitations of current procedures for varying atmospheric surface conditions, and to explore potential means to remedy them using textural classifiers; and (3) to compare synoptic cloud data from a control run experiment of the GISS climate model II with typical observed synoptic cloud patterns.

Barry, R. G.↗

NASA NTRS1988

Cloud classification from satellite data using a fuzzy sets algorithm: A polar example

Where spatial boundaries between phenomena are diffuse, classification methods which construct mutually exclusive clusters seem inappropriate. The Fuzzy c-means (FCM) algorithm assigns each observation to all clusters, with membership values as a function of distance to the cluster center. The FCM algorithm is applied to AVHRR data for the purpose of classifying polar clouds and surfaces. Careful analysis of the fuzzy sets can provide information on which spectral channels are best suited to the classification of particular features, and can help determine likely areas of misclassification. General agreement in the resulting classes and cloud fraction was found between the FCM algorithm, a manual classification, and an unsupervised maximum likelihood classifier.

Key, J. R.↗

NASA NTRS1988

Cloud cover determination in polar regions from satellite imagery

The principal objectives of this project are: to develop suitable validation data sets to evaluate the effectiveness of the ISCCP operational algorithm for cloud retrieval in polar regions and to validate model simulations of polar cloud cover; to identify limitations of current procedures for varying atmospheric surface conditions, and to explore potential means to remedy them using textural classifiers: and to compare synoptic cloud data from a control run experiment of the Goddard Institute for Space Studies (GISS) climate model 2 with typical observed synoptic cloud patterns. Current investigations underway are listed and the progress made to date is summarized.

Barry, R. G.↗

NASA NTRS1987

Cloud cover determination in polar regions from satellite imagery

A definition is undertaken of the spectral and spatial characteristics of clouds and surface conditions in the polar regions, and to the creation of calibrated, geometrically correct data sets suitable for quantitative analysis. Ways are explored in which this information can be applied to cloud classifications as new methods or as extensions to existing classification schemes. A methodology is developed that uses automated techniques to merge Advanced Very High Resolution Radiometer (AVHRR) and Scanning Multichannel Microwave Radiometer (SMMR) data, and to apply first-order calibration and zenith angle corrections to the AVHRR imagery. Cloud cover and surface types are manually interpreted, and manual methods are used to define relatively pure training areas to describe the textural and multispectral characteristics of clouds over several surface conditions. The effects of viewing angle and bidirectional reflectance differences are studied for several classes, and the effectiveness of some key components of existing classification schemes is tested.

Barry, R. G.↗