Showing papers on "Cloud computing published in 1992"

A Comprehensive Radiation Scheme for Numerical Weather Prediction Models with Potential Applications in Climate Simulations

Abstract: A comprehensive scheme for the parameterization of radiative transfer in numerical weather Prediction (NWP) models has been developed. The scheme is based on the solution of the δ-two-stream version of the radiative transfer equation incorporating the effects of scattering, absorption, and emission by cloud droplets, aerosols, and gases in each part of the spectrum. An extremely flexible treatment of clouds is obtained by allowing partial cloud cover in any model layer and relating the cloud optical properties to the cloud liquid water content. The latter quantity may either be a prognostic or diagnostic variable of the host model or specified a priori depending on cloud type, height, or similar criteria. The treatment of overlapping cloud layers is based on realistic assumptions, but any different approach requires only minor modifications of the code. The scheme has been tested extensively in the framework of the intercomparison of radiation codes in climate models (ICRCCM, WMO 1984, 1990). Rad...

On the determination of cloud cover from satellite sensors: The effect of sensor spatial resolution

Abstract: The effect of sensor spatial resolution on satellite-derived estimates of cloud fractional coverage is quantified on the basis of Landsat satellite radiance data. Cloud fraction is found to depend on cloud algorithm as much as it depends on sensor spatial resolution. Even for 28.5-m spatial resolution data, large cloud fraction differences exist between algorithms. Satellite cloud retrieval algorithms depend strongly on sensor spatial resolution and/or on the optical depth of the cloud field. When present, spatial resolution effects are small (less than 0.01) for pixel diameter less than 1/4 km and are large for pixel diameter larger than 1 km. The International Satellite Cloud Climatology Project bispectral threshold gives an increase in cloud fraction of 0.11 as spatial resolution degrades from 20 m to 8 km. The spatial coherence algorithm underestimates boundary layer cloud fraction by 0.18. The use of functional box counting and an assumption of fractal scale invariance overestimates the dependence of cloud fraction for spatial scales below 1 km.

Real-time computing

Abstract: Real{time systems are de ned as those systems in which the correctness of the system depends not only on the logical result of computation, but also on the time at which the results are produced. Real-time systems span a broad spectrum of complexity from very simple microcontrollers (such as a microprocessor controlling an automobile engine) to highly sophisticated, complex and distributed systems (such as air tra c control for

Cloud-radiation feedbacks in a general circulation model and their dependence on cloud modelling assumptions

Abstract: The general circulation model (GCM) used in this study includes a prognostic cloud scheme and a rather detailed radiation scheme. In a preceding paper, we showed that this model was more sensitive to a global perturbation of the sea surface temperatures than most other models with similar physical parametrization. The experiments presented here show how this feature might depend on some of the cloud modelling assumptions. We have changed the temperature at which the water clouds are allowed to become ice clouds and analyzed separately the feedbacks associated with the variations of cloud cover and cloud radiative properties. We show that the feedback effect associated with cloud radiative properties is positive in one case and negative in the other. This can be explained by the elementary cloud radiative forcing and has implications concerning the use of the GCMs for climate sensitivity studies.

An Analysis of Cloud Liquid Water Feedback and Global Climate Sensitivity in a General Circulation Model

Abstract: A set of general circulation model simulations is analyzed to determine how cloud distribution and cloud radiative properties might change as climate warms and to isolate and quantify the various feedback effects of clouds on climate sensitivity. For this study the NCAR Community Climate Model (CCM1) was modified so that the cloud radiative properties (albodo, emissivity, and absorptivity) are no longer prescribed, but are functions of the cloud liquid water content. Following the Cess and Potter approach for estimating climate sensitivity, we consider results from two sets of simulations. In one set, cloud liquid water is diagnosed from the simulated condensation rate and thus is free to vary with condensation, while in the other set, the cloud liquid water content is a fixed field (dependent only on altitude and latitude) that is obtained by averaging the results of the first set of experiments. The experiments make it possible to isolate the effects of cloud liquid water feedback. We find that...

A Description of the Air Force Real-Time Nephanalysis Model

Abstract: The Air Force Global Weather Central (AFGWC) Real-Time Nephanalysis (RTNEPH) is an automated cloud model that produces a 48-km gridded analysis of cloud amount, cloud type, and cloud height. Its primary input is imagery from polar-orbiting satellites. Six main programs make up the RTNEPH. These are the satellite data mapper, the surface temperature analysis and forecast model, the satellite data processor, the conventional data processor, the merge processor, and the bogus processor. The satellite data mapper remaps incoming polar-orbiter imagery to a polar-stereographic database. The surface temperature model produces an analysis and forecast of shelter and skin temperatures for comparison to satellite-measured infrared (IR) brightness temperatures. The satellite data processor reads in the new satellite data and produces a satellite-derived cloud analysis. The conventional data processor retrieves and reformats cloud information from airport observations. The merge processor combines the satell...

A New Method for Developing Cloud Specification Schemes in General Circulation Models

Abstract: In the present paper, we review a new method for relating cloud observations to large-scale variables of general circulation models. The method is based on an application of the cluster analysis to synoptic analyses Of prognostic model variables provided by the National Meteorological Center. Surface cloud observations are “clustered” according to the similarity of the principal-component loading scores of the corresponding vertical soundings. The method was tested by developing a simple cloud parameterization scheme, from the cluster-stratified cloud data, and comparing it with the observations. Parameterization results are compared qualitatively against satellite imagery and surface analysis, and quantitatively against a scheme based on one variable only, the relative humidity. Qualitative comparison shows that the new approach generates cloud parameterization consistent with observations, especially with cloud structures related to various synoptic-scale flows. Quantitative comparisons indicat...

An Automated Observing System for Passive Evaluation of Cloud Cover and Visibility

Abstract: : The Automated Observing System is a passive system for determination of cloud cover and sector visibility. This system was developed from related systems, the Whole Sky Imager for cloud field assessment, and the Horizon Scanning Imager for determination of the sector visibility. During the past funding interval, much of the effort on this system was directed toward development of night visibility capability. This report discusses the theoretical development of the equations and analytic approaches to night visibility. A variety of hardware and software adaptations are discussed, which allow the sensor to utilize multiple integration periods for increased system sensitivity. Sample results are included. Secondary developments discussed in this report include an in-depth sensitivity analysis of the daytime visibility system, as well as achievement of more accurate cloud algorithms and night cloud capability funded separately. This is followed by recommendations for improvements to tile current system, and for development of tactical systems based on the existing capability.... Visibility, Contrast transmittance, Atmosphere, Sector visibility, Weather sensors, Aviation weather observations, Weather, Atmospheric visibility, Cloud, Cloud cover, Cloud free line of sight.

High-performance computing, high-speed networks, and configurable computing environments: progress toward fully distributed computing.

Abstract: The next several years will see the maturing of a collection of technologies that will enable fully and transparently distributed computing environments. Networks will be used to configure independent computing, storage, and I/O elements into "virtual systems" that are optimal for solving a particular problem. This environment will make the most powerful computing systems those that are logically assembled from network-based components and will also make those systems available to a widespread audience. Anticipating that the necessary technology and communications infrastructure will be available in the next 3 to 5 years, we are developing and demonstrating prototype applications that test and exercise the currently available elements of this configurable environment. The Lawrence Berkeley Laboratory (LBL) Information and Computing Sciences and Research Medicine Divisions have collaborated with the Pittsburgh Supercomputer Center to demonstrate one distributed application that illuminates the issues and potential of using networks to configure virtual systems. This application allows the interactive visualization of large three-dimensional (3D) scalar fields (voxel data sets) by using a network-based configuration of heterogeneous supercomputers and workstations. The specific test case is visualization of 3D magnetic resonance imaging (MRI) data. The virtual system architecture consists of a Connection Machine-2 (CM-2) that performs surface reconstruction from the voxel data, a Cray Y-MP that renders the resulting geometric data into an image, and a workstation that provides the display of the image and the user interface for specifying the parameters for the geometry generation and 3D viewing. These three elements are configured into a virtual system by using several different network technologies. This paper reviews the current status of the software, hardware, and communications technologies that are needed to enable this configurable environment. These interdependent technologies include: (1) user interface and application program construction methodologies, (2) the interprocess communication (IPC) mechanisms used to connect the software modules of the application, (3) the network protocols and interface hardware used by the IPC for communicating between modules running on separate and independent computing system elements, (4) the telecommunications infrastructure that provides the low-level data transfer functions for the networks that connect the geographically distributed elements used by the application, and (5) the nature of the functional elements that will be connected to form virtual systems.

Cloud Scene Simulation Modeling the Enhanced Model

Abstract: : This report documents the development of the Enhanced Cloud Scene Simulation Model developed by TASC for Phillips Laboratory in support of the Smart Weapons Operability Enhancement (SWOE) Program under the Balanced Technology Initiative. The model simulates multi-dimensional cloud water density fields for input to radiative transfer models and scene generation systems. The enhanced cloud model incorporates additional capabilities and modifications to previous model versions. This document focuses on those new capabilities and briefly summarizes the technical tasks completed under the Cloud Scene Model Development Project. Cloud model, Fractal model, Scene simulation, Cumulus model.

Initialization of cloud water in a numerical weather prediction model

Abstract: In recent years many studies have shown the importance of treating condensation processes in a consistent manner in numerical weather prediction models. Among emerging improvements is the explicit treatment of cloud water, and in some cases precipitating water. An unresolved problem then is how to initialize the cloud water, especially since this quantity is not treated in the most commonly used analysis schemes. In this study, a method for initializing the cloud water in a numerical weather prediction (NWP) model will be presented and tested. The implications for the model's spin-up are investigated. Information from an earlier run (“first guess fields”) is used, together with satellite data. If necessary, humidity enhancement is performed where clouds are indicated by those sources. The results indicate that initialization of the cloud water field by itself does not have a large effect on the spin-up of precipitation and clouds. A much larger effect is obtained when the humidity field is enhanced. The spin-up time for precipitation is then reduced from 12 to 6 hours, while for cloud cover it is reduced to only 1–2 hours. The method is computationally very efficient, and is particularly useful over data-sparse areas, such as the oceans. An investigation of the different terms in the cloud water tendency equation is done and the results interpreted in terms of spin-up of cloud parameters. These tests confirm that the cloud water field only accounts for a small part of the spin-up effect. These also show that the production of cloud water per time step increases throughout the simulation.

Clustering applied to cloud wind determination

Abstract: Reliable cloud motion wind generation from Meteosat images requires good target selection. This is usually done by examining the infrared channel and selecting target windows which have a temperature variation between an upper limit and lower limit, i.e., windows containing essentially a single cloud layer. In this paper we apply an optimised multi-spectral clustering algorithm in an attempt to extract the principal cloud targets prior to target tracking. Experimental results show an increase in the number of trackable targets compared to conventional techniques based on raw data. The paper also examines the optimal target size and compares the performance of several target tracking techniques.

Neural net classifier for satellite imageries

Abstract: Accurate identification of cloud type is an important aspect of weather forecasting. One of the primary applications of the remotely sensed cloud cover data is to provide synoptic cloud cover information over extensive data-sparse regions; particularly the oceans and deserts. In southeast Asia, information on cloud cover data is obtained from the infrared and visible channels by Geostationary Meteorological Satellite. These imageries contain data of clouds. By extracting the textural features embedded in the images, information on cloud types can be derived and mapped spatially. An artificial neural network is used as a classifier to identify different cloud types through comprehensive training cycles. The architecture of the network used in the present study is multilayered with feedforward and backpropagation. The study makes use of a classification scheme based on the SYNOP code of the World Meteorological Organization (WMO). The average cloud classification accuracy obtained in this study is 40%.

Satellite retrievals of marine stratiform cloud systems

Abstract: The initial validation of a satellite-based cloud retrieval scheme of the spatial-coherence type that is being used to create a marine stratiform cloudiness (MSC) climatology is discussed. Initial results are presented which describe correlations between MSC properties and SST, which could be useful for providing guidelines for assessing potential climate feedbacks regarding these cloud types. The correlation of triangle-technique- and spatial-coherence-method-derived fractional cloud covers together with the regression line for all data points is shown.

Multi-Spectral Texture Analysis for Cloud Feature Discrimination

Abstract: Cloud classification is a difficult task due to the spectral homogeneity of cloud features. In recent years, researchers have devoted considerable attention to the development of new spectral and spatial measures, such as texture analysis, in order to distinguish between different cloud types. A new textural method for cloud classification, based on localized spatial filters was implemented, and the results are summarized in this paper. The textural measure being investigated is based on a class of filters known as Gabor filters. These filters discriminate textural features in a similar manner to that of human vision. This is particularly attractive for the cloud classification problem because the most accurate interpretation still involves "visual" subjective classification of images by a meteorologist or climatologist. The new method was applied to NOAA Advanced Very High Resolution Radiometer (AVHRR) imagery containing various cloud classes and meteorologic features. An extensive sensitivity analysis was performed in order to characterize the behavior of parameter settings. Currently, the method is being applied to additional imagery of various spectral and spatial characteristics.

On the Retrieval and Analysis of Multilevel Clouds

Abstract: An accurate satellite retrieval of cloud properties depends upon the detection and analysis of multilayered, overlapping cloud systems that surface observations show to be common. Multiple cloud layers are often found, for instance, in frontal situations, where cirrus overlays boundary layer convective cloud or low-to mid-level stratus cloud. Surface observers (Hahan et al., 1982) indicate that over ocean in the Northern Hemisphere between 30 deg. N and 60 deg. N, 51 percent of observations are of multilevel clouds. A satellite analysis by Coakley (1983) over the Pacific Ocean finds that more than 50 percent of 500 (250 sq km) frames exhibit evidence of multilayered cloud systems. The questions addressed in this study are the following: What error is introduced when inferring the cloud pressure from a Field-Of-View (FOV) that contains some arbitrary amount of transparent cloud overlaying a lower-level black cloud, such as stratus, by making the assumption that there is only a single cloud layer in the FOV, and what may be done to improve the cloud retrieval? The CO2 slicing methods (e.g. McCleese and Wilson, 1976; Smith and Platt, 1978; Chahine, 1974) have been shown to provide accurate means of inferring cirrus cloud altitude from passive infrared radiance measurements. The CO2 techniques have been applied to radiometric data from several instruments, notably the High Resolution Infrared Radiometric Sounder (HIRS/2, hereafter referred to as HIRS), the VISSR Atmospheric Sounder (VAS) (e.g., Menzel et al., 1983; Wylie and Menzel, 1989), and most recently to the High Resolution Interferometer Sounder (HIS) (Smith and Frey, 1990). The methods take advantage of the fact that infrared CO2 sounding channels spaced closely in wavenumber each have varying opacity to CO2, thereby causing each channel to be sensitive to a different level in the atmosphere. The techniques have been shown to be effective for single-layered, nonblack, mid- to high-level clouds such as cirrus, but are generally applied operationally to any given cloud occurrence. The CO2 slicing algorithms are most accurate for clouds than occur in a single, well-defined layer, or for multi-layered cloud cases in which the uppermost cloud layer is nearly black. Significant cloud height retrieval errors may ensue if the HIRS Field-Of-View (FOV) is cotaminated with low cloud. McCleese and Wilson (1976) have shown that the retrieved cloud height for the case of multiple cloud layers is a weighted average of the cloud heights actually present. The weight is approximately proportional to the product of the cloud heigt and the effective cloud amount. The effect of their result is that the uppermost cloud layer dominates the cloud pressure retrieval. Beyond stating that the higher cloud dominates the cloud pressure retrieval, there is no quantitative information to provide a way of estimating the errors in cloud pressure retrieval one should expect for certain common multilevel cloud situations or any suggestions on how to reduce the errors. In this paper we estimate the magnitude of the errors and use a simple algorithm to reduce the errors in optically thin cloud height retrival.

Satellite Analyses of Cirrus Cloud Properties During the FIRE Phase 2 Cirrus Intensive Field Observations over Kansas

Abstract: The First ISCCP (International Satellite Cloud Climatology Project) Regional Experiment (FIRE) Phase II Intensive Field Observations (IFO) were taken over southeastern Kansas between November 13 and December 7,1991, to determine cirrus cloud properties. The observations include in situ microphysical data; surface, aircraft, and satellite remote sensing; and measurements of divergence over meso- and smaller-scale areas using wind profilers. Satellite remote sensing of cloud characteristics is an essential aspect for understanding and predicting the role of clouds in climate variations. The objectives of the satellite cloud analysis during FIRE are to validate cloud property retrievals, develop advanced methods for extracting cloud information from satellite-measured radiances, and provide multiscale cloud data for cloud process studies and for verification of cloud generation models. This paper presents the initial results of cloud property analyses during FIRE-II using Geostationary Operational Environmental Satellite (GOES) data and NOAA Advanced Very High Resolution Radiometer (AVHRR) radiances.

Massively parallel neural network intelligent browse

Abstract: A massively parallel neural network architecture is currently being developed as a potential component of a distributed information system in support of NASA's Earth Observing System. This architecture can be trained, via an iterative learning process, to recognize objects in images based on texture features, allowing scientists to search for all patterns which are similar to a target pattern in a database of images. It may facilitate scientific inquiry by allowing scientists to automatically search for physical features of interest in a database through computer pattern recognition, alleviating the need for exhaustive visual searches through possibly thousands of images. The architecture is implemented on a Connection Machine such that each physical processor contains a simulated 'neuron' which views a feature vector derived from a subregion of the input image. Each of these neurons is trained, via the perceptron rule, to identify the same pattern. The network output gives a probability distribution over the input image of finding the target pattern in a given region. In initial tests the architecture was trained to separate regions containing clouds from clear regions in 512 by 512 pixel AVHRR images. We found that in about 10 minutes we can train a network to perform with high accuracy in recognizing clouds which were texturally similar to a target cloud group. These promising results suggest that this type of architecture may play a significant role in coping with the forthcoming flood of data from the Earth-monitoring missions of the major space-faring nations.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Cloud data set for neural network classification studies

Abstract: : A large set of GOES imagery has been analyzed to generate a cloud data set for neural network classification studies. The classification scheme uses 13 classes of clouds. The cloud data base is large enough to allow cross- validation tests of automated cloud classification techniques. The methods used to acquire the data on cloud type and a brief overview of the planned automated classification experiments are presented. The appendix contains an inventory of the cloud data set.

A proposed study of multiple scattering through clouds up to 1 THz

Abstract: A rigorous computation of the electromagnetic field scattered from an atmospheric liquid water cloud is proposed The recent development of a fast recursive algorithm (Chew algorithm) for computing the fields scattered from numerous scatterers now makes a rigorous computation feasible A method is presented for adapting this algorithm to a general case where there are an extremely large number of scatterers It is also proposed to extend a new binary PAM channel coding technique (El-Khamy coding) to multiple levels with non-square pulse shapes The Chew algorithm can be used to compute the transfer function of a cloud channel Then the transfer function can be used to design an optimum El-Khamy code In principle, these concepts can be applied directly to the realistic case of a time-varying cloud (adaptive channel coding and adaptive equalization) A brief review is included of some preliminary work on cloud dispersive effects on digital communication signals and on cloud liquid water spectra and correlations