Showing papers on "Cloud computing published in 1994"

Cloud Classification of AVHRR Imagery in Maritime Regions Using a Probabilistic Neural Network

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...

Analysis of cloud top height and cloud coverage from satellites using the O2 A and B bands

Abstract: Cloud height and cloud coverage detection are important for total ozone retrieval using ultraviolet and visible scattered light. Use of the O2 A and B bands, around 761 and 687 nm, by a satellite-borne instrument of moderately high spectral resolution viewing in the nadir makes it possible to detect cloud top height and related parameters, including fractional coverage. The measured values of a satellite-borne spectrometer are convolutions of the instrument slit function and the atmospheric transmittance between cloud top and satellite. Studies here determine the optical depth between a satellite orbit and the Earth or cloud top height to high accuracy using FASCODE 3. Cloud top height and a cloud coverage parameter are determined by least squares fitting to calculated radiance ratios in the oxygen bands. A grid search method is used to search the parameter space of cloud top height and the coverage parameter to minimize an appropriate sum of squares of deviations. For this search, nonlinearity of the atmospheric transmittance (i.e., leverage based on varying amounts of saturation in the absorption spectrum) is important for distinguishing between cloud top height and fractional coverage. Using the above-mentioned method, an operational cloud detection algorithm which uses minimal computation time can be implemented.

Advances in polarization diversity lidar for cloud remote sensing

Abstract: As soon as high-energy pulsed lasers became available in the mid-1960s, atmospheric scientists began assessing the information contents of various light detection and ranging (lidar) techniques in the field and laboratory. A particularly promising approach, which has recently gained increased stature as a result of growing interests in previously overlooked cloud types important to climate research (e.g. high-altitude cirrus), involves the polarization analysis of the backscattered laser return. Polarization diversity lidars collect data simultaneously in two or more channels to remotely determine the thermodynamic cloud phase, structure, and boundaries, and infer a variety of other climatically important cloud microphysical properties. As illustrated by the description of the mobile University of Utah Polarization Diversity Lidar system, commercially available dual-wavelength transmitters and improved electronic technologies to process high-resolution multichannel lidar signals represent significant advantages over earlier devices used in cloud studies. Polarization diversity can be added rather economically to more specialized lidars employing spectroscopic techniques, for other forms of atmospheric probing, such as Raman, differential absorption, and high spectral resolution lidars, in order to enhance instrument accuracy and versatility for cloud an aerosol research. Future engineering challenges remain in the "hardening" of compact lidar systems for extraterrestrial deployment, the achievement of unattended eye-safe operations, and in the improved integration of multiple remote sensor data streams and supporting in situ measurements to more fully characterize cloud systems and their effects on the Earth's radiation balance. >

An Operational System for Generating Cloud Drift Winds in the Australian Region and Their Impact on Numerical Weather Prediction

Abstract: The Australian Bureau of Meteorology has, since June 1992, produced cloud drift wind data for operational use. These data are used in the analysis cycle of the local operational numerical weather prediction system. This paper describes the methodology used for automatically producing cloud drift winds and also for their application to numerical weather analysis and prediction. Local processing of Geostationary Meteorological Satellite digital infrared data into cloud motion vectors has provided several advantages. It ensures timely availability of the data in the Australian National Meteorological Centre for the operational Regional Assimilation and Prediction (RASP) system. It allows quality control and, in particular, height assignment to be closely tied to the RASP system, which is consistent with the long-term requirement for the processing of these remotely sensed data to be done as part of the assimilation system. Importantly, use of the data has resulted in consistent improvements both in ...

Rainfall, snowfall forecast apparatus and method

Abstract: An apparatus is presented for providing a short time range forecast with relative high accuracy from weather radar images of cloud reflection data by incorporating physical properties of cloud in the forecasting method. The method consists of defining a plurality of lattice points on a radar image, and multiplying the reflection data from a group of neighboring lattice points obtained at a specific past point in time with selected coefficients. The products of multiplication are summed, and transformed into image data by specific function based on the properties relating to cloud. Squared errors of the difference between the computational reflection data and the observed reflection data are iterated to a value below a predetermined threshold value to select the coefficients, and these coefficients are used to provide forecasting of reflection data at a specific future point in time.

Cloud properties inferred from 8-12 micron data

Abstract: A trispectral combination of observations at 8-, 11-, and 12-micron bands is suggested for detecting cloud and cloud properties in the infrared. Atmospheric ice and water vapor absorption peak in opposite halves of the window region so that positive 8-minus-11-micron brightness temperature differences indicate cloud, while near-zero or negative differences indicate clear regions. The absorption coefficient for water increases more between 11 and 12 microns than between 8 and 11 microns, while for ice, the reverse is true. Cloud phases is determined by a scatter diagram of 8-minus-11-micron versus 11-minus-12-micron brightness temperature differences; ice cloud shows a slope greater than 1 and water cloud less than 1. The trispectral brightness temperature method was tested upon high-resolution interferometer data resulting in clear-cloud and cloud-phase delineation. Simulations using differing 8-micron bandwidths revealed no significant degradation of cloud property detection. Thus, the 8-micron bandwidth for future satellites can be selected based on the requirements of other applications, such as surface characterization studies. Application of the technique to current polar-orbiting High-Resolution Infrared Sounder (HIRS)-Advanced Very High Resolution Radiometer (AVHRR) datasets is constrained by the nonuniformity of the cloud scenes sensed within the large HIRS field of view. Analysis of MAS (MODIS Airborne Simulator) high-spatial resolution (500 m) data with all three 8-, 11-, and 12-micron bands revealed sharp delineation of differing cloud and background scenes, from which a simple automated threshold technique was developed. Cloud phase, clear-sky, and qualitative differences in cloud emissivity and cloud height were identified on a case study segment from 24 November 1991, consistent with the scene. More rigorous techniques would allow further cloud parameter clarification. The opportunities for global cloud delineation with the Moderate-Resolution Imaging Spectrometer (MODIS) appear excellent. The spectral selection, the spatial resolution, and the global coverage are all well suited for significant advances.

Climatological data for clouds over the globe from surface observations, 1982--1991: The total cloud edition

Abstract: TOTAL CLOUD Edition Superseded by the Extended Edited Synoptic Cloud Report Archive. See RELATED Datasets below.

Trends in Global Marine Cloudiness and Anthropogenic Sulfur

Abstract: A statistical analysis using published data on the global distribution of total cloud cover and cloud type amounts over the ocean, reduced from the Comprehensive Ocean–Atmosphere Data Set (COADS), shows a significant positive trend (4.2% increase from the 1930 baseline) in total oceanic cloud amount in the period between 1930 and 1981. The increase of total cloud amount for the Northern Hemisphere (5.8% ) was twice that for the Southern Hemisphere (2.9% ), The more consistent 30-yr ( 1952–1981 ) data show that the change in cloud amount ( 1952 base) was 1.5% for the globe, 2.3% for the Northern Hemisphere, and 1.2% for the Southern Hemisphere. The analysis also shows that the greatest cloud amount increase was for altocumulus and altostratus clouds and that this increase was most pronounced at midlatitudes (30°–50°N). The trend and the pattern of cloud amount variations appear to be in accord with the temporal trend and geographic distribution of S02 emissions. It is hypothesized that sulfate par...

Some Effects of Different Cloud Parameterizations in a Mesoscale Model and a Chemistry Transport Model

Abstract: Chemistry transport models often ignore the cloud parameters that can be provided by meteorological pre-processors like mesoscale meteorological models. They often recalculate these parameters with algorithms that differ from those used in the meteorological preprocessors. Hence, inconsistencies can occur between the treatment of clouds in the meteorological and chemical part of the model package. In this study the influence of five different cloud parameterization schemes used in a well-known mesoscale meteorological model on the results of a stand-alone version of a cloud and scavenging module is illustrated. The differences between the results provided by five model runs with different cloud modules and those recalculated by the stand-alone version are discussed. Such differences occur due to the inconsistencies between the different cloud parameterization schemes in the meteorological model and the cloud and scavenging module. The results of the cloud and scavenging module differ due to the d...

Support of Environmental Requirements for Cloud Analysis and Archive (SERCAA): Algorithm Descriptions

Abstract: : This report describes the SERCAA multiplatform, multisensor, multispectral cloud product analysis algorithms. These algorithms incorporate high-resolution sensor data from multiple military and civilian satellites, polar and geostationary, into a real-time cloud analysis model and apply multispectral cloud analysis techniques that improve the detection and specification of clouds, especially cirrus and low clouds. The SERCAA algorithms consist of a number of processes involved in integrating cloud analyses from multiple satellite platforms into a single cloud analysis product. The steps required to process the raw sensor data, collected from each of the satellite platforms, into each of the individual cloud analysis products include total cloud algorithms for DMSP, AVHRR, and geostationary platforms, cloud layer and type algorithms, and an analysis integration algorithm. The SERCAA data products for the CDFS II baseline include: total cloud cover fraction, number of cloud layers (up to four floating layers), cloud layer coverage fraction, cloud type, cloud height, and analysis confidence level.

Parameterization of subgrid scale convective cloud transport in a mesoscale regional chemistry model

Abstract: A parameterization of subgrid scale convective cloud vertical mixing has been developed in the National Oceanic and Atmospheric Administration Aeronomy Laboratory three-dimensional regional chemistry model. The parameterization is evaluated by comparing model results (with and without the cloud mixing parameterization) with observations of O{sub 3}, NO{sub x}, and NO{sub y} made onboard the National Center for Atmospheric Research Sabreliner aircraft during the 1990 Rural Oxidants in the Southern Environment campaign in Alabama. The authors` studies show that model results with and without the cloud transport significantly differ from each other when convective clouds are present, implying that cloud exchange is an important process in determining the distributions of trace species. Their studies also show that model results with cloud transport parameterization are in much better agreement with the aircraft observations than those without. 45 refs., 13 figs., 2 tabs.

The Use of a Two-Dimensional, Time-Dependent Cloud Model to Predict Convective and Stratiform Clouds and Precipitation

Abstract: A two-dimensional, time-dependent cloud model has been used in two field projects to forecast the convective development during the day from the morning sounding. In effect, the cloud model gives a dynamic analysis of the sounding as affected by heating and evaporation at the earth's surface, divergence of the winds throughout the atmosphere, and cloud shadow effects. During the initial project, the Cooperative Huntsville Meteorological Experiment, the results were mixed. Model runs were easily made when soundings were available, but displaying the results in a meaningful and useful way was the limiting factor. In a later experiment, the North Dakota Thunderstorm Project, the problem of displaying results was overcome and soundings were available from the local weather service forecast office with a high degree of reliability. The experimental model correctly forecasts convective development about 80% of the time, and precipitation or no precipitation more than 70% of the time.

Impact of multiple scattering on simulated infrared cloud scene images

Abstract: The 3D volumetric character of clouds is a critically important factor in determining cloud structure as seen in infrared imagery. Using a longwave cloud scene simulator which images a 3D cloud volume, the 3D structure has been shown to be particularly important when viewing at low grazing angles. In order to conduct analyses of cloud scene structure in MW and visible bands as well, the longwave simulator has been significantly upgraded to perform imaging of clouds with multiple scattering included. The multiple scattering algorithm is based on a WKB approximation method for the exact radiative transfer problem, and comprehends the spatial variations in optical properties within the cloud volume. As a first analysis, we have generated a cloud scene which is backlit by the sun, and systematically assess the contributions of the thermal, solar, and multiple scattering mechanisms within the imagery. As might be expected, multiple scattering has its greatest impact at the cloud edges in the MW band, where the `silver lining' is formed.

The computation of cloud base height from paired whole-sky imaging cameras

Abstract: A major goal for global change studies is to improve the accuracy of general circulation models (GCMs) capable of predicting the timing and magnitude of greenhouse gas-induced global warming. Research has shown that cloud radiative feedback is the single most important effect determining the magnitude of possible climate responses to human activity. Of particular value to reducing the uncertainties associated with cloud-radiation interactions is the measurement of cloud base height (CBH), both because it is a dominant factor in determining the infrared radiative properties of clouds with respect to the earth`s surface and lower atmosphere and because CBHs are essential to measuring cloud cover fraction. We have developed a novel approach to the extraction of cloud base height from pairs of whole sky imaging (WSI) cameras. The core problem is to spatially register cloud fields from widely separated WSI cameras; this complete, triangulation provides the CBH measurements. The wide camera separation (necessary to cover the desired observation area) and the self-similarity of clouds defeats all standard matching algorithms when applied to static views of the sky. To address this, our approach is based on optical flow methods that exploit the fact that modern WSIs provide sequences of images. We will describe the algorithm and present its performance as evaluated both on real data validated by ceilometer measurements and on a variety of simulated cases.

Improved cloud analysis for CDFS II through the SERCAA research and development program

Abstract: The Cloud Depiction and Forecasting System II (CDFS II) is a major new initiative that will transition the Air Force Global Weather Central (AFGWC) to a new satellite data processing system and include extensive changes in cloud analysis/forecasting at AFGWC. The present cloud analysis model, the RTNEPH, combines reduced resolution DMSP OLS or NOAA AVHRR data with conventional observations. The RTNEPH domain and analysis frequency are limited by its dependence on polar-orbiting satellites. In the CDFS II era (1998+), AFGWC cloud forecast models will benefit directly from improved automated nephanalysis capabilities from multiplatform sensor data. Support of Environmental Requirements for Cloud Analysis and Archive (SERCAA) project is a research and development program sponsored by the Strategic Environmental Research and Development Program that will provide both the next generation nephanalysis model for CDFS II and a new global cloud algorithm for use in determining the radiative and hydrological effects of clouds on climate and global change. SERCAA cloud analysis products will be available to a wide community of users both within and outside of the Department of Defense. The SERCAA project consists of two phases, the first has provided algorithms for retrieval of cloud spatial parameters for the CDFS II initiative. The second phase is concentrating on development of radiative and microphysical cloud parameter algorithms and on the archive structure.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Detection and classification of cloud data from geostationary satellite using artificial neural networks

Abstract: This paper presents a neural network-based approach for the detection/classification of cloud field from satellite data in both the visible and infrared (IR) range. Unlike many existing cloud detection schemes which use thresholding and statistical methods, this approach uses singular value decomposition (SVD) to extract image textural features in addition to mean value methodologies. The extracted features are then presented to a self-organizing feature map or Kohonen network for automatic detection and classification of cloud areas. The effectiveness of this method is demonstrated under many situations which are considered difficult for the conventional methods. The proposed method also possesses some interesting classification capabilities which can facilitate future studies on global climatology. >

Comparison of Cloud Fields from AGCM, In Situ and Satellite Measurements

Abstract: This paper focuses on the comparison of cloud amounts derived from an Atmospheric General Circulation Model (AGCM), Satellite-observed clouds, and Ground-based cloud observations.

Diagnosing Cloudiness from Global Numerical Weather Prediction Model Forecasts.

Abstract: : We investigated the utility of any information derivable from noncloud numerical weather prediction (NWP) model forecasts in inferring layer cloud amount distributions This effort involved identifying and preparing a suitable source of the predictand (cloud amount), generating and preparing a suitable source of the predictors (NWP variables and geographic information), and combining them to form diagnostic relationships in a model output statistics approach Both AFGWC RTNEPH cloud analyses and Phillips Laboratory Global Spectral Model (PL GSM) NWP forecasts were rendered on a 125 km equal-area grid in three cloud deck regimes (high, middle, low) Two statistical methods CLOUD CURVE ALGORITHM (CCA), a univariate method, and multiple linear regression (MLR) were used to relate the cloud amount to relative humidity (CCA) and to relative humidity and a large number of other NWP variables (MLR) We found that the CCA method preserves the sharpness of the cloud distribution while sacrificing skill, while MLR produced cloud diagnoses that were more skillful but less sharp The methods fall short of the error level standards established by Air Force requirements, but show potential for useful cloud forecast skill upon further refinement

Airborne and spaceborne cloud radar designs

Abstract: Presents some crucial design parameters and a strawman system design for a nadir-looking, 94-GHz spaceborne cloud profiling radar. This sensor is expected to provide cloud measurements at vertical resolution of 500 m and with a minimum detectable cloud reflectivity of slightly better than -30 dBZ. The radar design is intended to be accommodated by a spacecraft with limited resources. It uses a 2-m antenna and an extended interaction amplifier (EIA) that are readily available in either ground-based and airborne applications. For space application, improvements in the EIA lifetime and space qualification will be required. For various reasons, the spaceborne cloud radar system development is expected to be greatly benefited by the implementation of an aircraft cloud radar instrument. >

Neural networks and cloud classification

Abstract: The development of an efficient and accurate automated cloud classification method for use on satellite Images will be of great benefit to operational meteorology and climate studies. We have examined the possible use of neural networks as a classification tool for spectral and textural data extracted from Meteosat images. A large number of back-propagation neural network configurations were run and many were found to be highly effective, outperforming more traditional statistical classifiers. A Kohonen type competitive learning network was also tried, but was found to be considerably less successful on this data set. Some suggestions are made for future development based on the experience gained in this project.

An Improved Cloud Retrieval Algorithm Using HIRS2-MSU Radiance Measurements

Abstract: Cloud-top heights and cloud amounts are produced as part of the operational processing of polar-satellite data at the National Environmental Satellite Data and Information Service (NESDIS). These products were compared with similar products from the air force's real-time nephanalysis (RTNEPH), from the International Satellite Cloud Climatology Project, and from NASA Goddard's processing of satellite data. It was found that the amount of high-level cloud was too small in the NESDIS results, while the amount of low-level cloud was too large. An examination of the NESDIS algorithm revealed that the differences in cloud distributions were caused by the selection of channels used for the cloud retrievals. Cloud retrievals are most accurate at the levels at which the channels that are used are most sensitive. In addition, it was found that no one pair of channels was best at all levels. A new procedure was developed that varied the channels as a function of an initial estimate of the cloud height. This procedure produced improved cloud retrievals that were then compared with the RTNEPH results. The comparison showed that the two methods provide similar retrievals of cloud height and amount.

Cloud cover assessment grids for whole-sky images:

Abstract: The relative accuracy of cloud cover assessment from grid overlays on whole-sky images is discussed. Research shows that grid projection, density and cloud retrieval method are the major variables....

Virtual computing environment

Abstract: A network of supercomputers and high-performance workstations appears to be the only reasonable way to provide adequate computing resources for the Grand Challenge problems of the next century. Such a collection of computers and supporting software environments is called a virtual computing environment (VCE). The paper describes the motivation and goals of the VCE project, followed by a description of the system. The paper concentrates on the runtime aspects of the VCE, and concludes with a discussion of a small prototype system that has been built using the Isis distributed toolkit.<>

Automated Satellite Cloud Analysis - Tactical Nephanalysis (TACNEPH).

Abstract: : The TACNEPH program was a four year effort to develop a relocatable regional cloud analysis model for operational use in a transportable satellite ground station facility. Key features of the model are the ability to assimilate and analyze data from both military and civilian polar orbiting satellites in real time and to perform cloud analyses in the absence of any data source other than direct satellite sensor transmissions. In particular, data from the DMSP OLS, SSM/I, and SSM/T are used together with NOAA AVHRR multispectral imagery. In addition to direct broadcast satellite data, the only additional data requirements are for surface temperature climatology and geographic classification databases. Algorithms were developed to perform nephanalysis over the range of conditions expected to be encountered in a tactical environment (e.g., variable mix of sensor and supporting data and the absence of conventional observations). The TACNEPH program has the capability to operate in a 'gracefully degrading' mode wherein different algorithms are employed as various sources of data are lost. Cloud algorithms consist of a series of cloud tests, each sensitive to a different satellite sensor spectral signature. Since the model must be able to operate when no conventional data are available, clear scene characteristics are inferred from climatology and sensor data. A technique has been developed to predict clear-scene satellite brightness temperatures through dynamic adjustments to climatological surface temperatures derived from shelter measurements.