Showing papers on "Cloud computing published in 1995"

System and method for private addressing plans using community addressing

Abstract: A network hub system (200) is connected to a communications cloud (18) as well as messaging systems (202), (204), (206) and (208). A public access port (212) interacts with a public virtual messaging system (202a). A private access port (214) interacts with a private virtual messaging system (202b). Address translation tables are used to provide for identification of members of messaging communities which allow for the implementation of virtual private networks connected to hub system (200) and other hubs.

Classification of clouds over the western equatorial Pacific Ocean using combined infrared and microwave satellite data

Abstract: A new cloud classification scheme is presented that combines infrared and microwave satellite data. Because microwave radiation can penetrate deep into the cloud layer, this scheme is able to determine characteristics for both thin and deep clouds. Additionally, the new scheme can provide information on precipitation, which traditional infrared-visible cloud classification schemes have been unable to. The proposed cloud classification scheme utilizes the cloud top temperature obtained from infrared measurements and a microwave index that includes both emission and scattering signals. The following eight cloud classes are defined: warm nonprecipitating cloud, warm precipitating cloud, midtop nonprecipitating cloud, midtop precipitating cloud, thin high-top nonprecipitating cloud, deep high-top nonprecipitating cloud, anvil with stratiform precipitating cloud, and deep convective precipitating cloud. The classification scheme is validated by aircraft radar data obtained from Tropical Ocean-Global Atmosphere Coupled Ocean-Atmosphere Response Experiment. The new cloud classification scheme is used to investigate the clouds in the western equatorial Pacific Ocean warm pool region for the period from November 1992 to February 1993, allowing us to obtain for the first time the spectra of cloud coverage and precipitation distribution as a function of cloud types. The analysis shows that most of the cloudiness in this region is associated with warm nonprecipitating clouds. Precipitating pixels comprise only about 15% of total cloud pixels. Of the precipitating pixels, about 50% have cloud top temperatures warmer than −40°C. Using rainfall rates obtained from microwave satellite data, the relative contribution by each precipitating cloud type is investigated. It is found that the precipitating clouds with cloud top temperature warmer than −40°C contribute at least 23% of the total rainfall amount, while the remaining 77% of total precipitation is divided almost equally by anvil with stratiform precipitating cloud and the deep convective precipitating cloud.

Application of neural networks to AVHRR cloud segmentation

Abstract: The application of neural networks to cloud screening of AVHRR data over the ocean is investigated. Two approaches are considered, interactive cloud screening and automated cloud screening. In interactive cloud screening a neural network is trained on a set of data points which are interactively selected from the image to be screened. Because the data variability is limited within a single image, a very simple neural network topology is sufficient to generate an effective cloud screen. Consequently, network training is very quick and only a few training samples are required. In automated cloud screening, where a general network is designed to handle all images, the data variability can be significant and the resulting neural network topology is more complex. The latitudinal, seasonal and spatial dependence of cloud screening large AVHRR data sets is studied using an extensive data set spanning 7 years. A neural network and associated feature set are designed to cloud screen this data set. The sensitivity of the thermal infrared bands to high atmospheric water vapor concentration was found to limit the accuracy of cloud screening methods which rely solely on data from these channels. These limitations are removed when the visible channel data is used in combination with the thermal infrared data. A post processing algorithm is developed to improve the cloud screening results of the network in the presence of high atmospheric water vapor concentration. Post processing also is effective in identifying pixels contaminated by subpixel clouds and/or amplifier hysteresis effects at cloud-ocean boundaries. The neural network, when combined with the post processing algorithm, produces accurate cloud screens for the large, regionally distributed AVHRR data set. >

Cloud classification using whole-sky imager data

Abstract: Clouds are one of the most important moderators of the earth radiation budget and one of the least understood. The effect that clouds have on the reflection and absorption of solar and terrestrial radiation is strongly influenced by their shape, size, and composition. Physically accurate parameterization of clouds is necessary for any general circulation model (GCM) to yield meaningful results. The work presented here is part of a larger project that is aimed at producing realistic three-dimensional (3D) volume renderings of cloud scenes, thereby providing the important shape information for parameterizing GCMs. The specific goal of the current study is to develop an algorithm that automatically classifies (by cloud type) the clouds observed in the scene. This information will assist the volume rendering program in determining the shape of the cloud. Much work has been done on cloud classification using multispectral satellite images. Most of these references use some kind of texture measure to distinguish the different cloud types and some also use topological features (such as cloud/sky connectivity or total number of clouds). A wide variety of classification methods has been used, including neural networks, various types of clustering, and thresholding. The work presented here utilizes binary decision trees to distinguish the different cloud types based on cloud feature vectors.

Satellite Remote Sensing of Multiple Cloud Layers

Abstract: The goals of the current study are threefold: 1) to present a multispectral, multiresolution (MSMR) methodology for analysis of scenes containing multiple cloud layers; 2) to apply the MSMR method to two multilevel cloud scenes recorded by the NOAA Advanced Very High Resolution Radiometer (AVHRR) and the High Resolution Infrared Radiometer Sounder (HIRS/2) instruments during the First International Satellite Cloud Climatology Program (ISCCP) Regional Experiment (FIRE) on 28 November 1991; and 3) to validate the cloud-top height results from the case study analyses through comparison with lidar, radar, aircraft and rawin-sonde data. The measurements available from FIRE Cirrus II enable detailed examination of two complex cloud scenes in which cirrus and stratus appear simultaneously. A “fuzzy logic” classification system is developed to determine whether a 32×32 array of AVHRR data contains clear sky, low-level cloud, midlevel cloud, high-level cloud, or multiple cloud layers. With the addition of...

Cirrus Cloud Properties Derived from High Spectral Resolution Infrared Spectrometry during FIRE II. Part I: The High Resolution Interferometer Sounder (HIS) Systems

Abstract: The characteristics of the ER-2 aircraft and ground-based High Resolution Interferometer Sounder (HIS) instruments deployed during FIRE II are described. A few example spectra are given to illustrate the HIS cloud and molecular atmosphere remote sensing capabilities.

Comparison of cloud fields from atmospheric general circulation model, 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. This is distinctly different from Earth Radiation Budget Experiment (ERBE)-type comparisons because it does not mix potential errors in the cloud amount with those in the radiation code embedded in the model. Long term cloud climatologies were used to compare global cloud amounts and regional seasonal cycles. The results obtained were surprising in many respects. The AGCM successfully reproduced the signatures of the warm pool and North Pacific seasonal cycle cloudiness but failed in the low stratus region off the coast of South America, a known problem for AGCMs. The data sets also reproduced the anomaly signature associated with El Nino in the warm pool region, but the model amounts were lower. Global results had a similar success rate, with the model generally producing lower total cloud amounts compared to the satellite and in situ measurements. Also, an attempt was made to compare cloud vertical distributions between the data sets. Because of the inherent differences in the measuring processes among the three data sets, the cloud height may need to be validated using the corresponding radiation fields. Unfortunately there were also some large discrepancies between the two observed cloud data sets. We conclude that the character of the observed cloud data sets, while tremendously improved over the last decade, must be substantially enhanced before they will be useful in validating AGCMs by any but the crudest levels of comparison.

The Image Navigation Cloud Mask for the Multiangle Imaging Spectroradiometer (MISR)

Abstract: The authors have developed a cloud mask technique that may be applied to the efficient selection of “clear enough” scenes for image navigation. While the mask can be applied generally, the motivation for its development comes from its intended use on Multiangle Imaging Spectroradiometer (MISR) imagery. The difficulties in detecting clouds in the presence of land–water boundaries when using prenavigated imagery is overcome by using a simple two-step direct threshold technique. The two steps involve the thresholding of two observables derived for each pixel. The first is a 0.86-μm reflectance. The second is a new observable, D = | NDVI |bR1−2, where NDVI = (R2 − R1)(R2 + R1)−1, R2 is the 0.86-μm reflectance, R1 is the 0.67-μm reflectance, and b is chosen so as to maximize the separation between clear and cloudy pixels. The success of the cloud mask is shown by applying it to degraded AVIRIS data. The authors make comparisons with a more popular NDVI technique to show the advantage of our method.

Forecasting of Supercooled Clouds

Abstract: Using parameterizations of cloud microphysics, a technique to forecast supercooled cloud events is suggested. This technique can be coupled on the mesoscale with a prognostic equation for cloud water to improve aircraft icing forecasts. The procedure is validated using comparisons with airborne measurements from the Canadian Atlantic Storms Program. As an illustration of the application of this forecast technique, constant-pressure maps showing regions of cloud ice, supercooled cloud water, and cloud liquid water are presented for two particular cases.

A Pixel-Scale Algorithm of Cloud Type, Layer, and Amount for AVHRR Data. Part I: Nighttime

Abstract: An automated pixel-scale algorithm has been developed to retrieve cloud type, related cloud layer(s), and the fractional coverages for all cloud layers in each AVHRR (Advanced Very High Resolution Radiometer) pixel at night. In the algorithm, cloud-contaminated pixels are separated from cloud-free pixels and grouped into three generic cloud types. Cloud layers in each cloud type are obtained through a cloud-type uniformity check, a thermal uniformity check, and a channel 4 ( 11 μm) brightness temperature histogram analysis, within a grid area. The algorithm allows for pixels to be mixed among different cloud layers of different cloud types, as well as between cloud layers and the ocean or land surface. A “neighbor-cheek” method is developed to identify the cloud layers associated with each mixed pixel and to calculate the coverages of each of the cloud layers in the pixel. Digital color images are generated based on information on the location, cloud type, cloud layer, and cloud amount of each in...

A review of theoretical and observational studies in cloud and precipitation physics: 1991–1994

Abstract: It is becoming increasingly evident that clouds and precipitation play a critical role in many of the environmental or so-called “strategic” issues facing our society today, such as the “ozone hole” problem, climate change, ocean-atmospheric interactions and acid rain. Clouds and precipitation also play an important role in many applied problems such as aircraft icing and short-term forecasting. Solutions to these and other problems will require a deeper understanding of how clouds form, evolve and impact their surroundings. Scales associated with cloud formation range from the size of a sub-micron aerosol particle to the scale of a large hurricane. Scientific studies during the last four years have reaffirmed the notion that in order to adequately understand the role of clouds in these strategic and applied problems, fundamental studies on all scales important to cloud formation and evolution are required.

Cloud tracking using satellite data for predicting the probability of heavy rainfall events in the Mediterranean area

Abstract: The use of cloud tracking techniques and storm identification procedures is proposed in this paper with the aim of predicting the evolution of cloud entities associated with the highest rainfall probability within a given meteorological scenario. Suitable algorithms for this kind of analysis are based on the processing of digital images in the thermal infrared (IR) band from geostationary satellites: a selection of such algorithms is described in some detail together with a few real case applications. Three heavy rainfall events have been selected for this purpose with reference to the extreme meteorological situation observed during Fall 1992 and 1993 over the Mediterranean area. A window from 30 to 60 °N and from 20 °W to 30 °E has been identified for the analysis of data from the radiometer on board the ESA Meteosat platform. In conclusion, the suitability of cloud tracking techniques for predicting the probability of heavy rainfall events is discussed provided that the former are associated with proper modeling of small scale rainfall distribution.

Image processing for weather satellite cloud segmentation

Abstract: Image segmentation of weather satellite imagery is an important first step in an automated weather forecasting system. Accurate cloud extraction is also important in the determination of solar radiative transfer in atmospheric research, where satellite observations are used as inputs to global climate models to predict climatic change. Most of the current cloud extraction algorithms tend to be quite complicated to implement and execution time is potentially slow. The authors present a new algorithm which is simple to implement, fast in execution and provides good results in initial tests.

Lidar-Derived Distribution of Cloud Vertical Location and Extent.

Abstract: This paper summarizes the results of a statistical analysis of lidar-determined cloud geometrical properties measured during the 1989 and 1991 campaigns of the Experimental Cloud Lidar Pilot Study. Useful lidar descriptors are introduced to specify the bottom-, top-, and midcloud altitudes. These are used to describe the behavior of cloud vertical location and vertical extent during several months of observations using a dual wavelength (1064 and 532 nm) Nd:YAG lidar at Toronto. Frequency distributions of cloud height and cloud thickness are presented and the relationship of the lidar descriptors to cloud properties are discussed. These data are compared with other information on cloud geometry available in the literature.

Guidelines for Cloud Seeding to Augment Precipitation

Abstract: This manual of professional practice, \IGuidelines for Cloud Seeding to Augment Precipitation\N (ASCE Manual No. 81), is the outgrowth of a committee report by the same title published by the ASCE Weather Modification Committee in the March 1983 issue of \IJournal of Irrigation and Drainage Engineering\N. A task committee of the ASCE Climate and Weather Change Committee has expanded and updated this report to provide water resources managers and others who might become involved in the decision-making process for implementing a cloud seeding project with the necessary guidelines. The sections of this manual cover essentials of weather modification including the social, legal, environmental, and economic aspects as well as the scientific basis. Further, the manager is guided through the professional practice for operational cloud seeding, from materials and devices necessary to produce ice crystal-forming nuclei to the methods for distributing the artificially produced nuclei through cloud masses. In addition, methods of instrumentation that are used to provide input to real-time decisions and evaluate results are discussed. Finally, this manual outlines the steps that must be taken to implement a cloud seeding project. A glossary of terms is appended.

Comparison of Simulated Cloud Cover with Satellite Observations over the Western United States.

Abstract: Satellite imagery datasets and regional climate model results are intercompared for evaluation of model accuracy in the simulation of cloud cover. Both monthly average individual simulation times are analyzed. To provide a consistent comparison, satellite data are first mapped into the model's geographic projection, grid domain, and resolution. It is found that September 1988 monthly average cloud fraction results from the modeled simulations correspond to observations, in both spatial pattern and magnitude, with bias less than +/- 20% cloud fraction over the entire inland West. Agreement in the pattern of cloud fraction also is evident for monthly average cloud fraction in July, but there is no negative bias of 10%-30% cloud fraction in the model diagnosis of cloud cover. Correlations between the spatial distributions of model-derived and observed cloud fractions are found to exceed 0.80 for certain geographic regions of the West, and these correlations are largest over mountainous areas during summer. Case studies of a series of daily cloud cover demonstrate the ability of the model to simulate the effects of frontal passage on cloud distribution. The ability of the RegCM1 to simulate daily cloud fraction and diurnal cloud evolution is somewhat weak for the summer convective season. It is anticipated that a more recent version of the regional climate model may improve the simulation of summer season cloud cover, through changes in cloud parameterization and improvements in model resolution.

Cloud type separation using local correlation between visible and infrared satellite images

Abstract: Radiance fields provided by geostationnary satellites are fundamental for the knowledge of the spatial heterogeneity and life cycle of clouds and cloud systems However, detection and analysis of the cloud cover properties from VIS and/or JR radiance field is not obvious 1,2 and numerous methods, giving sometimes quite different results, have been proposed 3 In the present paper, we introduce a new parameter in the classification scheme we developed before 45 : the slope of the regression line between visible and infrared radiances Moreover, studying the time evolution of cloud classes requires to ensure the classification consistency from one hour to the other A new way of initializing the classification process is proposed and tested on a time series of Meteosat radiance fields taken over North Atlantic and West Europe during the 1989 ICE experiment The 10 day cloud classification built is compared with the Cl climatology cloud cover The time persistence of high clouds is studied and maps of the frequency of occurence of different cloud classes are built from the previous analysis After isolating high cloud cells on the classified images, a description of the shape and radiative properties of these individual cells is undertaken Preliminary results on cloud cell size distribution are presented

Hybrid neural network system for cloud classification from satellite images

Abstract: This paper presents an architecture and preliminary implementation results of a hybrid two-stage neural network system for cloud classification from satellite imagery. The system first performs pixel classification on the image spectral multi-channel data and descriptive data to discover possible areas covered by clouds and cloud contaminated pixel characteristics. Then it investigates the texture of image rectangular kernels composed of classified pixels belonging to classes recorded previously with some expected to represent clouds. The system determines cloud textures, integrates pixel information from within local image areas, and provides the final cloud classification. The method is based on the unsupervised classification approach. The hybrid neural network used consists of classical and modified learning multilayer self-organizing feature maps. The preliminary tests have been made on both artificial and satellite image data. The initial results are satisfactory and promising.