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At least 307 records · Page 17

The observational status of the Beta Cephei stars

The Beta Cephei stars are a group of variables of spectral type B, whose light and radial velocity periods lie between about three and seven hours. An up-to-date summary of the observational data for individual stars considered to be members of the Beta Cephei group is provided. The general properties of Beta Cephei stars as determined from a comparison between theory and observation are considered, taking into account location in the HR diagram and evolutionary state, physical properties, statistics and galactic distribution, and period-luminosity relation and pulsation mode. Recent trends concerning the observation of Beta Cephei stars are examined, giving attention to a search for new variables, ultraviolet observations from spacecraft, and high-resolution studies of profile variations.

Lesh, J. R.↗

Solar cycle variations in IMF intensity

Annual averages of logarithms of hourly interplanetary magnetic field (IMF) intensities, obtained from geocentric spacecraft between November 1963 and December 1977, reveal the following solar cycle variation. For 2-3 years at each solar minimum period, the IMF intensity is depressed by 10-15% relative to its mean value realized during a broad 9-year period centered at solar maximum. No systematic variations occur during this 9-year period. The solar minimum decrease, although small in relation to variations in some other solar wind parameters, is both statistically and physically significant.

King, J. H.↗

Satellite temperature monitoring and prediction system

The paper describes the Florida Satellite Freeze Forecast System (SFFS) in its current state. All data collection options have been demonstrated, and data collected over a three year period have been stored for future analysis. Presently, specific minimum temperature forecasts are issued routinely from November through March. The procedures for issuing these forecast are discussed. The automated data acquisition and processing system is described, and the physical and statistical models employed are examined.

Barnett, U. R.↗

Progress in passive microwave remote sensing - Nonlinear retrieval techniques

A variety of nonlinear retrieval methods have been applied to passive microwave remote sensing problems. These problems can be characterized in part by the degree to which their underlying physics and statistics can be understood and characterized in a simple way. Four examples of varying complexity are considered here; the simplest problem requires only analytic expressions for retrievals, whereas the most complex problem has been handled only with pattern classification techniques. The four examples are: (1) Doppler measurements of winds at 70 to 100 km, (2) retrieval of atmospheric water vapor profiles using the opaque 183-GHz water vapor resonance, (3) retrieval of snow accumulation rate by means of combined theoretical and empirical procedures, and (4) classification of diverse polar terrain by means of pattern recognition techniques.

Staelin, D. H.↗

On the establishment and maintenance of a modern conventional terrestrial reference system

The frame of the Conventional Terrestrial Reference System (CTS) is defined by an adopted set of coordinates, at a fundamental epoxh, of a global network of stations which contribute the vertices of a fundamental polyhedron. A method to estimate this set of coordinates using a combination of modern three dimensional geodetic systems is presented. Once established, the function of the CTS is twofold. The first is to monitor the external (or global) motions of the polyhedron with respect to the frame of a Conventional Inertial Reference System, i.e., those motions common to all stations. The second is to monitor the internal motions (or deformations) of the polyhedron, i.e., those motions that are not common to all stations. Two possible estimators for use in earth deformation analysis are given and their statistical and physical properties are described.

Bock, Y.↗

Are some BL Lac objects artefacts of gravitational lensing?

It is proposed here that a significant fraction of BL Lac objects are optically violently variable quasars whose continuum emission has been greatly amplified, relative to the line emission, by pointlike gravitational lenses in intervening galaxies. Several anomalous physical and statistical properties of BL Lacs can be understood on the basis of this model, which is immediately testable on the basis of absorption line studies and by direct imaging.

Ostriker, J. P.↗

Properties of radar backscatter of forests measured with a multifrequency polarimetric SAR

Fully polarimetric airborne synthetic aperture radar (AIRSAR) data, collected in Germany during the MAC Europe campaign, are calibrated using software packages developed at the Joint Research Center (JRC) in Italy for both L- and C-bands. During the period of the overflight dates, extensive ground truth was collected in order to describe the physical and statistical parameters of the canopy, the understory, and the soil. These parameters are compiled and converted into electromagnetic parameters suitable for input to the new polarimetric three-layer canopy model developed at the Wave Scattering Research Center (WSRC) at the University of Texas at Arlington. Comparisons between the theoretical predictions from the model and the calibrated data are carried out. Initial results reveal that the trend of the average phase difference can be predicted by the model, and that the backscattering ratio *shh/ svv is sensitive to the distribution of the primary branches.

Amar, F.↗

Turbulence near thunderstorm tops

For several years, scientists at San Jose State University, NASA-Ames, and the University of Arizona have carried out cooperative research programs to understand the causes and effects of severe turbulence. The primary sources of data for this work are Digital Flight Data Recorder (DFDR) tapes from airliners that have been involved in turbulence incidents. A significant result of the analysis of these data has been the identification and quantification of the turbulence causes. Turbulence signatures include breaking Kelvin-Helmholtz waves, large amplitude mountain lee waves, turbulence in and around thunderstorms, and maneuvering. The requirements that must be met for a turbulence incident to be included in the NASA study are rather straightforward: (1) severe or greater turbulence must have been reported (usually with passenger injuries) and (2) the flight data tapes must be available. Despite these rather general criteria, and the fact that our cases are drawn from a wide geographical area over the U.S. and the Atlantic Ocean, we have found an interesting bias in our sample. Of 12 cases at cruise altitude, four were definitely associated with thunderstorms and two are suspected thunderstorm cases. The others were due to mountain waves, CAT, high level windshear/maneuvering, or to causes not yet determined. Although our sample is small, these numbers have raised several questions, not the least of which are: How pervasive is the problem of aircraft encounters with severe turbulence in or near thunderstorm tops (TNTT)? Given the available visible and radar evidence of thunderstorms, Why do such incidents occur? Can anything be done to allevaite the problem? This paper outlines some very preliminary efforts to answer these questions. In the following sections, physical and statistical characteristics of TNTT are discussed (Section 2), TNTT causes are summarized (Section 3), current recommendations for TNTT avoidance are reviewed (Section 4), and some suggestions to ameliorate the problem are given (Section 5).

Lester, Peter F.↗

Uncertainty and Intelligence in Computational Stochastic Mechanics

Classical structural reliability assessment techniques are based on precise and crisp (sharp) definitions of failure and non-failure (survival) of a structure in meeting a set of strength, function and serviceability criteria. These definitions are provided in the form of performance functions and limit state equations. Thus, the criteria provide a dichotomous definition of what real physical situations represent, in the form of abrupt change from structural survival to failure. However, based on observing the failure and survival of real structures according to the serviceability and strength criteria, the transition from a survival state to a failure state and from serviceability criteria to strength criteria are continuous and gradual rather than crisp and abrupt. That is, an entire spectrum of damage or failure levels (grades) is observed during the transition to total collapse. In the process, serviceability criteria are gradually violated with monotonically increasing level of violation, and progressively lead into the strength criteria violation. Classical structural reliability methods correctly and adequately include the ambiguity sources of uncertainty (physical randomness, statistical and modeling uncertainty) by varying amounts. However, they are unable to adequately incorporate the presence of a damage spectrum, and do not consider in their mathematical framework any sources of uncertainty of the vagueness type. Vagueness can be attributed to sources of fuzziness, unclearness, indistinctiveness, sharplessness and grayness; whereas ambiguity can be attributed to nonspecificity, one-to-many relations, variety, generality, diversity and divergence. Using the nomenclature of structural reliability, vagueness and ambiguity can be accounted for in the form of realistic delineation of structural damage based on subjective judgment of engineers. For situations that require decisions under uncertainty with cost/benefit objectives, the risk of failure should depend on the underlying level of damage and the uncertainties associated with its definition. A mathematical model for structural reliability assessment that includes both ambiguity and vagueness types of uncertainty was suggested to result in the likelihood of failure over a damage spectrum. The resulting structural reliability estimates properly represent the continuous transition from serviceability to strength limit states over the ultimate time exposure of the structure. In this section, a structural reliability assessment method based on a fuzzy definition of failure is suggested to meet these practical needs. A failure definition can be developed to indicate the relationship between failure level and structural response. In this fuzzy model, a subjective index is introduced to represent all levels of damage (or failure). This index can be interpreted as either a measure of failure level or a measure of a degree of belief in the occurrence of some performance condition (e.g., failure). The index allows expressing the transition state between complete survival and complete failure for some structural response based on subjective evaluation and judgment.

Ayyub, Bilal M.↗

Incorporating Parallel Computing into the Goddard Earth Observing System Data Assimilation System (GEOS DAS)

Atmospheric data assimilation is a method of combining actual observations with model forecasts to produce a more accurate description of the earth system than the observations or forecast alone can provide. The output of data assimilation, sometimes called the analysis, are regular, gridded datasets of observed and unobserved variables. Analysis plays a key role in numerical weather prediction and is becoming increasingly important for climate research. These applications, and the need for timely validation of scientific enhancements to the data assimilation system pose computational demands that are best met by distributed parallel software. The mission of the NASA Data Assimilation Office (DAO) is to provide datasets for climate research and to support NASA satellite and aircraft missions. The system used to create these datasets is the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The core components of the the GEOS DAS are: the GEOS General Circulation Model (GCM), the Physical-space Statistical Analysis System (PSAS), the Observer, the on-line Quality Control (QC) system, the Coupler (which feeds analysis increments back to the GCM), and an I/O package for processing the large amounts of data the system produces (which will be described in another presentation in this session). The discussion will center on the following issues: the computational complexity for the whole GEOS DAS, assessment of the performance of the individual elements of GEOS DAS, and parallelization strategy for some of the components of the system.

Larson, Jay W.↗

I/O Parallelization for the Goddard Earth Observing System Data Assimilation System (GEOS DAS)

The National Aeronautics and Space Administration (NASA) Data Assimilation Office (DAO) at the Goddard Space Flight Center (GSFC) has developed the GEOS DAS, a data assimilation system that provides production support for NASA missions and will support NASA's Earth Observing System (EOS) in the coming years. The GEOS DAS will be used to provide background fields of meteorological quantities to EOS satellite instrument teams for use in their data algorithms as well as providing assimilated data sets for climate studies on decadal time scales. The DAO has been involved in prototyping parallel implementations of the GEOS DAS for a number of years and is now embarking on an effort to convert the production version from shared-memory parallelism to distributed-memory parallelism using the portable Message-Passing Interface (MPI). The GEOS DAS consists of two main components, an atmospheric General Circulation Model (GCM) and a Physical-space Statistical Analysis System (PSAS). The GCM operates on data that are stored on a regular grid while PSAS works with observational data that are scattered irregularly throughout the atmosphere. As a result, the two components have different data decompositions. The GCM is decomposed horizontally as a checkerboard with all vertical levels of each box existing on the same processing element(PE). The dynamical core of the GCM can also operate on a rotated grid, which requires communication-intensive grid transformations during GCM integration. PSAS groups observations on PEs in a more irregular and dynamic fashion.

Lucchesi, Rob↗

Passive Microwave Remote Sensing of Rainfall Considering the Effects of Wind and Nonprecipitating Clouds

It has long been shown both in theory and in observation that emission from rain drops in a raining cloud results in upwelling brightness temperature above that caused by the sea surface alone. High brightness temperatures at microwave frequencies (e.g. 37 and 19 GHz) have usually been quantitatively associated with rainfall using physical or statistical models. By comparing concurrent special sensor microwave/imager and radar data, however, we noticed many cases where there is no appreciable rainfall in a field of view (FOV) which exhibits high brightness temperature (T(sub B)) at 37 and 19 GHz. On the basis of calculations and past literature it is shown that such high brightness temperatures can be caused by nonprecipitating clouds and by wind. The effect of the wind is to create wave and high-emissivity foam on the sea surface. A model is developed to relate T(sub B) to the fractional coverage of rain, f, within a FOV. The parameters of the model are calibrated by fitting the model to the observed brightness temperature and fractional rain coverage data. The critical parameter of the model, T(sub B min.), which is the threshold brightness temperature for the presence of rain, depends on the strength of the storm. The strength of the storm is characterized by the fraction of the FOVs within a large area that have T(sub B) higher than 240 K, which is readily obtainable from satellite data alone. The instantaneous FOV rain rate R can then be obtained through the f approximately R relationship which is empirically derived using radar data. An algorithm has been proposed based on the T(sub B) approximately f and f approximately R relationship. Application of the algorithm to TOGA-COARE and Darwin storms results in reasonable instantaneous FOV rain estimate. When averaged over the entire radar scan, a more accurate and unbiased areal rain estimate can be achieved.

Qihang Li.↗

Impact of a Merged Precipitation Data on Global Soil Moisture Variability

Accurate soil moisture information has proved to be important to climate simulations and climate and weather forecasts. However, many difficulties exist that limit our understanding of soil moisture distribution and variability. One of them is the lack of accurate precipitation with appropriate spatial and temporal resolution. Precipitation as an input forcing to the land surface greatly influences soil moisture characteristics and variability. To improve precipitation data quality, an algorithm has been developed to generate a spatially and temporally continuous 3-hourly global precipitation data for the period of 1987 to present. This precipitation product is a combination of the precipitation from Special Sensor Microwave Imager (SSMI) with the Goddard Earth Observing System-1 Data Assimilation System (GEOS-1 DAS) employing a Physical-space Statistical Analysis System (PSAS). In this study we investigate the impact of this merged/analyzed precipitation data on the global soil moisture variability using an Off-line Land-surface GEOS Assimilation (OLGA) system. Two OLGA integrations starting from 1987 to 1993 are performed forced with the analyzed and GEOS-1 DAS precipitation respectively. We examine the spatial and temporal characteristics of soil moisture variability in response to the analyzed precipitation. The influence of this merged precipitation on the soil moisture variability and regional hydrological budget is estimated throughout the comparison with the results forced with the GEOS-1 DAS precipitation only. In the OLGA the sut@-grid scale horizontal heterogeneity is explicitly represented on the tile space. This provides a means to assess the role of the surface moisture heterogeneity in the interaction with the surface atmosphere and surface hydrological budget, and to validate OLGA results at tile space with in situ observation. ABRACOS (Anglo-Brazilian Amazonian Climate Observation Study), FIFE (First ISLSCP Field Experiment) I and HAPEX data will be used in the validation.

Yang, Runhua↗

Current and Future Plans of the NASA Data Assimilation Office (DAO)

The mission of the Data Assimilation Office (DAO) is to advance the state of the art of data assimilation and produce research-quality assimilated data sets which make optimal use of space-based observations. Development efforts over the last few years have focused on delivering a production data assimilation system in support of NASA's Terra platform. That system, called the Goddard Earth Observing System - version 2 or GEOS-2, represents a major upgrade to the baseline GEOS-1 system employed in NASA's first reanalysis effort. GEOS-2 includes a physical-space three dimensional variational analysis algorithm (the Physical-space Statistical Analysis System or PSAS) and numerous improvements to the general circulation model. The latter include a Soil-Vegetation-Atmosphere Transfer (SVAT) land surface scheme, a level 2.5 moist turbulence scheme and new Short Wave (SW) and Long Wave (LW) radiation code. The system also includes an off-line ozone assimilation system, and the capability to assimilate scatterometer surface winds, and TIROS Operational Vertical Sounder (TOVS) and Special Sensor Microwave Imager (SSM/I) moisture data. GEOS-2 is currently run at 1 degree horizontal resolution and 48 levels extending to O.Olmb. Experimental versions of GEOS-2 are run with a global stretched grid allowing enhanced (e.g. 1/4 deg) regional resolution. Other capabilities being developed include, the assimilation of Tropical Rainfall Measuring Mission (TRMM) precipitation and Global Positioning System (GPS) data, an off-line land surface assimilation system, and a retrospective analysis scheme. The DAO is also engaged in a number of collaborative efforts to help accelerate the development of the next generation data assimilation system. These include, a joint modeling effort between the DAO and NCAR/CGDD to develop a new Global Circulation Model (GCM), and a Department of Energy Lawrence Livermore National Laboratory (DOE/LLNL) collaboration on model parallelization. Plans for the next reanalysis will be discussed in the context of current and near term system quality and computing capabilities, and the need for multiple reanalysis products.

Atlas, Robert↗

A Global Data Assimilation System for Atmospheric Aerosol

We will give an overview of an aerosol data assimilation system which combines advances in remote sensing of atmospheric aerosols, aerosol modeling and data assimilation methodology to produce high spatial and temporal resolution 3D aerosol fields. Initially, the Goddard Aerosol Assimilation System (GAAS) will assimilate TOMS, AVHRR and AERONET observations; later we will include MODIS and MISR. This data assimilation capability will allows us to integrate complementing aerosol observations from these platforms, enabling the development of an assimilated aerosol climatology as well as a global aerosol forecasting system in support of field campaigns. Furthermore, this system provides an interactive retrieval framework for each aerosol observing satellites, in particular TOMS and AVHRR. The Goddard Aerosol Assimilation System (GAAS) takes advantage of recent advances in constituent data assimilation at DAO, including flow dependent parameterizations of error covariances and the proper consideration of model bias. For its prognostic transport model, GAAS will utilize the Goddard Ozone, Chemistry, Aerosol, Radiation and Transport (GOCART) model developed at NASA/GSFC Codes 916 and 910.3. GOCART includes the Lin-Rood flux-form, semi-Langrangian transport model with parameterized aerosol chemistry and physical processes for absorbing (dust and black carbon) and non-absorbing aerosols (sulfate and organic carbon). Observations and model fields are combined using a constituent version of DAO's Physical-space Statistical Analysis System (PSAS), including its adaptive quality control system. In this talk we describe the main components of this assimilation system and present preliminary results obtained by assimilating TOMS data.

daSilva, Arlindo↗

Preliminary Results from an Assimilation of TOMS Aerosol Observations Into the GOCART Model

At NASA Goddard we are developing a global aerosol data assimilation system that combines advances in remote sensing and modeling of atmospheric aerosols. The goal is to provide high resolution, 3-D aerosol distributions to the research community. Our first step is to develop a simple assimilation system for Saharan mineral aerosol. The Goddard Chemistry and Aerosol Radiation model (GOCART) provides accurate 3-D mineral aerosol size distributions that compare well with TOMS satellite observations. Surface, mobilization, wet and dry deposition, convective and long-range transport are all driven by assimilated fields from the Goddard Earth Observing System Data Assimilation System, GEOS-DAS. Our version of GOCART transports sizes from.08-10 microns and only simulates Saharan dust. TOMS radiance observations in the ultra violet provide information on the mineral and carbonaceous aerosol fields. We use two main observables in this study: the TOMS aerosol index (AI) which is directly related to the ratio of the 340 and 380 radiances and the 380 radiance. These are sensitive to the aerosol optical thickness, the single scattering albedo and the height of the aerosol layer. The Goddard Aerosol Assimilation System (GAAS) uses the Data Assimilation Office's Physical-space Statistical Analysis System (PSAS) to combine TOMS observations and GOCART model first guess fields. At this initial phase we only assimilate observations into the the GOCART model over regions of Africa and the Atlantic where mineral aerosols dominant and carbonaceous aerosols are minimal, Our preliminary results during summer show that the assimilation with TOMS data modifies both the aerosol mass loading and the single scattering albedo. Assimilated aerosol fields will be compared with assimilated aerosol fields from GOCART and AERONET observations over Cape Verde.

daSilva, Arlindo↗

The DAO Retrospective Data Assimilation System

The fixed-lag Kalman smoother of Cohn et al. (1994), or a version of a more computationally feasible approximation of it developed by Todling et al. (1998), is under implementation at the Data Assimilation Office (DAO) to build a retrospective data assimilation system (RDAS). The initial version of the RDAS uses the physical-space statistical analysis system and a modification of the incremental analysis update (IAU) procedure of Bloom et al. (1996); a follow up version of the RDAS Nill require the use of the adjoint of the DAO general circulation model (developed by Y. Yang and M Navon). The retrospective procedure is designed to produce improved analyses as well as improved assimilated fields consequently providing an improved climate representation through data assimilation.

Todling, Richardo↗

DAO's Finite-volume/Physical-space Data Assimilation System: Stratospheric Applications

In this talk we describe the next-generation data assimilation system being developed at NASA's Data Assimilation Office (DAO), with emphasis on the applications to stratospheric forecasts and stratospheric constituent transport. This data assimilation system includes the General Circulation Model jointly developed by the DAO and the Climate and Global Dynamics Division (CGDD) at NCAR. This model is based on the finite-volume dynamical core) developed at DAO with physical parameterizations from the NCAR Climate Community Model. The Physical-space Statistical Analysis System (PSAS) is used to combine a first guess from the NASA-NCAR GCM with observational data to provide an updated estimate of the state of the atmosphere. Case studies for the (northern) Winter of 2000 will be discussed.

daSilva, Arlindo↗