4,934 research outputs found
Application of Stochastic Radiative Transfer Theory to the ARM Cloud-Radiative Parameterization Problem
This project had two primary goals: (1) development of stochastic radiative transfer as a parameterization that could be employed in an AGCM environment, and (2) exploration of the stochastic approach as a means for representing shortwave radiative transfer through mixed-phase layer clouds. To achieve these goals, climatology of cloud properties was developed at the ARM CART sites, an analysis of the performance of the stochastic approach was performed, a simple stochastic cloud-radiation parameterization for an AGCM was developed and tested, a statistical description of Arctic mixed phase clouds was developed and the appropriateness of stochastic approach for representing radiative transfer through mixed-phase clouds was assessed. Significant progress has been made in all of these areas and is detailed in the final report
Understanding climate: A strategy for climate modeling and predictability research, 1985-1995
The emphasis of the NASA strategy for climate modeling and predictability research is on the utilization of space technology to understand the processes which control the Earth's climate system and it's sensitivity to natural and man-induced changes and to assess the possibilities for climate prediction on time scales of from about two weeks to several decades. Because the climate is a complex multi-phenomena system, which interacts on a wide range of space and time scales, the diversity of scientific problems addressed requires a hierarchy of models along with the application of modern empirical and statistical techniques which exploit the extensive current and potential future global data sets afforded by space observations. Observing system simulation experiments, exploiting these models and data, will also provide the foundation for the future climate space observing system, e.g., Earth observing system (EOS), 1985; Tropical Rainfall Measuring Mission (TRMM) North, et al. NASA, 1984
Determining the Parameters of Massive Protostellar Clouds via Radiative Transfer Modeling
A one-dimensional method for reconstructing the structure of prestellar and
protostellar clouds is presented. The method is based on radiative transfer
computations and a comparison of theoretical and observed intensity
distributions at both millimeter and infrared wavelengths. The radiative
transfer of dust emission is modeled for specified parameters of the density
distribution, central star, and external background, and the theoretical
distribution of the dust temperature inside the cloud is determined. The
intensity distributions at millimeter and IR wavelengths are computed and
quantitatively compared with observational data. The best-fit model parameters
are determined using a genetic minimization algorithm, which makes it possible
to reveal the ranges of parameter degeneracy as well. The method is illustrated
by modeling the structure of the two infrared dark clouds IRDC-320.27+029 (P2)
and IRDC-321.73+005 (P2). The derived density and temperature distributions can
be used to model the chemical structure and spectral maps in molecular lines.Comment: Accepted for publication in Astronomy Report
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