Michael F. Wehner

Mathematical comparison of combat computer models to exercise data

The powerful techniques of modern nonlinear statistical mechanics are used to compare battalion scale combat computer models (including simulations and wargames) to exercise data. This is necessary if large-scale combat computer models are to be extrapolated with confidence to develop battle-management, C^3 and procurement decision-aids, and to improve training. This modeling approach to battalion level missions is amenable to reasonable algebraic and@?or heuristic approximations to drive higher-echelon computer models. Each data set is fit to several candidate short-time probability distributions, using methods of very fast simulated re-annealing with a Lagrangian (time-dependent algebraic cost-function) derived from nonlinear stochastic rate equations. These candidate mathematical models are further tested by using path-integral numerical techniques we have developed to calculate long-time probability distribution spanning the combat scenario. We have demonstrated proofs of principle, that battalion level combat exercises can be well represented by the computer simulation JANUS(T), and that modern methods of nonlinear nonequilibrium statistical mechanics can well model these systems. Since only relatively simple drifts and diffusions were required, in larger systems, e.g., at brigade and division levels, it might be possible to adsorb other important variables (C^3, human factors, logistics, etc.) into more nonlinear mathematical forms. Otherwise, this battalion level model should be supplemented with a tree of branches corresponding to estimated values of these variables.

Application of statistical mechanics methodology to term-structure bond-pricing models

Recent work in statistical mechanics has developed new analytical and numerical techniques to solve coupled stochastic equations. This paper applies the very fast simulated re-annealing and path-integral methodologies to the estimation of the Brennan and Schwartz two-factor term structure model. It is shown that these methodologies can be utilized to estimate more complicated n-factor nonlinear models.

Performance of a distributed memory finite difference atmospheric general circulation model

Abstract A new version of the UCLA atmospheric general circulation model suitable for massively parallel computer architectures has been developed. This paper presents the principles for the codes design and examines performance on a variety of distributed memory computers. A two dimensional domain decomposition strategy is used to achieve parallelism and is implemented by message passing. This parallel algorithm is shown to scale favorably as the number of processors is increased. In the fastest configuration, performance roughly equivalent to that of multitasking vector supercomputers is achieved.

Toward a high performance distributed memory climate model

As part of a long range plan to develop a comprehensive climate systems modeling capability, the authors have taken the atmospheric general circulation model originally developed by Arakawa and collaborators at UCLA and have recast it in a portable, parallel form. The code uses an explicit time-advance procedure on a staggered three-dimensional Eulerian mesh. They have implemented a two-dimensional latitude/longitude domain decomposition message passing strategy. Both dynamic memory management and interprocess communication are handled with macro constructs that are preprocessed prior to compilation. The code can be moved about a variety of platforms, including massively parallel processors, workstation clusters, and vector processors, with a mere change of three parameters. Performance on the various platforms as well as issues associated with coupling different models for major components of the climate system are discussed.<<ETX>>

CLIMATE SYSTEM MODELING USING A DOMAIN AND TASK DECOMPOSITION MESSAGE-PASSING APPROACH

Abstract We have developed a Climate System Modeling Framework (CSMF) for high-performance computing systems, designed to schedule and couple multiple physics simulation packages in a flexible and transportable manner. Some of the major packages in the CSMF include models of atmospheric and oceanic circulation and chemistry, land surface and sea ice processes, and trace gas biogeochemistry. Parallelism is achieved through both domain decomposition and process-level concurrency, with data transfer and synchronization accomplished through message-passing. Both machine transportability and architecture-dependent optimization are handled through libraries and conditional compile directives. Preliminary experiments with the CSMF have been executed on a number of high-performance platforms, including the Intel Paragon, the TMC CM-5 and the Meiko CS-2, and we are in the very early stages of optimization. Progress to date is presented.

Efficient filtering techniques for finite-difference atmospheric general circulation models on parallel processors

Abstract Finite-difference-based atmospheric general circulation models using latitude/longitude meshes typically invoke filtering to suppress numerical instabilities occurring near the poles. We present a more efficient methodology for performing this filtering on parallel processors. This involves redistribution of the computational workload among all available processing elements, along with either the invocation of fast Fourier transforms to replace the slower numerical convolutions, or the smoothing of the transfer functions to enable use of fewer terms in the convolution sum. Results are presented on contemporary parallel architectures such as the Cray-T3D.

The influence of a Soil–Vegetation–Atmosphere Transfer scheme on the simulated climate of LLNL/UCLA AGCM

Abstract The sensitivity of the Lawrence Livermore National Laboratory (LLNL)/University of California, Los Angeles (UCLA) Atmospheric General Circulation Model (AGCM) to coupling with a Soil–Vegetation–Atmosphere Transfer (SVAT) scheme is investigated in this paper. The SVAT leads to general warming of the climate over continents in association with enhanced sensible heat fluxes. The wetness bias of AGCM is reduced drastically with reductions in latent heat fluxes and precipitation. There is reduction in cloudiness when the SVAT is coupled which results in increased solar radiation at the surface. Upward long wave radiation increases in association with increases in land surface temperature. Although the coupling of SVAT improves surface fluxes of heat and water vapor, these changes are mainly offset by changes in solar and infrared radiative fluxes. Global mean energy balance at the surface and the top of atmosphere is improved marginally.

High-performance climate system modeling using a domain and task decomposition message-passing approach

We have developed a climate system modeling framework (CSMF) for high-performance systems, designed to schedule and couple multiple physics simulation packages in a flexible and transportable manner. Some of the major packages in the CSMF include models of atmospheric and oceanic circulation and chemistry, land surface and sea ice processes, and trace gas biogeochemistry. Parallelism is achieved through both domain decomposition and process-level concurrency, with data transfer and synchronization accomplished through message-passing. Both machine transportability and architecture-dependent optimization are handled through libraries and conditional compile directives. Preliminary experiments with the CSMF have been executed on a number of high-performance platforms, including the Intel Paragon, the TMC CM-5 and the Meiko CS-2, and we are in the very early stages of optimization. Progress to date (1994) is presented.<<ETX>>