This article describes the Earth System Grid Federation (ESGF) mission and an international integration strategy for data, database and computational architecture, and stable infrastructure highlighted by the authors (the ESGF Executive Committee). These highlights are key developments needed over the next five to seven years in response to large-scale national and international climate community projects that depend on ESGF for success. Quality assurance and baseline performance from laptop to high performance computing characterizes available and potential data streams and strategies. These are required for interactive data collections to remedy gaps in handling enormous international federated climate data archives. Appropriate cyber security ensures protection of data according to projects but still allows access and portability to different ESGF and individual groups and users. A timeline and plan for forecasting interoperable tools takes ESGF from a federated database archive to a robust virtual laboratory and concludes the article.

The wealth of data to be collected from future Earth Observing systems is only the beginning of the process of being able to predict what will happen to our environment in response to natural and human induced changes. The models employed today will evolve to couple detailed processes in the solid earth, land surface, biosphere, atmosphere and oceans at orders of magnitude higher resolution into prediction systems that can be validated against these observations. These systems will stress the technology requirements for data movement, access, ingestion, computing throughput, and model construction. The Japanese Earth Simulator is the most recent advance in the technology that will support a whole Earth modelling capability, but is only the first step. Future demands will require 5 orders of magnitude improvement in computing technology over that of the Earth Simulator. Model complexity will demand software technologies that do not exist today for computing technology requirements - both hardware and software - that result from such prediction systems.

This paper summarizes the observational requirements for a future Earth System Observational and Modelling capability, in terms of the observed variables, the needed precision, and the spatial-temporal resolution. Architectural approaches are discussed, including an open-systems, evolutionary, sensor-Web approach.

This presentation discusses the NASA data assimilation project at the Data Assimilation Office at the NASA/Goddard Space Flight Center. The goal is to produce accurate gridded datasets of atmospheric fields by assimilating a range of observations along with physically consistent model forecasts. This work produces datasets that are used by the climate research community. The data come from conventional sources that are used for weather forecasts (e.g., radiosondes, earth-surface measurements, and satellite temperature retrievals), as well as new sources such as satellites that will be launched under the Mission To Planet Earth Enterprise. An end-to-end Goddard Earth Observing System (GEOS) Data Assimilation System (DAS) currently supports stratospheric flight missions and reanalysis projects for NASA. The current Core of this system (Model, and Analysis) is a multitasking algorithm that runs on Cray J90 and C90 computers at Goddard and NASA Ames Research Center. Future Core computing will be carried out at Ames, with a new production system scheduled to be ready for the EOS AM-1 satellite launch in June of 1998. The DAO has acquired SGI Origin 2000 computers, with an aggregate of 160 processors in place at Ames, and more planned for the future. The DAO is currently updating the control scripts and programs, and implementing a modular Fortran 90 Core system. During 1998 the Core system will be migrated to distributed-memory software using the Message Passing Interface. Part of this work is being carried out under the NASA High Performance Computing and Communications Earth and Space Sciences program. The algorithmic and performance issues involved in Core system are the main subject of this presentation.

Parallel computation for two-dimensional convective flows in cavities with adiabatic horizontal boundaries and driven by differential heating of the two vertical end walls are investigated using the Intel Paragon, Intel Touchstone Delta, Cray T3D and IBM SP2. The numerical scheme, including a parallel multigrid solver, and domain decomposition techniques for parallel computing are discussed in detail. Performance comparisons are made for the different parallel systems, and numerical results using various numbers of processors are discussed. © 1997 John Wiley & Sons, Ltd.

We have designed and implemented a set of highly efficient and highly scalable algorithms for an unstructured computational package, the PSAS data assimilation package, as demonstrated by detailed performance analysis of systematic runs on up to 512-nodes of an Intel Paragon. The preconditioned Conjugate Gradient solver achieves a sustained 18 Gflops performance. Consequently, we achieve an unprecedented 100-fold reduction in time to solution on the Intel Paragon over a single head of a Cray C90. This not only exceeds the daily performance requirement of the Data Assimilation Office at NASA's Goddard Space Flight Center, but also makes it possible to explore much larger and challenging data assimilation problems which are unthinkable on a traditional computer platform such as the Cray C90.

A parallel time-dependent incompressible flow solver and a parallel multigrid elliptic kernel are described. The flow solver is based on a second-order projection method applied to a staggered finite-difference grid. The multigrid algorithms implemented in the elliptic kernel, which is needed by the flow solver, are V-cycle and full V-cycle schemes. A grid-partition strategy is used in the parallel implementations of both the flow solver and the multigrid elliptic kernel on all fine and coarse grids. Numerical experiments and parallel performance tests show the parallel solver package is numerically stable, physically robust and computationally efficient. Both the multigrid elliptic kernel and the flow solver scale very well to a large number of processors on the Intel Paragon and the Cray T3D for computations with moderate granularity. The solver package has been carefully designed and coded so that it can be easily adapted to solving a variety of interesting two and three-dimensional flow problems. The solver package is portable to parallel systems that support MPI, PVM and Intel NX for interprocessor communications.