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P. M. Lyster, J. Guo, T. Clune, and J. W. Larson

Abstract

This paper quantifies the computational complexity and parallel scalability of two algorithms for four-dimensional data assimilation (4DDA) at NASA's Global Modeling and Assimilation Office (GMAO). The first, the Goddard Earth Observing System Data Assimilation System (GEOS DAS), uses an atmospheric general circulation model (GCM) and an observation-space-based analysis system, the Physical-Space Statistical Analysis System (PSAS). GEOS DAS is very similar to global meteorological weather forecasting data assimilation systems but is used at NASA for climate research. The second, the Kalman filter, uses a more consistent algorithm to determine the forecast error covariance matrix than does GEOS DAS. For atmospheric assimilation, the gridded dynamical fields typically have more than 106 variables; therefore, the full error covariance matrix may be in excess of a teraword. For the Kalman filter this problem will require petaflop s−1 computing to achieve effective throughput for scientific research.

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Kwo-Sen Kuo, William S. Olson, Benjamin T. Johnson, Mircea Grecu, Lin Tian, Thomas L. Clune, Bruce H. van Aartsen, Andrew J. Heymsfield, Liang Liao, and Robert Meneghini

Abstract

A 3D growth model is used to simulate pristine ice crystals, which are aggregated using a collection algorithm to create larger, multicrystal particles. The simulated crystals and aggregates have mass-versus-size and fractal properties that are consistent with field observations. The growth/collection model is used to generate a large database of snow particles, and the single-scattering properties of each particle are computed using the discrete dipole approximation to account for the nonspherical geometries of the particles. At 13.6 and 35.5 GHz, the bulk radar reflectivities of nonspherical snow particle polydispersions differ from those of more approximate spherical, homogeneous, ice–air particle polydispersions that have the same particle size distributions, although the reflectivities of the nonspherical particles are roughly approximated by polydispersions of spheres of 0.1–0.2 g cm−3 density. At higher microwave frequencies, such as 165.5 GHz, the bulk extinction (and scattering) coefficients of the nonspherical snow polydispersions are comparable to those of low-density spheres, but the asymmetry parameters of the nonspherical particles are substantially less than those of spheres for a broad range of assumed spherical particle densities. Because of differences in the asymmetry of scatter, simulated microwave-scattering depressions using nonspherical particles may well exceed those of spheres for snow layers with the same vertical water path. It may be concluded that, in precipitation remote sensing applications that draw upon input from radar and/or radiometer observations spanning a range of microwave frequencies, nonspherical snow particle models should be used to properly interpret the observations.

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Gerhard Theurich, C. DeLuca, T. Campbell, F. Liu, K. Saint, M. Vertenstein, J. Chen, R. Oehmke, J. Doyle, T. Whitcomb, A. Wallcraft, M. Iredell, T. Black, A. M. Da Silva, T. Clune, R. Ferraro, P. Li, M. Kelley, I. Aleinov, V. Balaji, N. Zadeh, R. Jacob, B. Kirtman, F. Giraldo, D. McCarren, S. Sandgathe, S. Peckham, and R. Dunlap IV

Abstract

The Earth System Prediction Suite (ESPS) is a collection of flagship U.S. weather and climate models and model components that are being instrumented to conform to interoperability conventions, documented to follow metadata standards, and made available either under open-source terms or to credentialed users.

The ESPS represents a culmination of efforts to create a common Earth system model architecture, and the advent of increasingly coordinated model development activities in the United States. ESPS component interfaces are based on the Earth System Modeling Framework (ESMF), community-developed software for building and coupling models, and the National Unified Operational Prediction Capability (NUOPC) Layer, a set of ESMF-based component templates and interoperability conventions. This shared infrastructure simplifies the process of model coupling by guaranteeing that components conform to a set of technical and semantic behaviors. The ESPS encourages distributed, multiagency development of coupled modeling systems; controlled experimentation and testing; and exploration of novel model configurations, such as those motivated by research involving managed and interactive ensembles. ESPS codes include the Navy Global Environmental Model (NAVGEM), the Hybrid Coordinate Ocean Model (HYCOM), and the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS); the NOAA Environmental Modeling System (NEMS) and the Modular Ocean Model (MOM); the Community Earth System Model (CESM); and the NASA ModelE climate model and the Goddard Earth Observing System Model, version 5 (GEOS-5), atmospheric general circulation model.

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