3 edition of High-performance computing and four-dimensional data assimilation found in the catalog.
High-performance computing and four-dimensional data assimilation
1996 by National Aeronautics and Space Administration, National Technical Information Service, distributor in [Washington, DC, Springfield, Va .
Written in English
|Other titles||High performance computing and four dimensional data assimilation.|
|Statement||Miloje S. Kakivic, project leader; principal investigator, Geoffrey C. Fox.|
|Series||[NASA contractor report] -- 206979., NASA contractor report -- NASA CR-206979.|
|Contributions||Fox, Geoffrey C., United States. National Aeronautics and Space Administration.|
|The Physical Object|
Sequential data assimilation framework for hydrologic state-parameter estimation and ensemble forecasting, in Proceedings of the 2nd international CAHMDA workshop on: The TerrestrialWater Cycle: Modelling and Data Assimilation Across Catchment Scales, edited by A.J. Teuling, H. Leijnse, P.A. Troch, J. Sheffield and E.F. Wood, pp. – Full text of "NASA Technical Reports Server (NTRS) CESDIS" See other formats.
Count Robert of Paris
works of Dr. Benjamin Franklin
Corporate plan =
Use of limited site-specific flood information in estimating flood peaks
A bytch named Karma
Diagnostic and therapeutic applications of breast imaging
North American railroads
Trace vectors in matrix analysis.
Get this from a library. High-performance computing and four-dimensional data assimilation: the impact on future and current problems: final report. [Miloje S Makivic; Geoffrey C Fox; United States.
National Aeronautics and Space Administration.]. Abstract. Driven by the emerging requirements of High Performance Computing (HPC) architectures, the main focus of this work is the interplay of computational and energetic aspects of a Four Dimensional Variational (4DVAR) Data Assimilation algorithm, based on Domain Decomposition (named DD-4DVAR).Author: Rossella Arcucci, Davide Basciano, Alessandro Cilardo, Luisa D’Amore, Filippo Mantovani.
•Four Dimensional Data Assimilation (4D DA) requires having information on the state of the system at many different times •In some approaches, information at different times is achieved by running a model forward (Tangent-Linear) and backward (Adjoint) in time •Optimal results with a.
Abstract. The Navy Coastal Ocean Model Four-Dimensional Variational Assimilation (NCOM 4DVAR) system is an analysis software package that is designed to supplement the current capability of the operational analysis/prediction system known as the Relocatable Navy Coupled Ocean Model (Relo NCOM) : Scott Smith, Hans Ngodock, Matthew Carrier, Jay Shriver, Philip Muscarella, Innocent Souopgui.
Four-Dimensional Model Assimilation of Data: High Performance Computing and Communications. As the report's summary states: "The HPCC program is driven by the recognition that unprecedented computational power and capability [are] needed to investigate and understand a wide range of scientific and engineering `grand challenge' problems.
PDF | Driven by the emerging requirements of High Performance Computing (HPC) architectures, the main focus of this work is the interplay of | Find, read and cite all the research you need on.
The STAR Institute with guidance from the Ohio Supercomputer Center is working on porting the Climate Four Dimensional Data Assimilation (CFDDA) technology developed by the National Center for Atmospheric Research to version 3 of the Weather and Research Forecast (WRF) model for use on systems within the Department of Defense High Performance.
A Uniform Memory Model for Distributed Data Objects on Parallel Architectures (V Balaji & R W Numrich) and other papers; Readership: Academics and researchers specializing in high performance computing, especially meteorological scientists, and supercomputer manufacturers.
A variety of high-resolution atmospheric-model applications are running on the high performance computer cluster at Dugway Proving Ground (DPG), with the objective of providing accurate weather information to forecasters, test managers and planners, and decision-support staff at ATEC test ranges.
The foundation of these applications is the 4D Weather (4DWX) forecasting system, which is built. Benefits: This data assimilation system was designed to take advantage of local High Performance Computing (HPC) Numerical Weather Prediction (NWP) and Data Assimilation.
Real-Time Four-Dimensional Data Assimilation (RT-FDDA) > see all products. The method of four dimensional variational data assimilation (4Dvar) is a widely known technique to enhance forecast skills of CTMs (Chemistry-Transport-Models) by ingesting in-situ and.
libROM is a collection of C++ classes that compute reduced order models and hyperreduced order models for systems of ordinary differential equations.
libROM includes parallel, adaptive methods for proper orthogonal decomposition, and parallel, non-adaptive methods for hyperreduction using the discrete empirical interpolation method. Nonoscillatory advection schemes contain switches, so that the derivative of the numerical solution at any time step with respect to that at the previous time step may be discontinuous.
In consequence, sensitivities calculated using the adjoint of the numerical scheme may be discontinuous or by: Fatode is a Fortran library for the integration of ordinary differential equations with direct and adjoint sensitivity analysis capabilities.
The paper describes the capabilities, implementation, code organization, and usage of this package. Fatode implements four families of methods: explicit Runge--Kutta for nonstiff problems, fully implicit Runge--Kutta, singly diagonally implicit Runge Cited by: This paper presents a fully non-Gaussian filter for sequential data assimilation.
The filter is named the “cluster sampling filter”, and works by directly sampling the posterior distribution following a Markov Chain Monte-Carlo (MCMC) approach, while the prior distribution is approximated using a Gaussian Mixture Model (GMM).Specifically, a clustering step is introduced after the forecast.
four dimensional variational (4D-Var) data assimilation, including a study of preconditioning. This new saddle point formulation  of 4D-Var allows parallelization in time dimension. Therefore, it represents a crucial step towards higher computational e ciency, since 4D-Var approaches otherwise require many sequential computations.
Air Pollution Modeling and Its Application XI. Editors: Gryning, Sven-Erik, Schiermeier Emerging Air Quality Modeling Technologies for High Performance Computing and Communication Environments.
Quantitative Evaluation of a Mesoscale Numerical Model Simulation Using Four-Dimensional Data Assimilation of Complex Airflow over the Kanto. Suggested Citation:"6 Status of Data Archives, Access, and Future Directions." National Research Council. Four-Dimensional Model Assimilation of Data: A Strategy for the Earth System Sciences.
Washington, DC: The National Academies Press. doi: / ×. Data assimilation aims at determining as accurately as possible the state of a dynamical system by combining heterogeneous sources of information in an optimal way. Generally speaking, the mathematical methods of data assimilation describe algorithms for forming optimal combinations of observations of a system, a numerical model that describes its evolution, and appropriate prior.
Data Assimilation for Geophysical Fluids. hence the implementation of efficient numerical methods is a challenge for high performance computing. Future developments of coupled models ocean-atmosphere will dramatically increase the need for efficient numerical methods for coupled models.
Nudging is a four-dimensional data assimilation Cited by: Many Task Computing for Real-Time Uncertainty Prediction and Data Assimilation in the Ocean. IEEE Transactions on Parallel and Distributed Systems, Special Issue on Many-Task Computing, I.
Foster, I. Raicu and Y. Zhao (Guest Eds.), 22, doi: /TPDS In this paper, we propose a Four-Dimensional Variational (4D-Var) data assimilation framework for wind energy potential estimation. The framework is defined as follows: we choose a numerical model which can provide forecasts of wind speeds then, an ensemble of model realizations is employed to build control spaces at observation steps via a modified Cholesky : Elias D.
Nino-Ruiz, Juan C. Calabria-Sarmiento, Luis G. Guzman-Reyes, Alvin Henao. Global Modeling and Assimilation Office Mail Code Greenbelt Rd International Conference on Terrestrial Systems Research: Monitoring, Prediction and High Performance Computing, Bonn, Germany D.
B McLaughlin, and D. Entekhabi (), Four-dimensional Data Assimilation and Down-Scaling of Remote Sensing SGP97 Observations for. Ocean data assimilation is increasingly recognized as crucial for the accuracy of the real-time ocean prediction systems. Here, the current status of ocean data assimilation in support of the operational demands of analysis and forecasting is reviewed, focusing on the methods currently adopted in operational prediction systems.
Seventh AMS Symposium on the Joint Center for Satellite Data Assimilation the Fifth Symposium on High Performance Computing for Weather, Water, Multiscale Analysis Schemes for the WRF Three-/Four-Dimensional Ensemble Variational Data Assimilation System (WRFDA 3/4DEnVar): Formulations and Case Results.
Derivative-free optimization Algorithm 1: Gauss–Newton 1 Set x 0 = xb 2 for k = 0,1,2, 3 Compute the departure vectors dbk and dok 4 Linearize G around x k (get G k and GT k) 5 Use a CG method to (approximately) solve the incremental 4D-Var (7) 6 Set x k+1 = x k +δx k 7 end for where M i,0 describes the system transition from time t 0 to time t i.A common method for solving the Cited by: 5.
() Four-Dimensional Data Assimilation: Comparison of Variational and Sequential Algorithms. Quarterly Journal of the Royal Meteorological Society() Critical fields of Josephson-coupled superconducting by: Research Focuses: Numerical weather prediction (NWP); probabilistic weather and model ensembles; four-dimensional data assimilation (e.g., nudging, adjoint methods, variational methods, ensemble Kalman filters, hybrid data assimilation methods); physical parameterizations and coupled models (e.g., boundary layer, land-surface, parameterized.
He got his master and bachelor degrees from Tsinghua University in Beijing, majoring in Fluid Mechanics, Engineering Mechanics, and Environmental Engineering. He earned his Ph.D. at the University of Iowa, with his dissertation of "Four-Dimensional Variational Data Assimilation Using Lidar Data" focusing on atmospheric boundary flow.
These analysis perturbations are added to the analysis obtained from the flow dependent, hybrid four‐dimensional variational data assimilation system (hybrid‐4DVar; Clayton et al., ) as a part of PS40 suite.
Forecast up to days from the new NEPS is routinely generated based on and UTC initial conditions, which include a Author: Ashu Mamgain, Abhijit Sarkar, E. Rajagopal. Eric Kostelich is interested in nonlinear dynamics, uncertainty quantification, and mathematical biology. He is one of the principal developers of a computationally efficient and very accurate method for estimating the initial conditions of numerical weather models from sparse sets of noisy observations, a procedure called data assimilation.
Coupled physical and biochemical data driven simulations of Massachusetts Bay in late summer: real-time and post-cruise data assimilation .pdf). Special issue on "The use of data assimilation in coupled hydrodynamic, ecological and bio-geo-chemical models of the oceans", J.
of Marine Systems, M. Gregoire, P. Brasseur and P.F.J. Lermusiaux. Simulate and predict using numerical atmospheric models, particularly the WRF model system now being developed by a number of organizations. The WRF model can be run in a variety of modes ranging from basic (e.g.
single vertical profiles of temperature, wind and humidity in a horizontally homogeneous domain) to very complex (full physics, terrain and inhomogeneous initial conditions in Cited by: 6.
Communication from the EC6 on ‘High Performance Computing: Europe’s Place in a Global Race’ COM() 45 final ‘The race for leadership in HPC systems is driven both by the need to address. Chemical state estimation for the middle atmosphere by four-dimensional variational data assimilation: System configuration Elbern, H.; Schwinger, J.; Botchorishvili, R.
Journal Article: Classification with Sums of Separable Functions Garcke, Jochen: Conference Paper. NIH: Large-Scale Computing and Visualization for Cardiopulmonary Imaging 3.
NIH: Multiscale Simulation of Gas Flow Distribution in the Human Lung 4. NSF: Data Assimilation of Dual Doppler Lidar Observations of the Urban Boundary Layer 5.
NSF CAREER: A Priori Test of Retrieval of Coherent Structures in the Atmospheric BoundaryFile Size: KB. These developments in data assimilation are likely to benefit from changes in high performance computing, and a possibility is the of operational data,” in Data Assimilation: Making Sense of Observations Multivariate ozone assimilation in four dimensional data assimilation,” in Proceedings of the Soda Workshop Cited by: Oct 9, “Multivariate relationships between the aerosols, moisture and winds in four-dimensional data assimilation for the global monitoring for environment and security”, funded by the European Space Agency (ESA, Plan for European Cooperating States) announces a position in data assimilation coupled to aerosols to begin on 1 January Full text of "NASA Technical Reports Server (NTRS) Center of Excellence in Space Data and Information Sciences" See other formats.
I started the Land Information System (LIS) project inand it continues to be developed at NASA and used for a wide range of applications.
LIS is a high performance land surface modeling and data assimilation system that won the NASA Software of the Year award.
Introduction to the Metacomputing Toolkit. High Performance Computing Tutorial, The National Center for Supercomputing Applications, University of Illinois at Urbana Champaign, April Gregor von Laszewski, Mary L.
Westbrook, Craig Barnes, and Ian Foster. Supercomputing Data Analysis with an Example on the APS CATs.E-mail: [email protected] assimilation is a method that combines observations (e.g. real world data) of a state of a system in particular three-dimensional and four-dimensional variational data assimilation (3D-Var and 4D-Var) conference on high performance computing, networking, storage and analysis (), IEEE ComputerAuthor: Melina A.
Freitag. For the assimilation at UTC 24 Januarythe background field used for the analysis was the analysis of radiosonde and surface data that was constructed for the control experiment. For the next 12 h, a 3-h cycle of model integration and VAS data assimilation was carried by: