Abstract
A parallel version of a local grid point model (LM) of the German Weather Service (DWD) has been implemented on the IBM SP computer system of the Potsdam Institute for Climate Impact Research for the computation of various regional climate scenarios. Multi-variate statistical techniques (pattern recognition, cluster analysis) are used to optimize the validation of different climate variables in different climate scenarios. Our visualization approach incorporates both domain and presentation knowledge in the form of different visualization techniques. The need for a standardized tool to configure any system will become more and more important. The first aim of this work was to develop some basic concepts for a Graphical User Interface (GUI) based on a Motif application.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
J. Bacher. Clusteranalyse. Oldenbourg, München, Germany, 1996.
2. A. Dickinson, P. Burton, J. Parker, and R. Baxter. Implementation and initial results from a parallel version of the meteorological office atmosphere prediction model. In G.-R. Hoffmann and N. Kreitz, editors, Coming of Age (Sixth ECMWF Workshop on the Use of Parallel Processors in Meteorology Proceedings), pages 177-194, Singapore, 1995. World Scientific.
G. Doms and U. Schättler. The nonhydrostatic limited-area model LM (lokal modell) of DWD, part I: Scientific documentation. Technical report, DWD, March 1997.
E.W. Forgy. Cluster analysis of multivariate data: Efficiency versus interpretability of classifications. Biometrics,21:768, 1965.
5. I. Foster and J. Michalakes. MPMM: A massively parallel mesoscale model. In G.-R. Hoffmann and T. Kauranne, editors, Parallel Supercomputing in Atmospheric Science (Fifth ECMWF Workshop on the Use of Parallel Processors in Meteorology Proceedings), pages 354-363, Singapore, 1993. World Scientific.
6. F.-W. Gerstengarbe and P.C. Werner. Applying non-hierarchical cluster analysis algorithms to climate classification: Some problems and their solution. In press in Theor. Appl. Climatol., 1999.
I. Jacobsen and E. Heise. A new economic method for the computation of the surface temperature in numerical models. Contr. Atmos. Phys., 55:128-141, 1982.
H. Jahnke. On a graphic consistency conception for cluster analysis procedure, chapter Cluster analysis as a procedure in inferential statistics, page 168. Vandenhoek & Ruprecht, Göttingen, Germany, 1988.
9. T. Kauranne, J. Oinonen, S. Saarinen, O. Serimaa, and J. Hietaniemi. The operational HIRLAM 2 model on parallel computers. In G.-R. Hoffmann and N. Kreitz, editors, Coming of Age (Sixth ECMWF Workshop on the Use of Parallel Processors in Meteorology Proceedings), pages 63-74, Singapore, 1995. World Scientific.
J. B. Klemp and R. B. Wilhelmson. The simulation of three-dimensional convective storm dynamics Journal of the Atmospheric Sciences, 35:1070-1096, 1978.
J.-F. Louis. A parametric model of vertical eddy fluxes in the atmosphere. Bound. Layer Meteor., 17:187-202, 1979.
B. Ritter and J.-F. Geleyn. A comprehensive radiation scheme for numerical weather prediction models with potential applications in climate simulations. Monthly Weather Review, 120:303-325, 1992.
SAS Institute Inc., Cary, NC. SAS/STAT User's Guide: Version 6, fourth edition, 1990.
U. Schättler and E. Krenzien. The parallel "Deutschland-Modell": A message-passing version for distributed memory computers. Parallel Computing, 23:2215-2226, 1997.
W. Skamarock and J. B. Klemp. The stability of time-splitting methods for the hydrostatic and nonhydrostatic elastic systems. Monthly Weather Review, 120:2109-2127, 1992.
16. SPSS Inc., Chicago, IL. SPSS for Windows manual, 1999.
StatSoft, Tulsa, OK. Statistica,Vol. III: Statistics II, 1994.
D. Steinhausen and K. Langer. Clusteranalyse: Einführung in Methoden und Verfahren der automatischen Klassifikation. Walter de Gruyter, Berlin, 1977.
M. Tiedtke. A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Monthly Weather Review, 117:1779-1799, 1989.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kücken, M., Schättler, U., Gerstengarbe, FW., Werner, P. (2000). Simulation and Visualization of Climate Scenarios on a Distributed Memory Platform. In: Engquist, B., Johnsson, L., Hammill, M., Short, F. (eds) Simulation and Visualization on the Grid. Lecture Notes in Computational Science and Engineering, vol 13. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-57313-2_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-57313-2_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67264-7
Online ISBN: 978-3-642-57313-2
eBook Packages: Springer Book Archive