A framework for detailed multiphase cloud modeling on HPC systems

Cloud processes are appreciated to be of increasing importance to the comprehension of the atmosphere. Therefore, weather models have recently been extended by detailed spectral descriptions of cloud processes. However, the high computational costs hinder their practical application. This paper introduces the novel framework FD4 (Four-Dimensional Distributed Dynamic Data structures), which is developed to parallelize and couple cloud models to atmospheric models in an efficient way and to enable a higher scalability on HPC systems. Results of first tests with the regional forecast model COSMO are presented.

[1]  Samuel Buis,et al.  PALM: a computational framework for assembling high‐performance computing applications , 2006, Concurr. Comput. Pract. Exp..

[2]  C. Kottmeier,et al.  The convective and orographically-induced precipitation study: A research and development project of the world weather research program , 2008 .

[3]  Richard D. Hornung,et al.  Parallel Adaptive Mesh Refinement , 2006, Parallel Processing for Scientific Computing.

[4]  Spyros N. Pandis,et al.  Size‐resolved aqueous‐phase atmospheric chemistry in a three‐dimensional chemical transport model , 2003 .

[5]  Jimy Dudhia,et al.  Spectral (Bin) microphysics coupled with a Mesoscale Model (MM5). Part I: Model description and first results , 2005 .

[6]  Jay Walter Larson,et al.  The Model Coupling Toolkit: A New Fortran90 Toolkit for Building Multiphysics Parallel Coupled Models , 2005, Int. J. High Perform. Comput. Appl..

[7]  Matthias S. Müller,et al.  The Vampir Performance Analysis Tool-Set , 2008, Parallel Tools Workshop.

[8]  M. Simmel,et al.  Condensation and activation in sectional cloud microphysical models , 2006 .

[9]  Jimy Dudhia,et al.  The Weather Research and Forecast Model: software architecture and performance [presentation] , 2005 .

[10]  A. Caboussat Archives of Computational Methods in Engineering Numerical Simulation of Two-phase Free Surface Flows , 2022 .

[11]  V. Grützun,et al.  Simulation of the influence of aerosol particle characteristics on clouds and precipitation with LM-SPECS: Model description and first results , 2008 .

[12]  John Shalf,et al.  Scalability challenges for massively parallel AMR applications , 2009, 2009 IEEE International Symposium on Parallel & Distributed Processing.

[13]  Georg Stadler,et al.  Scalable adaptive mantle convection simulation on petascale supercomputers , 2008, HiPC 2008.

[14]  Karen Dragon Devine,et al.  Partitioning and Dynamic Load Balancing for the Numerical Solution of Partial Differential Equations , 2006 .

[15]  Mark Lawrence,et al.  The atmospheric chemistry general circulation model ECHAM5/MESSy1: consistent simulation of ozone from the surface to the mesosphere , 2006 .

[16]  Alexander Khain,et al.  Effects of aerosols on precipitation from orographic clouds , 2007 .

[17]  S. Solomon The Physical Science Basis : Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change , 2007 .

[18]  George Karypis,et al.  Parmetis parallel graph partitioning and sparse matrix ordering library , 1997 .

[19]  Oswald Knoth,et al.  Non-dissipative cloud transport in Eulerian grid models by the volume-of-fluid (VOF) method , 2005 .