OMUSE: Oceanographic multipurpose software environment

This talk will give a brief introduction to OMUSE, the Oceanographic Multipurpose Software Environment, which is currently being developed. OMUSE is a Python framework that provides high-level object-oriented interfaces to existing or newly developed numerical ocean simulation codes, simplifying their use and development In this way, OMUSE facilitates the efficient design of numerical experiments that combine ocean models representing different physics or spanning different ranges of physical scales, for example coupling a global open ocean simulation with a regional coastal ocean model. OMUSE enables its users to write high-level Python scripts that describe simulations. The functionality provided by OMUSE takes care of the low-level integration with the code and deploying simulations on high-performance computing resources, allowing its users to focus on the physics of the simulation. We give an overview of the design of OMUSE and the modules and model components currently included. In particular, we will discuss the process of creating a new OMUSE interface to an existing code, and explain how OMUSE keeps track of the internal state of a running simulation. In addition, we will discuss the grid data types and grid remapping functionality that OMUSE provides. We also give an example of performing online data analysis on a running simulation, which is becoming increasingly important as models simulate a broader range of scales, generating large datasets that cannot be fully stored for offline analysis.

[1]  F. Inti Pelupessy,et al.  Multi-physics simulations using a hierarchical interchangeable software interface , 2011, Comput. Phys. Commun..

[2]  Philip W. Jones First- and Second-Order Conservative Remapping Schemes for Grids in Spherical Coordinates , 1999 .

[3]  M. Zijlema Computation of wind-wave spectra in coastal waters with SWAN on unstructured grids , 2010 .

[4]  Scott D. Peckham,et al.  A component-based approach to integrated modeling in the geosciences: The design of CSDMS , 2013, Comput. Geosci..

[5]  S. Valcke,et al.  The OASIS3 coupler: a European climate modelling community software , 2012 .

[6]  N. Drost,et al.  The Astrophysical Multipurpose Software Environment , 2013, 1307.3016.

[7]  Frank O. Bryan,et al.  Boundary impulse response functions in a century-long eddying global ocean simulation , 2010 .

[8]  J. B. Gregersen,et al.  OpenMI: Open modelling interface , 2007 .

[9]  Jason Maassen,et al.  High-Performance Distributed Multi-Model / Multi-Kernel Simulations: A Case-Study in Jungle Computing , 2012, 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum.

[10]  Cecelia DeLuca,et al.  The architecture of the Earth System Modeling Framework , 2003, Computing in Science & Engineering.

[11]  Jacopo Urbani,et al.  Jungle Computing: Distributed Supercomputing Beyond Clusters, Grids, and Clouds , 2011 .

[12]  J. McWilliams,et al.  A New Sea Surface Height–Based Code for Oceanic Mesoscale Eddy Tracking , 2014 .

[13]  Carsten Eden,et al.  Towards the impact of eddies on the response of the Southern Ocean to climate change , 2010 .

[14]  Henk A. Dijkstra,et al.  Critical Transition Analysis of the Deterministic Wind-Driven Ocean Circulation - A Flux-Based Network Approach , 2013, Int. J. Bifurc. Chaos.

[15]  Jason Maassen,et al.  A distributed computing approach to improve the performance of the Parallel Ocean Program (v2.1) , 2013 .

[16]  Andrew T. Wittenberg,et al.  Impacts on Ocean Heat from Transient Mesoscale Eddies in a Hierarchy of Climate Models , 2015 .

[17]  Henk A. Dijkstra,et al.  A New Regime of the Agulhas Current Retroflection: Turbulent Choking of Indian-Atlantic leakage , 2012 .

[18]  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..