About SURF

The SURF platform enables you to easily and quickly create your own high‐resolution limited area, nested ocean models.

This is the repository of an the Relocatable Ocean Modeling platform knwn as SURF, which enables the nesting of NEMO and SHYFEM codes into larger-scale ocean circulation models. SURF is distributed embedded in a Virtual Machine Environment in which the hydrodynamic model and several pre- and post-processing tools will be connected to the required inputs fields and the SURF numerical outputs. SURF has developed pre-processing for bathymetry, initial and lateral boundary conditions and atmospheric forcing, and it has a specific namelist interface to NEMO and/or SHYFEM namelist parameters. The aim is that this namelist will be part of a web-based interface, which is currently under construction.

SURF provides a numerical platform for the forecasting of hydrodynamic and thermodynamic fields at high spatial and temporal resolution. It is designed to be embedded in any region of a larger scale ocean prediction system at a coarser resolution, and includes multiple nesting capabilities. For each nesting, the coarser parent grid model fields provide the initial and lateral boundary conditions for the SURF child components. The sequential steps executed within the SURF-NEMO numerical platform can be grouped as follows:

  • Initialization: the user defines the simulation configuration parameters for the ocean model in the configuration file (horizontal and vertical grids, subgrid scale parameterizations, etc).
  • Access to the input datasets: the user specifies the location of the input data in the configuration file. The input data are the bathymetry, the coastline, the atmospheric forcing and the coarser resolution parent ocean model data.
  • Grid generation: this is an automated step, in which the horizontal and vertical grids for the child model are generated.
  • Input data regridding: this is an automated step, which generates the bottom topography, surface forcing and the initial and open lateral boundary conditions datasets on the child grid.
  • Forecast: another automated step, which produces the final outputs.
  • Post-processing: in this step the visualization and analysis procedures of the child model forecast are considered, and can be activated after the run execution (i.e. comparing parent/child fields, the simulation results with in-situ or satellite datasets, and converting the datasets).
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