Visualization of High Resolution Ocean Model Fields Peter Braccio (MBARI/NPS) Julie McClean (NPS) Joint NPS/NAVOCEANO Scientific Visualization Workshop.

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Visualization of High Resolution Ocean Model Fields Peter Braccio (MBARI/NPS) Julie McClean (NPS) Joint NPS/NAVOCEANO Scientific Visualization Workshop June 2000

Collaborators and Acknowledgements Mathew Maltrud (LANL) Bert Semtner (NPS) Jimmy Pelton: M.S. thesis (NPS) Pierre-Marie Poulain (NPS) Funding: ONR, NSF, DOE/CCPP Computer Resources: ARL, ARSC, LANL Data: AOML, JEDA,WOCE DACs

So... You’ve made spent years developing your computer simulation. You’ve got your funding. You’ve been allocated run time and disk space at a Super Computing Center. You’ve created your input forcing fields. You’ve run your code and happily used hours of computer time and terabytes of disk space.

Now, what do you do with all this output?

Analyze and Visualize

As computer simulations have increased in size and complexity, so have the associated problems associated with analysis and visualization.

State of the Art computer simulations of the world ocean are running at resolutions on the order of 1000x1000x40. Ocean models based on grid dimensions of 3600x2400x40 are already in the planning stages and will be in production shortly (July 2000).

With the increases in computing power and the decrease in the price of storage there shouldn’t be any problems in visualizing these models. However, if that was the case, we wouldn’t be here today.

0.1º, 40-level North Atlantic POP Model Description Multilevel PE model; Implicit free-surface Initialized from 15 yr run with ECMWF forcing [Smith et al., 2000]. Forced with daily NOGAPS wind stresses (93-97) and Barnier seasonal surface heat fluxes. KPP mixed layer Mercator grid: 11 3 boundary. Topography: ETOPO5 Surface salinity restored to Levitus. POP release June 1999 Source code: Web site: accio/pop_eval

POP Time-Averaged Variable Options

North Atlantic 0.1° POP Output Saved as 3- Daily Averages (.8 GB per file) Sea surface height 3D solutions of zonal and meridional velocities 3D solution of potential temperature 3D solution of salinity.

North Atlantic 0.1° POP Output Saved as Daily Snapshots (76 MB per file)

Sea Surface Temperature over the whole model domain

What is the size of the “problem”? Grid size 992x1280x D and 1 - 2D fields are recorded every 3 days This leads to a.8 Gb file every 3 model days -> 96 Gb of output per model year -> ~ 400 Gb file (formatted for visualization) -> ~ 2 Tb of output for a 5 year simulation

Computer systems, storage devices, and software are not in place (for the average research group) to visualize this amount of information.

Even though we are simulating a 3 dimensional environment, because of the lack of resources we usually end visualizing in only 2 dimensions. Using data mining techniques, we then extract the variables that we deem interesting for study out of the whole “data” set.

Simulation of Sea Surface Height Anomaly (cm) for years ; mean is removed for this period. Note the rich eddy field and well-resolved frontal structures, especially in the Gulf Stream and the equatorial currents.

North Atlantic 0.1° POP Output Saved as Daily Snapshots (76 MB per file)

Smith et al., 1999

Navy Global Prediction Vision A high-resolution global coupled air/ocean/ice model that assimilates data, providing initial conditions for forecasts. Very-high resolution regional coupled models nested into the global system at key locations.

The Future - Global 0.1º,40-level POP Initialize using MODAS annual T and S. Spin-up for 2 decades; force with a climatology constructed from ECMWF reanalysis ( ) or NCEP. Run for 5 years with realistic NOGAPS forcing. To be run on IBM SP P3 at NAVO

Global Grid Size 3600x2400x40

Development and Plans for Other Components of Global Coupled Predictive System Atmosphere: NOGAPS (NRL, FNMOC) Coupler: NRL Data Assimilation: MVOI or adjoint (NRL, FNMOC, NPS, OSU) Ice: PIPS (NRL, NPS)

Some thoughts on 3D visualization While system resources often preclude visualizing in three dimensions, there are other problems associated with this category of visualization.

Only see the part of the phenomena that is closest to you. The depth exaggeration (due to vastly different scales in the horizontal and vertical) is greatly pronounced Choices made when constructing the visualization have greater impact on it than when visualizing in 2D (due to the added factors of observer location and orientation) Some variables (i.e. subsurface trajectories) have to be run in the model in order to produce meaningful results.

Conclusions All research groups should invest in immersion visualization technologies (i.e.. Caves) to display real-time 3D simulations. Barring that, 2D slices of 3D fields are the way to go. Possible solutions are on the horizon, if we can leverage them.