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Abstract Computational oceanography is the study of ocean phenomena by numerical simulation, especially dynamical and physical phenomena. Progress in information technology has driven exponential growth in the number of global ocean observations and the fidelity of numerical simulations of the ocean in the past few decades. The growth has been exponentially faster for ocean simulations, however. We argue that this faster growth is shifting the importance of field measurements and numerical simulations for oceanographic research. It is leading to the maturation of computational oceanography as a branch of marine science on par with observational oceanography. One implication is that ultraresolved ocean simulations are only loosely constrained by observations. Another implication is that barriers to analyzing the output of such simulations should be removed. Although some specific limits and challenges exist, many opportunities are identified for the future of computational oceanography. Most important is the prospect of hybrid computational and observational approaches to advance understanding of the ocean. 

The turbulent channel flow database is produced from a direct numerical simulation (DNS) of wall bounded flow with periodic boundary conditions in the longitudinal and transverse directions, and no-slip conditions at the top and bottom walls. In the simulation, the Navier-Stokes equations are solved using a wall {normal, velocity {vorticity formulation. Solutions to the governing equations are provided using a Fourier-Galerkin pseudo-spectral method for the longitudinal and transverse directions and seventh-order Basis-splines (B-splines) collocation method in the wall normal direction. De-aliasing is performed using the 3/2-rule [3]. Temporal integration is performed using a low-storage, third-order Runge-Kutta method. Initially, the flow is driven using a constant volume flux control (imposing a bulk channel mean velocity of U = 1) until stationary conditions are reached. Then the control is changed to a constant applied mean pressure gradient forcing term equivalent to the shear stress resulting from the prior steps. Additional iterations are then performed to further achieve statistical stationarity before outputting fields. 

The data is from a direct numerical simulation on a 10243 periodic grid of the incompressible MHD equations. (See README-MHD linked document for equations and further details.) 

The data is from a direct numerical simulation of forced isotropic turbulence on a 10243 periodic grid, using a pseudo-spectral parallel code. Time integration of the viscous term is done analytically using integrating factor. The other terms are integrated using a second-order Adams-Bashforth scheme and the nonlinear term is written in vorticity form1. The simulation is de-aliased using phase-shift and a 2√2 /3 truncation2,3. Energy is injected by keeping constant the total energy in modes such that their wave-number magnitude is less or equal to 2. After the simulation has reached a statistical stationary state, 5028 frames of data, which includes the 3 components of the velocity vector and the pressure, are generated and ingested into the database. The duration of the stored data is about five large-eddy turnover times.