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Predefined Kernels#

parcels.application_kernels.advection module#

Collection of pre-built advection kernels.

parcels.application_kernels.advection.AdvectionAnalytical(particle, fieldset, time)[source]#

Advection of particles using ‘analytical advection’ integration.

Based on Ariane/TRACMASS algorithm, as detailed in e.g. Doos et al (https://doi.org/10.5194/gmd-10-1733-2017). Note that the time-dependent scheme is currently implemented with ‘intermediate timesteps’ (default 10 per model timestep) and not yet with the full analytical time integration.

parcels.application_kernels.advection.AdvectionEE(particle, fieldset, time)[source]#

Advection of particles using Explicit Euler (aka Euler Forward) integration.

Function needs to be converted to Kernel object before execution.

parcels.application_kernels.advection.AdvectionRK4(particle, fieldset, time)[source]#

Advection of particles using fourth-order Runge-Kutta integration.

Function needs to be converted to Kernel object before execution.

parcels.application_kernels.advection.AdvectionRK45(particle, fieldset, time)[source]#

Advection of particles using adaptive Runge-Kutta 4/5 integration.

Times-step dt is halved if error is larger than tolerance, and doubled if error is smaller than 1/10th of tolerance, with tolerance set to 1e-5 * dt by default.

Note that this kernel requires a Particle Class that has an extra Variable ‘next_dt’ and a FieldSet with constants ‘RK45_tol’ (in meters), ‘RK45_min_dt’ (in seconds) and ‘RK45_max_dt’ (in seconds).

parcels.application_kernels.advection.AdvectionRK4_3D(particle, fieldset, time)[source]#

Advection of particles using fourth-order Runge-Kutta integration including vertical velocity.

Function needs to be converted to Kernel object before execution.

parcels.application_kernels.advectiondiffusion module#

Collection of pre-built advection-diffusion kernels.

See this tutorial for a detailed explanation.

parcels.application_kernels.advectiondiffusion.AdvectionDiffusionEM(particle, fieldset, time)[source]#

Kernel for 2D advection-diffusion, solved using the Euler-Maruyama scheme (EM).

Assumes that fieldset has fields Kh_zonal and Kh_meridional and variable fieldset.dres, setting the resolution for the central difference gradient approximation. This should be (of the order of) the local gridsize.

The Euler-Maruyama scheme is of strong order 0.5 and weak order 1.

The Wiener increment dW is normally distributed with zero mean and a standard deviation of sqrt(dt).

parcels.application_kernels.advectiondiffusion.AdvectionDiffusionM1(particle, fieldset, time)[source]#

Kernel for 2D advection-diffusion, solved using the Milstein scheme at first order (M1).

Assumes that fieldset has fields Kh_zonal and Kh_meridional and variable fieldset.dres, setting the resolution for the central difference gradient approximation. This should be (of the order of) the local gridsize.

This Milstein scheme is of strong and weak order 1, which is higher than the Euler-Maruyama scheme. It experiences less spurious diffusivity by including extra correction terms that are computationally cheap.

The Wiener increment dW is normally distributed with zero mean and a standard deviation of sqrt(dt).

parcels.application_kernels.advectiondiffusion.DiffusionUniformKh(particle, fieldset, time)[source]#

Kernel for simple 2D diffusion where diffusivity (Kh) is assumed uniform.

Assumes that fieldset has constant fields Kh_zonal and Kh_meridional. These can be added via e.g. fieldset.add_constant_field(“Kh_zonal”, kh_zonal, mesh=mesh) or fieldset.add_constant_field(“Kh_meridional”, kh_meridional, mesh=mesh) where mesh is either ‘flat’ or ‘spherical’

This kernel assumes diffusivity gradients are zero and is therefore more efficient. Since the perturbation due to diffusion is in this case isotropic independent, this kernel contains no advection and can be used in combination with a separate advection kernel.

The Wiener increment dW is normally distributed with zero mean and a standard deviation of sqrt(dt).

parcels.application_kernels.EOSseawaterproperties module#

Collection of pre-built eos sea water property kernels.

parcels.application_kernels.EOSseawaterproperties.AdiabticTemperatureGradient(particle, fieldset, time)[source]#

Calculates adiabatic temperature gradient as per UNESCO 1983 routines.

Parameters

  • particle –

    The particle object with the following attributes:
    • Sarray_like

      Salinity in psu (PSS-78).

    • Tarray_like

      Temperature in ℃ (ITS-90).

    • pressurearray_like

      Pressure in db.

  • fieldset – The fieldset object

Returns

Adiabatic temperature gradient in ℃ db⁻¹.

Return type

array_like

References

  1. Fofonoff, P. and Millard, R.C. Jr UNESCO 1983. Algorithms for computation of fundamental properties of seawater. UNESCO Tech. Pap. in Mar. Sci., No. 44, 53 pp. http://unesdoc.unesco.org/images/0005/000598/059832eb.pdf

  2. Bryden, H. 1973. New Polynomials for thermal expansion, adiabatic temperature gradient and potential temperature of sea water. Deep-Sea Res. Vol20,401-408. doi:10.1016/0011-7471(73)90063-6

parcels.application_kernels.EOSseawaterproperties.PressureFromLatDepth(particle, fieldset, time)[source]#

Calculates pressure in dbars from depth in meters and latitude.

Returns

p – pressure [db]

Return type

array_like

References

  1. Saunders, Peter M., 1981: Practical Conversion of Pressure to Depth. J. Phys. Oceanogr., 11, 573-574. doi: 10.1175/1520-0485(1981)011<0573:PCOPTD>2.0.CO;2

parcels.application_kernels.EOSseawaterproperties.PtempFromTemp(particle, fieldset, time)[source]#

Calculates potential temperature as per UNESCO 1983 report.

Parameters

  • particle –

    The particle object with the following attributes:
    • Sarray_like

      Salinity in psu (PSS-78).

    • Tarray_like

      Temperature in ℃ (ITS-90).

    • pressurearray_like

      Pressure in db.

  • fieldset –

    The fieldset object with the following attributes:
    • refpressurearray_like, optional

      Reference pressure in db (default is 0).

  • time (float) – Simulation time (not used in this function but required for consistency with other kernels).

Returns

Potential temperature relative to reference pressure in ℃ (ITS-90).

Return type

array_like

References

  1. Fofonoff, P. and Millard, R.C. Jr UNESCO 1983. Algorithms for computation of fundamental properties of seawater. UNESCO Tech. Pap. in Mar. Sci., No. 44, 53 pp. Eqn.(31) p.39. http://unesdoc.unesco.org/images/0005/000598/059832eb.pdf

  2. Bryden, H. 1973. New Polynomials for thermal expansion, adiabatic temperature gradient and potential temperature of sea water. Deep-Sea Res. Vol20,401-408. doi:10.1016/0011-7471(73)90063-6

parcels.application_kernels.EOSseawaterproperties.TempFromPtemp(particle, fieldset, time)[source]#

Calculates temperature from potential temperature at the reference pressure PR and in situ pressure P.

Parameters

  • particle –

    The particle object with the following attributes:
    • Sarray_like

      Salinity in psu (PSS-78).

    • Tarray_like

      Potential temperature in ℃ (ITS-90).

    • pressurearray_like

      Pressure in db.

  • fieldset –

    The fieldset object with the following attributes:
    • refpressurearray_like, optional

      Reference pressure in db (default is 0).

  • time (float) – Simulation time (not used in this function but required for consistency with other kernels).

Returns

Temperature in ℃ (ITS-90).

Return type

array_like

References

  1. Fofonoff, P. and Millard, R.C. Jr UNESCO 1983. Algorithms for computation of fundamental properties of seawater. UNESCO Tech. Pap. in Mar. Sci., No. 44, 53 pp. Eqn.(31) p.39. http://unesdoc.unesco.org/images/0005/000598/059832eb.pdf

  2. Bryden, H. 1973. New Polynomials for thermal expansion, adiabatic temperature gradient and potential temperature of sea water. Deep-Sea Res. Vol20,401-408. doi:10.1016/0011-7471(73)90063-6

parcels.application_kernels.TEOSseawaterdensity module#

Collection of pre-built sea water density kernels.

parcels.application_kernels.TEOSseawaterdensity.PolyTEOS10_bsq(particle, fieldset, time)[source]#

Calculates density based on the polyTEOS10-bsq algorithm from Appendix A.2 of https://www.sciencedirect.com/science/article/pii/S1463500315000566 requires fieldset.abs_salinity and fieldset.cons_temperature Fields in the fieldset and a particle.density Variable in the ParticleSet

References

  1. Roquet, F., Madec, G., McDougall, T. J., Barker, P. M., 2014: Accurate polynomial expressions for the density and specific volume of seawater using the TEOS-10 standard. Ocean Modelling.

  2. McDougall, T. J., D. R. Jackett, D. G. Wright and R. Feistel, 2003: Accurate and computationally efficient algorithms for potential temperature and density of seawater. Journal of Atmospheric and Oceanic Technology, 20, 730-741.

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