Introduction

Introduction#

To forecast tides, instantiate either a pyfes.core.AbstractTidalModelComplex128 for double precision floating point calculations, or a pyfes.core.AbstractTidalModelComplex64 for single precision. While the latter is quicker and more memory-efficient, it sacrifices some accuracy. Regardless of the chosen precision, all internal computations are performed using double precision.

Tidal models are configured via a YAML file detailing the configurations for tide elevation calculations and the radial model for tide loading effects. These models can be set up to utilize either a Cartesian grid or LGP discretization. Additionally, the library’s API allows for further configuration. For more details, refer to the classes pyfes.config.Common, pyfes.config.Cartesian, and pyfes.config.LGP.

Data#

The source distribution features a data directory, which is located alongside this project and exclusively contains configuration files. It’s important to note that the tide atlas files are not part of the source distribution. To obtain the tide atlas files, they must be downloaded from the AVISO website.

Configuration file#

To instantiate a tidal model, you need to provide the YAML file describing the model to use. The YAML file is a text file containing two sections: tide and radial. The tide section contains the configuration of the tidal model used to calculate the tide elevations. The radial section contains the configuration of the model used to calculate the tide loading effect.

The tide or radial section contains one of the following keys:

  • cartesian

  • lgp

The cartesian key indicates that the numeric model used a Cartesian grid to describe the ocean. The lgp key indicates that the numeric model used a LGP discretization to describe the ocean.

Note

The configuration could contain environment variables. The environment variables are replaced by their values before the configuration is parsed. The syntax for environment variables is ${NAME}, where NAME is the name of the environment variable.

Cartesian grid#

The section cartesian contains the following keys:

  • lat: the name of the netCDF variable containing the latitude of the grid points. Optional, default: lat.

  • lon: the name of the netCDF variable containing the longitude of the grid points. Optional, default: lon.

  • amplitude: the name of the netCDF variable containing the amplitude of the tidal constituents. Optional, default: amplitude.

  • dynamic: The list of the waves to be considered as part of the given altlas (evaluated dynamically from the model). The wave declared in this list will be considered as part of the model components and will be disabled from the admittance calculation and and in the long-period equilibrium wave calculation routine (lpe_minus_n_waves). Optional, default: [].

  • phase: the name of the netCDF variable containing the phase of the tidal constituents. Optional, default: phase.

  • epsilon: the tolerance used to determine if the longitude axis is periodic or not. If the step size times the number of elements in the longitude axis is equal to 360 degrees within the given tolerance, then the longitude axis is considered periodic. Otherwise, it is considered non-periodic. Optional, default: 1e-6.

  • paths: a dictionary containing the paths to the netCDF files containing the tidal constituents. The keys are the names of the tidal constituents, and the values are the paths to the netCDF files containing the tidal constituents.

LGP discretization#

The section lgp contains the following keys:

  • lat: the name of the netCDF variable containing the latitude of the mesh points. Optional, default: lat.

  • lon: the name of the netCDF variable containing the longitude of the mesh points. Optional, default: lon.

  • amplitude: the pattern of the netCDF variable containing the amplitude of the tidal constituents. The pattern must contain the string {constituent}, which is replaced by the name of the tidal constituent. Example: amp_{constituent}. Optional, default: {constituent}_amplitude.

  • phase: the pattern of the netCDF variable containing the phase of the tidal constituents. The pattern must contain the string {constituent}, which is replaced by the name of the tidal constituent. Example: pha_{constituent}. Optional, default: {constituent}_phase.

  • codes: The name of the netCDF variable containing the LPG codes of the mesh points.

  • max_distance: The maximum distance between a mesh point and a requested point outside the mesh. If the distance is greater than this value, the estimation is undefined, otherwise the estimation is extrapolated. Optional, default: 0.0 (extrapolation is not allowed).

  • path: the path to the netCDF file containing the tidal constituents.

  • triangles: the name of the netCDF variable containing the vertices of the triangles of the mesh. Optional, default: triangle

  • type: the type of LPG discretization. The possible values are lgp1 and lpg2. Optional, default: lgp1.

Example#

The following example shows how to instantiate a tidal model using the configuration file fes2014b.yaml.

The configuration file fes2014b.yaml:

radial:
    cartesian:
        lat: lat
        lon: lon
        amplitude: amp
        phase: pha
        epsilon: 1e-6
        paths:
            M2: ${PATH}/M2.nc
            S2: ${PATH}/S2.nc
            K1: ${PATH}/K1.nc
            O1: ${PATH}/O1.nc
            P1: ${PATH}/P1.nc
            Q1: ${PATH}/Q1.nc
tide:
    lgp:
        lat: lat
        lon: lon
        dynamic: ['A5']
        amplitude: amp_{constituent}
        phase: pha_{constituent}
        codes: codes
        max_distance: 0.0
        path: ${PATH}/fes2014b_elevations.nc
        triangles: triangle
        type: lgp1

The Python code to instantiate the tidal model and evaluate the tide elevation:

import pyfes

cfg = pyfes.load_config('fes2014b.yaml')
...
tide, lp, _ = pyfes.evaluate_tide(cfg['tide'], dates, lons, lats, num_threads=1)
load = pyfes.evaluate_radial(cfg['radial'], dates, lons, lats, num_threads=1)[0]
geocentric_tide = tide + load + lp

Note

A full example of tide prediction is available in the gallery.

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