potential

  • Predict gravity tides and tide-generating forces

Source code

pyTMD.predict.generating_force(t: ndarray, XYZ: Dataset, SXYZ: Dataset, LXYZ: Dataset, **kwargs)[source]

Compute the tide-generating force due to the gravitational attraction of the moon and sun [72, 73]

Parameters:
t: np.ndarray

Days relative to 1992-01-01T00:00:00

XYZ: xr.Dataset

Dataset with cartesian coordinates

SXYZ: xr.Dataset

Dataset with Earth-centered Earth-fixed coordinates of the sun

LXYZ: xr.Dataset

Dataset with Earth-centered Earth-fixed coordinates of the moon

lmax: int, default 4

Maximum degree of spherical harmonic expansion

GM: float, default 3.986004418e14

Geocentric gravitational constant (m3 s-2)

mass_ratio_solar: float, default 332946.0482

Mass ratio between Earth and Sun

mass_ratio_lunar: float, default 0.0123000371

Mass ratio between Earth and Moon

Returns:
F: xr.Dataset

Tide-generating force (m s-2)

pyTMD.predict.gravity_tide(t: ndarray, XYZ: Dataset, SXYZ: Dataset, LXYZ: Dataset, deltat: float = 0.0, a_axis: float = 6378136.6, **kwargs)[source]

Compute the estimated gravity tides due to the gravitational attraction of the moon and sun [27, 73]

Parameters:
t: np.ndarray

Days relative to 1992-01-01T00:00:00

XYZ: xr.Dataset

Dataset with cartesian coordinates

SXYZ: xr.Dataset

Dataset with Earth-centered Earth-fixed coordinates of the sun

LXYZ: xr.Dataset

Dataset with Earth-centered Earth-fixed coordinates of the moon

deltat: float or np.ndarray, default 0.0

Time correction for converting to Ephemeris Time (days)

a_axis: float, default 6378136.3

Semi-major axis of the Earth (meters)

lmax: int, default 3

Maximum degree of spherical harmonic expansion

h2: float, default 0.6078

Degree-2 Love number of vertical displacement

k2: float, default 0.30102

Degree-2 Love number of gravitational potential

h3: float, default 0.292

Degree-3 Love number of vertical displacement

k3: float, default 0.093

Degree-3 Love number of gravitational potential

GM: float, default 3.986004418e14

Geocentric gravitational constant (m3 s-2)

mass_ratio_solar: float, default 332946.0482

Mass ratio between Earth and Sun

mass_ratio_lunar: float, default 0.0123000371

Mass ratio between Earth and Moon

Returns:
G: xr.Dataset

Gravity tides (m s-2)

pyTMD.predict.potential._out_of_phase(XYZ: Dataset, SXYZ: Dataset, LXYZ: Dataset, F2_solar: ndarray, F2_lunar: ndarray)[source]

Wrapper function to compute the out-of-phase corrections induced by mantle anelasticity [56]

Parameters:
XYZ: xr.Dataset

Dataset with cartesian coordinates

SXYZ: xr.Dataset

Dataset with Earth-centered Earth-fixed coordinates of the sun

LXYZ: xr.Dataset

Dataset with Earth-centered Earth-fixed coordinates of the moon

F2_solar: np.ndarray

Factors for the sun

F2_lunar: np.ndarray

Factors for the moon

Returns:
G: xr.Dataset

Gravity tide corrections

pyTMD.predict.potential._out_of_phase_diurnal(XYZ: Dataset, LSXYZ: Dataset, F2: ndarray, dh2: float = -0.0025, dk2: float = -0.00144)[source]

Computes the out-of-phase corrections induced by mantle anelasticity in the diurnal band [56]

Parameters:
XYZ: xr.Dataset

Dataset with cartesian coordinates

LSXYZ: xr.Dataset

Dataset with Earth-centered Earth-fixed coordinates of the sun or moon

F2: np.ndarray

Factors for the sun or moon

dh2: float, default -0.0025

Love number correction for the diurnal band

dk2: float, default -0.00144

Love number correction for the diurnal band

Returns:
G: xr.Dataset

Gravity tide corrections

pyTMD.predict.potential._out_of_phase_semidiurnal(XYZ: Dataset, LSXYZ: Dataset, F2: ndarray, dh2: float = -0.0022, dk2: float = -0.0013)[source]

Computes the out-of-phase corrections induced by mantle anelasticity in the semi-diurnal band [56]

Parameters:
XYZ: xr.Dataset

Dataset with cartesian coordinates

LSXYZ: xr.Dataset

Dataset with Earth-centered Earth-fixed coordinates of the sun or moon

F2: np.ndarray

Factors for the sun or moon

dh2: float, default -0.0022

Love number correction for the semi-diurnal band

dk2: float, default -0.0013

Love number correction for the semi-diurnal band

Returns:
G: xr.Dataset

Gravity tide corrections

pyTMD.predict.potential._frequency_dependence(XYZ: Dataset, MJD: ndarray, deltat: float | ndarray = 0.0)[source]

Wrapper function to compute the frequency dependent in-phase and out-of-phase corrections [56]

Parameters:
XYZ: xr.Dataset

Dataset with cartesian coordinates

MJD: np.ndarray

Modified Julian Day (MJD)

deltat: float or np.ndarray, default 0.0

Time correction for converting to Ephemeris Time (days)

Returns:
G: xr.Dataset

Gravity tide corrections

pyTMD.predict.potential._frequency_dependence_diurnal(XYZ: Dataset, MJD: ndarray, deltat: float | ndarray = 0.0)[source]

Computes the frequency dependent in-phase and out-of-phase corrections of the diurnal band [56]

Parameters:
XYZ: xr.Dataset

Dataset with cartesian coordinates

MJD: np.ndarray

Modified Julian Day (MJD)

deltat: float or np.ndarray, default 0.0

Time correction for converting to Ephemeris Time (days)

Returns:
G: xr.Dataset

Gravity tide corrections

pyTMD.predict.potential._frequency_dependence_long_period(XYZ: Dataset, MJD: ndarray, deltat: float | ndarray = 0.0)[source]

Computes the frequency dependent in-phase and out-of-phase corrections induced by mantle anelasticity in the long-period band [56]

Parameters:
XYZ: xr.Dataset

Dataset with cartesian coordinates

MJD: np.ndarray

Modified Julian Day (MJD)

deltat: float or np.ndarray, default 0.0

Time correction for converting to Ephemeris Time (days)

Returns:
G: xr.Dataset

Gravity tide corrections