Atmospheric Delays

Bases: Enum

Ionospheric available analysis centers

COD = auto()

COR = auto()

EHR = auto()

ESA = auto()

ESR = auto()

IGR = auto()

IGS = auto()

JPL = auto()

UPC = auto()

UHR = auto()

UPR = auto()

UQR = auto()

Bases: ValueError

Wrong analysis center name for ionospheric data retrieval

Bases: RuntimeError

Error encountered while reading the Ionospheric IONEX TEC Map file

Bases: FileNotFoundError

Could not find the specified ionospheric map file

Bases: Enum

Method for generating mapping function for Ionospheric delay evaluation from TEC data

IPP = auto()

GROUND = auto()

GROUND_CONVERTED = auto()

Bases: Enum

Solution type of TEC maps for Ionospheric delay evaluation from TEC data

FINAL = 'FIN'

RAPID = 'RAP'

Bases: Enum

Time resolution of TEC maps for Ionospheric delay evaluation from TEC data

HALF_HOUR = '30M'

HOUR = '01H'

TWO_HOURS = '02H'

Ionospheric Delay Estimator (first order approximation, slant range) from CDDIS IONEX TEC MAP class. The delay is due to the refractive index of the medium the signal is passing through.

The ionosphere is the zone of the terrestrial atmosphere that contains a partially ionised medium, as result of the X and UV rays of Solar Radiation and the incidence of charged particles.

The propagation speed of the electromagnetic signals in the ionosphere depends on its electron density:

• during the day, sun radiation causes ionisation of neutral atoms producing free electrons and ions (TEC)
• during the night, the recombination process prevails, where free electrons and ions are recombined, lowering TEC

Ionosphere is a dispersive media, with the following Relation of Dispersion between \(\omega\) and \(k\) of the incoming wave:

where \(\omega_p\) is the critical frequency of the ionospheric plasma, meaning that only signals with \(\omega > \omega_p\) through the ionized plasma medium. Therefore, ionosphere causes a frequency dependent path delay for microwave signals.

acquisition_time = acquisition_time

ionospheric_delay_scaling_factor = ionospheric_delay_scaling_factor

carrier_freq = fc_hz

tec_mapping_method = tec_mapping_method

solution_type = tec_solution_type

time_resolution = tec_time_resolution

analysis_center = IonosphericAnalysisCenters[analysis_center.upper()]

ionospheric_map_folder = None

__init__(acquisition_time: PreciseDateTime, analysis_center: str, fc_hz: float, ionospheric_delay_scaling_factor: float, tec_mapping_method: TECIncidenceAngleMethod, tec_solution_type: TECMapSolutionType = TECMapSolutionType.FINAL, tec_time_resolution: TECMapTimeResolution = TECMapTimeResolution.HOUR, map_folder: Path | str | None = None) -> None

read_ionosphere_map_file(ionosphere_map_file: Path) -> tuple[list, list, np.ndarray, np.ndarray]

estimate_delay(sat_xyz_coords: ndarray, point_targets_coords: ndarray) -> np.ndarray

Bases: FileNotFoundError

Could not find the specified grid points station coordinates file

Bases: ValueError

Grid resolution provided is not supported

Bases: Enum

Tropospheric map data model

DORIS = auto()

GNSS = auto()

GRID = auto()

SLR = auto()

VLBI = auto()

Bases: Enum

Tropospheric map resolution for GRID model

FINE = '1x1'

MEDIUM = '2.5x2'

COARSE = '5x5'

Bases: Enum

Tropospheric map data type

GRAD = auto()

LHG = auto()

RAYTR = auto()

V3GR = auto()

VMF1 = auto()

VMF3 = auto()

VMF3o = auto()

Bases: Enum

Tropospheric map data version

EI = auto()

FC = auto()

OP = auto()

RADIATE = auto()

TRP = auto()

Bases: Enum

Tropospheric grid interpolation method using griddata from scipy.interpolate, check the function for the available methods

LINEAR = auto()

NEAREST = auto()

CUBIC = auto()

Tropospheric Delay Estimator from Vienna Mapping Function 3 data. The delay is due to the refractive index of the medium the signal is passing through.

The Troposphere is neutral and non-dispersive for frequencies up to 30 GHz, so the path through this medium is not depended on the carrier frequency for microwave signals, in contrast to the ionosphere.

The refractive index of the troposphere is a function of pressure, temperature and partial water vapor pressure. The total delay can be decomposed into a wet and dry (hydrostatic) component. - hydrostatic part comprises approximately 90% of the overall delay and is well suited to pressure driven modelling. Closely linked to topography, reflector height has to be taken into account. - wet component undergoes rapid changes due to the high spatiotemporal variability of water vapor.

The equation to be solved is the following:

where \(\Delta L(\epsilon)\) is the total delay time and it is composed by an hydrostatic component \(h\) and a wet component \(w\). \(mf(\epsilon)\) are the wet and hydrostatic mapping functions generated in this algorithm starting from the Vienna Mapping Function 3 data and the spherical harmonics Legendre polynomials.

acquisition_time = acquisition_time

interp_method = interpolation_method

map_model = map_model

map_type = map_type

map_version = map_version

map_grid_res = map_grid_res

tropospheric_map_folder = None

__init__(acquisition_time: PreciseDateTime, interpolation_method: TroposphericGridInterpolationMethod, map_folder: Path = None, map_model: TroposphericMapModel = TroposphericMapModel.GRID, map_grid_res: TroposphericGRIDResolution = TroposphericGRIDResolution.FINE, map_type: TroposphericMapType = TroposphericMapType.VMF3, map_version: TroposphericMapVersion = TroposphericMapVersion.OP) -> None

read_vmf3_files(files: list[Path]) -> list[pd.DataFrame]

estimate_delay(point_targets_coords: ndarray, sat_xyz_coords: ndarray) -> tuple[np.ndarray, np.ndarray]