corrtide.py

March 27, 2020 ยท View on GitHub

  • Calculates tidal elevations for correcting altimetry data
  • Can use OTIS format tidal solutions provided by Ohio State University and ESR
  • Can use Global Tide Model (GOT) solutions provided by Richard Ray at GSFC

Calling Sequence

python corrtide.py '/path/to/data/*.h5' \
    -D /path/to/tidedirectory -T CATS2008 \
    -v lon lat t_sec h_cor -a

Inputs

  1. input HDF5 or ascii file

Command Line Options

  • -D X, --directory=X: Working data directory for tide models
  • -T X, --tide=X: Tide model to use in correction
    • CATS0201
    • CATS2008
    • CATS2008_load
    • TPXO9-atlas
    • TPXO9.1
    • TPXO8-atlas
    • TPXO7.2
    • TPXO7.2_load
    • AODTM-5
    • AOTIM-5
    • AOTIM-5-2018
    • GOT4.7
    • GOT4.7_load
    • GOT4.8
    • GOT4.8_load
    • GOT4.10
    • GOT4.10_load
  • -v X, --variable=X: variable names of lon/lat/time/height in HDF5 file
  • -c X, --cols=X: column positions of lon/lat/time/height in ASCII file
  • -e X: reference epoch of input time in secs
  • -a: apply tide correction instead of saving to separate file

Output options:

  1. Applies tide correction and saves the cor as additional variable.
  2. Generates external file with correction for each point (x,y,t,cor).

Dependencies:

  • calc_astrol_longitudes.py: computes the basic astronomical mean longitudes
  • calc_delta_time.py: calculates difference between universal and dynamic time
  • convert_xy_ll.py: convert lat/lon points to and from projected coordinates
  • infer_minor_corrections.py: return corrections for 16 minor constituents
  • load_constituent.py: loads parameters for a given tidal constituent
  • load_nodal_corrections.py: load the nodal corrections for tidal constituents
  • predict_tide_drift.py: predict tidal elevations using harmonic constants
  • read_tide_model.py: extract tidal harmonic constants from OTIS tide models
  • read_netcdf_model.py: extract tidal harmonic constants from netcdf models
  • read_GOT_model.py: extract tidal harmonic constants from GSFC GOT models