For performing "level 0" (L0) and "level 1" (L1) processing on raw moored ADCP data in Python using the UHDAS pycurrents package.

L0 processing does not include any processing. Raw ADCP data is combined with metadata from a csv file and exported in netCDF format.

L1 processing contains minimal processing. Raw ADCP data is also combined with metadata from a csv file and exported in netCDF format. The difference from L0 is that L1 processing comprises:

• Corrections for magnetic declination
• Calculation of sea surface height from pressure values and latitude
• Rotation into enu coordinates if this is not already the coordinate system of the dataset
• Flagging leading and trailing ensembles from before and after deployment and setting them to nan's
• Flagging negative pressure values

add_var2nc.py adds a geographic_area variable to a netCDF file from either the L0 or L1 process and exports a new netCDF file containing this addition.

ADCP IOS header file.py produces an IOS Shell header file for each netCDF file that makes the netCDF file searchable on the IOS Water Properties website (https://www.waterproperties.ca/).

1. Before creating a virtual environment for the package, create a folder for the virtual environment and enter the folder in terminal, e.g. "adcp"
2. Create a virtual environment called "adcp37" with Python version 3.7:
   conda create -n adcp37 python=3.7
3. Activate the virtual environment:
   conda activate adcp37
4. Add the conda-forge to your channel:
   conda config --add channels conda-forge
   conda config --set channel_priority strict
5. Install required packages:
   conda install numpy scipy pip pandas netCDF4 xarray gsw matplotlib shapely
   pip install datetime
6. Clone pycurrents with Mercurial:
   hg clone --verbose http://currents.soest.hawaii.edu/hg/pycurrents
7. Install pycurrents:
   pip install -e ./pycurrents
8. Clone this pycurrents_ADCP_processing repository with git:
   git clone https://github.com/hhourston/pycurrents_ADCP_processing.git
9. cd to the pycurrents_ADCP_processing directory and run "setup.py":
   python setup.py install

• Linux (or Unix-like) environment
• Python 3.7

Sample usage of ADCP_processing_L1.py, ADCP_processing_L0.py, add_var2nc.py and ADCP_IOS_Header_file.py is laid out in create_nc.py. An example of how to create uniform time data (for replacing invalid time data in a raw ADCP file) can be found in generate_time_range.py.

ADCP_pycurrents_L1.py is based off of the gist, RdiToNetcdf.ipynb, by Jody Klymak (https://github.com/jklymak), and includes contributions from Di Wan (https://github.com/onedwd) and Eric Firing (https://github.com/efiring). add_var2nc.py was written by Di Wan. ADCP_IOS_Header_file.py was written by Lu Guan (https://github.com/guanlu129).

Documentation:

• pycurrents package and log: https://currents.soest.hawaii.edu/hgstage/pycurrents/file/tip
• netCDF documentation: https://www.unidata.ucar.edu/software/netcdf/docs/index.html
• RDI Broadband primer: https://www.comm-tec.com/Docs/Manuali/RDI/BBPRIME.pdf
• RDI Ocean Surveyor technical manual: http://www.teledynemarine.com/Documents/Brand%20Support/RD%20INSTRUMENTS/Technical%20Resources/Manuals%20and%20Guides/Ocean%20Surveyor_Observer/Ocean%20Surveyor%20Technical%20Manual_Apr18.pdf
  • Raw data variable terminology starts on page 148; pycurrents outputs variables with these names

Conventions:

• BODC SeaDataNet quality flags: https://www.bodc.ac.uk/data/documents/series/37006/#QCflags
• BODC SeaDataNet P01 vocabulary search: http://seadatanet.maris2.nl/v_bodc_vocab_v2/search.asp?lib=p01&screen=0
• BODC SeaDataNet P06 data storage unit search: http://seadatanet.maris2.nl/v_bodc_vocab_v2/search.asp?lib=p06&screen=0
• CF Conventions standard name table: http://cfconventions.org/standard-names.html
• GF3 codes (no longer maintained): https://www.nodc.noaa.gov/woce/woce_v3/wocedata_1/sss/documents/liste_param.htm