/station-m-currents

Analysis of current meter data collected at Station M, located on the abyssal plain 220 km offshore of central California

Primary LanguageJupyter Notebook

Analysis of current meter data collected by the Monterey Bay Aquarium Research Institute (MBARI) at Station M, located on the abyssal plain 220 km offshore of central California. Current meter data were collected near the seabed at ~4000 m depth from October 2014–October 2018. Data, methods and results are described in the publication:

Connolly, T. P., P. R. McGill, R. G. Henthorn, D. A. Burrier, C. Michaud (2020) Near-bottom currents at Station M in the abyssal Northeast Pacific, Deep Sea Research II, 173, 104743. https://doi.org/10.1016/j.dsr2.2020.104743

DOI for for v1.0 of this repository on Zenodo: DOI

Data

Current meter data

Station M current meter data are publicly available on Zenodo: http://dx.doi.org/10.5281/zenodo.3612575 DOI

Wind data

Winds from the NCEP North American Regional Reanalysis (NARR) product are used in rover_compare_wind.ipynb, available at: https://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/NARR/monolevel/catalog.html

The analysis uses the 10 m wind velocity in the uwnd.10.*.nc and vwnd.10.*.nc files from 2014-2018. These can be stored locally, or the code can be modified to use the ESRL OpenDAP server.

Using the data in the analysis

Download all current meter data files to one common base directory and extract the zip files as three separate sub-directories. Modify the data_dir variable in datapath.py to point to the location of this base directory. If using the NCEP NARR winds, modify the ncep_dir variable to point to the location of the locally-stored .nc files (they should all be in one directory).

Required packages

The Python notebooks and modules in this repository make use of the following packages:

The conda environment can be recreated with the environment.yml file, following the instructions at https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html

Analysis

Analysis of benthic rover data

rover_analysis.ipynb Figure 1 - Example time series Figure 2 - Running average time series Figure 3 - Seasonal time series Figure 4 - Benthic rover spectrum

rover_compare_wind.ipynb Figure 5 - Wind forcing and wavelets

  • First run rover_analysis.ipynb to create rover_processed_2015_2018.csv
  • Requires NCEP NARR output (see above)

Analysis of ADCP data

ADCP_spectrum_complexpca.ipynb Figure 6 - ADCP rotary spectra Figure 10 - complex PCA

Analysis of World Ocean Atlas data

stratification_woa.ipynb Figure 7 - Stratification (World Ocean Atlas)

Bottom boundary layer - tidal analysis and idealized modeling

ADCP_utide_analysis.ipynb Run UTide analysis on ADCP data and save results in a NetCDF file

ADCP_utide_results_theory_M2.ipynb Figure 8 - Plot results of harmonic analysis and modeling for M2 constituent

ADCP_utide_results_theory_K1.ipynb Figure 9 - Plot results of harmonic analysis and modeling for K1 constituent

ADCP_utide_testcases_soulsby.ipynb Shows results of analytical model, which can be compared with examples in Soulsby (1983)

Bottom boundary layer - time dependent numerical model

time_dependent_model_loggrid_multiple_zo.ipynb Run time-dependent numerical model for different values of $z_o$

time_dependent_model_analysis.ipynb Figure 11 - Plot RMSE for different $$ values modeled friction velocity for one month