Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations: Preprocessed Satellite and In-situ observation datasets

Preprocessed satellite sea surface salinity (SSS), temperature (SST), and sea surface height anomalies (SSHA) and Argo-based mixed layer depth (MLD) profiles. Original data can be found at:

(SST): Remote Sensing Systems. 2017. MW optimum interpolated SST data set. Ver. 5.0. PO.DAAC, CA, USA.  Further information available at at https://doi.org/10.5067/GHMWO-4FR05. Data can be accessed at https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/REMSS/mw_OI/v5.0/.

(SSS): Oleg Melnichenko. 2018. Aquarius L4 Optimally Interpolated Sea Surface Salinity. Ver. 5.0. PO.DAAC, CA, USA. Further information at https://doi.org/10.5067/AQR50-4U7CS. Data can be accessed at https://podaac-tools.jpl.nasa.gov/drive/files/SalinityDensity/aquarius/L4/IPRC/v5/7day. 

(SSH): Zlotnicki, Victor; Qu, Zheng; Willis, Joshua. 2019. SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL1609. Ver. 1812. PO.DAAC, CA, USA. Information available at https://doi.org/10.5067/SLREF-CDRV2. Data can be accessed at https://podaac-tools.jpl.nasa.gov/drive/files/SeaSurfaceTopography/merged_alt/L4/cdr_grid

(MLD) Argo-based ocean surface mixed layer depths using the buoyancy gradient definition of Whitt Nicholson and Carranza (2019) processed dataset available at https://doi.org/10.5281/zenodo.4291175.

Preprocessing code can be found at https://github.com/NCAR/ml-ocean-bl/mloceanbl/preprocess_mld.py and .../preprocess_sss_sst_ssh.py.

SSS, SST, SSH were regridded and resampled onto a 1/2 degree lat/lon 7day grid. Data, along with anomalies, is contained in the file sss_sst_ssh_anomalies.nc.

Smoothed argo-based mixed layer depths are used to calculate climatologies and standardized climatologies. 4 degree lat/lon gridded climatologies are stored in mldb_climatology_climatologystd_binned.nc. Meanwhile,  mldb_full_anomalies_stdanomalies_climatology_stdclimatology.nc contains contains the mld, anomalies, standard anomalies, climatologies, and standardized climatologies with corresponding argo locations, times, and corresponding weeks. 

All of these data files are preprocessed and organized to be used with the ml-ocean-bl github code found at https://github.com/NCAR/ml-ocean-bl/mloceanbl/. See https://github.com/NCAR/ml-ocean-bl/notebooks for use cases and examples. 

Contact D. Foster with any questions.