NetCDF Header

Surface seawater concentration of acetone: monthly climatology

netcdf file:///data/07f089eb-fcd8-4fd0-9a0d-38b45cedd9dc/Csw_Acetone_f09f09_Monthly_WangJGR2020_v20190916a.nc-505673d2-39fc-4997-b70a-28fff991ed73 {
  dimensions:
    time = UNLIMITED;   // (12 currently)
    lat = 192;
    lon = 288;
  variables:
    int time(time=12);
      :_FillValue = -2147483647; // int
      :long_name = "date";
      :units = "YYYYMMDD";

    double lat(lat=192);
      :units = "degrees_north";
      :long_name = "latitude";
      :_FillValue = -900.0; // double

    double lon(lon=288);
      :units = "degrees_east";
      :long_name = "longitude";
      :_FillValue = -900.0; // double

    float acetone_conc_nM_Monthly(time=12, lat=192, lon=288);
      :_FillValue = -99999.0f; // float
      :missing_value = 9.96921E36f; // float
      :units = "nanomoles per liter (nM)";

  // global attributes:
  :createdby = "Siyuan Wang (NCAR), siyuan@ucar.edu";
  :title = "Surface seawater concentration of acetone predicted by an observationally trained machine learning algorithm (random forest).";
  :note = "Training dataset: Yang et al 2014a; Yang et al 2014b; Dixon et al 2014; Beale et al 2013; Kameyama et al 2010; Hudson et al 2007; Marandino et al 2005; Marandino et al (Knorr06)";
  :ref = "Wang et al. JGR 2020. Global Atmospheric Budget of Acetone: Air-Sea Exchange and the Contribution to Hydroxyl Radicals";
  :creation_date = "Mon Jun 29 16:35:00 MDT 2020";
}