docs: def lsm_data_to_subset_netcdf(self, netcdf_file_path,
                                          data_var_map_array,
                                          resample_method=None):
          """Writes extracted data to the NetCDF file format
  
          .. todo:: NetCDF output data time is always in UTC time. Need to convert to local timezone for GSSHA.
  
          .. warning:: The NetCDF GSSHA file is only supported in GSSHA 7 or greater.
  
          .. note::
              GSSHA CARDS:
                  * HMET_NETCDF pointing to the netcdf_file_path
                  * LONG_TERM (see: http://www.gsshawiki.com/Long-term_Simulations:Global_parameters)
  
          Parameters:
              netcdf_file_path(string): Path to output the NetCDF file for GSSHA.
              data_var_map_array(list): Array to map the variables in the LSM file to the
                                        matching required GSSHA data.
              resample_method(Optional[gdalconst]): Resample input method to match hmet data to GSSHA grid for NetCDF output. Default is None.
  
  
          GRIDtoGSSHA Example:
  
          .. code:: python
  
              from gsshapy.grid import GRIDtoGSSHA
  
              #STEP 1: Initialize class
              g2g = GRIDtoGSSHA(gssha_project_folder='/path/to/gssha_project',
                                gssha_project_file_name='gssha_project.prj',
                                lsm_input_folder_path='/path/to/wrf-data',
                                lsm_search_card='*.nc',
                                lsm_lat_var='XLAT',
                                lsm_lon_var='XLONG',
                                lsm_time_var='Times',
                                lsm_lat_dim='south_north',
                                lsm_lon_dim='west_east',
                                lsm_time_dim='Time',
                                )
  
              #STEP 2: Generate NetCDF DATA
  
              #EXAMPLE DATA ARRAY 1: WRF GRID DATA BASED
              #SEE: http://www.meteo.unican.es/wiki/cordexwrf/OutputVariables
  
              data_var_map_array = [
                                    ['precipitation_acc', ['RAINC', 'RAINNC']],
                                    ['pressure', 'PSFC'],
                                    ['relative_humidity', ['Q2', 'PSFC', 'T2']], #MUST BE IN ORDER: ['SPECIFIC HUMIDITY', 'PRESSURE', 'TEMPERATURE']
                                    ['wind_speed', ['U10', 'V10']], #['U_VELOCITY', 'V_VELOCITY']
                                    ['direct_radiation', ['SWDOWN', 'DIFFUSE_FRAC']], #MUST BE IN ORDER: ['GLOBAL RADIATION', 'DIFFUSIVE FRACTION']
                                    ['diffusive_radiation', ['SWDOWN', 'DIFFUSE_FRAC']], #MUST BE IN ORDER: ['GLOBAL RADIATION', 'DIFFUSIVE FRACTION']
                                    ['temperature', 'T2'],
                                    ['cloud_cover' , 'CLDFRA'], #'CLOUD_FRACTION'
                                   ]
  
              g2g.lsm_data_to_subset_netcdf("E/GSSHA/gssha_wrf_data.nc",
                                            data_var_map_array)
  
          HRRRtoGSSHA Example:
  
          .. code:: python
  
              from gsshapy.grid import HRRRtoGSSHA
  
              #STEP 1: Initialize class
              h2g = HRRRtoGSSHA(
                                #YOUR INIT PARAMETERS HERE
                               )
  
              #STEP 2: Generate NetCDF DATA
  
              #EXAMPLE DATA ARRAY 2: HRRR GRID DATA BASED
              data_var_map_array = [
                                    ['precipitation_rate', 'prate'],
                                    ['pressure', 'sp'],
                                    ['relative_humidity', '2r'],
                                    ['wind_speed', ['10u', '10v']],
                                    ['direct_radiation_cc', ['dswrf', 'tcc']],
                                    ['diffusive_radiation_cc', ['dswrf', 'tcc']],
                                    ['temperature', 't'],
                                    ['cloud_cover_pc' , 'tcc'],
                                   ]
  
              h2g.lsm_data_to_subset_netcdf("E:/GSSHA/gssha_wrf_data.nc",
                                            data_var_map_array)
          """
          self._check_lsm_input(data_var_map_array)
  
          output_datasets = []
          #DATA
          for gssha_var, lsm_var in data_var_map_array:
              if gssha_var in self.netcdf_attributes:
                  self._load_converted_gssha_data_from_lsm(gssha_var, lsm_var, 'netcdf')
                  #previously just added data, but needs to be hourly
                  gssha_data_var_name = self.netcdf_attributes[gssha_var]['gssha_name']
                  self._convert_data_to_hourly(gssha_data_var_name)
                  if resample_method:
                      self._resample_data(gssha_data_var_name)
                  else:
                      self.data = self.data.lsm.to_projection(gssha_data_var_name,
                                                              projection=self.gssha_grid.projection)
  
                  output_datasets.append(self.data)
              else:
                  raise ValueError("Invalid GSSHA variable name: {0} ...".format(gssha_var))
          output_dataset = xr.merge(output_datasets)
          #add global attributes
          output_dataset.attrs['Convention'] = 'CF-1.6'
          output_dataset.attrs['title'] = 'GSSHA LSM Input'
          output_dataset.attrs['history'] = 'date_created: {0}'.format(datetime.utcnow())
          output_dataset.attrs['proj4'] = self.data.attrs['proj4']
          output_dataset.attrs['geotransform'] = self.data.attrs['geotransform']
  
          output_dataset.to_netcdf(netcdf_file_path)