Description:
  Author: V. Agudetse

Analysis:
  Horizon: Seasonal
  Variables:
    name: psl
    freq: monthly_mean
    # units: {tas: C}
  Datasets:
    System:
      name: ECMWF-SEAS5
    Multimodel: False
    Reference:
      name: ERA5
  Time:
    sdate: '1101'
    fcst_year: # '2020'
    hcst_start: '2000'
    hcst_end: '2010'
    ftime_min: 1
    ftime_max: 3
  Region:
    latmin: 20
    latmax: 80
    lonmin: -80
    lonmax: 40
  Regrid:
    method: bilinear
    type: "r180x90" #to_reference #'r360x181'
  Workflow:
    Anomalies:
      compute: yes # yes/no, default yes
      cross_validation: yes # yes/no, default yes
      save: 'all' # 'all'/'none'/'exp_only'/'fcst_only'
    Calibration:
      method: raw
      save: 'none' # 'all'/'none'/'exp_only'/'fcst_only'
    Indices:
      NAO: {obsproj: TRUE, save: 'all', plot_ts: TRUE, plot_sp: yes}
      # Downscaling:
      #   # Assumption 1: leave-one-out cross-validation is always applied
      #   # Assumption 2: for analogs, we select the best analog (minimum distance)
      #   type: intbc # mandatory, 'none', 'int', 'intbc', 'intlr', 'analogs', 'logreg'.
      #   int_method: conservative # regridding method accepted by CDO.
      #   bc_method: bias # If type intbc. Options: 'bias', 'calibration', 'quantile_mapping', 'qm', 'evmos', 'mse_min', 'crps_min', 'rpc-based'.
      #   lr_method: # If type intlr. Options: 'basic', 'large_scale', '4nn'
      #   log_reg_method: # If type logreg. Options: 'ens_mean', 'ens_mean_sd', 'sorted_members'
      #   target_grid: /esarchive/recon/ecmwf/era5/monthly_mean/tas_f1h/tas_200002.nc # nc file or grid accepted by CDO
      #   nanalogs: # If type analgs. Number of analogs to be searched
      #   save: 'all' # 'all'/'none'/'exp_only'
    Skill:
      metric: RPS RPSS BSS10 # RPS RPSS CRPS CRPSS FRPSS BSS10 BSS90 EnsCorr Corr mean_bias mean_bias_SS
      save: 'all' # 'all'/'none'
    Probabilities:
      percentiles: [[1/3, 2/3], [1/10, 9/10]]
      save: 'percentiles_only' # 'all'/'none'/'bins_only'/'percentiles_only'
    Visualization:
      plots: skill_metrics, forecast_ensemble_mean
      multi_panel: no
      projection: cylindrical_equidistant
    Indicators:
      index: no
  ncores: 10
  remove_NAs: yes
  Output_format: S2S4E
Run:
  Loglevel: INFO
  Terminal: yes
  output_dir: /esarchive/scratch/vagudets/auto-s2s-outputs/
  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
  startR_workflow:
    modules: anomalies skill # Modules to run inside Compute(), in order
    return: hcst, fcst, obs # Choose only if they will fit into memory
    save: hcst, fcst, obs # Only if returned
    chunk_along: {time: 2} # list: {dimension_1: # of chunks, dimension_2, # of chunks, ...}
    run_on: local # options: local, as_machine, nord3
    chunk_wallclock: '01:00:00' # Wallclock for each chunk, hh:mm:ss. Only if run_on is not 'local'
    expid: a6cb # Only if run_on is not 'local'. leave empty to create a new one? O
    hpc_user: bsc32762 # your hpc username. Only if run_on is not 'local'
    slurm_directives: ['#SBATCH --constraint=medmem', '#SBATCH --exclusive'] # Only if run_on is not 'local'


