Wish list for observational fields taken over from AMWG diagnostics

[oyvindseland]

New feature type

New plot and/or plot enhancement, New infrastructure or infrastructure enhancement

What is this new feature?

I am setting up a wish list for observational fields to include from AMWG diagnostics
It is set up with in general the highest a priority on top, except that I have also grouped the same type of input files using the same source together.

I am starting with the list but can also point to the files if needed.

Surface wind stress (ERS or NCEP or Large-Yeager?)

Radiative flxues from CERES-EBAF
ALBEDO
ALBEDOC
FSNTC

Icefrac (HadISST or SSM/I)

CloudSat
Total cloud cover
low cloud amount
medium cloud amount
high cloud amount.

Assistance required?

Yes, I will need some assistance before making a PR

Extra info

No response

[adagj]

@oyvindseland
Adding observations to ADF is straightforward. If you go to adf_variable_defaults.yaml, you will see an overview of all variables, including observations. Just add fields to the obs_* entries.

E.g. for FSNTC

FSNTC:
  colormap: "Blues"
  contour_levels_range: [120, 320, 10]
  diff_colormap: "BrBG"
  diff_contour_range: [-20, 20, 2]
  scale_factor: 1
  add_offset: 0
  new_unit: "Wm$^{-2}$"
  mpl:
    colorbar:
      label : "Wm$^{-2}$"
  obs_file: "CERES_EBAF_Ed4.1_2001-2020.nc"
  obs_name: "CERES_EBAF_Ed4.1"
  obs_var_name: "toa_sw_clr_c_mon"
  category: "TOA energy flux"

If you have observation files from the AMWG diagnostics that you want to use, just copy the files to the ADF-obs folder and add the filename to obs_file and the variable name to obs_var_name. The obs_name is simply what will be written on the figures.

If you want to derive a new variable that is not in the NorESM output, e.g. RESTOM or ALBEDO or similar, you can define it in adf_diag.py.

I.e. in the function def derive_variables(self, res=None, hist_str=None, vars_to_derive=None, ts_dir=None, constit_dict=None, overwrite=None)

if var == "RESTOM":
    der_val = ds["FSNT"] - ds["FLNT"]
if var == "TOTCF":
    der_val = ds["SWCF"] + ds["LWCF"]
if var == "ALBEDO":
    der_val = YOU ADD HERE!

You need to list the variables you use for the derivation in you config yaml file , and define the variable properties in adf_variable_defaults.yaml. Something like:

ALBEDO:
  colormap: "Blues"
  contour_levels_range: [-1, 1, .1]
  diff_colormap: "BrBG"
  diff_contour_range: [-1, 1, .1]
  scale_factor: 1
  add_offset: 0
  new_unit: "Fraction"
  derivable_from: ['FSDS','FSNS']
  mpl:
    colorbar:
      label : "Wm$^{-2}$"
  obs_file: "ERAI_all_climo.nc"
  obs_name: "ERAI"
  obs_var_name: "ALBEDO"
  category: "Sfc energy flux"

[oyvindseland]

Thank you. I will try it after Easter. Should I make also make pull requests to the repository?

[adagj]

I used

if var =="ALBEDO":
    der_val = (ds['FSDS']-ds['FSNS'])/ds['FSDS'].where(ds['FSDS']>0) 

And it worked fine it seems: https://ns2345k.web.sigma2.no/datalake/diagnostics/ADF/adagj/n1850.ne16pg3_tn14.noresm3_0_beta12.ndiff_surface_align.20260317_617_634_vs_Obs/ALBEDO_ANN_LatLon_Mean.png

[adagj]

Thank you. I will try it after Easter. Should I make also make pull requests to the repository?

Sure!