ADF#
A number of plots are provided from ADF. The full output from the stand-alone ADF configuration is in the link below.
Note that in standalone format (eg, CUPiD run not through CESM workflow), ADF is currently run by users via the following process:
Install ADF and activate cupid-analysis
Use the
CUPiD/helper_scripts/generate_adf_config_file.pyscript to generate an ADF config file based on a CUPiD configuration file.cd CUPiD/examples/external_diag_packages../../helper_scripts/generate_adf_config_file.py --cupid-config-loc . --adf-template ../../externals/ADF/config_amwg_default_plots.yaml --out-file ADF_config.yaml
Run ADF with the newly created configuration file.
../../externals/ADF/run_adf_diag ADF_config.yaml
# Parameters
case_name = "b.e30_alpha08b.B1850C_LTso.ne30_t232_wgx3.313"
base_case_name = "b.e30_alpha08b.B1850C_LTso.ne30_t232_wgx3.308"
case_nickname = "BLT1850_313"
base_case_nickname = "BLT1850_308"
CESM_output_dir = "/glade/campaign/cesm/development/cross-wg/diagnostic_framework/CESM_output_for_testing"
start_date = "0001-01-01"
end_date = "0044-01-01"
climo_start_year = 24
climo_end_year = 44
base_start_date = "0001-01-01"
base_end_date = "0105-01-01"
base_climo_start_year = 85
base_climo_end_year = 105
obs_data_dir = (
"/glade/campaign/cesm/development/cross-wg/diagnostic_framework/CUPiD_obs_data"
)
ts_dir = None
lc_kwargs = {"threads_per_worker": 1}
serial = False
adf_root = "../../examples/313v308/ADF_output/"
key_plots = [
"Surface_Wind_Stress_ANN_LatLon_Vector_Mean.png",
"PRECT_ANN_LatLon_Mean.png",
"PS_DJF_SHPolar_Mean.png",
"TaylorDiag_ANN_Special_Mean.png",
]
compare_obs = False
subset_kwargs = {}
product = "/glade/u/home/dsanders/cesm_dev/CUPiD/examples/313v308/computed_notebooks//atm/ADF.ipynb"
if base_case_year_range:
base_case_yr_range_str = f"_{base_case_year_range[0]}_{base_case_year_range[1]}"
alt_base_case_yr_range_str = (
f"_{base_case_year_range[0]}_{str(int(base_case_year_range[1])-1)}"
)
else:
base_case_yr_range_str = ""
alt_base_case_yr_range_str = ""
possible_adf_comparison_names = [
f"{case_name}_{case_year_range[0]}_{case_year_range[1]}_vs_{base_case_name}{base_case_yr_range_str}"
]
possible_adf_comparison_names.append(
f"{case_name}_{case_year_range[0]}_{str(int(case_year_range[1])-1)}_vs_{base_case_name}{base_case_yr_range_str}"
)
possible_adf_comparison_names.append(
f"{case_name}_{case_year_range[0]}_{str(int(case_year_range[1])-1)}_vs_{base_case_name}{alt_base_case_yr_range_str}"
)
possible_adf_comparison_names.append(
f"{case_name}_{case_year_range[0]}_{case_year_range[1]}_vs_{base_case_name}{alt_base_case_yr_range_str}"
)
Key Metrics from ADF#
Some important things to look at from ADF include a comparison table and a few maps: