LDF

The output from the stand-alone LDF configuration is in the link below

Note that LDF is currently run by users. Temporarily, we suggest users walk through the following process:

1. Install LDF and activate cupid-analysis

2. Use the CUPiD/helper_scripts/generate_ldf_config_file.py script to generate a LDF config file based on a CUPiD configuration file.

   • cd CUPiD/examples/external_diag_packages

   • ../../helper_scripts/generate_ldf_config_file.py --cupid-config-loc . --ldf-template ../../externals/LDF/config_clm_unstructured_plots.yaml --out-file LDF_config.yaml

3. Run LDF with the newly created configuration file.

   • ../../externals/LDF/run_adf_diag LDF_config.yaml

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import os

from IPython.core.display import HTML, Image
from IPython.display import display
import pandas as pd

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ldf_root = "."
case_name = None
base_case_name = None
start_date = ""
end_date = ""
base_start_date = None
base_end_date = None
key_plots = None
# ldf_root will be external_diag_packages/computed_notebooks/LDF/

# 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
ldf_root = "../../examples/313v308/LDF_output/"
key_plots = [
    "GPP_ANN_LatLon_Mean.png",
    "ELAI_ANN_LatLon_Mean.png",
    "TOTECOSYSC_GlobalMean_ANN_TimeSeries_Mean.png",
    "RegionalClimo_Amazonia_RegionalClimo_Mean.png",
]
subset_kwargs = {}
product = "/glade/u/home/dsanders/cesm_dev/CUPiD/examples/313v308/computed_notebooks//lnd/LDF.ipynb"

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# Want some base case parameter defaults to equal control case values
if base_case_name is not None:
    if base_start_date is None:
        base_start_date = start_date

    if base_end_date is None:
        base_end_date = end_date

# convert start-date and end-date to year range
case_year_range = [int(start_date.split("-")[0]), int(end_date.split("-")[0])]

if (not base_start_date) and (not base_end_date):
    base_case_year_range = None
else:
    base_case_year_range = [
        int(base_start_date.split("-")[0]),
        int(base_end_date.split("-")[0]),
    ]

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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_ldf_comparison_names = [
    f"{case_name}_{case_year_range[0]}_{case_year_range[1]}_vs_{base_case_name}{base_case_yr_range_str}"
]
possible_ldf_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_ldf_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_ldf_comparison_names.append(
    f"{case_name}_{case_year_range[0]}_{case_year_range[1]}_vs_{base_case_name}{alt_base_case_yr_range_str}"
)

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ldf_found = False
for ldf_comparison_name in possible_ldf_comparison_names:
    ldf_root_candidate = os.path.join(ldf_root, ldf_comparison_name)
    if os.path.exists(ldf_root_candidate):
        ldf_found = True
        ldf_root = ldf_root_candidate
        display(
            HTML(
                f'<a href="../LDF/{ldf_comparison_name}/website/index.html" target="_blank" style="font-size: 30px">Full LDF output</a>'
            )
        )
        break

if not ldf_found:
    print("No LDF output found for the specified case and date range.")

Full LDF output

Key Metrics from LDF

Some important things to look at from LDF include a comparison table and a few maps:

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comparison_table = os.path.join(ldf_root, "amwg_table_comp.csv")
if os.path.isfile(comparison_table):
    table = pd.read_csv(comparison_table)
    display(HTML(table.to_html(index=False, float_format="{:6g}".format)))

variable	unit	test	control	diff
TSA	K	280.539	280.5	0.039
TV	K	281.162	281.136	0.026
PREC	mm d$^{-1}$	2.282	2.295	-0.013
ELAI	m${-2}$ m${-2}$	1.139	1.162	-0.023
FSDS	W m$^{-2}$	197.501	197.359	0.142
FLDS	W m$^{-2}$	295.572	295.423	0.149
QBOT	kg/kg	0.0065	0.00648	2e-05
ASA	% reflected	29.191	29.131	0.06
RNET	W m$^{-2}$	61.186	70.966	-9.78
FSH	W m$^{-2}$	33.476	33.227	0.249
ET	W m$^{-2}$	36.769	36.912	-0.143
FCTR	W m$^{-2}$	17.118	17.137	-0.019
FGEV	W m$^{-2}$	11.474	11.466	0.008
FCEV	W m$^{-2}$	8.177	8.309	-0.132
QRUNOFF_TO_COUPLER	mm d$^{-1}$	0.955	0.966	-0.011
SNOWDP	m	2.254	2.252	0.002
GPP	PgC y$^{-1}$	92.994	93.576	-0.582
NPP	PgC y$^{-1}$	37.596	37.806	-0.21
NEE	PgC y$^{-1}$	1.741	0.55	1.191
NBP	PgC y$^{-1}$	-2.346	-1.14	-1.206
NPP_NUPTAKE	gC/m^2/s	2.78e-06	2.81e-06	-3e-08
BTRANMN	unitless	0.594	0.586	0.008
TOTECOSYSC	gC/m^2	18680.7	18530.5	150.207
TOTSOMC_1m	PgC	1458.34	1445.32	13.013
ALTMAX	m	31.941	32.163	-0.222
TWS	mm	7550.03	7566.55	-16.516
SOILWATER_10CM	kg/m2	27.799	27.852	-0.053
GRAINC_TO_FOOD	PgC y$^{-1}$	0.43	0.419	0.011
FAREA_BURNED	s-1	2.5e-09	2.46e-09	4e-11

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for path_to_key_plot in key_plots:
    full_path = os.path.join(ldf_root, path_to_key_plot)
    if os.path.isfile(full_path):
        display(Image(full_path))