Requirements

The data checker relies on the following libraries:

numpy, xarray, argparse, dateutil.relativedelta, datetime, json, sys, os, pathlib, re, stat, logging, typing

Install requirements with:

pip install numpy xarray python-dateutil

How to run

1. Add ${checkerdir}/src to PYTHONPATH in ~/.bashrc, where ${checkerdir} is the full path to the checker directory:
   export PYTHONPATH="${PYTHONPATH}:${checkerdir}/src"

2. Configure the config file (config_lu.json for landuse or config_em.json for emissions), which contains the following settings:
   • directory: the directory with the files requiring checking;
   • log_path: where to save logs (relative path inside the checker directory);
   • base_path: full path to the checker directory;
   • required_file_types: for the landuse files there are "multiple-management", "multiple-states", "multiple-transitions";
   • required_variables: variables which are mandatory to be in the files (for each file type independently)
   • required_coords: coordinates which are mandatory to be in the files (for each file type independently);
   • required_attributes: general attributes which are mandatory for the files;
   • required_attributes_in_vars: variable-specific attributes which are mandatory for the files.

3. Configure the file ${checkerdir}/src/variable-info_landuse.json or ${checkerdir}/src/variable-info_emissions.json which contains the variable ranges requirements (for each file type independently).

4. Run: python run_script.py config_lu.json or python run_script.py config_em.json.

Checkers

FileNameChecker: ${checkerdir}/src/checkers/checker_00_file_name.py

Check filetype ("multiple-management", "multiple-states", or "multiple-transitions") and the filename (it should match a pattern multiple-<...>_input4MIPs_landState_<...>_gn_YYYY-YYYY.nc). It uses functions from ${checkerdir}/src/utils/misc_utils.py.

StandardComplianceChecker: ${checkerdir}/src/checkers/checker_01_standard_compliance.py

Check file permissions, dimension variables, compulsory attributes, _FillValue.

SpatialCompletenessChecker: ${checkerdir}/src/checkers/checker_02_spatial_completeness.py

Create the reference mask based on the reference file and check the presence of missing values. It uses functions from ${checkerdir}/src/utils/misc_utils.py.

SpatialConsistencyChecker: ${checkerdir}/src/checkers/checker_03_spatial_consistency.py

Check that the lon/lat grid points correspond to the reference file.

TemporalConsistencyChecker: ${checkerdir}/src/checkers/checker_04_temporal_consistency.py

Check timesteps for consistency. It uses functions from ${checkerdir}/src/utils/path_utils.py.

i is the number of the timestep in the file:
time = "2020-01-01" [0], "2025-01-01" [1], "2030-01-01" [2], "2035-01-01" [3], "2040-01-01" [4], "2045-01-01" [5], "2050-01-01" [6], "2055-01-01" [7], "2060-01-01" [8], "2070-01-01" [9], "2080-01-01" [10], "2090-01-01" [11], "2100-01-01" [12]

During the check, the 'time' array is replaced by the 'timestep' array:
timesteps = [50, 55, 60, 65, 70, 75, 80, 85, 90, 100, 110, 120, 130]

timediff is the difference between two consequtive timesteps: timediff = timesteps[i] - timesteps[i-1].
Here we have timefiff of either 5 or 10 years:

• for i<=8 (before 2060) timediff should be 5 years
• for other i (after 2060) timediff should be 10 years

ValidRangesChecker: ${checkerdir}/src/checkers/checker_05_valid_ranges.py

Check that data values are in the required range (defined in ${checkerdir}/src/variable-info.json). It uses functions from ${checkerdir}/src/utils/misc_utils.py.

StatesTransitionsChecker: ${checkerdir}/src/checkers/checker_06_states_transitions.py

1. For each multiple-states_<XXX>: check that the sum of all variables is close to 1.

2. For each multiple-transitions_<XXX>: take the corresponding file multiple-states_<XXX> (with the same <XXX>) and check that the sum of the gross landuse transitions matches the difference in states between two consecutive years (except for the variables secdf, primf, secdn, primn).

Algorithm for (2):

• In multiple-states_<...>, we have variables 'c3ann' 'c3nfx' 'c3per' ... , so for each variable var we take its value for the year Y: var_states_Y, and its value for the year Y+1: var_states_(Y+1).

• In multiple-transitions_<...>, we have 'c3ann_to_c3nfx' 'c3ann_to_c3per' 'c3ann_to_c4ann' ... , i.e. X_to_var and var_to_X with var from multiple-states_<...>.
  We calculate (for every year Y):
  sum(X_to_var) - the sum of all variables in multiple-transitions_<...> for the year Y with names to_{var}, and
  sum(var_to_X) - the sum of all variables in multiple-transitions_<...> for the year Y with names {var}_to,
  e.g. for c3ann at the year Y:
  sum(X_to_var) = sum ['c3nfx_to_c3ann', 'c3per_to_c3ann', 'c4ann_to_c3ann', 'c4per_to_c3ann', 'primf_to_c3ann', 'primn_to_c3ann', 'secdf_to_c3ann', 'secdn_to_c3ann', 'urban_to_c3ann', 'pastr_to_c3ann', 'range_to_c3ann']
sum(var_to_X) = sum ['c3ann_to_c3nfx', 'c3ann_to_c3per', 'c3ann_to_c4ann', 'c3ann_to_c4per', 'c3ann_to_secdf', 'c3ann_to_secdn', 'c3ann_to_urban', 'c3ann_to_pastr', 'c3ann_to_range']

• We want this equation to be true:
  sum(X_to_var) - sum(var_to_X) = var_states_(Y+1) - var_states_Y,
  so for each variable we calculate delta which should be close to 0:
  delta = [ sum(X_to_var) - sum(var_to_X) ] - [ states_(Y+1) - states_Y) ]

Other files

• ${checkerdir}/run_script.py: run the "main" function; ${checkerdir}/src/checkers/directory_checker.py and ${checkerdir}/scripts/check_file.py: configure the parameters and run all checkers;
• ${checkerdir}/src/utils: functions which are used by checkers.

Logging

For each run, the checker creates a new logging directory (its name includes the dataset name, current date and time) in ${checkerdir}/logs (the "logs" name can be modified in config_lu.json in "log_path").

There are files:

• <...>_errors.log - only errors;
• <...>_output.log - all information about the checking.