RF-ClimParam / config.json
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{
"model_name": "RF-ClimParam",
"model_type": "joint-multi-output-random-forest-regressor",
"architectures": ["JointRandomForestRegressor"],
"framework": "PyTorch",
"domain": "earth-science",
"task": "multi-resolution-subgrid-parameterization",
"implementation": {
"entry_point": "model/rf_climparam.py",
"scope": "independent paper-method engineering reproduction",
"train_script": "scripts/train.py",
"inference_script": "scripts/inference.py",
"evaluation_script": "scripts/result.py",
"synthetic_data_script": "scripts/fake_data.py"
},
"architecture": {
"family": "bootstrapped extra-random regression forest",
"rf_tend_input": 145,
"rf_tend_output": 144,
"rf_diff_input": 62,
"rf_diff_output": 17,
"joint_multi_output_leaves": true,
"scales": ["x4", "x8", "x16", "x32"]
},
"paper_model": {
"trees": 10,
"min_samples_leaf": {"x4": 20, "x8": 20, "x16": 20, "x32": 7},
"training_samples": {"x4": 5000000, "x8": 5000000, "x16": 5000000, "x32": "not_reported"},
"max_depth": "not_reported",
"max_features": "not_reported",
"bootstrap": "not_reported",
"split_criterion": "not_reported",
"implementation": "scikit-learn-0.21.2-RandomForestRegressor"
},
"data": {
"protocol": "rf_climparam_v2",
"format": "NPZ",
"high_resolution": {"km": 12, "grid": [576, 1440], "vertical_levels": 48},
"coarse_grids": {
"x4": {"km": 48, "grid": [144, 360]},
"x8": {"km": 96, "grid": [72, 180]},
"x16": {"km": 192, "grid": [36, 90]},
"x32": {"km": 384, "grid": [18, 45], "online_native_grid": [18, 48]}
},
"domain_km": [6912, 17280],
"snapshot_hours": 3,
"default_timestep_seconds": 24,
"coarsening": "N by N horizontal average",
"synthetic_layout": "one complete C-order flattened structured snapshot per coarse scale",
"default_training_sampling": "one longitude column per latitude"
},
"paper": {
"arxiv": "2001.03151",
"doi": "10.1038/s41467-020-17142-3",
"official_code_data_osf_doi": "10.17605/OSF.IO/36YPT"
},
"configuration_sources": [
"conf/config.yaml", "model/rf_climparam.py", "scripts/fake_data.py",
"scripts/train.py", "scripts/inference.py", "scripts/result.py"
]
}