File size: 1,534 Bytes
c059069 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | seed: 2020
data:
root: data
format_version: rf_climparam_v2
scales: [x4, x8, x16, x32]
snapshots_per_scale: 1
train_columns_per_latitude: 1
vertical_levels: 48
low_level_diffusion_levels: 15
snapshot_hours: 3
training_days: 337.5
high_resolution:
resolution_km: 12
grid: [576, 1440]
domain_km: [6912, 17280]
coarse_grids:
x4: {factor: 4, resolution_km: 48, grid: [144, 360]}
x8: {factor: 8, resolution_km: 96, grid: [72, 180]}
x16: {factor: 16, resolution_km: 192, grid: [36, 90]}
x32: {factor: 32, resolution_km: 384, grid: [18, 45], online_native_grid: [18, 48]}
model:
rf_tend: {input_features: 145, output_features: 144}
rf_diff: {input_features: 62, output_features: 17}
engineering:
trees: 2
max_depth: 5
min_samples_leaf: 3
max_features: sqrt
split_candidates: 8
bootstrap: true
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
runtime:
device: cpu
paths:
checkpoint: result/checkpoints/rf_climparam.pt
training_metrics: result/training/metrics.json
inference: result/output/predictions.npz
evaluation_dir: result/evaluation
evaluation:
precipitation_seconds: 10800
online_proxy_scale: x32
online_proxy_grid: [18, 48]
prediction_chunk_size: 4096
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