project:
  name: PBL-Emulator
  format_version: "1.0"
  seed: 19
  independent_code_license: Apache-2.0

paper_specification:
  title: Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model
  doi: 10.5194/gmd-12-4261-2019
  dimensions: {input: 16, levels: 17, variables_per_level: 5}
  output_variables: [U, V, W, tk, QVAPOR]
  architectures: {FFN_hidden_dense: 34, hierarchical_hidden_dense_per_level: 3, width: 16}
  training: {optimizer: Adam, learning_rate: 0.001, epochs: 1000, batch_size: 64, early_stopping_patience: 10}
  temporal_protocol: "3-hourly input and same-timestamp diagnostic output"

engineering_assumptions:
  default_architecture: HAC
  synthetic_data: "structured engineering data, not WRF observations or paper data"
  split: "chronological 70/15/15 percent"
  vertical_order: bottom_to_top

paths:
  data: data/pbl_emulator_synthetic.npz
  checkpoint: result/checkpoints/pbl_emulator.pt
  training_metrics: result/training/metrics.json
  predictions: result/output/predictions.npz
  evaluation_metrics: result/evaluation/metrics.json
  figure: result/evaluation/profiles.png

data:
  samples: 768
  seed: 7
  interval_hours: 3

model:
  architecture: HAC
  width: 16
  levels: 17
  output_variables: 5

training:
  epochs: 6
  batch_size: 64
  learning_rate: 0.001
  early_stopping_patience: 10
  seed: 19
  resume: false
  num_workers: 0

paper_model:
  epochs: 1000
  batch_size: 64
  width: 16