{
  "model_name": "ClimODE",
  "model_type": "climode",
  "architectures": [
    "ClimODE",
    "ClimateEncoderFreeUncertain"
  ],
  "framework": "PyTorch",
  "domain": "atmosphere",
  "task": "global-weather-forecasting-with-neural-odes",
  "implementation": {
    "entry_point": "model/climode.py",
    "scope": "OneScience adapter of the physics-informed neural transport ODE with optional attention and probabilistic output; it is not an AutoModel-compatible Transformers implementation"
  },
  "architecture": {
    "family": "physics-informed neural ODE with advective transport and convolutional velocity/noise networks",
    "input_format": "BCHW",
    "input_grid_shape": [
      32,
      64
    ],
    "input_channels": 5,
    "output_channels": 5,
    "time_step_hours": 6,
    "solver": "euler",
    "absolute_tolerance": 0.005,
    "relative_tolerance": 0.005,
    "use_attention": true,
    "use_uncertainty": true,
    "use_positional_encoder": false,
    "resnet_repetitions": [
      5,
      3,
      2
    ],
    "resnet_hidden_channels": [
      128,
      64,
      10
    ],
    "history_frames_for_context": 3
  },
  "data": {
    "dataset": "ERA5",
    "source_grid_shape": [
      721,
      1440
    ],
    "model_grid_shape": [
      32,
      64
    ],
    "regrid_method": "bilinear with periodic longitude handling",
    "variables": [
      "z",
      "t",
      "t2m",
      "u10",
      "v10"
    ],
    "variable_sources": {
      "z": "geopotential_500",
      "t": "temperature_850",
      "t2m": "2m_temperature",
      "u10": "10m_u_component_of_wind",
      "v10": "10m_v_component_of_wind"
    },
    "input_steps": 1,
    "output_steps": 1,
    "normalization": "ClimODE min-max normalization; source statistics are loaded from the configured static directory",
    "uncertainty_output": "mean and standard deviation fields when use_uncertainty is enabled"
  },
  "configuration_sources": [
    "conf/config.yaml",
    "model/climode.py",
    "scripts/data_loader.py",
    "scripts/train.py"
  ]
}