OneScience-Group/GraphDOP

1{2  "model_name": "GraphDOP",3  "model_type": "graphdop",4  "architectures": [5    "GraphDOP"6  ],7  "framework": "PyTorch",8  "domain": "climate-and-atmosphere",9  "task": "observation-driven-medium-range-weather-forecasting",10  "implementation": {11    "entry_point": "model/graphdop.py",12    "scope": "pure-PyTorch minimal reproduction using gridded ERA5 placeholders and fixed regular-mesh graphs instead of the paper's irregular Level-1 observations and dynamic graphs"13  },14  "architecture": {15    "family": "GNN encoder-Transformer processor-GNN decoder",16    "input_format": "B T C H W",17    "encoder": "per-grid-cell MLP, adaptive pooling to the latent mesh, then residual mean-aggregation GNN layers",18    "processor": "pre-normalized Transformer encoder over latent-mesh tokens with learned positional embeddings",19    "decoder": "latent-mesh GNN, bilinear upsampling, and a per-grid-cell output MLP",20    "edge_features": [21      "forward bearing",22      "Haversine distance"23    ],24    "activation": "GELU",25    "normalization": "LayerNorm",26    "loss": "channel-weighted mean squared error",27    "repository_default_config": {28      "purpose": "connectivity validation with synthetic gridded data",29      "grid_shape": [30        32,31        3232      ],33      "mesh_shape": [34        8,35        836      ],37      "in_channels": 6,38      "out_channels": 6,39      "input_steps": 2,40      "output_steps": 2,41      "latent_dim": 64,42      "num_encoder_layers": 2,43      "num_decoder_layers": 2,44      "num_processor_blocks": 1,45      "attention_heads": 4,46      "hidden_dim": 64,47      "channel_weights": [48        1,49        1,50        1,51        1,52        1,53        154      ]55    },56    "paper_reference_config": {57      "latent_grid": "O96 reduced Gaussian grid with 40320 nodes",58      "latent_dim": 1024,59      "observation_graph": "dynamic graph over irregular Level-1 observations",60      "training_steps": 70000,61      "training_hardware": "64 H100 GPUs"62    }63  },64  "data": {65    "dataset": "ERA5",66    "role": "regular-grid placeholder for the paper's multi-instrument observations",67    "variables": [68      "atms_brightness_temperature",69      "gpsro_bending_angle",70      "ascat_sigma0",71      "significant_wave_height",72      "2m_temperature",73      "10m_wind_speed"74    ],75    "time_step_hours": 6,76    "input_length": 2,77    "output_length": 2,78    "channels": 6,79    "spatial_size": [80      32,81      3282    ],83    "storage_format": "HDF5 fields with T C H W layout",84    "train_years": [85      1951,86      195287    ],88    "validation_years": [89      195390    ],91    "test_years": [92      195493    ]94  },95  "configuration_sources": [96    "README.md",97    "conf/config.yaml",98    "model/graphdop.py",99    "scripts/train.py",100    "scripts/inference.py",101    "scripts/fake_data.py",102    "configuration.json"103  ]104}105