Output
The output of the FootNet model contains footprints, and reference information for matching corresponding receptors.
The output of the FootNet is a time-integrated footprint with dimensions (400, 400) with units of ppm /(µmol m-2 s-1). The output footprint has a spatial resolution of 1 km.
The footprints are provided as numpy arrays for easier integration with existing inversion frameworks or an option to save the footprints on disk.
Output format options
- It is recommended to use numpy arrays if using FootNet on-the-fly in an inversion.
- If the user prefers storing the footprints then it is easier to use NetCDF format.
- User can also write their own storage functions to add any additional storage format.
Surface Measurement Example
from SurfaceFootNet import SurfaceFootNet
# Loading FootNet model
model_path = "../models/SurfaceFootNet_in_sample.pth"
model = SurfaceFootNet(model_path=model_path)
foots, reference_indices, reference_timestamps, reference_rlons, reference_rlats = model.run_inference(receptors, input_met)
Column Measurement Example
from ColumnFootNet import ColumnFootNet
# Loading FootNet model
model_path = "../models/ColumnFootNet_in_sample.pth"
model = ColumnFootNet(model_path=model_path)
foots, reference_indices, reference_timestamps, reference_rlons, reference_rlats = model.run_inference(receptors, input_met)