metadata

license: mit
datasets:
  - CEMAC/synthetic_lee_waves
metrics:
  - mse
pipeline_tag: image-to-image
library_name: fastai
tags:
  - climate

Model Card for LeeWaveNet

This repository contains four neural-network models, trained using fastai, for detecting and determining characteristics of trapped lee waves using maps of 700 hPa vertical velocity as input.

The base model segmodel.pkl generates a segmentation mask indicating where trapped lee waves are present. This model uses a U-Net architecture with Resnet-34 (pre-trained on ImageNet) as the encoder model.
Three alternative model heads have been trained on synthetic data: amplitude_0.0625.pkl, wavelength_0.125.pkl and orientation_0.25.pkl. These predict the amplitude, wavelength and orientation of detected waves respectively.

For full details, please see the article by Coney et al. (2023).