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).