Improving surface melt estimation over the Antarctic Ice Sheet using deep learning

Welcome to the repository for post-processing RACMO2 surface melt using deep learning and MODIS albedo

Current version: 1.0 (7 December 2021)

1. Repository Overview

This repository includes the code for building the MLP model, as well as its implementation.

The provided Jupyter Notebook can be used for training the MLP model.
We provide an example at AWS 14, demonstrating the MLP implementation

(1) with albedo from AWS/MODIS

(2) with meterorogical parameters provided by AWS/RACMO2

2. Model Structure

3. Data Availability

MODIS/Terra Surface Reflectance Daily L2G Global 1 km and 500 m SIN Grid product is available via the Land Processes Distributed Active Archive Center (LP DAAC) (https://doi.org/10.5067/MODIS/MOD09GA.006, last access: 3 December 2021). MODIS/Terra+Aqua Albedo Daily L3 Global 500 m SIN Grid product is also available via LP DAAC (https://doi.org/10.5067/MODIS/MCD43A3.006, last access: 3 December 2021). Sentinel-1 images are provided by the European Space Agency (ESA) (https://sentinel.esa.int/web/sentinel/sentinel-data-access, last access: 3 December 2021). Automatic weather station observations from AWS 14, 17, and 18 are available via https://doi.pangaea.de/10.1594/PANGAEA.910473. RACMO2 simulations (https://www.projects.science.uu.nl/iceclimate/models/antarctica.php#2-1, last access: 3 December 2021) are provided by van Wessem et al. (2018) which are available on request to the original authors. Some RACMO2 data are also available via Zenodo: https://zenodo.org/record/3677642.

4. Model Execution via cmd line

Related Publication:

Hu, Z., Kuipers Munneke, P., Lhermitte, S., Izeboud, M., and van den Broeke, M.: Improving Surface Melt Estimation over Antarctica Using Deep Learning: A Proof-of-Concept over the Larsen Ice Shelf, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-102, in review, 2021.