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

Improving surface melt estimation over the Antarctic Ice Sheet using deep learning: A proof of concept over the Larsen Ice Shelf

Hu, Zhongyang (Universiteit Utrecht)
Kuipers Munneke, Peter (Universiteit Utrecht)
Lhermitte, S.L.M. (TU Delft Mathematical Geodesy and Positioning)
Izeboud, M. (TU Delft Mathematical Geodesy and Positioning)
Van Den Broeke, Michiel (Universiteit Utrecht)

2021

Accurately estimating the surface melt volume of the Antarctic Ice Sheet is challenging and has hitherto relied on climate modeling or observations from satellite remote sensing. Each of these methods has its limitations, especially in regions with high surface melt. This study aims to demonstrate the potential of improving surface melt simulations with a regional climate model by deploying a deep learning model. A deep-learning-based framework has been developed to correct surface melt from the regional atmospheric climate model version 2.3p2 (RACMO2), using meteorological observations from automatic weather stations (AWSs) and surface albedo from satellite imagery. The framework includes three steps: (1) training a deep multilayer perceptron (MLP) model using AWS observations, (2) correcting Moderate Resolution Imaging Spectroradiometer (MODIS) albedo observations, and (3) using these two to correct the RACMO2 surface melt simulations. Using observations from three AWSs at the Larsen B and C ice shelves, Antarctica, cross-validation shows a high accuracy (root-mean-square error of 0.95ĝ€¯mmĝ€¯w.e.ĝ€¯d-1, mean absolute error of 0.42ĝ€¯mmĝ€¯w.e.ĝ€¯d-1, and a coefficient of determination of 0.95). Moreover, the deep MLP model outperforms conventional machine learning models and a shallow MLP model. When applying the trained deep MLP model over the entire Larsen Ice Shelf, the resulting corrected RACMO2 surface melt shows a better correlation with the AWS observations for two out of three AWSs. However, for one location (AWS 18), the deep MLP model does not show improved agreement with AWS observations; this is likely because surface melt is largely driven by factors (e.g., air temperature, topography, katabatic wind) other than albedo within the corresponding coarse-resolution model pixels. Our study demonstrates the opportunity to improve surface melt simulations using deep learning combined with satellite albedo observations. However, more work is required to refine the method, especially for complicated and heterogeneous terrains.

http://resolver.tudelft.nl/uuid:213c756d-3d14-4035-af20-23189acbd63c

https://doi.org/10.5194/tc-15-5639-2021

1994-0416

The Cryosphere, 15 (12), 5639-5658

Institutional Repository

journal article

© 2021 Zhongyang Hu, Peter Kuipers Munneke, S.L.M. Lhermitte, M. Izeboud, Michiel Van Den Broeke