EGU23-4044, updated on 22 Feb 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

DailyMelt: Diffusion-based Models for Spatiotemporal Downscaling of (Ant-)arctic Surface Meltwater Maps

Björn Lütjens1, Patrick Alexander5, Raf Antwerpen5, Guido Cervone4, Matthew Kearney2, Bingkun Luo3, Dava Newman1, and Marco Tedesco5
Björn Lütjens et al.
  • 1Massachusetts Institute of Technology, United States of America (
  • 2Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology
  • 3Harvard-Smithsonian Center for Astrophysics, Harvard University
  • 4Institute for Computational and Data Sciences, Pennsylvania State University
  • 5Lamont-Doherty Earth Observatory, Columbia University

Motivation. Ice melting in Greenland and Antarctica has increasingly contributed to rising sea levels. Yet, the exact speed of melting, existence of abrupt tipping points, and in-detail links to climate change remain uncertain. Ice shelves essentially prevent the ice sheet from slipping into the ocean and better prediction of collapses is needed. Meltwater at the surface of ice shelves indicates ice shelf collapse through destabilizing ice shelves via fracturing and flexural processes (Banwell et al., 2013) and is likely impacted by a warming climate ( Kingslake et al., 2017). Maps of meltwater have been created from in-situ and remote observations, but their low and irregular spatiotemporal resolution severely limits studies (Kingslake et al., 2019).

Research Gap. In particular, there does not exist daily high-resolution (< 500m) maps of surface meltwater. We propose the first daily high-resolution surface meltwater maps by developing a deep learning-based downscaling method, called DailyMelt, that fuses observations and simulations of varying spatiotemporal resolution, as illustrated in Fig.1. The created maps will improve understanding of the origin, transport, and controlling physical processes of surface meltwater. Moreover, they will act as unified source to improve sea level rise and meltwater predictions in climate models. 

Data. To synthesize surface meltwater maps, we leverage observations from satellites (MODIS, Sen-1 SAR) which are high-resolution (500m, 10m), but have substantial temporal gaps due to repeat time and cloud coverage. We fuse them with simulations (MAR) and passive microwave observations (MEaSURE) that are daily, but low-resolution (6km, 3.125km). In a significant remote sensing effort, we have downloaded, reprojected, and regridded all products into daily observations for our study area over Greenland’s Helheim glacier. 

Approach and expected results. Within deep generative vision models, diffusion-based models promise sharp and probabilistic predictions. We have implemented SRDiff (Li H. et al., 2022) and tested it on spatially downscaling external data. As a baseline model, we have implemented a statistical downscaling model that is a local hybrid physics-linear regression model (Noel et al., 2016). In our planned benchmark, we expect a baseline UNet architecture that minimizes RMSE to create blurry maps and a generative adversarial network that minimizes adversarial loss to create sharp but deterministic maps. We have started with spatial downscaling and will include temporal downscaling. 

In summary, we will create the first daily high-resolution (500m) surface meltwater maps, have introduced the first diffusion-based model for downscaling Earth sciences data, and have created the first benchmark dataset for downscaling surface meltwater maps.



Banwell, A. F., et al. (2013), Breakup of the Larsen B Ice Shelf triggered by chain reaction drainage of supraglacial lakes, Geophys. Res. Lett., 40 

Kingslake J, et al. (2017), Widespread movement of meltwater onto and across Antarctic ice shelves, Nature, 544(7650)

Kingslake J., et al. (2019), Antarctic Surface Hydrology and Ice Shelf Stability Workshop report, US Antarctic Program Data Center

Li H., et al. (2022), SRDiff: Single image super-resolution with diffusion probabilistic models, Neurocomputing, 479

Noël, B., et al. (2016), A daily, 1 km resolution data set of downscaled Greenland ice sheet surface mass balance (1958–2015), The Cryosphere, 10

How to cite: Lütjens, B., Alexander, P., Antwerpen, R., Cervone, G., Kearney, M., Luo, B., Newman, D., and Tedesco, M.: DailyMelt: Diffusion-based Models for Spatiotemporal Downscaling of (Ant-)arctic Surface Meltwater Maps, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-4044,, 2023.