Deep learning for quality control of surface physiographic fields using satellite Earth observations

Kimpson, Tom; Choulga, Margarita; Chantry, Matthew; Balsamo, Gianpaolo; Boussetta, Souhail; Dueben, Peter; Palmer, Tim

doi:https://doi.org/10.5194/hess-27-4661-2023

Articles | Volume 27, issue 24

https://doi.org/10.5194/hess-27-4661-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-27-4661-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 27, issue 24

Research article

|

22 Dec 2023

Research article |

| 22 Dec 2023

Deep learning for quality control of surface physiographic fields using satellite Earth observations

Tom Kimpson, Margarita Choulga, Matthew Chantry, Gianpaolo Balsamo, Souhail Boussetta, Peter Dueben, and Tim Palmer

Data sets

ERA5 hourly data on pressure levels from 1940 to present H. Hersbach et al. https://doi.org/10.24381/cds.bd0915c6

MYD11A1 MODIS/Aqua Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid V006 Z. Wan et al. https://doi.org/10.5067/MODIS/MYD11A1.006

Model code and software

tomkimpson/ML4L: 0.0.1 (0.0.1) T. Kimpson https://doi.org/10.5281/zenodo.10403785

Short summary

Lakes play an important role when we try to explain and predict the weather. More accurate and up-to-date description of lakes all around the world for numerical models is a continuous task. However, it is difficult to assess the impact of updated lake description within a weather prediction system. In this work, we develop a method to quickly and automatically define how, where, and when updated lake description affects weather prediction.