EMS Annual Meeting Abstracts
Vol. 20, EMS2023-482, 2023, updated on 06 Jul 2023
EMS Annual Meeting 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Developement of a land cover map for hectometric numerical weather prediction using machine learning

Geoffrey Bessardon, Thomas Rieutord, and Emily Gleeson
Geoffrey Bessardon et al.
  • Met Éireann, Research and Applications Division, Dublin 9, Ireland (geoffrey.bessardon@met.ie)

Developing hectometric scale numerical weather prediction (NWP) models requires an accurate, high-resolution physiography dataset to represent surface heterogeneity in calculating surface-atmosphere exchanges. Land cover is one of the main components of physiography datasets. Unfortunately, the resolution of land cover datasets currently used in NWP is too coarse to fulfil the needs of hectometric NWP.

High-resolution remote sensing imagery and machine learning techniques have enabled the emergence of 10 m resolution global land cover maps such as  the Environmental Systems Research Institute 2020 (ESRI2020), European Space Agency (ESA) WorldCover, and the second generation Coordination of Information on the Environment land cover  (CLC+). These dataset labels are too generic to be directly implemented into NWP. Other high-resolution datasets can provide information about specific themes, such as the Copernicus dominant leaf type (DLT) 2018 and the Global map of Local Climate Zones, or national/regional information, such as the Swedish national land cover database 2018. While plenty of high-resolution physiography resources exist, none cover NWP needs.

This work aims at developing a high-resolution version of ECOCLIMAP-SG, the land cover map used operationally at Met Éireann, using supervised machine learning techniques. Supervised machine-learning techniques are widely used for land cover mapping but require a reference dataset. Here we propose to develop a reference dataset using multiple data sources and bagging multiple decision trees. We will complement this dataset with artificial (or synthetic) data using Geometric Synthetic Minority Oversampling Technique (G-SMOTE). Eventually, we will compare supervised machine-learning techniques such as convolutional neural networks or random forests.

How to cite: Bessardon, G., Rieutord, T., and Gleeson, E.: Developement of a land cover map for hectometric numerical weather prediction using machine learning, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-482, https://doi.org/10.5194/ems2023-482, 2023.