Version 1
: Received: 11 September 2024 / Approved: 11 September 2024 / Online: 12 September 2024 (15:37:00 CEST)
How to cite:
Rieutord, T.; Bessardon, G.; Gleeson, E. High-Resolution Land Use Land Cover Dataset for Meteorological Modelling–Part 2: ECOCLIMAP-SG-ML an Ensemble Land Cover Map. Preprints2024, 2024090942. https://doi.org/10.20944/preprints202409.0942.v1
Rieutord, T.; Bessardon, G.; Gleeson, E. High-Resolution Land Use Land Cover Dataset for Meteorological Modelling–Part 2: ECOCLIMAP-SG-ML an Ensemble Land Cover Map. Preprints 2024, 2024090942. https://doi.org/10.20944/preprints202409.0942.v1
Rieutord, T.; Bessardon, G.; Gleeson, E. High-Resolution Land Use Land Cover Dataset for Meteorological Modelling–Part 2: ECOCLIMAP-SG-ML an Ensemble Land Cover Map. Preprints2024, 2024090942. https://doi.org/10.20944/preprints202409.0942.v1
APA Style
Rieutord, T., Bessardon, G., & Gleeson, E. (2024). High-Resolution Land Use Land Cover Dataset for Meteorological Modelling–Part 2: ECOCLIMAP-SG-ML an Ensemble Land Cover Map. Preprints. https://doi.org/10.20944/preprints202409.0942.v1
Chicago/Turabian Style
Rieutord, T., Geoffrey Bessardon and Emily Gleeson. 2024 "High-Resolution Land Use Land Cover Dataset for Meteorological Modelling–Part 2: ECOCLIMAP-SG-ML an Ensemble Land Cover Map" Preprints. https://doi.org/10.20944/preprints202409.0942.v1
Abstract
While the surface of the Earth plays a key role in weather forecasting through its interaction with the atmosphere, in ensemble numerical weather predictions the uncertainty on the surface is only represented with perturbations in the parameterisations representing the surface processes. Data representing the surface, such as the land cover, are not perturbed. As fully data-driven forecasts without parameterisations are growing in importance, sampling the uncertainty on the land cover data brings a new way of making ensemble forecasts. Our work describes a method of generating ensemble land cover maps for numerical weather prediction. The target land cover map has the ECOCLIMAP-SG labels, used in the SURFEX surface model, and therefore is expected to have all relevant labels for surface-atmosphere interactions. The method translates the ESA WorldCover map to ECOCLIMAP-SG labels and resolution using auto-encoders. The land cover ensemble members are obtained by sampling the land cover probabilities in the output of the neural network. This paper builds upon the work done in a companion paper describing the high-resolution version of ECOCLIMAP-SG, called ECOCLIMAP-SG+, used for the training and evaluation of the neural network. The output map presented here, called ECOCLIMAP-SG-ML, improves upon the ECOCLIMAP-SG map in terms of resolution (from 300~m to 60~m), overall accuracy (from 0.41 to 0.63) and the ability to produce ensemble members.
Keywords
land cover land use; machine learning; meteorology
Subject
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
