Machine Learning Emulation of Urban Land Surface Processes

Autor: Meyer, David, Grimmond, Sue, Dueben, Peter, Hogan, Robin, van Reeuwijk, Maarten

Publikováno v: Meyer, D., Grimmond, S., Dueben, P., Hogan, R., & van Reeuwijk, M. (2022). Machine Learning Emulation of Urban Land Surface Processes. Journal of Advances in Modeling Earth Systems, 14(3)

Can we improve the modeling of urban land surface processes with machine learning (ML)? A prior comparison of urban land surface models (ULSMs) found that no single model is 'best' at predicting all common surface fluxes. Here, we develop an urban ne

Externí odkaz: http://arxiv.org/abs/2112.11429

Copula-based synthetic data augmentation for machine-learning emulators

Autor: Meyer, David, Nagler, Thomas, Hogan, Robin J.

Publikováno v: Geoscientific Model Development, 14(8), 5205--5215 (2021)

Can we improve machine-learning (ML) emulators with synthetic data? If data are scarce or expensive to source and a physical model is available, statistically generated data may be useful for augmenting training sets cheaply. Here we explore the use

Externí odkaz: http://arxiv.org/abs/2012.09037

Twelve Times Faster yet Accurate: A New State‐Of‐The‐Art in Radiation Schemes via Performance and Spectral Optimization.

Autor: Ukkonen, Peter¹ (AUTHOR) peterukk@gmail.com, Hogan, Robin J.^2,3 (AUTHOR)

Publikováno v: Journal of Advances in Modeling Earth Systems. Jan2024, Vol. 16 Issue 1, p1-17. 17p.

Preface to the special issue "EarthCARE Level 2 algorithms and data products": Editorial in memory of Tobias Wehr.

Autor: Hogan, Robin J.^1,2 (AUTHOR) r.j.hogan@ecmwf.int, Illingworth, Anthony J.¹ (AUTHOR), Kollias, Pavlos³ (AUTHOR), Okamoto, Hajime⁴ (AUTHOR), Wandinger, Ulla⁵ (AUTHOR)

Publikováno v: Atmospheric Measurement Techniques. 2024, Vol. 17 Issue 10, p3081-3083. 3p.

Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.