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  Downscaling of meteorological variables by machine learning for driving models at snowdrift-permitting scales

Howe, L., Essery, R. (2023): Downscaling of meteorological variables by machine learning for driving models at snowdrift-permitting scales, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-4713

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Item Permalink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5021121 Version Permalink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5021121_1
Genre: Conference Paper

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 Creators:
Howe, Leam1, Author
Essery, Richard1, Author
Affiliations:
1IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations, ou_5011304              

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 Abstract: Meteorological variables required for driving snow energy-balance models are now widely available at ~1km scales from operational convection-permitting forecast models but not at ~100 m snowdrift-permitting scales. Simple statistical approaches to downscaling with lapse rates or linear regression are unreliable for variables such as precipitation rate and wind speed that do not have straightforward relationships with elevation, but more reliable downscaling with atmospheric dynamics models is computationally expensive. We explore the application of convolutional neural networks as a tool for downscaling that is more informative than simple statistical methods and faster than dynamical downscaling. Applications are illustrated by modelling late-lying snowdrifts in the Cairngorm Mountains of Scotland.

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Language(s): eng - English
 Dates: 2023-07-112023-07-11
 Publication Status: Finally published
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 Identifiers: DOI: 10.57757/IUGG23-4713
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Title: XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
Place of Event: Berlin
Start-/End Date: 2023-07-11 - 2023-07-20

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Title: XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
Source Genre: Proceedings
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Publ. Info: Potsdam : GFZ German Research Centre for Geosciences
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