Using machine learning to produce a cost-effective national building height map of Ireland to categorise local climate zones
![Thumbnail](/xmlui/themes/Mirage2/images/white_rectangle.jpeg)
File Type:
PDFItem Type:
articleDate:
2022-05-02Access:
openAccessCitation:
Eoghan Keany, Geoffrey Bessardon, Emily Gleeson, 'Using machine learning to produce a cost-effective national building height map of Ireland to categorise local climate zones', [article], Met Éireann, 2022-05-02Description:
In numerical weather prediction (NWP) the estimation of the different surface fluxes (radiative and non-radiative) requires surface parameters calculated from land cover map information. Estimating these fluxes is essential for weather prediction as most of the atmospheric energy and water exchanges happen at the surface. A land cover map represents identifiable elements that the map producer wants to distinguish and is created using a mixture of remotely-sensed and in-situ observations. Land cover elements include, for example, the types of forest, crops, urban density and so on.Corporate name:
Met ÉireannPublisher:
Met ÉireannType of material:
articleCollections
Availability:
Full text availableKeywords:
Climate zones, Machine learning, Height mapMetadata
Show full item recordLicences: