| dc.contributor.author | Keany, Eoghan | |
| dc.contributor.author | Bessardon, Geoffrey | |
| dc.contributor.author | Gleeson, Emily | |
| dc.date.accessioned | 2022-05-04T11:02:17Z | |
| dc.date.available | 2022-05-04T11:02:17Z | |
| dc.date.issued | 2022-05-02 | |
| dc.identifier.citation | 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, 2022-05-02 | |
| dc.identifier.uri | http://hdl.handle.net/2262/98528 | |
| dc.description | 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. | en |
| dc.language.iso | en | en |
| dc.publisher | Met Éireann | en |
| dc.relation.isversionof | https://doi.org/10.5194/asr-19-13-2022, 2022 | |
| dc.rights | Y | en |
| dc.subject | Climate zones | en |
| dc.subject | Machine learning | en |
| dc.subject | Height map | en |
| dc.title | Using machine learning to produce a cost-effective national building height map of Ireland to categorise local climate zones | en |
| dc.type | article | en |
| dc.type.supercollection | edepositireland | |
| dc.contributor.corporatename | Met Éireann | en |
| dc.publisher.place | IE | en |
| dc.rights.ecaccessrights | openAccess | |
