E32 Verlengen van AI-neerslagnowcasting door blending met weermodellen

dc.rights.license	CC-BY-NC-ND
dc.contributor.advisor	Siebes, Arno
dc.contributor.author	Luo, Kevin
dc.date.accessioned	2024-08-07T23:05:38Z
dc.date.available	2024-08-07T23:05:38Z
dc.date.issued	2024
dc.identifier.uri	https://studenttheses.uu.nl/handle/20.500.12932/47134
dc.description.abstract	This thesis was done for the company Weather Impact to assess the potential of blending of KNMI harmonie data and DGMR. Linear blending was selected and tested on data from various days. For linear blending, the python package Pysteps was used. The results of linear blending showed some improvements for low intensity precipitation but struggled with high intensity precipitation.
dc.description.sponsorship	Utrecht University
dc.language.iso	EN
dc.subject	E32 Verlengen van AI-neerslagnowcasting door blending met weermodellen
dc.title	E32 Verlengen van AI-neerslagnowcasting door blending met weermodellen
dc.type.content	Master Thesis
dc.rights.accessrights	Open Access
dc.subject.keywords	Weather forecasting, nowcasting, blending, Numerical weather prediction
dc.subject.courseuu	Applied Data Science
dc.thesis.id	36221