Addressing up-scaling methodologies for convection-permitting EPSs using statistical and machine learning tools

Autor:	T. Comito, C. Clancy, C. Daly, A. Hally
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	Science Physics QC1-999 Meteorology. Climatology QC851-999
Zdroj:	Advances in Science and Research, Vol 18, Pp 145-156 (2021)
Druh dokumentu:	article
ISSN:	1992-0628 1992-0636
DOI:	10.5194/asr-18-145-2021
Popis:	Convection-permitting weather forecasting models allow for prediction of rainfall events with increasing levels of detail. However, the high resolutions used can create problems and introduce the so-called “double penalty” problem when attempting to verify the forecast accuracy. Post-processing within an ensemble prediction system can help to overcome these issues. In this paper, two new up-scaling algorithms based on Machine Learning and Statistical approaches are proposed and tested. The aim of these tools is to enhance the skill and value of the forecasts and to provide a better tool for forecasters to predict severe weather.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/733c5a1e96dd422abde328a057220a82 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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