dc.contributor.author | Herrera, M | |
dc.contributor.author | Ramallo-Gonz alez, A | |
dc.contributor.author | Eames, ME | |
dc.contributor.author | Ferreira, A | |
dc.contributor.author | Coley, D | |
dc.date.accessioned | 2018-03-06T15:22:45Z | |
dc.date.issued | 2018-03-21 | |
dc.description.abstract | Heat waves give rise to order of magnitude higher mortality rates than other
weather-related natural disasters. Unfortunately both the severity and amplitude
of heat waves are predicted to increase worldwide as a consequence of
climate change. Hence, meteorological services have a growing need to identify
such periods in order to set alerts, whilst researchers and industry need representative
future heat waves to study risk. This paper introduces a new locationspecific
mortality risk focused definition of heat waves and a new mathematical
framework for the creation of time series that represents them. It focuses on
identifying periods when temperatures are high during the day and night, as
this coincidence is strongly linked to mortality. The approach is tested using
observed data from Brazil and the UK. Comparisons with previous methods
demonstrate that this new approach represents a major advance that can be
adopted worldwide by governments, researchers and industry | en_GB |
dc.description.sponsorship | This research has been performed in the project COLBE on The creation of localized
current and future weather for the built environment, EPSRC [grant code EP/K002724/1].
Ramallo-Gonz´alez would like to acknowledge Fundaci´on S´eneca-Agencia de Ciencia y Tecnolog´ıa
de la Regi´on de Murcia, CARM. | en_GB |
dc.identifier.citation | Published online 21 March 2018. | en_GB |
dc.identifier.doi | 10.1016/j.envsoft.2018.03.007 | |
dc.identifier.uri | http://hdl.handle.net/10871/31884 | |
dc.language.iso | en | en_GB |
dc.publisher | Elsevier | en_GB |
dc.relation.source | All data created during this research are openly available
from the University of Bath data archive at: https://doi.org/10.15125/BATH-XXXX. | en_GB |
dc.rights.embargoreason | Under embargo until 21 March 2019 in compliance with publisher policy. | en_GB |
dc.rights | © 2018 Elsevier Ltd. All rights reserved. | |
dc.subject | quantile regression | en_GB |
dc.subject | models ensemble | en_GB |
dc.subject | weather files | en_GB |
dc.subject | built environment | en_GB |
dc.subject | heat waves | en_GB |
dc.title | Creating Extreme Weather Time Series through a Quantile Regression Ensemble | en_GB |
dc.type | Article | en_GB |
dc.identifier.issn | 1364-8152 | |
dc.description | This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record. | en_GB |
dc.identifier.journal | Environmental Modelling and Software | en_GB |