Creating Extreme Weather Time Series through a Quantile Regression Ensemble

Manuel Herrera, Alfonso Ramallo-González, Matt Eames, Aida A. Ferreira, David Coley

Research output: Contribution to journalArticle

2 Citations (Scopus)
38 Downloads (Pure)

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 location-specific 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.

Original languageEnglish
Pages (from-to)28-37
Number of pages10
JournalEnvironmental Modelling and Software
Volume110
Early online date21 Mar 2018
DOIs
Publication statusPublished - 1 Dec 2018

Keywords

  • Built environment
  • Heat waves
  • Models ensemble
  • Quantile regression
  • Weather files

ASJC Scopus subject areas

  • Software
  • Environmental Engineering
  • Ecological Modelling

Fingerprint Dive into the research topics of 'Creating Extreme Weather Time Series through a Quantile Regression Ensemble'. Together they form a unique fingerprint.

  • Datasets

    Quantile Regression Ensemble Summer Year (QRESY)

    Herrera Fernandez, M. (Creator), Ramallo-González, A. (Creator), Eames, M. (Creator), Ferreira, A. A. (Creator) & Coley, D. (Creator), University of Bath, 21 Mar 2018

    Dataset

    Cite this