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An approach to homogenize daily peak wind gusts: An application to the Australian series

Artikel i vetenskaplig tidskrift
Författare Cesar Azorin-Molina
Jose A. Guijarro
Tim R. McVicar
Blair C. Trewin
Andrew J. Frost
Deliang Chen
Publicerad i International Journal of Climatology
Volym 39
Nummer/häfte 4
Sidor 2260-2277
ISSN 0899-8418
Publiceringsår 2019
Publicerad vid Institutionen för geovetenskaper
Sidor 2260-2277
Språk en
Länkar doi.org/10.1002/joc.5949
Ämnesord Australia, Climatol V3.1, daily peak wind gusts, homogenization
Ämneskategorier Geovetenskap och miljövetenskap


© 2018 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society. Daily Peak Wind Gust (DPWG) time series are important for the evaluation of wind-related hazard risks to different socioeconomic and environmental sectors. Yet, wind time series analyses can be impacted by several artefacts, both temporally and spatially, which may introduce inhomogeneities that mislead the study of their decadal variability and trends. The aim of this study is to present a strategy in the homogenization of a challenging climate extreme such as the DPWG using 548 time series across Australia for 1941–2016. This automatic homogenization of DPWG is implemented in the recently developed Version 3.1 of the R package Climatol. This approach is an advance in homogenization of climate records as it identifies 353 break points based on monthly data, splits the daily series into homogeneous subperiods, and homogenizes them without needing the monthly corrections. The major advantages of this homogenization strategy are its ability to: (a) automatically homogenize a large number of DPWG series, including short-term ones and without needing site metadata (e.g., the change in observational equipment in 2010/2011 was correctly identified); (b) use the closest reference series even not sharing a common period with candidate series or presenting missing data; and (c) supply homogenized series, correcting anomalous data (quality control by spatial coherence), and filling in all the missing data. The NCEP/NCAR reanalysis wind speed data were also trialled in aiding homogenization given the station density was very low during the early decades of the record; however, reanalysis data did not improve the homogenization. Application of this approach found a reduced range of DPWG trends based on site data, and an increased negative regional trend of this climate extreme, compared to raw data and homogenized data using NCEP/NCAR. The analysis produced the first homogenized DPWG dataset to assess and attribute long-term variability of extreme winds across Australia.

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