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

Abstract

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 shortterm 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.

Uncertainty evaluation of Climatol's adjustment algorithm applied to daily air temperature time series

“…While the proposed approach to the evaluation of the adjustment uncertainty on the daily time scale appears attractive and theoretically rigorous, it can potentially lead to some problems that may limit its practical applicability. For instance, one of the limitations can be related to difficulties with a construction of the statistical ensemble for E R with a sufficient number of its individual realizations in order to perform the calculations according to (6). Another example of limitations can be explained as follow: typically, at the end of the time domain T , all station signals in E R contain undisturbed segments (see, for example, Fig.…”

“…The R package Climatol is a homogenization software that has been widely used in recent years for removing inhomogeneities from collections of raw time series of different climate variables and different time resolution (e.g., Mamara et al ., 2013; Sanchez‐Lorenzo et al ., 2015; Guijarro et al ., 2018; Meseguer‐Ruiz et al ., 2018; Azorin‐Molina et al ., 2019; Coll et al ., 2020; Dumitrescu et al ., 2020). The effectiveness of the software has been evaluated in several benchmark tests (Venema et al ., 2012; Killick, 2016; Guijarro et al ., 2017; Guijarro et al ., 2019) where it demonstrated good results, which are comparable in terms of accuracy to other well established and tested homogenization algorithms.…”

Climatology of Near‐Surface Daily Peak Wind Gusts Across Scandinavia: Observations and Model Simulations

“…Further information are available at http://www.climatol.eu/ (last accessed February 16, 2021). Climatol has been widely and successfully applied to homogenize wind series in previous studies (e.g., Shi et al., 2019; Zhang et al., 2020) and also to homogenize DPWG (Azorin‐Molina et al., 2016, 2019). For this reason, it was chosen here to perform the homogenization of DPWG series across Scandinavia.…”

Homogenization of a combined hourly air temperature dataset over Romania

“…The Climatol method has been successfully used to homogenize air temperature data (Mamara et al ., ; Kolendowicz et al ., ), solar radiation (Sanchez‐Lorenzo et al ., ), wind speed (Azorin‐Molina et al ., ), wind gusts (Azorin‐Molina et al ., ; Azorin‐Molina et al ., ) and precipitation (Luna et al ., ). Climatol was also tested against realistic benchmark datasets and returned very good results comparable to other homogenization methods which are currently used by the climatology community (Venema et al ., ; Guijarro et al ., ).…”