The influence of station density on climate data homogenization

Abstract

ABSTRACT:Relative homogenization methods assume that measurements of nearby stations experience similar climate signals and rely therefore on dense station networks with high-temporal correlations. In developing countries such as Peru, however, networks often suffer from low-station density. The aim of this study is to quantify the influence of network density on homogenization. To this end, the homogenization method HOMER was applied to an artificially thinned Swiss network.Four homogenization experiments, reflecting different homogenization approaches, were examined. Such approaches include diverse levels of interaction of the homogenization operators with HOMER, and different application of metadata. To evaluate the performance of HOMER in the sparse networks, a reference series was built by applying HOMER under the best possible conditions. Applied in completely automatic mode, HOMER decreases the reliability of temperature records. Therefore, automatic use of HOMER is not recommended. If HOMER is applied in interactive mode, the reliability of temperature and precipitation data may be increased in sparse networks. However, breakpoints must be inserted conservatively. Information from metadata should be used only to determine the exact timing of statistically detected breaks. Insertion of additional breakpoints based solely on metadata may lead to harmful corrections due to the high noise in sparse networks.

Effects of undetected data quality issues on climatological analyses

“…On a daily timescale, this reduction is 0.10 (TX) and 0.13 (TN) for station pairs within 300 km of distance and 0.06 (PRCP) for stations within 100 km of distance. These findings confirm the assumption by Gubler et al (2017) that the strong differences in correlation coefficients between station networks of the Peruvian Andes and Switzerland may not be explained by unequal climate regimes alone. Hypothesizing that UDQIs occur more frequently in the station networks of developing than developed countries, a higher frequency of such errors can be expected in tropical areas than in mid-latitudes.…”

“…Reducing the spatial density of available data normally decreases the quality of the results such as for data homogenization (Caussinus and Mestre, 2004;Domonkos, 2013;Gubler et al, 2017). With the present study, however, we have demonstrated that removing time series segments affected by UDQIs increases the overall quality of the dataset, and the results of climatological analyses are consequently more coherent and reliable.…”

“…For the success of the widely applied relative homogenization method, highly correlated time series are required (Cao and Yan, 2012;Gubler et al, 2017;Plummer et al, 2003;Trewin, 2013;Venema et al, 2012). Similarly, the important spatial consistency test in the QC process depends on suitable neighbouring stations (Durre et al, 2010;Plummer et al, 2003).…”

“…et al, 2000). According to Gubler et al (2017), not only climatological factors may be responsible for such differences, but also factors related to the quality of the observations, such as station siting and observation practices. Besides potentially reducing the correlation between station pairs, data quality issues may also induce inhomogeneities in time series (WMO, 2008).…”

Homogenization of a combined hourly air temperature dataset over Romania

“…Gubler et al . () performed four experiments on Peruvian and Swiss networks in order to assess the influence of station density on climate data homogenization.…”