Homogenization of monthly precipitation time series in Croatia

Abstract

Various types of studies require a sufficiently long series of data processed identically over the entire area. For climate analysis, it is necessary that analysed time series are homogeneous, which means that their variations are caused only by variations in weather and climate. Unfortunately, most of the climatological series are inhomogeneous and contain outliers that may significantly affect the analysis results. The 137 stations with precipitation measurement belonging to the meteorological station network governed by the Meteorological and Hydrological Service of Croatia were selected for the present analysis. Most of the data series cover a period from the late 1940s or early 1950s through the year 2010. For quality control and homogenization, an approach based on the software ProClimDB/Anclim was applied. In this study, we describe the results from the quality control and homogenization process for monthly precipitation sums as well as the spatial relationship of precipitation in the Croatian region. The precipitation network in Croatia is fairly homogeneous as only 23% of the 137 analysed stations are found to be inhomogeneous.

Quality control and homogenization of daily meteorological data in the trans-boundary region of the Jhelum River basin

“…However, they cannot identify the errors such as where a data value is significantly different from the previous or the following value in the same time series (Feng et al, 2004). To detect these kinds of outliers, Tukey's method, known as Inter Quartile Range (IQR) method, developed by Tukey (1997) was used in the present study, as in González-Rouco et al, (2001), Štěpánek et al (2013) and Zahradníček et al (2014) to detect the outliers in the climatic datasets. There are three main steps to detect outliers: 1) to find out the inter quartile range (IQR)-which is the difference between the first quartile (Q1) and the third quartile (Q3); 2) to calculate lower and upper extremes-the lower and upper extremes are calculated by subtracting 1.5×IQR from Q1 and adding 1.5×IQR into Q3, respectively; 3) values beyond these limits are considered to be possible outliers.…”

“…It is the primary emphasis of quality control to treat with outliers before application of any homogenization approach, which can mislead homogenization results (González-Rouco et al, 2001;Štěpánek et al, 2013;Zahradníček et al, 2014). There is a lack of generally recommended methodology for quality control of meteorological data.…”

“…There is a lack of generally recommended methodology for quality control of meteorological data. Thus, in the present study, a combination of different methods applied in Feng et al (2004), Štěpánek et al (2013) and Zahradníček et al (2014) was used to identify erroneous data resulting from observation sources and digitization.…”

“…This method detects outliers by comparing test station values with neighbor stations' values (Feng et al, 2004). Since no single method is generally recommended to deal with spatial outliers (Štěpánek et al, 2013), a combination of several methods was applied in the present study to identify outliers as done in Štěpánek et al (2013) and Zahradníček et al (2014). In this study, ProclimDB software developed by Štěpánek et al (2010) was used for this purpose.…”

“…Many countries such as Australia (Trewin, 2013), Spain (Vicente-Serrano et al, 2010), Croatia (Zahradníček et al, 2014), Czech Republic (Štěpánek et al, 2009) and China (Feng et al, 2004) have developed homogenized meteorological datasets for climate analysis. However, in Pakistan and India, no quality controlled and homogeneous datasets are available for climate research.…”