Homogenization of Ukrainian air temperature data

We present a new data set of quality‐controlled and homogenized daily maximum (Tmax) and minimum (Tmin) temperature for Italy. The data set includes 144 Tmax and 139 Tmin long‐term series, covering the period 1961–2017. First, the paper provides a description of data sources and quality controls implemented for the detection of erroneous daily observations. Next, the temperature records used for this work are introduced. Following strict data continuity and completeness requirements, we identified more than 500 time series with at least 20 years of valid data (raw data set), which were spatially partitioned using a hierarchical clustering approach. For each cluster, the time series homogeneity was assessed using two different statistical automatic approaches: ACMANT and Climatol. The results of the homogenization process are illustrated only for the long‐term series subset. Both homogenization methods revealed the presence of non‐climatic discontinuities in most of the temperature series. Although Climatol detected a slightly lower number of breakpoints than ACMANT, the two methods are in good agreement with respect to the statistics which describe the number and timing of the breakpoints. Since no metadata are available, the plausibility of the homogenized time series was evaluated using different statistical measures: RMSE, Spearman correlation coefficient and trends estimation. Our results show that the homogenized data sets are more spatially coherent than the raw time series. In particular, the analysis of the annual temperature trends shows more realistic and reliable climatic patterns when the homogenized data sets are considered. For our data, the homogenization process only marginally changes the annual and seasonal warming trend values found for the area‐averaged anomaly raw series. The Tmax and Tmin homogenized data set will be regularly updated and is intended to be used for a variety of climate studies that require data at daily resolution, as the analysis of climate extremes.

Section: Datamentioning

confidence: 99%

Section: Homogenizationmentioning

confidence: 99%

A new homogenized daily data set for temperature variability assessment in Italy

Fioravanti

Piervitali

Desiato

2019

“…The homogeneity of the monthly mean air temperature series collected in Ukraine has been considered in several studies (Osadchyi et al ., ; Osadchyi et al ., ; Skrynyk et al ., ; Osadchyi et al ., ). However, homogenization of Ukrainian extreme, minimum (Tn) and maximum (Tx) temperature data at different timescales (daily, monthly, annual) has not been conducted so far.…”

Section: Introductionmentioning

confidence: 97%

“…The results presented alongside the results published in Osadchyi et al . () constitute a complete homogeneous database of monthly air temperatures (mean, minimum and maximum) for the territory of Ukraine covering the period of 1946–2015 (1946–2014 for mean temperature). The database, therefore, can be considered as a good basis for regional/national climate studies.…”

Section: Introductionmentioning

confidence: 99%

Quality control and homogenization of monthly extreme air temperature of Ukraine

Skrynyk

Aguilar²,

Skrynyk

et al. 2018

Self Cite

Digital databases of monthly means of daily extreme, minimum (Tn) and maximum (Tx) air temperature collected in Ukraine were created. The databases contain temperature records of 371/340 (Tn/Tx) climatological stations which recorded data on the territory of Ukraine anytime during 1881-2015. The significant part of the data sets (~48/47%) was obtained after conducting a data rescue process, recovering historical paper records. Due to a great deal of missing data during World War I and II, homogenization was possible to perform only for 178 time series covering the period of 1946-2015. A homogenization procedure, conducted by means of the HOMER software, revealed 323 break points for minimum temperature (~1.8 breaks per station) and 310 for maximum temperature (~1.7 breaks per station). Approximately 37/33% (Tn/Tx) of the breaks can be explained by metadata. Shift amplitudes in Tn time series ranged between −1.18 and 1.71 C, while for maximum temperature the range was slightly less from −0.99 to 1.15 C. For both temperatures, mean values of shift amplitudes were near zero (−0.04/−0.05 C). To validate the homogenization results, several statistical procedures were performed to compare inhomogeneous and homogeneous time series. The conducted statistical calculations provide the proofs that inhomogeneity of the Ukrainian extreme air temperature data should be taken into account when analysing regional climate change and variability. K E Y W O R D S extreme (minimum/maximum) air temperature, homogenization of time series, HOMER software, Ukraine

“…Osadchyi et al . () applied HOMER approach to improve the quality of the monthly air temperature dataset collected in Ukraine from 178 stations between 1946 and 2014. Hewaarachchi et al .…”

Section: Introductionmentioning

confidence: 99%

Homogenization of a combined hourly air temperature dataset over Romania

Dumitrescu

Cheval

Guijarro

2019

Daily and sub‐daily homogenization of climate variables have been intensively investigated in the last decades, but to the best of our knowledge, this is the first study on homogenization of hourly temperature in Romania. This paper describes the creation of a homogenized hourly air temperature data set at a country scale by combining data from four independent meteorological networks. The air temperature measurements for the period 2009 and 2017 were obtained from the following networks: Romanian National Meteorological Administration (ANM), National Network for Monitoring Air Quality (RNMCA), Regional Basic Synoptic Network (RBSN), and Meteorological Terminal Aviation Routine Weather Report network (METAR). The climatological limits, persistence, temporal variation (step test), and spatial consistency were the quality control tests used to isolate the errors due to malfunctioning of the temperature sensors, data coding or transmission. The Climatol homogenization method was successfully applied for identifying and correcting any suspicious values. The missing data were filled by considering the similarities between each station and the reference series. Comparing the output with the original data, it is apparent that the removal of the break points, correction and homogenization resulted in a new data set with statistical properties very similar to the raw data, but more reliable for climate research due to the increased homogeneity. Eventually, the procedure can be implemented in operational use for collecting more data from other networks.