Homogenization of a combined hourly air temperature dataset over Romania

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.This work was supported by a grant of Ministry of Research and Innovation, Romania, CNCS—UEFISCDI, project number PN‐III‐P1‐1.1‐PD‐2016‐1579, within PNCDI III

Benchmarking homogenization algorithms for monthly data

The COST (European Cooperation in Science and Technology) Action ES0601: Advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies. The algorithms were validated against a realistic benchmark dataset. Participants provided 25 separate homogenized contributions as part of the blind study as well as 22 additional solutions submitted after the details of the imposed inhomogeneities were revealed. These homogenized datasets were assessed by a number of performance metrics including i) the centered root mean square error relative to the true homogeneous values at various averaging scales, ii) the error in linear trend estimates and iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Moreover, state-of-the-art relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that currently automatic algorithms can perform as well as manual ones