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    Can reference radiosounding measurements be used to improve historical time series?

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    Upper-air radiosounding observations are undoubtedly a primary data source for the study of climate and for the atmospheric reanalysis. Nevertheless, historical radiosounding time series are affected by several systematic uncertainties due to change in the measurement sensors. As an alternative to the few existing approaches, in the frame of the Copernicus Climate Change Service (C3S), a novel approach, named RHARM (Radiosounding HARMonization), has been developed to provide a homogenized dataset of temperature, humidity and wind radiosounding profiles available from the Integrated Global Radiosonde Archive (IGRA) along with an estimation of the total uncertainty for each profile. Estimation of uncertainties has never been developed in previous homogenization algorithms. The homogenization is carried out for a substantial subset of IGRA radiosounding stations. Comparisons of trends calculated at 300 hPa over Europe in the period 2000–2018 using ERA5 ECWMF atmospheric reanalysis, IGRA and RHARM datasets show a good agreement for temperature with mutual differences within 0.05 K/da. For relative humidity, ERA5 shows a trend of −0.7%/da, while the trend for IGRA and RHARM is of 0.5%/da and 0.8%/da, respectively. The usefulness of comparing, for the first time, ERA5 and RHARM time series taking advantage of the RHARM uncertainty is also discussed

    Estimating climate extreme indices and the related uncertainties using U. S. Climate Reference Network (USCRN) near-surface temperature

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    The availability of near-surface temperature records from reference networks enables the quantification of measurement uncertainties, which may have an asymmetrical nature due to effects contributing only to the warm or cold bias. In this work, two extreme indices (Consecutive Frost Days and Consecutive Ice Days) and the related uncertainties are calculated for the period 2006-2020 from the U. S. Climate Reference Network (USCRN) and discussed with the calculation without considering the uncertainties. Overall, the results show an accumulated yearly number exceeding 15 periods of CFD and CIDs, with the largest values at the highest latitude covered by the USCRN considered stations. Propagating asymmetric uncertainties for the specified indices revealed a pronounced, latitude-dependent, influence on the indices' estimations due to accounting for the measurement uncertainties. Positive uncertainties show larger values compared to negative ones for the considered Indices. The assessment of uncertainty is a crucial component in enhancing research and decision-making connected to climate change, as it underscores the incomplete understanding of variability in the climate system and the limitations of climate models and observational instruments

    Going Beyond Counting First Authors in Author Co-citation Analysis

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    The present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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