Number of the records: 1
Trends of precipitation variables on different datasets
- 1.
SYSNO ASEP 0572326 Document Type A - Abstract R&D Document Type The record was not marked in the RIV R&D Document Type Není vybrán druh dokumentu Title Trends of precipitation variables on different datasets Author(s) Beranová, Romana (UFA-U) RID, ORCID
Huth, Radan (UFA-U) RID, ORCIDNumber of authors 2 Source Title EGU General Assembly 2023. - Munich : The European Geosciences Union, 2023
EGU23-5356Number of pages 1 s. Action EGU General Assembly 2023 Event date 23.04.2023 - 28.04.2023 VEvent location Vídeň Country AT - Austria Event type WRD Language eng - English Country DE - Germany Keywords precipitation ; trend ; intensity Subject RIV DG - Athmosphere Sciences, Meteorology OECD category Meteorology and atmospheric sciences Institutional support UFA-U - RVO:68378289 Annotation It is a well-established fact that different types of data (station, gridded, reanalysis) possess different statistical characteristics, e.g. for higher-order moments, extremes, and trends. In this contribution we examine the long-term changes in precipitation characteristics on different data sources over Europe. We calculate and display differences between the datasets and attempt to identify causes for the differences and for specific behavior of the datasets. We used data from stations across Europe (ECA&D project), gridded data (E-OBS) and reanalysis (NCEP/NCAR, JRA-55). We mainly analyze the trends of the seasonal total amount, intensity and probability of precipitation. Long-term trends of seasonal values of precipitation variables and their statistical significance are calculated by non-parametric methods (Mann-Kendall test, Kendall statistic). The analysis is conducted on a seasonal basis, with emphasis on winter and summer. We found that each of the datasets has its advantages and drawbacks. Trends in reanalysis deviate considerably from the other datasets mainly because the type and amount of data assimilated into them change in time. The weakness of the grid data sets is the unstable number of stations entering the interpolation in time, and the lack of representativeness of some climate stations is the main disadvantage of the station data. Workplace Institute of Atmospheric Physics Contact Kateřina Adamovičová, adamovicova@ufa.cas.cz, Tel.: 272 016 012 ; Kateřina Potužníková, kaca@ufa.cas.cz, Tel.: 272 016 019 Year of Publishing 2024
Number of the records: 1