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Commonly studying climate is gaining more and more space thanks to the expansion of the tools of statistical climatology and the development of the informatical background. Trend analyses of annual and seasonal mean temperature values clearly show that the Hungarian values are mostly in line with the global trend, and in some cases exceed it. Since a sufficient number of measurements are only available from the 1970s, we used hourly temperature values (03 UTC, 09 UTC, 15 UTC, 21 UTC) of the period 1971–2020 for station data series in Hungary for the trend analysis. In order to make the examined datasets sufficiently representative, we homogenized the station data series, filled in data gaps, and performed quality control using the MASH software. To ensure spatial representativeness, we interpolated the homogenized station data onto a dense, regular grid network with the MISH system. In addition, we used the ANOVA method to examine the expected values, standard deviations assigned to the hourly values, and we analyzed on maps how the values within the day changed in each region over 50 years. [ABSTRACT FROM AUTHOR]

This study concerns the interpolation of daily and hourly precipitation data in regions where small but intense thunderstorms, such as supercells, have occurred, and which, due to their size, often evade conventional meteorological stations. Consequently, relying solely on these measurements for interpolation can introduce errors and yield incomplete representations. To mitigate these issues, this research incorporates radar background information. The study selects days marked by significant precipitation during summer season and employs the Meteorological Interpolation based on Surface Homogenized Data (MISH) method for interpolation, both with and without radar-derived background data. Furthermore, our research also investigates the adaptability of the MISH method in handling radar anomalies, which encompass errors, missing data, and spurious measurements resulting from unintended radar reflections. Additionally, it examines whether the precipitation measured by radar can be used for climatic purposes on its own (without traditional measurements). Statistical techniques are employed to assess the improvement in interpolation quality with the inclusion of radar data and to quantify the relationship between interpolations with and without supplementary radar information. The study underscores the critical role of combining measurement data and radar products in the interpolation framework. This approach has implications for societal and agricultural sectors and offers potential benefits for hazard forecasting accuracy. [ABSTRACT FROM AUTHOR]

The article presents a recent update of a comprehensive dataset of long‐term series of precipitation data from instrumental observations in the Greater Alpine Region (GAR), that is, the region of Europe including the Alpine mountain range and their nearer surroundings (4°–19° E in longitude and 43°–49° N in latitude). A comparison to different national homogenized datasets is also presented. Results show that in the national homogenized datasets more breaks have been detected due to higher station density. They also demonstrate the necessity of constant exchange with data providers. The resulting trends in all datasets are mainly weak and only a minority of them is statistically significant. In most cases the similarity of statistical index numbers are promising, with, for example, small RMSE between the presented new HISTALP homogenization and the time series of the national homogenized datasets. Nevertheless, for some stations higher differences occur and break signals are not what would be expected due to possible causes in the station history. The differences between the national and the HISTALP new homogenization—due to, for example, different methods used, different points in time when the homogenization took place, different options of data handling (combination of station data, gap filling routines, ...) and different reference stations—illustrate the inherent uncertainty unavoidably associated to homogenization and point out the need of careful communication and use of the data. On the other hand, the results highlight the advantage of consistently homogenized datasets, versus the risks associated with mixing results from different homogenizations. [ABSTRACT FROM AUTHOR]

Gridded analyses of observed precipitation are an important data resource for environmental modelling, climate model evaluation and climate monitoring. In Europe, datasets that resolve the rich mesoscale variations widely exist for the national territories, but similar datasets covering the entire continent are more recent. Here, we evaluate daily precipitation in two newly available pan‐European datasets: E‐OBS (v19.0e), a statistical analysis from rain‐gauge data, and ERA5, the new global reanalysis from ECMWF. Special interest is on how the refinements of grid spacing, the methodological upgrades and the quantification of uncertainty (ensemble), bear on capabilities at the mesoscale. The evaluation is conducted in three subregions, the Alps, the Carpathians and Fennoscandia, and involves as reference high‐quality regional datasets derived from dense rain‐gauge data. The study suggests that E‐OBS and ERA5 agree qualitatively well with the reference datasets. Major mesoscale patterns in the climatology (mean, wet‐day frequency, 95% quantile) are reproduced. The improvement over earlier versions of the datasets is evident. ERA5 was found to overestimate mean precipitation in all regions, related to too many wet days. The accuracy of E‐OBS was found to depend on station density, with spatial and temporal variations clearly less accurate in data sparse regions. In comparison, E‐OBS turned out to be superior to ERA5 in regions with dense data, but the two datasets are on a par in regions with sparse data, and partly ERA5 has advantages. For both datasets we find that the spatial resolution is coarser than the grid spacing, with overly smooth fields and an underestimation of high quantiles. Also, both datasets were found to be clearly overconfident in their uncertainty characterization (too small ensemble spread). Overall, the two datasets advance the characterization of precipitation on a pan‐European scale, but users are advised to take residual limitations into account in applications. [ABSTRACT FROM AUTHOR]