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
Climate studies, particularly those that are related to climate change, require long, high-quality controlled data sets, which are representative both spatially and temporally. Changing the conditions of measurements, for example relocating the station, or changing the frequency and timing of measurements, or changing the instruments used can cause breaks in the time series. To avoid these problems, data errors and inhomogeneities are eliminated and the data gaps are filled by using the MASH (Multiple Analysis of Series for Homogenization, Szentimrey, 1999, 2008) homogenization procedure. The Hungarian meteorological observation network was upgraded significantly in the last decades. Homogenization of the data series raises the question of how to homogenize long and short data series together within the same process. It is possible to solve this with the MASH method due it has solid mathematical foundations, which make it suitable for such purposes. The solution includes the synchronization of the common parts’ inhomogeneities within three (or more) different MASH processing of the three (or more) datasets with different lengths depending on the time periods and elements. After the homogenization process, the station data series were interpolated to a 0.1 degree regular grid covering the whole area of Hungary. The MISH (Meteorological Interpolation based on Surface Homogenized Data Basis; Szentimrey and Bihari, 2007) program system was used for this purpose. The MISH procedure was developed specifically for the interpolation of various meteorological elements. In the case of mean temperature, we also renewed the MISH modeling, as compared to previous years, the number of homogenized stations doubled due to the new work, so it was expedient to model the climate statistical parameters with this extended station system. Time series of daily mean temperature and precipitation sum for the period 1870–2020 for Hungary were used in this study. As a result, the longest ever homogenized, gridded daily data sets became available for Hungary. The method described here can also be applied to produce representative datasets for other meteorological elements.
⎯ The rainfall intensity for various return periods are commonly used for hydrological design. In this study, we focus on rare, short-term, 60-minute precipitation extremes and related return values which are one of the relevant durations in the planning and operating demands of drainage and sewerage systems in Hungary. Time series of 60-minute yearly maxima were analyzed at 96 meteorological stations. To estimate the return values for a given return period, the General Extreme Value (GEV) distribution was fit to the yearly maxima. The GEV fit and also the Gumbel fit (GEV Type I.) were tested. According to the goodness of fit test results, both GEV and Gumbel distributions, are adequate choices. The return values for 2, 4, 5, 10, 20, and 50 year return periods are illustrated on maps, and together with their 95% confidence intervals, are listed in tables for selected stations. The maps of return values demonstrate that the spatial patterns of the return values are similar, although the enhancing effect of orography can be explored in the Transdanubia region and in the North Hungarian Range. As the return period is increasing, so the range of the confidence are widening as it is expected.
The PannEx is a GEWEX-initiated, community driven research network in the Pannonian Basin. One of the main scientific issues to address in PannEx is the investigation of precipitation extremes. Meteorological Services in the PannEx area collected the hourly precipitation data and commonly used a computer program, which was developed in the INTENSE project, to produce a set of global hydro-climatic indices. Calculations are carried out on data aggregated 1-, 3- and 6-h intervals. Selected indices are analyzed in this paper to assess the general climatology of the short-term precipitation in the Pannonian basin. The following indices are illustrated on maps and graphs: the annual mean and maxima of 1-h, 3-h and 6-h sums, the count of 3-hr periods greater than 20 mm thresholds, the maximum length of wet hours, the timing of wettest hour and the 1-h precipitation intensity. The seasonal trends of the 1-h precipitation intensity were tested from 1998 to 2019. Analysis of sub-daily precipitation has been limited by the availability of data on a global or a regional scale. The international effort made in this work through collaboration in the PannEx initiative contributes to enlarging the data availability for regional and global analysis of sub-daily precipitation extremes.
Összefoglaló. A WMO 2021 elején kiadott állapotértékelője szerint a COVID–19 miatti korlátozások ellenére az üvegházhatású gázok légköri koncentrációja tovább emelkedett. A tengerszint emelkedés a közelmúltban gyorsult, rekordmagas volt a jégvesztés Grönlandon, az Antarktisz olvadása is gyorsulni látszik. Szélsőséges időjárás pusztított, élelmiszer-ellátási gondok léptek fel, és 2020-ban a COVID–19 hatásával együtt nőtt a biztonsági kockázat több régióban is. Az éghajlatváltozás felerősíti a meglévő kockázatokat, és újabb kockázatok is fellépnek majd a természeti és az ember által alkotott rendszerekben. Az éghajlatváltozás hatása a hazai mérési sorokban is megjelenik. Az Országos Meteorológiai Szolgálat (OMSZ) homogenizált, ellenőrzött mérései szerint 1901 óta 1,2 °C-ot nőtt az évi középhőmérséklet. Két normál időszakot vizsgálva egyértelmű a magasabb hőmérsékletek felé tolódás, a csapadék éven belüli eloszlása megváltozott, az őszi másodmaximum eltűnőben van. Nőtt az aszályhajlam, gyakoribbá váltak a hőhullámok, intenzívebb a csapadékhullás, emiatt az éghajlatvédelemi intézkedések mellett a jól megalapozott alkalmazkodás is indokolt. A biztonsági kockázatok csökkenthetők az OMSZ és Országos Katasztrófavédelmi Főigazgatóság közötti együttműködés által. Summary. The first part of the article gives an overview of the state of the global climate in 2020 based on the report compiled by the World Meteorological Organization (WMO, 2021) and network of partners from UN. According to this report, the 2020 was one of the three warmest years on record, despite a cooling La Niña event. The global mean temperature for 2020 (January to October) was 1.2 ± 0.1 °C above the 1850–1900 baseline, used as an approximation of pre-industrial levels. The latest six years have been the warmest on record. 2011-2020 was the warmest decade on record. The report on the “State of the Global Climate 2020” illustrates the state of the key indicators of the climate system, including greenhouse gas concentrations, increasing land and ocean temperatures, sea level rise, melting ice and glacier and extreme weather. It also highlights impacts on socio-economic development, migration and displacement and food security. All key climate indicators and associated impact information published in this report highlight continuing climate change, an increasing occurrence and intensification of extreme events, and severe losses and damage, affecting people, societies and economies. Extreme weather events triggered an estimated 10 000 000 displacements in 2020. Because of COVID-19 lockdowns, response and recovery operations were leading to delays in providing assistance. After decades of decline, the increase in food insecurity since 2014 is being driven by conflict and economic slowdown as well as climate variability and extreme weather events. Climate change will amplify existing risks and create new risks for natural and human systems. Risks are unevenly distributed and are generally greater for disadvantaged people and communities in countries at all levels of development. The global changes have local effects in Hungary as it is shown in the second part of the article. The climate monitoring at the Hungarian Meteorological Service is based on measurements stored in the Climate data archive. We apply data management tools to produce high quality and representative datasets to prepare climate studies. The data homogenization makes possible to eliminate inhomogeneities due to change in the measuring practice and station movements. Applying spatial interpolation procedure for meteorological data provide the spatial representativeness of the climate data used for monitoring. The surface temperature increase is slightly higher in Hungary than the global change from 1901. The annual precipitation decreased by 3% from 1901, although this change is not significant statistically. The monthly temperatures shifted to warmer monthly averages in the most recent normal period between 1991 and 2020 comparing to the 1961–1990 in each months. The annual course of the monthly precipitations changed, especially autumn. The monthly sum in September and in October increased substantially. The frequency of heatwave days increased by more than two weeks in the Little Plain and in the southern part of the Great Hungarian Plain from 1981, which is the most intense warming period globally. The intensification of the precipitation in the recent years is obvious in our region. The cooperation of the Disaster Risk Management and the Hungarian Meteorological Service could expand the adaptive capacity of the society to climate change.
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