Integrated spatial assessment of wind erosion risk in Hungary

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Analysis of the correlation between the incidence of food-borne diseases and climate change in Hungary

It is increasingly accepted globally, that many food-borne diseases are associated with climate change. The goal of the present research is to investigate whether changes in the annual number of the registered food-borne diseases in Hungary can be correlated to any climate parameter, as it is reasonable to suppose that it can be linked to climate change. Ten climate parameters and indices were examined as potential influencing factors. A multiple linear regression model was employed, using the backward elimination method to find the climate factors that have a significant effect on the annual number of food-borne diseases. It was found that the annual mean temperature was the only significant predictor of the annual number of registered food-borne diseases, and that 22.0% of the total variance in the annual number of food-borne diseases can be explained by the annual mean temperature. It should be noted that this relationship is negative, given that they are derived from time series with opposite trends. This phenomenon may be explained by the process of evolution and adaptation of the infecting fauna

Return values of 60-minute extreme rainfall for Hungary

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

Republic of Macedonia -25

Abstract The MISH (Meteorological Key words: SPI, interpolation, MISH, gridding The MISH method The MISH (Meteorological Interpolation based on Surface Homogenized Data Basis) method for the spatial interpolation of surface meteorological elements was developed at the Hungarian Meteorological Service . This is a meteorological system not only in respect of the aim but in respect of the tools as well. It means that using all the valuable meteorological information -climate and supplementary model or background information -is intended. For that purpose developing an adequate mathematical background was also necessary of course. In the practice many kinds of interpolation methods exist therefore the question is the difference between them. According to the interpolation problem the unknown predictand value is estimated by using the known predictor values. The type of the adequate interpolation formula depends on the probability distribution of the meteorological elements! Additive formula is appropriate for normal distribution (e.g. temperature) while some multiplicative formula can be applied for quasi lognormal distribution (e.g. precipitation). The expected interpolation error depends on certain interpolation parameters as for example the weighting factors. The optimum interpolation parameters minimize the expected interpolation error and these parameters are certain known functions of different climate statistical parameters e.g. expectations, deviations and correlations. Consequently the modelling of the climate statistical parameters is a key issue to the interpolation of meteorological elements. The various geostatistical kriging methods applied in GIS are also based on the above mathematical theory -To model the climate statistical parameters by using long homogenized data series. -To calculate the modeled optimum interpolation parameters which are certain known functions of the modeled climate statistical parameters

Globális és hazai éghajlati trendek, szélsőségek változása: 2020-as helyzetkép = Global Trends and Climate Change in Hungary in 2020

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

Changes of Temperature and Precipitation Extremes Following Homogenization = Hőmérsékleti és csapadék szélsőségek vizsgálata homogenizált adatokon

Climate indices to detect changes have been defined in several international projects on climate change. Climate index calculations require at least daily resolution of time series without inhomogeneities, such as transfer of stations, changes in observation practice. In many cases the characteristics of the estimated linear trends, calculated from the original and from the homogenized time series are significantly different. The ECA&D (European Climate Assessment & Dataset) indices and some other special temperature and precipitation indices of own development were applied to the Climate Database of the Hungarian Meteorological Service. Long term daily maximum, minimum and daily mean temperature data series and daily precipitation sums were examined. The climate index calculation processes were tested on original observations and on homogenized daily data for temperature; in the case of precipitation a complementation process was performed to fill in the gaps of missing data. Experiences of comparing the climate index calculation results, based on original and complemented-homogenized data, are reported in this paper. We present the preliminary result of climate index calculations also on gridded (interpolated) daily data. A klímaváltozás detektálása céljából több nemzetközi, a klímaváltozással foglalkozó programban klímaindexek sorát definiáltak. Az éghajlati szélsőségek vizsgálatához, avagy az extrém indexek számításához inhomogenitásoktól (állomás áttelepítések, a mérési időpontok és a mérési módszerek változásai) mentes, jó minőségű napi adatsorok szükségesek. Sok esetben ugyanis jelentősen eltér az eredeti és a homogenizált adatok alapján számolt lineáris trend értéke nemcsak nagyságában, hanem előjelében is. Ez pedig a klímaváltozás tekintetében téves következtetések levonásához vezethet. Az OMSZ klimatológiai adatbázisban történt fejlesztés során megvalósítottuk az ECA&D (European Climate Assessment & Dataset) projektben alkalmazott extrém klímaindexek sorozatát és ezeket kiegészítettük néhány általunk kifejlesztett karakterisztikával. Az extrémumokban fellépő változások nyomon követésére lineáris trendelemzést végeztünk mind az eredeti, mind a homogenizált sorokon, ennek a vizsgálatnak a tapasztalatairól számolunk be ebben a dolgozatban. Bemutatjuk emellett a rácsponti klímaindex számítások előzetes eredményeit is

Changes of Temperature and Precipitation Extremes following Homogenization

Climate indices to detect changes have been defined in several international projects on climate change. Climate index calculations require at least daily resolution of time series without inhomogeneities, such as transfer of stations, changes in observation practice. In many cases the characteristics of the estimated linear trends, calculated from the original and from the homogenized time series are significantly different. The ECA&D (European Climate Assessment & Dataset) indices and some other special temperature and precipitation indices of own development were applied to the Climate Database of the Hungarian Meteorological Service. Long term daily maximum, minimum and daily mean temperature data series and daily precipitation sums were examined. The climate index calculation processes were tested on original observations and on homogenized daily data for temperature; in the case of precipitation a complementation process was performed to fill in the gaps of missing data. Experiences of comparing the climate index calculation results, based on original and complemented-homogenized data, are reported in this paper. We present the preliminary result of climate index calculations also on gridded (interpolated) daily data