8 research outputs found
Joint examination of climate time series based on a statistical definition of multidimensional extreme
The joint examination of the climate time series may be efficient methodology for the characterization of extreme weather and climate events. In general, the main difficulties are connected with the different probability distribution of the variables and the handling of the stochastic connection between them. The first problem can be solved by the standardization procedures, i.e., to transform the variables into standard normal ones. For example, there are the Standardized Precipitation Index (SPI) series for the precipitation sums assuming gamma distribution, or the standardization of temperature series assuming normal distribution. In case of more variables, the problem of stochastic connection can be solved on the basis of the vector norm of the transformed variables defined by their covariance matrix. We will present the developed mathematical methodology and some examples for its meteorological applications
Többdimenziós éghajlati idősorok extrémumainak vizsgálata
Az Ă©ghajlatváltozás tanulmányozásához elengedhetetlen a szĂ©lsĹ‘sĂ©gek vizsgálata. A szĂ©lsĹ‘sĂ©gek vizsgálata törtĂ©nhet egyrĂ©szt Ăşgy, hogy az extrĂ©m Ă©ghajlati esemĂ©nyek idĹ‘sorát vizsgáljuk, másrĂ©szt Ăşgy, hogy az Ă©ghajlati idĹ‘sorok extrĂ©mumait vizsgáljuk. Ez utĂłbbi esetben, ha egyetlen elemet vizsgálunk, a szĂ©lsĹ‘sĂ©g az adott idĹ‘sor maximuma vagy minimuma. Jelen tanulmányban az Ă©ghajlati idĹ‘sorok szĂ©lsĹ‘Ă©rtĂ©keit határozzuk meg Ăşgy, hogy több meteorolĂłgiai elemet egyĂĽttesen vizsgálunk Ă©s Ăgy határozzuk meg az extrĂ©mumokat. Rögtön felmerĂĽl a kĂ©rdĂ©s, hogy többdimenziĂłs idĹ‘sornál van-e Ă©rtelme szĂ©lsĹ‘Ă©rtĂ©krĹ‘l beszĂ©lni, Ă©s ha igen, milyen mĂłdon határozhatĂł meg. Ehhez kapcsolĂłdĂłan bemutatjuk az Ăşn. norma mĂłdszert, definiáljuk a vektorváltozĂł extrĂ©mumát, Ă©s pĂ©ldákon keresztĂĽl mutatjuk be a mĂłdszer alkalmazását csapadĂ©k- Ă©s hĹ‘mĂ©rsĂ©klet-idĹ‘sorok egyĂĽttes vizsgálatával. Tanulmányunkhoz a magyarországi napi átlaghĹ‘mĂ©rsĂ©kleti Ă©s csapadĂ©k idĹ‘sorokat használtuk fel az 1901‒2019 idĹ‘szakra. Az alábbiakban bemutatjuk az egyĂĽttes vizsgálat során kapott legfontosabb eredmĂ©nyeket, Ă©s összevetjĂĽk az egydimenziĂłs esetben kapott szĂ©lsĹ‘sĂ©gekkel. Amennyiben ezzel a mĂłdszerrel visszakapjuk az eredeti egydimenziĂłs idĹ‘sorok szĂ©lsĹ‘sĂ©geit, Ăşgy az Ă©ghajlat-változás vizsgálatához nem ad többletet a bemutatni kĂvánt mĂłdszer. ElöljárĂłban összegezhetjĂĽk, hogy elemzĂ©seink azt jelzik, hogy vannak olyan Ă©vek, amelyek csak a csapadĂ©k vagy csak az átlaghĹ‘mĂ©rsĂ©klet szempontjábĂłl nem számĂtanak extrĂ©mnek, de egyĂĽtt vizsgálva a kĂ©t elemet mĂ©gis kimondhatjuk, hogy szĂ©lsĹ‘sĂ©ges Ă©vek voltak. Ezek alapján tehát a többdimenziĂłs Ă©ghajlati idĹ‘sorok extrĂ©mumainak vizsgálata kiegĂ©szĂti, Ă©s ezáltal hatĂ©konyabbá teszi az Ă©ghajlatváltozás vizsgálatát ahhoz kĂ©pest, mintha csak az egydimenziĂłs idĹ‘sorokat vizsgálnánk
Creation of a representative climatological database for Hungary from 1870 to 2020
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
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
Extreme Months: Multidimensional Studies in the Carpathian Basin
In addition to the one-dimensional mathematical statistical methods used to study the climate and its possible variations, the study of several elements together is also worthwhile. Here, a combined analysis of precipitation and temperature time series was performed using the norm method based on the probability distribution of the elements. This means, schematically speaking, that each component was transformed into a standard normal distribution so that no element was dominant. The transformed components were sorted into a vector, the inverse of the correlation matrix was determined and the resulting norm was calculated. Where this norm was at the maximum, the extreme vector, in this case the extreme month, was found. In this paper, we presented the results obtained from a joint analysis of the monthly precipitation and temperature time series for the whole territory of Hungary over the period 1871–2020. To do this, multidimensional statistical tests that allowed the detection of climate change were defined. In the present analysis, we restricted ourselves to two-dimensional analyses. The results showed that none of the tests could detect two-dimensional climate change on a spatial average for the months of January, April, July and December, while all the statistical tests used indicated a clear change in the months of March and August. As for the other months, one or two, but not necessarily all tests, showed climate change in two dimensions
Extreme Months: Multidimensional Studies in the Carpathian Basin
In addition to the one-dimensional mathematical statistical methods used to study the climate and its possible variations, the study of several elements together is also worthwhile. Here, a combined analysis of precipitation and temperature time series was performed using the norm method based on the probability distribution of the elements. This means, schematically speaking, that each component was transformed into a standard normal distribution so that no element was dominant. The transformed components were sorted into a vector, the inverse of the correlation matrix was determined and the resulting norm was calculated. Where this norm was at the maximum, the extreme vector, in this case the extreme month, was found. In this paper, we presented the results obtained from a joint analysis of the monthly precipitation and temperature time series for the whole territory of Hungary over the period 1871–2020. To do this, multidimensional statistical tests that allowed the detection of climate change were defined. In the present analysis, we restricted ourselves to two-dimensional analyses. The results showed that none of the tests could detect two-dimensional climate change on a spatial average for the months of January, April, July and December, while all the statistical tests used indicated a clear change in the months of March and August. As for the other months, one or two, but not necessarily all tests, showed climate change in two dimensions
Globális és hazai éghajlati trendek, szélsőségek változása: 2020-as helyzetkép = Global Trends and Climate Change in Hungary in 2020
A WMO 2021 elejĂ©n kiadott állapotĂ©rtĂ©kelĹ‘je szerint a COVID–19 miatti korlátozások ellenĂ©re az ĂĽvegházhatásĂş gázok lĂ©gköri koncentráciĂłja tovább emelkedett. A tengerszint emelkedĂ©s a közelmĂşltban gyorsult, rekordmagas volt a jĂ©gvesztĂ©s Grönlandon, az Antarktisz olvadása is gyorsulni látszik. SzĂ©lsĹ‘sĂ©ges idĹ‘járás pusztĂtott, Ă©lelmiszer-ellátási gondok lĂ©ptek fel, Ă©s 2020-ban a COVID–19 hatásával egyĂĽtt nĹ‘tt a biztonsági kockázat több rĂ©giĂłban is. Az Ă©ghajlatváltozás felerĹ‘sĂti a meglĂ©vĹ‘ kockázatokat, Ă©s Ăşjabb kockázatok is fellĂ©pnek majd a termĂ©szeti Ă©s az ember által alkotott rendszerekben. Az Ă©ghajlatváltozás hatása a hazai mĂ©rĂ©si sorokban is megjelenik. Az Országos MeteorolĂłgiai Szolgálat (OMSZ) homogenizált, ellenĹ‘rzött mĂ©rĂ©sei szerint 1901 Ăłta 1,2 °C-ot nĹ‘tt az Ă©vi közĂ©phĹ‘mĂ©rsĂ©klet. KĂ©t normál idĹ‘szakot vizsgálva egyĂ©rtelmű a magasabb hĹ‘mĂ©rsĂ©kletek felĂ© tolĂłdás, a csapadĂ©k Ă©ven belĂĽli eloszlása megváltozott, az Ĺ‘szi másodmaximum eltűnĹ‘ben van. NĹ‘tt az aszályhajlam, gyakoribbá váltak a hĹ‘hullámok, intenzĂvebb a csapadĂ©khullás, emiatt az Ă©ghajlatvĂ©delemi intĂ©zkedĂ©sek mellett a jĂłl megalapozott alkalmazkodás is indokolt. A biztonsági kockázatok csökkenthetĹ‘k az OMSZ Ă©s Országos KatasztrĂłfavĂ©delmi FĹ‘igazgatĂłság közötti egyĂĽttműködĂ©s által