@article{MTMT:33254113, title = {Extreme Months: Multidimensional Studies in the Carpathian Basin}, url = {https://m2.mtmt.hu/api/publication/33254113}, author = {Kovácsné Izsák, Beatrix Cecília and Szentimrey, Tamás and Lakatos, Mónika and Pongrácz, Rita}, doi = {10.3390/atmos13111908}, journal-iso = {ATMOSPHERE-BASEL}, journal = {ATMOSPHERE}, volume = {13}, unique-id = {33254113}, abstract = {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.}, year = {2022}, eissn = {2073-4433}, orcid-numbers = {Kovácsné Izsák, Beatrix Cecília/0000-0003-1323-5389; Pongrácz, Rita/0000-0001-7591-7989} } @article{MTMT:32922814, title = {Joint examination of climate time series based on a statistical definition of multidimensional extreme}, url = {https://m2.mtmt.hu/api/publication/32922814}, author = {Szentimrey, Tamás and Kovácsné Izsák, Beatrix Cecília}, doi = {10.28974/idojaras.2022.2.1}, journal-iso = {IDŐJÁRÁS}, journal = {IDŐJÁRÁS / QUARTERLY JOURNAL OF THE HUNGARIAN METEOROLOGICAL SERVICE}, volume = {126}, unique-id = {32922814}, issn = {0324-6329}, year = {2022}, eissn = {0324-6329}, pages = {159-184}, orcid-numbers = {Kovácsné Izsák, Beatrix Cecília/0000-0003-1323-5389} } @article{MTMT:32758445, title = {Creation of a representative climatological database for Hungary from 1870 to 2020}, url = {https://m2.mtmt.hu/api/publication/32758445}, author = {Kovácsné Izsák, Beatrix Cecília and Szentimrey, Tamás and Lakatos, Mónika and Pongrácz, Rita and Szentes, Olivér}, doi = {10.28974/idojaras.2022.1.1}, journal-iso = {IDŐJÁRÁS}, journal = {IDŐJÁRÁS / QUARTERLY JOURNAL OF THE HUNGARIAN METEOROLOGICAL SERVICE}, volume = {126}, unique-id = {32758445}, issn = {0324-6329}, year = {2022}, eissn = {0324-6329}, pages = {1-26}, orcid-numbers = {Kovácsné Izsák, Beatrix Cecília/0000-0003-1323-5389; Pongrácz, Rita/0000-0001-7591-7989} } @misc{MTMT:32758492, title = {TÖBBDIMENZIÓS ÉGHAJLATI IDŐSOROK EXTRÉMUMAINAK VIZSGÁLATA}, url = {https://m2.mtmt.hu/api/publication/32758492}, author = {Kovácsné Izsák, Beatrix Cecília and Szentimrey, Tamás and Pongrácz, Rita and Lakatos, Mónika}, unique-id = {32758492}, year = {2021}, orcid-numbers = {Pongrácz, Rita/0000-0001-7591-7989} } @CONFERENCE{MTMT:32627494, title = {Multidimensional extremes: joint study of precipitation and temperature time series}, url = {https://m2.mtmt.hu/api/publication/32627494}, author = {Kovácsné Izsák, Beatrix Cecília and Szentimrey, Tamás and Lakatos, Mónika and Pongrácz, Rita}, booktitle = {EMS Annual Meeting Abstracts}, unique-id = {32627494}, year = {2021}, pages = {EMS2021-223}, orcid-numbers = {Kovácsné Izsák, Beatrix Cecília/0000-0003-1323-5389; Pongrácz, Rita/0000-0001-7591-7989} } @CONFERENCE{MTMT:32054972, title = {Creation of a representative climatological database for Hungary from 1870 to 2020}, url = {https://m2.mtmt.hu/api/publication/32054972}, author = {Kovácsné Izsák, Beatrix Cecília and Szentimrey, Tamás and Pongrácz, Rita and Lakatos, Mónika and Szentes, Olivér}, booktitle = {EGU General Assembly 2021: Conference Abstracts}, unique-id = {32054972}, year = {2021}, pages = {EGU21-872}, orcid-numbers = {Kovácsné Izsák, Beatrix Cecília/0000-0003-1323-5389; Pongrácz, Rita/0000-0001-7591-7989} } @inbook{MTMT:31919784, title = {JOINT HOMOGENIZATION OF TIME SERIES WITH UNEQUAL LENGTH BY APPLYING THE MASH PROCEDURE}, url = {https://m2.mtmt.hu/api/publication/31919784}, author = {Kovácsné Izsák, Beatrix Cecília and Lakatos, Mónika and Pongrácz, Rita and Szentimrey, Tamás and Szentes, Olivér}, booktitle = {TENTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND FIFTH CONFERENCE ON SPATIAL INTERPOLATION TECHNIQUES IN CLIMATOLOGY AND METEOROLOGY (Budapest, Hungary, 12-14 October 2020, Online)}, unique-id = {31919784}, abstract = {The Hungarian Meteorological Service (HMS) is celebrating its 150th anniversary this year. Thanks to the continuous recording of archive data, new data was added to the database. These should be checked and homogenized for the period 1871-1900 before being subjected to climatic analyses.Homogenization of the data series raises the problem that how to homogenize together the long and short data series, since the meteorological observation system was upgraded substantially in the last decades. It is possible to solve these problems with method MASH (Multiple Analysis of Series for Homogenization, Szentimrey) due to its adequate mathematical principles for such purposes. When the station network is upgraded and we have short data series besides the long series, the common section must be homogeneous together with the long as well as with the short data series in addition these two or more systems have to be homogeneous themselves too. MASH is able to fulfill these criteria, as it is based on hypothesis testing and it involves an iteration procedure. The solution is that we synchronize the common part’s inhomogeneities within two or more different MASH processing for the two or more datasets with different length.}, year = {2021}, pages = {46-58}, orcid-numbers = {Pongrácz, Rita/0000-0001-7591-7989} } @inbook{MTMT:31919778, title = {MATHEMATICAL QUESTIONS OF SPATIAL INTERPOLATION AND SUMMARY OF MISH}, url = {https://m2.mtmt.hu/api/publication/31919778}, author = {Szentimrey, Tamás}, booktitle = {TENTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND FIFTH CONFERENCE ON SPATIAL INTERPOLATION TECHNIQUES IN CLIMATOLOGY AND METEOROLOGY (Budapest, Hungary, 12-14 October 2020, Online)}, unique-id = {31919778}, abstract = {We focus on the basic mathematical and theoretical questions of spatial interpolation of meteorological elements. Nowadays in meteorology the most often applied procedures for spatial interpolation are the geostatistical interpolation methods built also in GIS software. The mathematical basis of these methods is the geostatistics that is an exact but special part of the mathematical statistics. However special meteorological spatial interpolation methods for climate variables also can be developed on the basis of the mathematical statistical theory. The main difference between the geostatistical and meteorological interpolation methods can be found in the amount of information used for modelling the necessary statistical parameters. In geostatistics the usable information or the sample for modelling is only the predictors, which are a single realization in time. While in meteorology we have spatiotemporal data, namely the long data series which form a sample in time and space as well. The long data series is such a specialty of the meteorology that makes possible to model efficiently the statistical parameters in question. The planned topics to be discussed are as follows.–Interpolation formulas and loss functionsdepending on the spatial probability distribution of climate variables. –Estimation and modelling of climate statistical parameters (e.g.: spatial trend, covariance or variogram) for interpolation formulas using spatiotemporal sample and supplementarymodel variables (topography). Use of background information (e.g.: dynamical model results, satellite, radar data) for spatial interpolation.The earlier versions of our method MISH (Meteorological Interpolation based on Surface Homogenized Data Basis; Szentimrey and Bihari) were developed formerly at the Hungarian Meteorological Service. At MISH method we use spatiotemporal data for modelling the climate statistical parameters and the interpolation system is based on these results. The earlier modelling system was elaborated for the monthly and daily expected values and the spatial correlations. At the new version MISHv2.01 the monthly and daily standard deviations and the daily temporal correlations also can be modelled. Consequently the modelling subsystem of MISH is completed for all the first two spatiotemporal moments on monthly and daily scales. If the joint spatiotemporal probability distribution of the given variable is normal then the above spatiotemporal moments determined uniquely this distribution that is the mathematical model of the climate. Another developments are modelling of the interpolation error RMSE (Root Mean Square Error) in order to characterize quantitatively the uncertainty of the interpolation, furthermore real time Quality Control for daily and monthly data. We will present a summary of the method MISH.}, year = {2021}, pages = {59-69} } @inbook{MTMT:31919773, title = {MATHEMATICAL QUESTIONS OF HOMOGENIZATION AND SUMMARY OF MASH}, url = {https://m2.mtmt.hu/api/publication/31919773}, author = {Szentimrey, Tamás}, booktitle = {TENTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND FIFTH CONFERENCE ON SPATIAL INTERPOLATION TECHNIQUES IN CLIMATOLOGY AND METEOROLOGY (Budapest, Hungary, 12-14 October 2020, Online)}, unique-id = {31919773}, abstract = {There are several methods and software for the homogenization of climate data series but there is not any exact mathematical theory of the homogenization. At the examinations mainly the physical experiences are considered while the mathematical formulation of the problems is neglected in general. Moreover occasionally there are some mathematical statements at the description of the methods in the papers –e. g. capability to adjust the higher order moments –but without any proof and this way is contrary to the mathematical conventions of course. As we see the basic problem of the homogenization is the unreasonable dominance of thepractical procedures over the theory and it is the main obstacle of the progress. Therefore we try to formulate some questions of homogenization in accordance with the mathematical conventions. The planned topics to be discussed are as follows.–The mathematical definition of the inhomogeneity and the aim of homogenization. It is necessary to clarify that the homogenization of climate data series is a distribution problem instead of a regression one.–Relation of monthly and daily data series homogenization.–Mathematical overview on the methodology of spatial comparison of series, inhomogeneity detection, adjustment of series in accordance with the publication of WMO Guidelines on Homogenisation (2020).–Relation of theoretical evaluation and benchmark for methods, validation statistics.The earlier versions of our method MASH (Multiple Analysis of Series for Homogenization; Szentimrey) were developed formerly at the Hungarian Meteorological Service. These procedures aimed to homogenize the daily and monthlydata series in the mean i.e. the first order moment. The new versionMASHv4.01 has been developed for joint homogenization of mean and standard deviation using some mathematical results. Theoretically in case of normal distribution the homogenization of mean and standard deviation is sufficient since if the first two moments are homogenous then the higher order moments are also homogeneous. An interactive automatic algorithm also was developed in this new version in order to make the homogenization easier for the users. We will present a summary of the software MASH where our intention was to develop a flexible, interactive automatic, artificial intelligence (AI) system that simulates the human intelligence and mimics the human analysis on the basis of advanced mathematics. We finish the paper with some comments connected to the fact that during the seminar we were obliged to express our skepsis on the credibility of the MULTITEST benchmark results to the authors.}, year = {2021}, pages = {4-17} } @inbook{MTMT:31919767, title = {COMPARATIVE STUDY OF CARPATCLIM, E-OBS AND ERA5 DATASET}, url = {https://m2.mtmt.hu/api/publication/31919767}, author = {Lakatos, Mónika and Szentimrey, Tamás and Kovácsné Izsák, Beatrix Cecília and Szentes, Olivér and Hoffmann, Lilla and Bíróné Kircsi, Andrea and Konkolyné Bihari, Zita}, booktitle = {TENTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND FIFTH CONFERENCE ON SPATIAL INTERPOLATION TECHNIQUES IN CLIMATOLOGY AND METEOROLOGY (Budapest, Hungary, 12-14 October 2020, Online)}, unique-id = {31919767}, abstract = {Recently the pan-European observational dataset E-OBS has been considered as a reference for several European climate analyses. Moreover, the usage of the newly available global reanalysisERA5 is increasing for climate change studies. CARPATCLIM is a regional climate dataset for the Carpathian region, which is situated in central-eastern Europe. The E-OBS and ERA5 dataset were tested against CARPATLIM and against other regional datasets inthe framework of the COPERNICUS C3S_311a_Lot4 project. The common time period of E-OBS, ERA5 and CARPATCLIM is the period of 1979-2010. Different measures, evaluation statistics were computed for comparison of the gridded Tx, Tn and precipitation fields for this period. Analysis of Variance (ANOVA) method was applied for instance, which is an adequate statistical method to explore the statistical structure of different datasets. ANOVA can be used effectively for the characterization of the spatiotemporal statistical properties of CARPATCLIM, E-OBS and ERA5. In addition, different evaluation scores, yearly cycle, absolute and monthly extremes, quantiles, wet days frequency, several climate indices for temperature were computed and reported in the COPERNICUS C3S_311a_Lot4 project. Trend analysis (exponential trend model for precipitation and linear trend model for temperature) and homogeneity test for the gridded data were applied too. The differences between the datasets come from the station density behind the grids and also the methods used for homogenization and gridding determine the results. The main outcomes of this comparative study are presented on graphs and maps in this paper.}, year = {2021}, pages = {84-101}, orcid-numbers = {Kovácsné Izsák, Beatrix Cecília/0000-0003-1323-5389} }