@article{MTMT:36036870,
	title = {Clustering of the Black Sea Region meteorological stations of Türkiye with fuzzy c-means, k-means, and silhouette index analysis methods by precipitation, temperature and wind speed},
	url = {https://m2.mtmt.hu/api/publication/36036870},
	author = {Keskin, Aslı Ulke and Kır, Gurkan and Zeybekoglu, Utku},
	doi = {10.28974/idojaras.2025.1.6},
	journal-iso = {IDŐJÁRÁS},
	journal = {IDŐJÁRÁS / QUARTERLY JOURNAL OF THE HUNGARIAN METEOROLOGICAL SERVICE},
	volume = {129},
	unique-id = {36036870},
	issn = {0324-6329},
	abstract = {Recent years have seen a marked increase in the number of disasters causedby the effects of global climate change. In response, a range of studies have been conductedin Türkiye and worldwide with the aim of reducing the impact of climate change.Theclassification of regions affected by climate change into similar classes in terms of climateparameters is crucial for the application of consistent methods in studies conducted in theseregions. Consequently, the formulation of effective strategies to mitigate the repercussionsof climate change in these regions is contingent upon the accurate determination of theaforementioned strategy.The observation records evaluated within the scope of the studywere obtained from 31 stations of the Turkish State Meteorological Service in the BlackSea Region, encompassing the period between 1982 and 2020, encompassing precipitation,temperature, and wind speed records.. The maximum number of clusters was determinedas 5, the cluster analysis study was carried out by using fuzzy c-means and k-meansmethods for 2, 3, 4, and 5 cluster numbers according to these three data together form amatrix. The determination of the optimum cluster numbers was carried out by silhouetteindex analysis. For the data matrix where precipitation, temperature, and wind speed wereevaluated together, the most appropriate classification was obtained by the k-means methodby choosing the number of clusters as 4.},
	year = {2025},
	eissn = {0324-6329},
	pages = {89-105}
}

@article{MTMT:36036740,
	title = {Assessment of hydroclimatic trends in Southeast Europe – Examples from two adjacent countries (Bosnia & Herzegovina and Serbia)},
	url = {https://m2.mtmt.hu/api/publication/36036740},
	author = {Pešić, Ana Milanović and Jakovljević, Dejana and Rajčević, Vesna and Gnjato, Slobodan},
	doi = {10.28974/idojaras.2025.1.5},
	journal-iso = {IDŐJÁRÁS},
	journal = {IDŐJÁRÁS / QUARTERLY JOURNAL OF THE HUNGARIAN METEOROLOGICAL SERVICE},
	volume = {129},
	unique-id = {36036740},
	issn = {0324-6329},
	abstract = {Water quantity is often analyzed throughout mean annual and seasonaldischarges in various studies worldwide. This paper aims to present water discharge trendsin the lower parts of the Una, Sana, and Vrbanja rivers in Bosnia and Herzegovina and thelargest Serbian national river Velika Morava and its tributaries Jasenica and Resava riversin Serbia for the period 1961–2020. Also, the paper examines air temperature andprecipitation trends and their connection with discharge trends. Mann-Kendall test wasapplied for the determination of trends in air temperature, precipitation, and discharges; theSen's nonparametric estimator was utilized for establishing the magnitude of the trend,while the t-test was used for determining the statistical significance of the trend. In orderto determine possible changes, two periods were observed: 1961–1990 and 1991–2020.Results showed statistically insignificant changes in discharges and precipitation trends onannual and seasonal levels. On the other hand, a significant air temperature increase wasrecorded in the period 1991–2020, with the highest increase during the summer. The mostsignificant increase was observed in Banja Luka due to urban heat island effect in this city.},
	year = {2025},
	eissn = {0324-6329},
	pages = {69-87}
}

@article{MTMT:36036288,
	title = {An observational study of a long-lived monsoon depression over the South China Sea},
	url = {https://m2.mtmt.hu/api/publication/36036288},
	author = {Chan, Pak-wai and He, Junyi},
	doi = {10.28974/idojaras.2025.1.3},
	journal-iso = {IDŐJÁRÁS},
	journal = {IDŐJÁRÁS / QUARTERLY JOURNAL OF THE HUNGARIAN METEOROLOGICAL SERVICE},
	volume = {129},
	unique-id = {36036288},
	issn = {0324-6329},
	abstract = {In general, there would be one monsoon depression affecting the South ChinaSea every summer. Such depressions are relatively short-lived and mostly last for a fewdays. In early June 2023, there was a relatively long-lived monsoon depression over theSouth China Sea with a lifespan of around 10 days. The paper documents the life of thismonsoon depression, including the meteorological observations. This depression is foundto have the typical structure of a monsoon depression, namely, very weak winds near thecenter and higher wind speed with intense convection associated with a burst of southwestmonsoon in its periphery. The strong southwest monsoon was also observed as a boundarylayer jet in the upper air observations. The study is unique from the perspective that thereare more meteorological observations over the northern part of the South China Sea,including the weather buoys and oil platforms, which provide unprecedentedmeteorological observations of the depression. It is hoped that this paper could stimulatefurther studies of monsoon depressions in this region in the future.},
	year = {2025},
	eissn = {0324-6329},
	pages = {39-51}
}

@article{MTMT:36036272,
	title = {Temporal and spatial analysis of lightning density in Türkiye},
	url = {https://m2.mtmt.hu/api/publication/36036272},
	author = {Çiçek, İhsan and Türkoğlu, Necla and Demirörs, Zerrin and Gözet, Edanur and Ateş Yilmaz, Batuhan and Alan, İlker},
	doi = {10.28974/idojaras.2025.1.2},
	journal-iso = {IDŐJÁRÁS},
	journal = {IDŐJÁRÁS / QUARTERLY JOURNAL OF THE HUNGARIAN METEOROLOGICAL SERVICE},
	volume = {129},
	unique-id = {36036272},
	issn = {0324-6329},
	abstract = {In this study, a temporal analysis of lightning density was performed onlightning data obtained from the Türkiye State Meteorological Service (TSMS) for theperiod 2017–2021, with the analysis encompassing hourly, monthly, seasonal, and annualscales. ArcGIS version 10.4.1 was used. When the annual lightning density was evaluatedby regions, the highest values were observed in the Inner Aegean, Marmara, SouthwestAnatolia, Western Black Sea, and Eastern Anatolia Regions. The Central Anatolia Regionhas the lowest lightning density. Lightning density is also the highest in late spring, earlysummer when the ground temperature and, thus, instability is highest. May and June weredetermined to have the highest lightning density, whereas December, January, andFebruary had the lowest lightning density. Considering lightning activity hourly, thehighest number of lightning strikes occurred at noon, while the lowest number occurred atnight and during the morning hours. Upon examining the relationship of lightning withlatitude and longitude values, it was concluded that the relationship with latitude valueswas more significant and positive. Lightning changes as a function of altitude: it increasesbetween 30-150 m and 500-1000 m, while it decreases between 150-500 m and above1000 m.},
	year = {2025},
	eissn = {0324-6329},
	pages = {15-37}
}

@article{MTMT:35631726,
	title = {The connection between time of concentration and rainfall intensity based on rainfall-runoff modeling},
	url = {https://m2.mtmt.hu/api/publication/35631726},
	author = {Négyesi, Klaudia and Nagy, Eszter Dóra},
	doi = {10.28974/idojaras.2024.4.3},
	journal-iso = {IDŐJÁRÁS},
	journal = {IDŐJÁRÁS / QUARTERLY JOURNAL OF THE HUNGARIAN METEOROLOGICAL SERVICE},
	volume = {128},
	unique-id = {35631726},
	issn = {0324-6329},
	abstract = {The study aims to examine the relation between rainfall intensities and timesof concentration based on rainfall-runoff modeling using the recently developed featuresof the Hydrologic Engeneering Center – Hydrologic Modeling System (HEC-HMS)modeling software. The time of concentration is generally considered a constantcharacteristic of a catchment. However, various publications have shown that response timeis a dynamic property and a function of rainfall intensity. Model simulations wereperformed to gain more insight into the relationship mentioned. The applicability of thedynamic time of concentration was examined with the help of a recent version of the HECHMS software that can interpret the dynamic relationship between time of concentrationand rainfall intensity. The models were built for characteristic and dynamic cases. In thecharacteristic case, the time of concentration values of the catchments were calculatedusing the commonly applied Wisnovszky empirical equation, while in the dynamic case,the applicability of the rainfall intensity, i.e., the time of concentration function, wasexamined. The applicability of the new HEC-HMS feature was reviewed, and therelationship between the time of concentration and rainfall intensity was confirmed. Thedynamic approach improved the models’ performance, especially where the Wisnovszkyequation yields an inadequate estimation of the time of concentration based on the results.},
	year = {2024},
	eissn = {0324-6329},
	pages = {439-450},
	orcid-numbers = {Nagy, Eszter Dóra/0000-0002-6235-3499}
}

@article{MTMT:35061398,
	title = {Spatiotemporal imputation of missing rainfall values to establish climate normals},
	url = {https://m2.mtmt.hu/api/publication/35061398},
	author = {O’Sullivan, Brian and Kelly, Gabrielle},
	doi = {10.28974/idojaras.2024.2.6},
	journal-iso = {IDŐJÁRÁS},
	journal = {IDŐJÁRÁS / QUARTERLY JOURNAL OF THE HUNGARIAN METEOROLOGICAL SERVICE},
	volume = {128},
	unique-id = {35061398},
	issn = {0324-6329},
	abstract = {Spatial kriging interpolation has been a widely popular geostatistical method for decades, and it is commonly used to predict both gridded and missing climatic variables. Climate data is typically monitored across a variety of timescales, from daily measurements to thirty-year periods, known as long-term averages (LTAs). LTAs can be constructed from daily, monthly, or annual measurements so long as any missing values in the data are infilled first. Although spatial kriging is an available method for the prediction of missing data, it is limited to a single moment in time for each imputation. Not only can missing values only be predicted with observations measured at the same instance in time, but the entire imputation process must be repeated up to the number of timesteps in which missing data is present. This study investigates the imputation performance of spatiotemporal regression kriging, an extension of spatial regression kriging which simultaneously accounts for data across both space and time. Hence, missing data is predicted using observations from other points in time, and only a single imputation process is required for the entire data set. Spatiotemporal regression kriging has been evaluated against a variety of geostatistical methods, including spatial kriging, for the imputation of monthly rainfall totals for the Republic of Ireland. Across all tests, the spatiotemporal methods presented have outperformed any purely spatial methods considered. Furthermore, three different regression methods were considered when de-trending the data before interpolation. Of those tested, generalized least squares (GLS) was shown to provide the best results, followed by elastic-net regularization when GLS proved computationally unavailable. Finally, the data set has been infilled using the best performing imputation method, and precipitation LTAs are presented for the Republic of Ireland from 1981–2010.},
	year = {2024},
	eissn = {0324-6329},
	pages = {237-249}
}

@article{MTMT:35061395,
	title = {Development of new version MASHv4.01 for homogenization of standard deviation},
	url = {https://m2.mtmt.hu/api/publication/35061395},
	author = {Szentimrey, Tamás},
	doi = {10.28974/idojaras.2024.2.5},
	journal-iso = {IDŐJÁRÁS},
	journal = {IDŐJÁRÁS / QUARTERLY JOURNAL OF THE HUNGARIAN METEOROLOGICAL SERVICE},
	volume = {128},
	unique-id = {35061395},
	issn = {0324-6329},
	abstract = {The earlier versions of our method MASH (Multiple Analysis of Series for Homogenization; Szentimrey) were developed for homogenization of the daily and monthly data series in the mean, i.e., the first order moment. The software MASH was developed as an interactive automatic, artificial intelligence (AI) system that simulates the human intelligence and mimics the human analysis on the basis of advanced mathematics. This year we finished the new version MASHv4.01 that is able to homogenize also the standard deviation, i.e., the second order moment. The problem of standard deviation is related to the monthly and daily data series homogenization.},
	year = {2024},
	eissn = {0324-6329},
	pages = {219-235}
}

@article{MTMT:35061394,
	title = {Comparison of historical and modern precipitation measurement techniques in Sweden},
	url = {https://m2.mtmt.hu/api/publication/35061394},
	author = {Joelsson, L. Magnus T. and Södling, Johan and Kjellström, Erik and Josefsson, Weine},
	doi = {10.28974/idojaras.2024.2.4},
	journal-iso = {IDŐJÁRÁS},
	journal = {IDŐJÁRÁS / QUARTERLY JOURNAL OF THE HUNGARIAN METEOROLOGICAL SERVICE},
	volume = {128},
	unique-id = {35061394},
	issn = {0324-6329},
	abstract = {Precipitation gauges used for observations in the 19th century are reconstructed and pairs of gauges are installed at two, climatologically different, regular weather observation sites (Norrköping and Katterjåkk). Norrköping is a quite well sheltered site with a low degree of frozen precipitation, while Katterjåkk is an open site with a high degree of frozen precipitation. One of the gauges at each site is equipped with a wind shield. Parallel observations are conducted from November 2016 through May 2021. Regular observations are also conducted manually with modern gauges and with automatic gauges at the sites. The wind shield effects (larger observed precipitation sums due to the inclusion of a wind shield) for the sheltered (Norrköping) and the open (Katterjåkk) sites are 7% and 16% for snow and 2% and 1% for rain, respectively. The modern gauges generally collect more precipitation than the historical shielded gauges, the difference is 0–8% for rain and almost up to 50% for snow. However, these differences can, in part, be ascribed to micrometeorologal conditions at the sites. The differences between observation methods are larger for snow and sleet than for rain. There are also larger differences in the open site than in the sheltered site. The most closely placed modern gauge relative to the historical gauges (automatic gauge in Norrköping, manual gauge in Katterjåkk) gives the most similar precipitation sums, suggesting that micrometeorology is more important than the observation method. The undercatch due to lacking wind shields in historical observations can probably not explain more than 20% of the increased observed precipitation in the late 19th and early 20th century. The question of potential influence on climatological precipitation series due to the transition from historical to modern observation methods remains unconcluded.},
	year = {2024},
	eissn = {0324-6329},
	pages = {195-218}
}

@article{MTMT:35061386,
	title = {Operational homogenization of daily climate series in Spain: experiences with different variables},
	url = {https://m2.mtmt.hu/api/publication/35061386},
	author = {Lorenzo, Belinda and Guijarro, José A. and Chazarra, Andrés and Rodríguez-Ballesteros, César and Moreno, José V. and Romero-Fresneda, Ramiro and Huarte, Maite and Morata, Ana},
	doi = {10.28974/idojaras.2024.2.2},
	journal-iso = {IDŐJÁRÁS},
	journal = {IDŐJÁRÁS / QUARTERLY JOURNAL OF THE HUNGARIAN METEOROLOGICAL SERVICE},
	volume = {128},
	unique-id = {35061386},
	issn = {0324-6329},
	abstract = {Calculation of the new climatological standard normals for the period 1991–2020 was a motivation to carry out the homogenization of the required climatic variables in the Spanish Meteorological Agency (AEMET). The national observation network has undergone changes along its history that often introduce non-climatic interferences to the series. On the other hand, for the calculation of various parameters and climatic indices, it is essential to have complete daily series. With this in mind, homogenization of daily series of precipitation, maximum and minimum temperatures, sunshine hours, relative humidity, station level pressure, mean wind speed, and maximum wind gust was carried out. This paper shows how the homogenization process was performed, covering the period 1975–2020 with carefully selected daily data sets from the national climatological database. The homogenization software Climatol v.4.0 was used for this process, and derived variables such as average temperature, sea level pressure, and vapor pressure were calculated from their related homogenized series. The peculiarities and issues of each variable are explored and, finally, the homogenization results were used to readily calculate the 1991–2020 climatological standard normals with the dedicated software CLINO_tool v.1.5.},
	year = {2024},
	eissn = {0324-6329},
	pages = {155-170}
}

@article{MTMT:35061382,
	title = {Statistical modeling of the present climate by the interpolation method MISH – theoretical considerations},
	url = {https://m2.mtmt.hu/api/publication/35061382},
	author = {Szentimrey, Tamás},
	doi = {10.28974/idojaras.2024.2.1},
	journal-iso = {IDŐJÁRÁS},
	journal = {IDŐJÁRÁS / QUARTERLY JOURNAL OF THE HUNGARIAN METEOROLOGICAL SERVICE},
	volume = {128},
	unique-id = {35061382},
	issn = {0324-6329},
	abstract = {Our method MISH (Meteorological Interpolation based on Surface Homogenized Data Basis; Szentimrey and Bihari) was developed for spatial interpolation of meteorological elements. According to mathematical theorems, the optimal interpolation parameters are known functions of certain climate statistical parameters, which fact means we could interpolate optimally if we knew the climate. Furthermore, the data assimilation methods also need to know the climate if Bayesian estimation theory is to be correctly applied. Therefore, we have developed the MISH system also to model the climate statistical parameters, i.e. present climate, by using long data series. It is a nonsense that we try to model the future climate but we do not know the present climate.},
	year = {2024},
	eissn = {0324-6329},
	pages = {143-154}
}