2024. június 19. szerda
IDŐJÁRÁS - angol nyelvű folyóirat

Vol. 127, No. 3 * Pages 267–420 * July - September 2023


Journal of the Hungarian Meteorological Service

letöltés [pdf: 3023 KB]
Investigation and analysis of the Iranian autumn rainfall thickness pattern
Hossein Jahantigh
DOI:10.28974/idojaras.2023.3.1 (pp. 267–283)
 PDF (5181 KB)   |   Abstract

The purpose of this study was to investigate and analyze the trend of autumn precipitation thickness pattern in Iran. For this purpose, two environmental and atmospheric databases have been used. Environmental data is prepared and networked in two stages, in the first stage with the help of 1434 stations and in the second stage with the help of 1061 stations. Atmospheric data includes geopotential height data obtained from the National Center for Environmental Prediction and the National Center for Atmospheric Research (NCEP / NCAR). The spatial resolution of this data is 2.5 × 2.5 degrees. The thickness of the atmosphere, which is usually between 500 and 1000 hectopascals, is shown. This thickness is considered as the thickness of the whole atmosphere. The results of the autumn precipitation trend showed that although autumn precipitation on monthly and annual scales has experienced an increasing trend in most regions, in less than 5% of Iran, the upward trend has been significant. The most intense upward trend is observed in the form of spots in the central and northern parts of the Zagros Mountain, while the greatest decreasing trend has been observed in the form of cores along the Caspian coastal cities. The results of the autumn precipitation thickness pattern showed that the autumn precipitation thickness pattern is affected by deflection and instability due to high latitude cold and humid weather and low latitude hot and humid weather occurred in North Africa, in such a way that the Black Sea and the Mediterranean Sea provide the required moisture in high latitudes and the Red Sea and the Persian Gulf in low latitudes.


Estimating the sunshine duration using multiple linear regression in Kocaeli, Turkey
Mine Tulin Zateroglu
DOI:10.28974/idojaras.2023.3.2 (pp. 285–298)
 PDF (739 KB)   |   Abstract

This study aims to estimate and evaluate the characteristic behavior of sunshine duration for long-term records. Sunshine duration and other climate variables such as cloudiness, precipitation, relative humidity, etc., have been measured in meteorological stations for a long time all over the world. But in some cases, such as missing data or unavailable station, the estimation of sunshine duration play a crucial role. Statistical models can be used to predict the sunshine duration over climate variables. To evaluate the behavior of sunshine duration, several climate variables were analyzed for different time scales. The data used in this study were collected from a ground-based meteorological station. In the first, all data were arranged according to different time scales as monthly, seasonal, and annual average values. Prediction models were constructed for each time scale. This study used multiple linear regression (MLR) to build the models and the Pearson correlation analysis to determine the relations between the climate elements. The created models for estimating sunshine duration were validated as well. According to the results, MLR can be utilized and recommended for the prediction of the sunshine duration over climate variables.


Research trend the in meteorology and atmospheric sciences category based on essential science indicators during 2011–2021
Bao-Zhong Yuan and Jie Sun
DOI:10.28974/idojaras.2023.3.3 (pp. 299–319)
 PDF (3976 KB)   |   Abstract

This study analyzed 1,636 top papers in the subject category of meteorology and atmospheric sciences about eleven years from 2011 to 2021, which included 1,636 highly cited papers and 24 hot papers in the field belonged to 20 Web of Science categories and 14 research areas. All top papers, written in English, were from 13,878 authors, 2,913 organizations, and 124 countries or territories, and published in 72 journals in the field. The top five journals are the  Nature Climate Change (15.9% of the studied paper), Atmospheric Chemistry and Physics (12.1%), Journal of Climate (7.0%), Bulletin of the American Meteorological Society (6.7%), and Journal of Geophysical Research Atmospheres (4.6%), each published more than 76 papers. Top five countries were the USA, England, PR China, Germany, and France. Furthermore, top five organizations of National Oceanic and Atmospheric Administration (NOAA), National Center for Atmospheric Research (NCAR), National Aeronautics and Space Administration (NASA), Chinese Academy of Sciences, and University Colorado were popular based on contribution of articles more than 134 papers each. All keywords were separated into eight clusters for different research topic. Visualizations offer exploratory information on the current state in a scientific field or discipline, as well as can indicate possible developments in the future.


Impacts of large scale climate drivers on precipitation in Sindh, Pakistan using machine learning techniques
Sapna Tajbar, Ali Mohammad Khorshiddoust, and Saeed Jahanbakhsh Asl
DOI:10.28974/idojaras.2023.3.4 (pp. 321–346)
 PDF (4011 KB)   |   Abstract

Sindh province of Pakistan has a long history of severe droughts. Several large scale climate drivers (LSCD) are known for their effect on precipitation worldwide but studies in the Sindh region are missing; wide variety of LSCDs and lagged associative information. This study aimed to identify the significant LSCDs in Sindh province of Pakistan and improve the forecast skill of monthly precipitation by employing the principal component analysis (PCA), artificial neural network (ANN), Bayesian regularization neural network (BRNN), and multiple regression analysis (MRA), while considering the 12 months lagged LSCDs such as Nino-1+2, Nino-3, Nino-3.4, Nino-4, Quasi-Biennial Oscillation (QBO) at 30 and 50hPa (QBOI and QBOII), sea surface temperature (SST), 2m air temperature (T2M), 500 hPa and 850 hPa geopotential heights (H500 and H850), surface and 500 hPa zonal velocity (SU and U500), latent and sensible heat fluxes over land (LHFOL and SHFOL), and surface specific humidity (SSH). Global Land Data Assimilation System (GLDAS), Tropical Rainfall Measuring Mission (TRMM), Modern-Era Retrospective Analysis for Research and Application (MERRA-2), NOAA, Freie University Berlin, and Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) datasets were used. Results manifested that significant LSCDs with 99% confidence level were SU, U500, T2M, SST, SHFOL, LHFOL, SSH, and H850. During test period, compared with MR models of 0.39 to 0.64 and principal components of 0.31 to 0.57, the ANN and BRNN models had better predictive skills with correlation coefficients of 0.57 to 0.83 and 0.52 to 0.76, respectively. It can be concluded that the ANN and BRNN models enable us to predict monthly precipitation in Sindh region with lagged LSCDs.


Considerations regarding the evolution of extreme temperatures in the Banat Plain in the last six decades
Mihai Dudaş and Petru Urdea
DOI:10.28974/idojaras.2023.3.5 (pp. 347–377)
 PDF (8528 KB)   |   Abstract

During cold winter nights we often hear the question "where is the global warming, should it not be warmer?". Low temperatures that can still be recorded in the Banat Plain during winter or media reports of cold waves affecting various regions worldwide seem to the common man to be in total contradiction with the concerns of the scientific community about global warming. With this article we are trying to follow the evolution of some meteorological parameters that can affect the population in one way or another, namely number of tropical days, number of winter days, number of tropical nights, number of frosty nights, absolute maximum and minimum temperatures. Thus, the data obtained from the three national meteorological services (Romanian, Hungarian, and Serbian) operating on the territory of the Banat Plain were grouped in a common database and analyzed both in Excel and with the help of the non-parametric Mann-Kendall test, obtaining a series of conclusions on the evolution of the abovementioned parameters, as well as on the way how the increase in the risk of high temperatures is compensated (or not) by the decrease in the risk of low temperatures.


Contribution to the study of climate change in Serbia using continentality, oceanity, and aridity indices
Dragan Burić, Jovan Mihajlović, Vladan Ducić, Milan Milenković, and Goran Anđelković
DOI:10.28974/idojaras.2023.3.6 (pp. 379–399)
 PDF (2506 KB)   |   Abstract

The aim of the study is to present some specific climatic conditions on the territory of the Republic of Serbia based on the analysis of four climate indices, which can help in understanding contemporary climate changes. Temperature and precipitation data from 31 meteorological stations for the period 1951–2010 were used. The relative homogeneity of the data series was done using the MASH v3.02 method. The indices used are: Johansson Continentality Index, Kerner Oceanity Index, De Martonne Aridity Index, and Pinna Combinative Index. Geospatial analysis of the distribution of the values of the four mentioned indices was done using the QGIS package 2.8.1. The results of the research show that the continentality effect is present in most of Serbia, while oceanity is observed locally, mainly in the western and southwestern parts of the country. The further analysis showed that there is no dry and semi–dry Mediterranean climate in Serbia. Considering that it is dry in the warmest part of the year (July–September), when the need for water is increased, which is clearly shown by the Walter climate diagram, as well as the fact that an increase in temperature and a decrease in precipitation during the vegetation period were observed in the second 30–year period (1981–2010), it can be concluded that in Serbia there is a tendency towards arid climate. The results presented in this paper can help decision makers to plan certain climate change adaptation measures.


Evaluation of wind comfort with computational fluid dynamics simulations for pedestrian sidewalks around buildings
Alper Aydemir, Fikriye Ezgi Karahüseyin, and Yaşar Can Yılmaz
DOI:10.28974/idojaras.2023.3.7 (pp. 401–420)
 PDF (3272 KB)   |   Abstract

Wind power could be one of the most clean and powerful renewable resources for electrical energy production, but on the other hand, uncontrolled wind flow especially in urban places could cause undesired situations as damage to buildings, decrease in pedestrian comfort, environmental damage, or even life loss. Construction of high-rise buildings, widely spread structures within cities, and environmental changes forces, engineers to find quick, reliable, and also economically viable solutions during design stages, but wind comfort of sidewalks generally not considered enough even if they are located in crowded areas. The web-based computer aided engineering (CAE) program named Simscale which runs on the basis of sophisticated graphical interface was used as computational fluid dynamics (CFD) software to determine wind speeds under influence of buildings in the Nuh Naci Yazgan University campus. Also, field measurements carried out in campus area for a short term period were compared with long term hourly wind speed data obtained from the Turkish State Meteorological Service (MGM) station located in Kayseri to identify most optimal wind speed data for the research area. Results of analysis showed that wind speed increased in the  mostly used paths of campus, which means that the layout of buildings negatively affected the wind comfort. CFD analysis softwares could be used to determine the possible consquences of wind with less economic investment in a short time, and they could be used in accordance with comfort criterias as well as safety regulations.




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