Cloudiness, Weather Modification and Their Effects on Populations, Environment and Land

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 4171

Special Issue Editors


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Department of Geospatial and Environmental Science, Faculty of Geography, University of Belgrade, Studenstki Trg 3/III, 11000 Belgrade, Serbia
Interests: GIS; remote sensing; water science; meteorology; climatology; environment; digital cartography; atmosphere; statistics; numerical analysis
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Department of Geography, Tourism and Hotel Management, University of Novi Sad, Trg Dositeja Obradovića 3, Novi Sad 21000, Serbia
Interests: physical geography; natural hazards; rainfall erosivity; climate extremes; climatology; meteorology
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Department of Geography, Tourism and Territorial Planning, Faculty of Geography, Tourism and Sport, University of Oradea, University Str, C Hall, Office 221, 410087 Oradea, Romania
Interests: sustainable tourism; tourism development; nature-based tourism
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The cloudiness and cloud cover changes in the atmospheric layers have a significant influence on the extreme climate effects. The estimation of cloudiness is of great importance for the analysis of climatological parameters, water content inside of them, as well as its connection with biomes and the climate of the world. Changing weather has a great impact on life on our planet. In recent decades, many governments, especially in semi-arid and arid countries, performed inadvertent weather modification in order to obtain an additional water supply for drinking, agriculture, etc., through cloud seeding, dew and fog harvesting systems. There are many advanced methods and techniques that can analyze the lower and upper layers of clouds, dew and fog. For this purpose, mostly advanced GIS and remote sensing techniques such as kriging, semi-kriging, interpolation, zonal statistics, classification, graded points, advanced raster analysis, batch processing with processing framework, nearest neighbor analysis, unsupported and supported pixel classification, object oriented classification, overlay vector classification, soft pixel classification, machine learning, AHP, AHP fuzzy network, etc. are used. In the future, weather and weather modification, including climate change and extremes, will have a greater impact on the environment, the population and land. Therefore, scientific solutions oriented towards the usage of alternative water resources in the atmosphere may pose a significant sustainable approach in a changing environment affected by climate change.

Dr. Aleksandar Dj Valjarević
Dr. Tin Lukić
Dr. Cezar Morar
Guest Editors

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Keywords

  • atmosphere
  • cloudiness
  • modifications
  • weather
  • climate change
  • water resources
  • economic impact
  • numerical methods
  • geospatial analysis
  • population
  • environment
  • land

Published Papers (2 papers)

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Research

18 pages, 3191 KiB  
Article
The Heritage Climate Index (HERCI): Development, Assessment and Application for Tourism Purposes in Geoheritage and Cultural Heritage Sites
by Uroš Durlević, Nina Čegar, Milica Dobrić, Sandra Vukašinović, Tin Lukić, Vladica Stevanović, Dragan Radovanović and Aleksandar Valjarević
Atmosphere 2023, 14(8), 1265; https://doi.org/10.3390/atmos14081265 - 9 Aug 2023
Viewed by 1448
Abstract
The development of climate indices and their application can influence the preferences of tourists and the time frame for visiting the locality. This study develops a new Heritage Climate Index (HERCI) that finds its application in assessing climate comfort for visiting geoheritage and [...] Read more.
The development of climate indices and their application can influence the preferences of tourists and the time frame for visiting the locality. This study develops a new Heritage Climate Index (HERCI) that finds its application in assessing climate comfort for visiting geoheritage and cultural heritage objects. The study analyzed the geoheritage site in Western Serbia (Stopića Cave) and the cultural heritage site in Eastern Serbia (Golubac Fortress). The index was developed to represent climatic comfort on a monthly basis and consisted of five climatic elements. The values of the HERCI index were obtained based on the multi-criteria decision-making model—the Best–Worst method (BWM). The results were classified into five classes, depending on the degree of conformity. After a comparative analysis of the index results for four localities and their attendance for the period 2012–2021 and 2019–2022, it was determined that there is a very high level of correlation (>0.9). This is the first study to use the BWM to develop and analyze a climate index. From the aspect of tourism policy, this study significantly contributes to tourism organizations and tourists in better understanding climate comfort and making decisions about the organization’s time frame and realization of the travel. Full article
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23 pages, 8031 KiB  
Article
Modelling of Wildfire Susceptibility in Different Climate Zones in Montenegro Using GIS-MCDA
by Gojko Nikolić, Filip Vujović, Jelena Golijanin, Ante Šiljeg and Aleksandar Valjarević
Atmosphere 2023, 14(6), 929; https://doi.org/10.3390/atmos14060929 - 25 May 2023
Cited by 7 | Viewed by 1826
Abstract
Montenegro has different influences on the weather and climate; in general, according to Köppen’s classification, there are two climate zones: warm temperate (C) and cold temperate (D). The aim of this study is to determine the susceptibility to wildfires in the Montenegrin coastal [...] Read more.
Montenegro has different influences on the weather and climate; in general, according to Köppen’s classification, there are two climate zones: warm temperate (C) and cold temperate (D). The aim of this study is to determine the susceptibility to wildfires in the Montenegrin coastal municipality of Budva and the northern municipality of Rožaje, which are located in different climatic conditions, using multicriteria GIS decision analysis (GIS-MCDA). Nine natural and anthropogenic criteria were used for the analysis. Open geospatial data were used as input data for all criteria. The assignment of weighting coefficients for the criteria in relation to wildfire susceptibility importance was based on the Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (F-AHP) procedures. The results for the AHP and F-AHP models were obtained using the Weighted Linear Combination (WLC) method. According to the AHP model, the very high and high category covers 80.93% of the total area in Budva and 80.65% in Rožaje. According to the F-AHP model, the very high and high category occupies 80.71% of the total area in Budva and 82.30% in Rožaje. The validation shows that the models of GIS-MCDA perform fair in both climatic zones. The proposed models, especially in the absence of geospatial data, can be a strategic and operational advantage in the development of plans and strategies for protection against wildfires. Full article
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