The Mediterranean is recognized as one of the most sensitive regions regarding climate change. The northern sector winds are a dominant feature of summer low-tropospheric circulation over the Aegean basin in eastern Mediterranean (EMed). This study is an updated assessment that uses state-of-the-art tools in order to investigate the projected changes of the meridional wind speed and Etesian regime during summer period (June-July-August) over the 21^st century. The analysis is based on 17 Global Climate Models simulations (GCMs) available from Coupled Model Intercomparison Project Phase 6 (CMIP6) covering the historical period (from 1971 to 2014) and the future period (from 2015 to 2100) under two Shared Socioeconomic Pathways (SSPs), an intermediate and a very high emission scenario (SSP2-4.5 and SSP5-8.5). Additionally, results from GCMs analysis are compared to ERA5 reanalysis for the historical period from 1971 to 2000. Our findings suggest that the majority of GCMs reproduce the spatial pattern of Etesians but underestimate the meridional wind speed about 0.5 to 1.0 m/s, as compared to ERA5. During the future period, the meridional wind speed is projected to be increased over the Aegean basin, mainly during the last period of 21st century. Findings show that the majority of GCM simulations (12 out of 17) show an increase of meridional wind speed about 0.2 to 1.4 m/s for SSP5-8.5 and 0.2 to 0.6 m/s for SSP2-4.5, as compared to historical period from 1971 to 2000.

Severe Local Storms affect the Cuban territory at any time of the year and constitute one of the main variations in climate. These variations seem to be associated with very low-frequency oscillations or cycles in background climatic conditions, within processes of natural climate variability. One of these oscillations is El Niño – Southern Oscillation, which plays an important role as a forcing element in Cuba's climatic variability. Taking into account the above, in this research the objetives are: find the monthly distribution of the Severe Local Storms in the regions of Cuba, finding the months of greatest severity, as well as its relationship with the phases of the El Niño - Southern Oscillation event and analyze the relationship between the days with reports of Severe Local Storms and the intensity of El Niño - Southern Oscillation phases in the rainy and dry season. Among the materials and methods used are El Niño – Southern Oscillation Index database and the Severe Local Storms reports database. It was shown that the months with the highest occurrence of severe activity coincide with the rainy period of the year and the least active with the dry season, resulting in the months from May to August as those with the highest frequency of Severe Local Storms, highlighting July as the month of maximum severe activity for the west and center and June for the eastern zone. In addition, it was observed that the phase that most favored the occurrence of Severe Local Storms was the Neutral phase of the El Niño – Southern Oscillation event, followed by the warm phase and, to a lesser extent, the cold phase.

During recent years, by expanding cities, air pollution is one of the most important problem made by humans. Tehran, as the capital of Iran, is expanding gradually and its population is rising day by day. Therefore, the increase of human activities causes many problems, such as air pollution in this area. In this study, Sentinel-5 data for Tehran city are used from last month of 2018 to present, and collected by Google Earth Engine. Three main pollutants have been studied, such as aerosol, ozone and CO. Different statistics of each pollutant are calculated, and temporal evolutions are investigated using variance analysis and linear regressions. The medians and the interquartile ranges are -0.555 and, 1.248 for aerosols, 0.131 and 0.015 mol m^-2 for ozone, and 0.037, and 0.011 for CO mol m^-2. The annual evolution shows that aerosols amounts are higher in summer against the winter months, whereas ozone is high in March and low in October. CO amounts are minimum in summer and maximum at the end of fall and winter, with high variability. The aerosol trend is increasing, whereas statistics have shown a decreasing trend for ozone, with both trends being statistically significant at a 95% confidence level. However, the CO trend is totally stable. These results can be used to detect pollution events, and to protect human life in future. Moreover, these parameters can be studied in other areas, especially the industrial ones, to understand the most polluted sites and periods.

The occurrence of earthquakes, which can strike suddenly without any warning, has always posed a potential threat to humanity. However, researchers worldwide have been diligently studying the mechanisms and patterns of these events in order to develop warning systems and improve detection methods. One of the most reliable indicators for predicting large earthquakes has been the examination of electron availability in the ionosphere. This study focuses on analyzing the behavior of the Total Electron Content (TEC) in the ionosphere during the 30-day period leading up to the three most devastating earthquakes of the past decade. Specifically, the data were examined from the cGPS stations closest to the epicenters: MERS for the Turkey earthquake with 7.8Mw on 02-06-2023, CHLM for the Nepal earthquake with 7.8Mw on 04-25-2015, and MIZU for the Japan earthquake with 9.1Mw on 03-11-2011. Notable positive and negative anomalies were observed for each earthquake, and the vertical Total Electron Content (vTEC) for each PRN (pseudo-random noise) was plotted to determine the specific time of the TEC anomaly. The spatial distribution of vTEC for the anomalous specific time revealed that the anomalies were in close proximity to the earthquake epicenters, particularly within denser fault zones.

Abstract: In Cuba, short-term predictions have been developed for wind speed in the Gibara wind farms. These predictions present an absolute mean error (EMA) that sometimes exceeds 3 m/s. This study has the aim of verify the wind forecast generated by SisPI using the Synoptic Situation Types Catalog (TSS), a wind speed observation data provided by the anemometers installed in the wind turbine. The study period spanned from May 2020 to April 2021. For the evaluation were used the metrics: root mean square error (RMSE) and EMA, and the analysis was made in the rainy and dry seasons, through the methodology developed by (Patiño, 2022). Results indicate that the subtype 3 (Extended undisturbed anticyclonic flow) was the one with the highest frequency of cases between very good and good in both seasonal periods. Subtype 19 (migratory anticyclone in an advanced state of transformation) was the system that produced the worst results in the dry season, with the largest number of cases of bad wind speed forecasts. The results of the statisticians: bias (BIAS) and Pearson's Correlation Coefficient (r), were very favorable.

The real-time monitoring of crucial meteorological parameters derived from geostationary satellites is considered of high importance given the fact that they provide high local-scale accuracy about their spatiotemporal evolution, and consequently, the potential damages from extreme weather to infrastructure and private properties can be eliminated. The scope of this study is an attempt to automatically visualize in real-time rainfall and hail estimations coming from a known satellite-based algorithm that uses Meteosat multispectral imagery exclusively. The application is fully automated, written in the Python programming environment using open-source libraries, and provides colored graphs about the spatial variation of the examined parameters with the same temporal resolution as the Meteosat imagery. Additional functions of this application include warnings for extreme situations each time pre-defined threshold values are exceeded, as well as geographical areas that are vulnerable to heavy rainfall and/or hail occurrences. This application is a pilot operating over the Greek periphery. Also, there is a capability to create small video animations for the spatiotemporal evolution of the rainfall and hail estimations up to 6 hours before the latest available satellite images.

Cuba presents two well-marked periods in the year, the dry one (November-April) and the rainy one (May-October); the latter is part of the tropical wave season. Among the main characteristics of these systems are wavelengths between 2000-2500 km, propagation speed between 20-40 km/h and period in the range of 3 to 4 days. As they pass, they cause changes in the weather, mainly in the rainfall regime, as well as being, in some cases, precursors of tropical cyclones in the North Atlantic and in the Caribbean Sea. Regarding the variability in its quantity and intensity, recent studies suggest that it could be related to the El Niño-Southern Oscillation (ENSO) event. For this reason, the present investigation has as general objective to examine the behavior of the precipitation associated with the transit of tropical waves through Cuba during the years 2012-2020 for different phases of the ENSO (El Niño and La Niña). In order to obtain the information on the rainfall associated with the waves, the data from the meteorological stations belonging to the Institute of Meteorology of Cuba and the network of rain gauges of the National Institute of Hydraulic Resources were consulted on the dates that coincide with the passage of these systems for the country. In addition, the annual behavior of the ENSO based on the Southern Oscillation Index (ONI) was analyzed. To determine the years of greatest influence and their graphic representation, the Microsoft Excel 2019 program was used. The results showed that during the Neutral and La Niña years there are few differences in the number of cases with intense rainfall associated with waves. On the contrary, the results reveal a decrease in rainfall in the El Niño phase.

Urban ventilation plays a crucial role in maintaining air quality and mitigating the heat island effect in urban areas. It facilitates the movement of fresh air, disperses pollutants, and regulates temperature, creating a healthier and more comfortable environment for residents. Proper urban ventilation design and planning can contribute to reducing air pollution, improving the overall livability of cities, and enhancing the well-being and quality of life of urban dwellers. In this context, the present study investigates the relationship between drag parameters and ventilation efficiency in urban-like regular arrays, with the aim of uncovering correlations that can enhance the understanding of airflow dynamics in urban environments. Three-dimensional computational fluid dynamics (CFD) simulations employing the standard k-ε turbulence model are performed. These arrays are sorted into two groups: the first consists of cubic arrays with various wall-to-wall distances, while the second comprises cuboid arrays with various side widths. The planar area density (λ_P) ranges from 0.0625 to 0.56. Grid sensitivity test and validation against wind tunnel data are conducted, followed by simulations under the same inflow conditions. The drag force (F), drag coefficient (C_d), spatially averaged velocity (U_ave), and air change rate (ACH) are calculated. According to the results, F shows a significant increase for 0.0625 < λ_P < 0.25 and a slight decrease for 0.25 < λ_P < 0.56, while Cd shows a linear increase with λ_P. The further linear regression analysis shows that U_ave and ACH are strongly negatively correlated with F and C_d, which supports the effectiveness of drag parameters in reflecting ventilation efficiency, particularly for evaluating urban block-scale ventilation. These findings could have significant implications for urban planning, architectural design, and the development of sustainable and efficient urban spaces.

Air pollution poses a significant threat to human health, climate stability, and ecological balance the proposed climate monitoring system utilizes Raspberry Pi as a central procession unit and integrates various sensors, which also incorporates sensors to measure the concentrations of PM1, PM2.5, PM10, and black carbon. This method meets the need for effective and immediate air quality monitoring and offers useful information to communities, academics, and policymakers. Through IoT connectivity, the gathered data is sent to a cloud-based platform for analysis and visualization.

The system offers a user-friendly interface that presents actionable insights for informed decision-making. Its warning capabilities alert users when pollution levels exceed thresholds and also this system contributes to a comprehensive understanding of air pollution. By measuring particulate matter and black carbon levels, it supports the development of effective air quality management strategies. The system helps to take proactive measures and create cleaner and healthier environments.

In conclusion, the proposed Climate Monitoring System utilizing Raspberry Pi, sensors, IoT connectivity, and machine learning techniques offers an effective and real-time solution for monitoring air quality. The integration of IoT connectivity allows remote access to air quality data, while machine learning algorithms analyse the data and initiate alerts.

The variability of North Atlantic Subtropical High and its inlfuence on the behavior of tropical cyclogenesis are charactized an analized. The monthly database of the Center for Environmental Prediction and the Center for Atmospheric Research of the United States was consulted, for the months of May to October between 1950-2019. The variables used were the central pressure to determine the position of the North Atlantic Subtropical Anticyclone on the surface, and the geopotential to obtain the position of the anticyclonic center at 850 hPa, as well as the geopotential at 500 and 200 hPa over the central región of the anticyclone. on surface. The results showed that this system weakens at surface level and intensifies at other tropospheric levels and has moved to the northwest with respect to its mean position. On the other hand, the relationship with tropical cyclone activity in the Atlantic basin was assured and updated. Currently, low levels play an important role in tropical cyclogenesis, surface pressure, as well as the intensity and position of the anticyclonic cell at 850 hPa, as well as the position and extension of the anticyclonic ridge at this level, the parameters with highest coincidence. The anticyclone parameters in May maintain great significance, but in the present century the anticyclone parameters in the months of June and July have increased their significance in terms of their position and intensity respectively.