In this study, long-term analyses of thunderstorm events occurring in Marmara Region airports were carried out. In the analyses made using airport reports, the events that occurred between 2001-201...

This study aims to reveal the long-term station-based characteristics of precipitation in Istanbul, a mega city located on the continents of Europe and Asia, with complex topography and coastline along the Marmara and Black Seas. Using data from five different stations, three located in the European continent and two in the Asian continent, with measurement periods ranging from 72 to 93 years, wet and dry days have been identified, statistics on precipitation conditions during the warm and cold seasons have been generated, categorization based on precipitation intensities has been performed, and analyses have been conducted using extreme precipitation indices. At stations located in the northern part of the city, higher annual total precipitation has been observed compared to those in the south. A similar situation applies to the number of wet days. While during the cold season, the wet and dry day counts are nearly the same across all stations, this condition exhibits significant differences in favor of dry days during the warm season. Apart from dry conditions, “moderate” precipitation is the most frequently observed type across all stations. However, “extreme” events occur significantly more often (6%) during the warm season compared to the cold season (2%). Long-term anomalies in terms of annual precipitation totals have shown similarity between stations in the north and south, which has also been observed in longitudinally close stations. Despite the longer duration of the cold season and stronger temperature gradients, extreme rainfall events are more frequent during the warm season, primarily due to thunderstorm activity. While trend analyses revealed limited significant trends in precipitation intensity categories and extreme indices, the study highlights the importance of comprehensive examination of extreme rainfall events on both station-based and regional levels, shedding light on potential implications for regional climate change. Lastly, during the cold season, the inter-station correlation in terms of annual total precipitation amounts has been considerably higher compared to the warm season.

This study analyzes heat waves (HWs), cold spells (CSs), and mean temperature trends in Türkiye’s three major metropolises (Istanbul, Ankara, and Izmir) using long-term station data. HW and CS events were defined via a percentile-based threshold approach, utilizing daily maximum (T_max) and minimum (T_min) temperature data from a total of 15 meteorological stations. Temporal trends in annual and seasonal wave frequencies, alongside mean temperature series, were evaluated using the Mann–Kendall test and Sen’s slope estimator. The findings indicate that HW frequencies have significantly increased across the majority of stations, whereas CS frequencies have decreased at most locations. It was determined that while HWs predominantly concentrate in summer and CSs in winter, heat extremes can extend into transitional seasons. Mean temperatures exhibit a statistically significant upward trend across all stations. Furthermore, HWs have become more prominent and CSs have dissipated more rapidly in urban and coastal stations. These results reveal that the risk of heat extremes is escalating while cold extreme events are weakening in Türkiye’s major cities due to warming climate conditions.

This study is the first comprehensive analysis of hail events in Türkiye over a long-term period, utilizing high spatial (0.25° × 0.25°) and temporal resolution (hourly) data to examine the convective environment in which these events occur. Information on hail events across Türkiye for the 23-year period from 2001 to 2023 was obtained from the European Severe Weather Database (ESWD). Subsequently, duplicate events were removed from the 3860 reports classified according to Quality Control (QC) categories, resulting in 874 unique hail events. To understand the atmospheric conditions and convective environment in which hail events occurred, proximity soundings were created for each event, considering the spatial and temporal resolutions of the events. A total of 21 convective parameters were calculated for each hail event, and detailed analyses were conducted for different hail sizes (ranging from 2 to 8 cm). The prominent findings of the study are as follows: The months of May and June, when atmospheric instability peaks, showed a 2–3 times higher frequency of hail events compared to other months. Although hail events occurred almost every hour of the day, high frequencies were particularly observed between 1100 and 1600 UTC. The Central Anatolia Region and Mediterranean Region had the highest frequency of hail events (21% each) among geographical regions. The convective parameters that best reflected the convective environment of hail events were Most Unstable Convective Available Potential Energy (MUCAPE), Most Unstable Convective Inhibition (MUCIN), Maximum Updraft Speed for the MU Parcel (WMAX), Most Unstable WMAXSHR, and Most Unstable Lifting Condensation Level (MUEL). While hail sizes varied between 2 and 8 cm across all events, correlations were found between increasing hail sizes and convective parameters .

This study presents an integrated framework for offshore wind resource assessment and wind farm micrositing in the Northern Aegean Sea of Türkiye by combining machine learning-assisted measure–correlate–predict (MCP) modelling, long-term reanalysis data and computational fluid dynamics (CFD). One year of measurements from a 41 m meteorological mast on Küçük Ada, offshore Aliağa, İzmir, was analyzed together with a 21-year ECMWF Reanalysis v5 (ERA5) dataset. The measurements indicated a mean annual wind speed of 8.07 m/s, a wind shear exponent of 0.049, a Weibull shape parameter of 2.06 and a persistent northeasterly wind regime. Long-term conditions were reconstructed using 64 meteorological and cyclic predictors derived from four ERA5 grid points and their bilinear interpolation to the mast location. Five H₂O algorithm families were evaluated using randomized grid searches and 12-fold temporal cross-validation. Distributed Random Forest provided the best performance for 100 m wind speed, with RMSE = 1.969 m/s, MAE = 1.504 m/s, bias = −0.027 m/s and an out-of-fold Pearson correlation coefficient of r = 0.884. TreeSHAP analysis was applied to interpret predictor contributions. High-resolution WindSim simulations with 28.75 million cells, ALOS PALSAR topography and CORINE land-cover data supported turbine micrositing. The proposed 1.43 GW wind farm yielded 5494.5 GWh/year after wake losses, with a capacity factor of 43.9% and an overall wake loss of 5.2%. The framework provides a robust basis for offshore wind development in Türkiye.

This study investigated the relationship between the height of the atmospheric boundary layer (ABL) and variation in PM10 concentrations in Ankara during the course of 2023. By analyzing data from a nearby meteorological station and an air quality measurement station it was found that the average PM10 level for the year was 51.5 µg/m³. The highest monthly average was reported in January at 90.99 µg/m³ while the lowest was recorded in April at 24.42 µg/m³.PM10 data varied according to the month. The ABL height changed dramatically with the season reaching heights of over 1000 m in summer and as low as 100 m in winter. Lower ABL heights and higher heating activities correlated with higher PM10 levels. Higher PM10 concentrations have also been associated with winds from the east and southwest and a strong correlation was observed between the variation in PM10 air pollution levels and the use of fossil fuels for heating throughout the winter. These results stress the impact of seasonal temperature variations on PM10 levels and ABL height indicating the need for focused approaches to enhance air quality particularly during winter.