https://www.ims-jmas.net/ Journal of Meteorology and Atmospheric Science en daily 1 Sat, 21 Dec 2024 00:00:00 +0330 Sat, 21 Dec 2024 00:00:00 +0330 https://www.ims-jmas.net/article_237772.html Evaporation is a fundamental process in the hydrological cycle. In warm climates, water losses due to evaporation from rivers, canals, and open-water surfaces represent a significant challenge; therefore, the application of reliable predictive models is essential. This study evaluates the performance of three advanced models—Multilayer Perceptron (MLP) neural network, Random Forest (RF) algorithm, and Gaussian Process Regression (GPR)—for predicting evaporation. Six years of daily meteorological data, including mean wind speed, mean air temperature, mean relative humidity, and evaporation, recorded at the Dehkhoda Agro-Industrial meteorological station in Ahvaz, were used. Model predictions were compared against observed values, and all analyses were conducted in MATLAB 2022. Evaluation metrics, including the coefficient of determination (R²) and root mean square error (RMSE), revealed that the MLP model achieved the highest accuracy during the training stage (R² = 0.92, RMSE = 1.44), outperforming the validation (R² = 0.87, RMSE = 1.77) and testing (R² = 0.91, RMSE = 1.68) stages. The overall performance of the MLP model (R² = 0.93, RMSE = 1.40) confirmed its high predictive capability. The overall results for the RF and GPR models were R² = 0.93, RMSE = 1.41, and R² = 0.93, RMSE = 1.42, respectively, indicating that all three models demonstrated high accuracy in predicting evaporation. Based on these findings, the MLP model is recommended as the primary approach for evaporation prediction, followed by RF and GPR https://www.ims-jmas.net/article_237773.html Extreme precipitation events are among the most significant climatic phenomena that can cause widespread damage on local to regional scales within a short period of time. One of the primary systems influencing heavy summer rainfall over Asia is the Indian monsoon, whose variations in intensity and position can substantially alter precipitation patterns in surrounding regions. During the summer of 2022, large parts of Iran experienced intense rainfall and devastating floods. This event affected 26 provinces, caused the death of more than 80 people, and led to severe losses in infrastructure and agriculture. Concurrently, India experienced an exceptionally strong monsoon season; the country’s total rainfall in July 2022 was about 17 % above the long-term climatological mean.Using a large-scale synoptic approach, this study investigates the dynamic and statistical mechanisms associated with this extreme event. The NCEP/NCAR reanalysis data were used to analyze atmospheric circulation fields, the ERA5 reanalysis to calculate the Indian Monsoon Index (IMI), and monthly satellite-based GPM precipitation data to examine rainfall over Iran. Anomalies of geopotential height, horizontal wind, and vertically integrated moisture flux convergence were computed at several pressure levels for July 2022. In addition, to evaluate the temporal linkage between the monsoon and rainfall, the Pearson correlation coefficient between the IMI and monthly precipitation at twenty points with the highest positive rainfall anomalies in southern Iran was calculated for both simultaneous (Lag 0) and one-month-lagged (Lag +1) phases.The results indicate that the IMI value in July 2022 exceeded its long-term mean by more than two standard deviations, representing one of the strongest monsoon phases in the past four decades. Correlation coefficients between the IMI and precipitation exceeded 0.4 and were statistically significant at the 95% confidence level south of 30° N, while the relationship weakened at higher latitudes. The moisture-anomaly pattern further revealed enhanced moisture transport from the Bay of Bengal and the Arabian Sea toward southern Iran, leading to increased convergence and intensified rainfall. Overall, the findings highlight that the exceptionally strong Indian monsoon phase in summer 2022 was the principal driver of Iran’s extreme precipitation and associated floods. https://www.ims-jmas.net/article_238035.html Global warming and climate change are among the most critical environmental challenges of the present era, exerting profound impacts on the climatic dynamics of arid regions. This study aims to detect the trends of heat wave characteristics and their relationship with temperature anomalies in Yazd, Iran. For this purpose, daily temperature data during the period 1960–2017 were employed. Heat wave indices, including the number of events, frequency of heat wave days, duration, magnitude, intensity, and the mean temperature of the hottest day within each wave, were calculated using the ClimPACT software in the R (version 2.10) environment. To investigate correlations, temperature anomalies at the Yazd station were extracted and compared with global land surface temperature anomalies. The results indicated that the highest number of heat wave events occurred in 2010 and 2016. The frequency of heat wave days showed a statistically significant increasing trend, reaching 47 and 42 days in 2010 and 2016, respectively. A considerable increase in the duration and intensity of heat waves was also observed; the longest duration was recorded in 2013 with 13 consecutive days, while the maximum magnitude of heat waves exceeded 31 °C during the last two decades. Correlation analysis revealed a strong association between all heat wave indices and temperature anomalies, with the frequency of heat wave days showing the highest coefficient of determination (R²=0.6108). Overall, the findings highlight the intensification, persistence, and severity of heat waves in Yazd over recent decades, providing clear evidence of ongoing climate change in this arid region. Understanding future trends of heat waves can significantly contribute to risk management strategies, climate adaptation planning, and efficient energy consumption.