Environmental data modeling, particularly wind speed variations across Iraq's diverse regions, supports renewable energy assessment and climate risk management amid fossil fuel dependency challenges. This study applies the Ramos-Louzada (RL) distribution and its extensions Generalized RL (GRL), Inverse Power RL (IPRL), Exponentiated GRL (EGRL), Inverse RL (IRL), and Ramos-Louzada Exponential (RLE) distribution to hourly 2023 wind speed data from four Iraqi cities: Basrah, Al-Sulaymaniyah, Tikrit, and Al-Kut. Parameters are estimated via maximum likelihood, with model performance evaluated using several criteria. Results reveal location-specific fits, with average wind speeds ranging from 2.75 m/s (Al-Sulaymaniyah) to 4.76 m/s (Basrah), all positively skewed and moderately kurtotic. The IRL distribution outperforms others across all sites, achieving highest coefficient of determination (R2) (0.9758–0.9877) and lowest root mean square error (0.0332–0.0432), Akaike information criterion, Bayesian information criterion, and the Kolmogorov–Smirnov statistic (KS), surpassing IPRL (second-best) and RL baselines. While, RLE distribution consistently ranks lowest. Further, IRL distribution also exceeds Weibull distribution benchmarks, with superior R2 and reduced KS by up to 52%. These findings highlight RL extensions' flexibility for heavy-tailed, skewed wind regimes, informing wind energy potential, site-specific turbine design, and environmental forecasting in Iraq.
Ahmed et al. (Mon,) studied this question.