Ultra-short-term wind forecasting (< 30 min) is essential for the reliable operation and economic planning of wind energy systems, particularly in regions with high atmospheric variability. Atmospheric stability influences wind dynamics and predictive accuracy, but its role in propagating errors along the wind-energy-revenue chain has received limited attention. To address this gap, the relationship is qualitatively evaluated at a tropical site on the Yucatán Peninsula using 10-minute SoDAR measurements collected over a year of climatic variability. Representative forecasting approaches with different learning mechanisms, including persistence, support vector regression (SVR), and light gradient boosting machine (LGBM), were assessed across various forecast horizons, lag structures, and predictor configurations that included turbulence-related variables. The findings indicate that atmospheric stability significantly influences both prediction performance and the consequences of prediction errors. LGBM consistently outperforms both persistence and SVR, particularly in multi-step forecasting scenarios, and exhibits low sensitivity to lags and predictors. In contrast, SVR shows a stronger dependence on model configuration and improves when turbulent variables (turbulence intensity, turbulent kinetic energy, and vertical velocity) are incorporated, especially under stable conditions. Propagating forecast errors through a representative power curve and actual market prices shows that small biases near rated power can cause revenue deviations of 2.3–4.8 %. These results provide a systematic assessment of how atmospheric stability shapes forecasting skill and the operational and economic risk of ultra-short-term wind power forecasting.