The paper by Gneiting, Balabdaoui, and Raftery discusses the evaluation of probabilistic forecasts, focusing on calibration and sharpness. Probabilistic forecasts are evaluated through predictive densities or cumulative distribution functions. The authors propose a diagnostic approach based on maximizing the sharpness of predictive distributions subject to calibration. Calibration refers to the statistical consistency between forecasts and observations, while sharpness measures the concentration of predictive distributions. They introduce tools such as the probability integral transform (PIT) histogram, marginal calibration plots, sharpness diagrams, and proper scoring rules to assess these properties. The paper includes a case study on probabilistic forecasts of wind speed at the Stateline wind energy center, demonstrating the effectiveness of their diagnostic approach in ranking and improving forecast methods. The results highlight the importance of both calibration and sharpness in evaluating probabilistic forecasts.The paper by Gneiting, Balabdaoui, and Raftery discusses the evaluation of probabilistic forecasts, focusing on calibration and sharpness. Probabilistic forecasts are evaluated through predictive densities or cumulative distribution functions. The authors propose a diagnostic approach based on maximizing the sharpness of predictive distributions subject to calibration. Calibration refers to the statistical consistency between forecasts and observations, while sharpness measures the concentration of predictive distributions. They introduce tools such as the probability integral transform (PIT) histogram, marginal calibration plots, sharpness diagrams, and proper scoring rules to assess these properties. The paper includes a case study on probabilistic forecasts of wind speed at the Stateline wind energy center, demonstrating the effectiveness of their diagnostic approach in ranking and improving forecast methods. The results highlight the importance of both calibration and sharpness in evaluating probabilistic forecasts.