Kleijer Yuan*
Meteorological models are crucial tools for predicting weather and climate patterns. However, these models often exhibit biases due to imperfections in model physics, initial conditions, and parameterizations. Bias correction methods are employed to adjust model outputs, enhancing their accuracy and reliability. This review examines various bias-correction techniques used in meteorological modeling, evaluating their effectiveness, advantages, and limitations. We explore statistical methods, dynamical approaches, and machine learning techniques, providing a comprehensive overview of current practices and future directions in the field. The review aims to guide researchers and practitioners in selecting appropriate bias-correction methods for improving meteorological predictions.
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