Forecasts of maximum and minimum air temperatures are essential to mitigate the damage of extreme weather events such as heat waves and tropical nights. The Numerical Weather Prediction (NWP) model has been widely used for forecasting air temperature, but generally it has a systematic bias due to its coarse grid resolution and lack of parametrizations. This study used random forest (RF), support vector regression (SVR), artificial neural network (ANN) and a multi‐model ensemble (MME) to correct the Local Data Assimilation and Prediction System (LDAPS; a local NWP model over Korea) model outputs of next‐day maximum and minimum air temperatures ( and ) in Seoul, South Korea. A total of 14 LDAPS model forecast data, the daily maximum and minimum air temperatures of in‐situ observations, and five auxiliary data were used as input variables. The results showed that the LDAPS model had an R² of 0.69, a bias of −0.85 °C and an RMSE of 2.08 °C for forecast, whereas the proposed models resulted in the improvement with R² from 0.75 to 0.78, bias from −0.16 to −0.07 °C and RMSE from 1.55 to 1.66 °C by hindcast validation. For forecasting , the LDAPS model had an R² of 0.77, a bias of 0.51 °C and an RMSE of 1.43 °C by hindcast, while the bias correction models showed R² values ranging from 0.86 to 0.87, biases from −0.03 to 0.03 °C, and RMSEs from 0.98 to 1.02 °C. The MME model had better generalization performance than the three single machine learning models by hindcast validation and leave‐one‐station‐out cross‐validation.

中文翻译：

---

基于机器学习的偏差校正方法对城市极端气温的数值天气预报模型预测的比较评估

预测最高和最低气温对于减轻极端天气事件（如热浪和热带夜晚）的破坏至关重要。数值天气预报（NWP）模型已被广泛用于预测气温，但是由于其粗略的网格分辨率和缺乏参数设置，通常具有系统偏差。这项研究使用随机森林（RF），支持向量回归（SVR），人工神经网络（ANN）和多模型集成（MME）来纠正本地数据同化和预测系统（LDAPS；韩国的本地NWP模型）在韩国首尔的第二天最高和最低气温（和）的模型输出 。总共14个LDAPS模型预测数据，每日最高和最低本地气温观测值和五个辅助数据用作输入变量。结果表明，LDAPS模型的R ²为0.69，预测的偏差为-0.85°C，RMSE为2.08°C ，而所提出的模型导致R ²从0.75改善到0.78，而偏差为-通过后铸验证，可得到0.16至-0.07°C的温度和RMSE（1.55至1.66°C）为了进行预测，LDAPS模型的R ²为0.77，偏差为0.51°C，RMSE为1.43°C（后铸），而偏差校正模型显示R ²值范围从0.86至0.87，偏置范围从-0.03至0.03°C，RMSE从0.98至1.02°C。通过后播验证和留一站外交叉验证，MME模型具有比三个单机学习模型更好的泛化性能。