Compound Parameterization to Improve the Accuracy of Radiation Emulator in a Numerical Weather Prediction Model

Abstract

To improve the numerical weather prediction model over Korea using a neural network (NN) radiation emulator, two types of compound parameterization (CP) were developed. Although the CP returning to the original parameterization causes a considerable increase in the computational time, this increase can be compensated by the infrequent use of the radiation scheme, thus maintaining the 60-fold speedup of the radiation process with the NN emulator. The first CP is based on the prediction of the heating rate error using the additional NN for all given input variables. In contrast, the second CP uses the cloud fraction to estimate the uncertainty of the NN emulator. As a result of model simulations for independent cases, including extreme flood events, the first CP was the most effective for passive use, whereas the second was useful in active use and exhibited the lowest error. Thus, these CP methods can help improve weather forecasting.