Development of an Optimal Postprocessing Model Using the Microgenetic Algorithm to Improve Precipitation Forecasting in South Korea

• Published in: Artificial intelligence for the earth systems Vol. 3; no. 1
• Main Authors: Kim, Junsu, Kim, Yeon-Hee, Bok, Hyejeong, Jang, Sungbin, Cho, Eunju, Kim, Seungbum
• Format: Journal Article
• Language: English
• Published: 01.01.2024
• ISSN: 2769-7525; 2769-7525
• DOI: 10.1175/AIES-D-23-0069.1

Abstract We developed an advanced postprocessing model for precipitation forecasting using a microgenetic algorithm (MGA). The algorithm determines the optimal combination of three general circulation models: the Korean Integrated Model, the Unified Model, and the Integrated Forecast System model. To measure model accuracy, including the critical success index (CSI), probability of detection (POD), and frequency bias index, the MGA calculates optimal weights for individual models based on a fitness function that considers various indices. Our optimized multimodel yielded up to 13% and 10% improvement in CSI and POD performance compared to each individual model, respectively. Notably, when applied to an operational definition that considers precipitation thresholds from three models and averages the precipitation amount from the satisfactory models, our optimized multimodel outperformed the current operational model used by the Korea Meteorological Administration by up to 1.0% and 6.8% in terms of CSI and false alarm ratio performance, respectively. This study highlights the effectiveness of a weighted combination of global models to enhance the forecasting accuracy for regional precipitation. By utilizing the MGA for the fine-tuning of model weights, we achieved superior precipitation prediction compared to that of individual models and existing standard postprocessing operations. This approach can significantly improve the accuracy of precipitation forecasts. Significance Statement We developed an optimized multimodel for predicting precipitation occurrence using advanced techniques. By integrating various weather models with their optimized weights, our approach outperforms the method of using an arithmetic average of all models. This study underscores the potential to enhance regional precipitation forecasts, thereby facilitating more precise weather predictions for the public.