Machine-learning based ocean atmospheric duct forecasting: a hybrid model-data-driven approach

The atmospheric duct is a vital radio wave environment. Conventional methods of forecasting the atmospheric duct mainly include statistical analysis based on sounding observation data and mesoscale numerical model - based prediction. The former can provide accurate duct information but is highly dependent on the acquisition of data sets. The latter is more practical but still lacks accuracy. This paper introduces machine learning to establish a novel meteorological parameter correction model for atmospheric duct prediction. In detail, using the weather research and forecasting (WRF) model data and spatiotemporal characteristics as input, sounding data as label and extreme gradient boosting (XGBoost) model for training, the meteorological parameter correction effect is the best, i. e., the accuracy of forecast meteorological parameters is improved by about 65.4%. Combining the mapping relationship between meteorological parameters and corrected atmospheric refractive index ( CARI ), and the transition mechanism of CARI to duct parameters, a new duct forecasting mechanism is proposed. Due to the high efficiency of numerical model and the accuracy of sounding data, the new duct forecasting mechanism has excellent performance. By comparing the duct forecasting results, the forecasting accuracy of the new duct forecasting model is significantly higher than that of the mesoscale model. © 2023, Beijing University of Posts and Telecommunications. All rights reserved.