A novel decomposition-reconstruction-prediction hybrid model to predict precipitable water vapor from GNSS observations

Precipitable water vapor (PWV) is one of the key factors in weather disaster preparedness and water forecasting. Due to the nonlinearity and nonstationarity of the PWV sequence, the existing models cannot achieve stable prediction accuracy. Therefore, this paper proposes a novel decomposition-reconstruction-prediction hybrid prediction model, named improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN)-permutation entropy (PE)-autoregressive integrated moving average with exogenous (ARIMAX)-one-dimensional convolution neural network-bidirectional long-short-term memory (1D CNN-BiLSTM), for predicting PWV. Firstly, the PWV sequence derived from the Global Navigation Satellite System (GNSS) is decomposed using the ICEEMDAN. Secondly, the PE of each decomposed modal is calculated and the PWV sequence is reconstructed into low- and high-frequency components. Then, considering spatio-temporal information, geographic information and meteorological data (longitude, latitude, altitude, day of year, hour of day, surface air pressure and temperature) aided modeling, the low- and high-frequency components are predicted using ARIMAX and 1D CNN-BiLSTM, respectively, and superimposed to obtain the predicted value and prediction intervals. Finally, the proposed model is validated for performance using the PWV derived from six GNSS stations. Compared with other models, the results show that ICEEMDAN-PE-ARIMAX-1D CNN-BiLSTM significantly improves PWV prediction performance, the mean root mean square errors for the six stations are 0.3765 mm, 0.7517 mm and 1.4696 mm for the 1 h, 12 h, and 24 h forecasts, respectively.