Pred-SF: A Precipitation Prediction Model Based on Deep Neural Networks

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 5; p. 2609
• Main Authors: Tang, Rongnian, Zhang, Pu, Wu, Jingjin, Chen, Youlong, Dong, Lingyu, Tang, Song, Li, Chuang
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 27.02.2023; MDPI
• Subjects: Analysis; Data integration; Datasets; Deep learning; ERA5; Geospatial data; Humidity; Meteorological data; Modal data; Multisensor fusion; Neural networks; Numerical methods; Numerical weather forecasting; Partial differential equations; Precipitation; Precipitation (Meteorology); Pred-SF; predict precipitation; Prediction models; PredRNN-V2; Rain; self-cyclic; Sensors; Spatial data; step by step; Time series; Weather forecasting; ERA5; predict precipitation; GPM; step by step; self-cyclic; Pred-SF; PredRNN-V2
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23052609

How to predict precipitation accurately and efficiently is the key and difficult problem in the field of weather forecasting. At present, we can obtain accurate meteorological data through many high-precision weather sensors and use them to forecast precipitation. However, the common numerical weather forecasting methods and radar echo extrapolation methods have insurmountable defects. Based on some common characteristics of meteorological data, this paper proposes a Pred-SF model for precipitation prediction in target areas. The model focuses on the combination of multiple meteorological modal data to carry out self-cyclic prediction and a step-by-step prediction structure. The model divides the precipitation prediction into two steps. In the first step, the spatial encoding structure and PredRNN-V2 network are used to construct the autoregressive spatio-temporal prediction network for the multi-modal data, and the preliminary predicted value of the multi-modal data is generated frame by frame. In the second step, the spatial information fusion network is used to further extract and fuse the spatial characteristics of the preliminary predicted value and, finally, output the predicted precipitation value of the target region. In this paper, ERA5 multi-meteorological mode data and GPM precipitation measurement data are used for testing to predict the continuous precipitation of a specific area for 4 h. The experimental results show that Pred-SF has strong precipitation prediction ability. Some comparative experiments were also set up for comparison to demonstrate the advantages of the combined prediction method of multi-modal data and the stepwise prediction method of Pred-SF.