Using a 3D convolutional neural network and gated recurrent unit for tropical cyclone track forecasting

Abstract

The tropical cyclone (TC) track forecast is an essential task in meteorological operations. An accurate forecast should be based on a comprehensive understanding and description of TCs. A TC has a complex three-dimensional structure, and the surrounding atmosphere is the driving force for its development. Traditional forecasting methods performed relatively well for the TCs with stable moving speed and direction. However, the forecast accuracy still leaves some space to improve. In recent years, machine learning methods that can extract features from a large amount of historical data have been used in meteorological services and have shown excellent performance. To better forecast 6, 12, 18, and 24 h TC tracks in the Western North Pacific, a hybrid optimization model, combining the 3D convolutional neural network (3DCNN), gated recurrent unit (GRU), and smoothing algorithm is designed, which is called smoothed 3D-GRU. The 3DCNN is used to explore the potential relationship between environmental variables and TC movements at different pressure levels. The GRU is used to convert the TC track forecasting problem into a spatio-temporal sequence problem. The smoothing algorithm is used as a post-processing method to suppress unreasonable jumps of the model output. The mean spherical distances (MSDs) of the proposed smoothed 3D-GRU model at four prediction times are 27.89, 52.37, 79.16, and 112.05 km, which are lower than the comparative machine learning-based forecasting algorithms. Compared with the numerical prediction methods, the MSDs of the smoothed 3D-GRU model are lower in most situations. In general, the smoothed 3D-GRU model can provide reliable guidance for the TC trajectory prediction.