Prediction of Tropical Easterly Waves Using Deep Learning

Tropical easterly waves (TEWs) directly impact people through wind, rain, and tropical cyclone formation in the Pacific and Atlantic Oceans. The structure and intensity of a TEW can be affected by a myriad of internal and external factors during a wave’s lifetime. Most existing statistical models of TEW intensification have been specifically designed to predict tropical cyclone formation from these waves. Understanding TEW behavior across a wide range of intensities, timescales, and geographic regions would provide insight into the general framework of TEW evolution. We use a novel TEW dataset, ERA5 reanalysis, and GridSAT brightness temperature data to train a neural network to predict vorticity and convective intensity in TEWs at lead times of 1 to 5 days over Africa, in the tropical North Atlantic, and in the eastern North Pacific. This network uses TEW-centered input data to generate a 50-member ensemble of predictions for each output variable at each lead time. We verify the network's predictive performance against forecasts from operational modeling. We identify input variables that contribute most significantly to the network’s output predictions and associated mean errors and ensemble uncertainty, and show how these findings vary for waves in different locations and of different initial strengths. Physically intuitive mechanisms seen in this investigation can help us better understand how TEWs evolve along an intensity / organization spectrum ranging from weak, dry waves to full-fledged tropical cyclones.