## 3 Measured Tropical-Storm Winds  
The tropical storm Rumbia, which occurred in 2018, is selected as an example for short-term wind-speed forecasting. It was the third tropical storm that hit the east coast of China and caused widespread damage to properties with concomitant economic losses and casualties. Rumbia originally developed as a tropical depression in the southeast of the Ryukyu Islands and then moved northward on 13 August. It went towards the west on 16 August and made landfall at Shanghai in the early morning of 17 August. During its landfall, the intensity of the wind power achieved its maximum. After it moved into the continent, the wind power of Rumbia began to decrease and finally disappeared. The Sutong Bridge, which was the longest cable-stayed bridge in the world, is very close to Shanghai, so the wind data of Rumbia were well recorded by the anemometers in the structural health monitoring system. Details on the anemometers can be found in [41]. The data from the anemometer, which is located in the middle of the main span, with a height of 76.9 m, are employed for the forecasting. The data span from 20:48 on 16 August to 08:48 on 17 August. In order to obtain sufficient reaction time to take emergency measures, the time interval during the wind speed forecasting is taken as 1 min. Figure 4 depicts the 1 min peak wind speeds of the measured wind samples, in which the zero time instant corresponds to 20:48 on 16 August. The wind speed in Figure 4 shows a typical wind sample measured in the outer region of the tropical storm Rumbia. The wind speed generally increases before 07:45 on 17 August and then decreases after the peak wind speed, which equals to 33 m/s, although a dramatical plunge occurs between 03:56 and 04:03. The observation above indicates that a distinct time-varying trend exists in the wind sample. Hence, the wind sample is visibly judged as a non-stationary process, which will be quantiti  
Figure 4: Measured wind speed data. (**a**) Peak wind speed in a 1 min time interval; (**b**) PDF of measured wind speeds. To analyze the statistical property of the wind sample, the PDF of the wind speeds is calculated, as shown in Figure 4b. It is observed that the wind speeds are mostly larger than 10 m/s and are not well satisfied with the normal distribution. In such a case, the models based on a Gaussian assumption may present a relatively poor prediction. Considering the non-stationarity, non-Gaussianity, and even nonlinearity in tropical-storm winds, it is necessary to discuss the applicability of different forecasting approaches. To facilitate the wind speed forecasting, the measured data in Figure 4 are classified as three parts with a length ratio of 3:1:1. The first part is the training set to build the basic forecasting model. The second part is the validation set that provides an unbiased evaluation of the forecasting model when tuning hyperparameters, and this part can eliminate the overfitting problem. The last part is the test set, which is utilized to verify the forecasting performance. The stationarity and statistical properties of the three parts are different to some extent. This is the big challenge for short-term forecasting of tropical storms. The linear and nonlinear models are both established based on in situ measured data, so the wind characteristics and topography effects are inherently involved in the prediction. As the wind direction of the tropical storm will not change significantly in a short amount of time, the trained model by the previously collected data in a determined period can represent the upstream topography. However, for the wind speed near the eye wall, the wind direction changes rapidly, thus the model trained by the previously collected data would not work for the future data on this condition, as the upstream terrain may be different.