# Typhoon Intensity Forecasting Based on LSTM Using the Rolling Forecast Method
## 1 Introduction  
Tropical cyclones are low-pressure cyclones that occur in tropical or subtropical oceans and have a great impact on the surrounding environmental systems. Tropical cyclones can grow into tropical depressions (TDs), tropical storms (TSs), strong tropical storms (STSs), typhoons (TYs), strong typhoons (STYs), and super typhoons (super TYs) (defined by the China Meteorological Administration). When the maximum wind speed near the center of a tropical cyclone reaches Level 12 (32.7 m/s or more), it is called a typhoon. A typhoon is a kind of disastrous weather system with strong destructive force. When a typhoon arrives, it causes a storm surge, heavy rainfall, a tide change, and other disasters, which bring huge losses to the lives and property of people. Based on the data recorded by the China Meteorological Administration (CMA), on average, no less than seven typhoons hit the southeast coastal region of China every year, which is the most highly developed and populated region in China. Therefore, it is of great practical significance to forecast typhoons in an accurate and timely manner. Several methods for typhoon forecasting exist. At present, most researchers use numerical prediction methods based on dynamics theory [1; 2; 3; 4]. However, these methods are expensive and complex [5], and it is difficult to gain internal skills using them [6]. In recent years, with the rise of artificial neural networks (ANNs), especially feed-forward neural network (FNNs), some researchers have tried to forecast typhoons based on an FNN. This method, based on statistical theory and big data, does not require knowledge of the detailed physical processes during the development of typhoons, which shows the positive meaning for typhoon forecasting. However, for typhoon forecasting, there are too many prediction factors that influence the prediction results. [PERSON] et al. (2017) applied some forecasting models based on FNNs with different prediction factors to predict the storm surges caused by typhoons. [PERSON] et al. (2018) combined a neural network with the locally linear embedding (LLE) algorithm to predict the precipitation caused by typhoons. [PERSON] et al. (2019) applied a prediction model based on an FNN using the prediction factors selected by multivariate stepwise regression analysis to predict typhoon tracks. The above prediction results show the effective and suitable prediction factors for making the prediction results better for typhoon forecasting. In recent years, [PERSON] et al. (2019) studied typhoon intensity forecasting using a prediction model based on an FNN and applied the model for typhoons Nakri (2014) and Molave (2015), which achieved better results than the National Meteorological Center (NMC), the Joint Typhoon Warning Center (JTWC), the Japan Meteorological Agency (JMA), and the Korea Meteorological Agency (KMA) in 48 h using the minimum pressure and the maximum wind speed near the typhoon's center, as well as the position data of the typhoon center for the current time and for every 6 h in the previous 18 h. However, they did not obtain ideal results for more than 48 h. On the contrary, for the regional forecasting of wind speed during a typhoon landfall, the results could be worse with more uncertainty ([PERSON] et al., 2019). These results show the limit of the abilities of prediction models based on an FNN to predict a typhoon. Therefore, it is very necessary to find a practical method to forecast typhoon intensity in a more timely and accurate manner, especially with a longer lead time of more than 48 h.  
In general, typhoon intensity is measured by the minimum pressure and the maximum wind speed near the typhoon's center, so typhoon intensity forecasting is actually pressure and wind speed forecasting.