Real-Time Rainfall Forecasts Based on Radar Reflectivity during Typhoons: Case Study in Southeastern Taiwan

Abstract

This study developed a real-time rainfall forecasting system that can predict rainfall in a particular area a few hours before a typhoon’s arrival. The reflectivity of nine elevation angles obtained from the volume coverage pattern 21 Doppler radar scanning strategy and ground-weather data of a specific area were used for accurate rainfall prediction. During rainfall prediction and analysis, rainfall retrievals were first performed to select the optimal radar scanning elevation angle for rainfall prediction at the current time. Subsequently, forecasting models were established using a single reflectivity and all elevation angles (10 prediction submodels in total) to jointly predict real-time rainfall and determine the optimal predicted values. This study was conducted in southeastern Taiwan and included three onshore weather stations (Chenggong, Taitung, and Dawu) and one offshore weather station (Lanyu). Radar reflectivities were collected from Hualien weather surveillance radar. The data for a total of 14 typhoons that affected the study area in 2008–2017 were collected. The gated recurrent unit (GRU) neural network was used to establish the forecasting model, and extreme gradient boosting and multiple linear regression were used as the benchmarks. Typhoons Nepartak, Meranti, and Megi were selected for simulation. The results revealed that the input data set merged with weather-station data, and radar reflectivity at the optimal elevation angle yielded optimal results for short-term rainfall forecasting. Moreover, the GRU neural network can obtain accurate predictions 1, 3, and 6 h before typhoon occurrence.

Integrating ANFIS and Qt Framework to Develop a Mobile-Based Typhoon Rainfall Forecasting System

“…(3) This study employed Qt for cross-operating platform development, the C++ was adopted for implementation, and the program code was converted by the Qt-Creator into native code to avoid the compatibility problem of different operating platforms and to increase the operating efficiency (4) This study employed cluster analysis and ANFIS to implement TRF Kernel, including the optimal parameters of groups established beforehand and the one-three hours ahead typhoon rainfall forecast information. Hence, the urban rainstorm disaster prevention is much more timely (5) The architecture of the TRF system is not a fully cloud-based system. It cannot be easy to co-operate with other well-known cloud service, such as Google, Microsoft, and Amazon…”

“…The typhoon rainfall forecasting can be divided in two methods: physical numerical method and statistical method [1][2][3][4][5][6]. As the physical numerical method employs numerical methods to solve complex fluid dynamic nonlinear equations.…”

Modular Neural Networks with Fully Convolutional Networks for Typhoon-Induced Short-Term Rainfall Predictions

“…The development of weather surveillance radars has enabled quantitative precipitation estimation with extremely high spatial resolutions. Weather radars, which have the advantages of wide coverage and round-the-clock observation, are critical devices for meteorological observation [ 15 ]. Accordingly, the application of two-dimensional (2-D) radar images compensates for the insufficient 1-D spatial rainfall data collected from land-based observation stations.…”