Improving Dual-Polarization Radar-based rainfall estimation using Long Short-Term Memory Neural Networks

Extreme hydrological events, which are highly concerned by local governments, hydraulic units and hazard response centers due to their potential to bring heavy rainfall and cause serious floods, have frequently occurred and impacts on Taiwan urban area in recent years under the circumstance of climate change and global warming. The frequent occurrence of high intense storm always leads to flooding-related disasters within a short period, which makes rainfall monitoring a disaster prevention. Therefore, this study utilizes Long Short-Term Memory Neural Networks (LSTM) and Back Propagation Neural Networks (BPNN) to extract the characteristics of radar observations and forecast rainfall with time 1-step-ahead to 6-step-ahead (T+1~T+6) in Taiwan’s capital, Taipei City. The data collection was included in the Shulin dual-polarization radar (RCSL) observations, such as differential phase shift, specific differential phase, reflectivity and doppler radial wind field, and rain gauge data from May 2021 to November 2021 in the Taipei City. With a view to capturing the movement of hydrometeors continually changes within the time step, an algorithm which can calculate velocity and direction of specific hydrometeors on two-dimensional matrix were developed and applied to simulate location of the specific hydrometeors on n-step-ahead (T+n). Finally, the rainfall forecast can be achieved by using the simulated location of specific hydrometeors and its physical properties from radar observations as input data to fit rainfall from the gauge. This study aims to investigate the relationship between short-duration rainfall and radar observations by artificial neural network (ANN), and forecast the rainfall  within a short period.

 

Keywords: Dual-Polarization Radar; Rainfall Estimation; Artificial Intelligence (AI), Artificial neural network (ANN); Long Short-Term Memory Neural Networks(LSTM)