Research Topic:
Weather data processing and rainfall prediction using particle filter

Person in charge: Kazuki Ohara  

Research Brief


In recent years localized heavy rain rain called guerrilla rain is a problem. It is necessary to predict the occurrence of disasters caused by localized heavy rain in advance and minimize the damage by accurately grasping the situation. As one of countermeasures, phased array radars capable of measuring three dimensional raindrop distribution have been developed. However, the amount of data acquired by the phased array radar is enormous and it imposes broadband data link on the infrastructure to transfer the observed raw data to remote node. It will not be realistic when the number of radar nodes are increased. This study drastically reduces rainfall data by modeling observed data with three dimensional volume. By using this method, it is possible to transfer data on an inexpensive internet line with a transmission speed of about several [Mbps]. In addition, we propose a system that predicts transmitted data using a particle filter. In the proposed system, a particle model is constructed by using the moving direction and increasing or decreasing direction of each rain clump in the three dimensional volume model as state vectors.