Study of advanced data assimilation and cloud resolving ensemble technique

为解决在无强天气或地形强迫的情况下局地强对流暴雨预报的困难，开展了中尺度资料同化和集合预报的研究与发展，建立了风暴尺度4DVAR资料同化方案、JMA非静力模式的局地集合变换Kalman滤波方案和集合变分资料同化方案，并应用于局地暴雨和台风的资料同化试验。中尺度观测数据的中尺度同化试验（例如，GPS数据和多普勒激光雷达数据）。发展了一个中尺度集合预报系统的初始和侧边界扰动方法，并将该系统应用于云分辨模式。通过对中尺度集合预报的检验，证明了局地暴雨概率预报的可行性。

To overcome the difficulties to predict local convective rains without strong synoptic or orographic forcing, research and development on mesoscale data assimilation and ensemble forecast were conducted.A storm scale 4DVAR data assimilation scheme, local ensemble transform Kalman filter for the JMA nonhydrostatic model, an ensemble variational data assimilation scheme were developed and applied to data assimilation experiments for local heavy rainfall events and typhoons. Mesoscale assimilation experiments for mesoscale observation data (e.g., GPS data and Doppler lidar data) were conducted. Initial and lateral boundary perturbation methods for a mesosocale ensemble prediction system were developed and the system was applied to a cloud resolving model. Feasibility of the probabilistic forecast of local heavy rainfall was shown through validation of mesoscale ensemble forecasts.