云中相变潜热的吸收和释放，对中小尺度云降水宏微观物理过程的发展起到至关重要的作用。已有研究表明通过Nudging技术将相变潜热同化进模式可以有效改善预报精度。然而由于云中相变潜热无法直接测量，因此其在资料同化方面的研究较少。潜热同化的关键是如何较为准确地估计出观测与模式间的相变潜热差值。本项目针对层状云降水过程，应用具有详细微物理过程的一维层状云模式和观测的雷达反射率因子场，分析得到各水物质的空间分布，对比同化时刻WRF模式的水物质场，得到各水物质的空间差值，进而计算出相变潜热差值，再根据大气热力学关系转换为位温场差值，通过Nudging技术调整格点同化时刻位温廓线的形态和强度。本方案详细考虑了冰相微物理过程以及各水物质的空间分布，更适合研究有冷云过程参与的降水系统，因此更具有物理意义，有助于提高数值模式定量降水预报的准确度，进一步认识云中热力场与微物理过程之间的相互反馈机制。

The absorption and release of latent heat in cloud plays very important role in macrophysical and microphysical processes of meso-micro scale cloud precipitation systems. Previous studies have already showed that latent heat nudging can effectively improve forecast accuracy. However, it is impossible to directly detect Latent heat in cloud, so the application of it in data assimilation is rare. The key of latent heat nudging is how to accurately estimate the latent heat change between observation and model in cloud. Aiming at the rainfall processes of stratiform, this research will first derive the spatial distribution of hydrometeors through observed radar reflectivity field and one dimensional stratiform cloud model including detailed microphysical processes, and then compare the derived hydrometeors field with WRF output to get the latent heat changes at Nudging time. After that, potential temperature field can be calculated by the relationship of atmospheric thermodynamics. Finally, both the scale and the shape of the potential temperature profiles are modified through the Nudging module in WRF. This method considers detailed ice phase microphysical processes and spatial distribution of hydrometeors in cloud, which has more physical significance and would improve the accuracy of precipitation forecasting of NWP models and the understanding of feedback mechanism between thermal field and cloud microphysical processes further.