边界层高度是一个可以用来描述边界层结构特征的基本参数变量，同时在边界层参数化、污染扩散、以及长期气候特征等问题中都是一个重要的参数。它和边界层内的气象（譬如水汽和位温）以及化学要素有很好的相关。所以如果能将边界层高度观测同化到模式中来改进模式初始场中相关的物理量，势必能提高模式预报的准确率。 然而目前关于边界层高度同化的研究鲜有报道。本项目将基于地基激光雷达和CALIOPSO天基激光雷达资料进行多种方法诊断（反演）边界层高度研究，并和其他资料（例如微波辐射计）诊断的结果进行比较，改进反演算法；然后通过实测资料和模式预报的样本集合探索边界层高度和其他物理量相关性研究，开展EnKF同化边界层高度同化方法研究，以此来改进边界层气象的预报.

Planetary boundary layer height (PBLH) is an important parameter to illustrate the structure of the atmospheric boundary layer. It plays important roles in boundary layer parameterization, dispersion of air pollutants, and long term climate characteristics. PBLH shows significant correlation with meteorological and chemical variables in the lower atmosphere. If PBLH is assimilated to optimize the meteorological initial conditions, short term forecasting/simulation of boundary layer meteorology can be improved. To our best knowledge, observed PBLH is not assimilated to improve meteorology forecasting so far. In this project, PBLH will be diagnosed from ground-based and airborne Lidar data used a few methods. The best methods to estimate PBLH will be identified/developed by evaluating with the PBLH diagnosed from the traditional measurements (e.g., temperature and wind profiles). The correlation between PBLH and meteorological variables will be examined. Once such information is studied, we will use ensemble Kalman filter (EnKF) to assimilate PBLH to improve forecasting/simulation of boundary layer meteorology. The improved meteorology forecasting/simulation could be used to drive air quality forecasting/simulation.