基于集合卡尔曼滤波的地表水热通量同化研究

Assimilation of surface water heat flux using Ensemble Kalman Filter

地表水热通量是研究地表能量转换与水文过程中的重要参数,本文借助通用陆面模式CLM3.0(Community Land Model3.0)为动力框架,利用集合卡尔曼滤波作为同化算法构建单站点的地表水热通量同化系统,并利用Ameriflux通量观测网上Chestnut Ridge、ARM SGP Main以及Tonzi Ranch三个站点的通量观测数据进行直接同化地表水热通量试验。结果表明,在三种不同下垫面下,RMSE直接同化水热通量能够很好地改善地表总水热通量的估算效果。经过同化通量观测值,模式输出的通量值的RMSE均有减小。在代表农田下垫面的ARM SGP Main站,感热通量的RMSE由67.49W/m2下降至14.07W/m2,潜热通量的RMSE由70.07W/m2下降至14.35W/m2;在代表森林下垫面的Chestnut Ridge站,感热通量的RMSE由82.56W/m2下降至48.56W/m2,潜热通量的RMSE由42.99W/m2下降至38.92W/m2;在代表草地下垫面的Tonzi Ranch站,感热通量的RMSE由62.99W/m2下降至17.85W/m2,潜热通量的RMSE由44.76W/m2下降至36.01W/m2。相对于通过同化地表温度和湿度间接改善地表水热通量预报的研究结果,直接同化地表水热通量的结果好于前者。但值得注意的是,针对集合同化方法,不同初始场误差、观测误差和大气强迫数据误差的扰动强度都会对同化结果造成影响。从同化系统对3种误差的敏感性分析结果来看:观测误差的影响最大且减小观测误差能够减小同化后的RMSE值,估计观测误差的方法是否合理会直接影响同化结果的好坏;初始场误差对同化后的RMSE值影响最小;另外,增加大气强迫数据误差和初始场误差能减小同化后的RMSE值。

Water and heat fluxes exchange between biosphere and bottom atmosphere are indispensable parts in understanding the surface energy conversion and hydrological cycle processes happening on the land surface. Estimation and prediction of fluxes have immense research significance in fields of environmental protection, agricultural production and climate prediction. Land surface model is a powerful tool to obtain space-time continuous fluxes despite its poor simulation accuracy. The state-of-the-art data assimilation method provides a way to solve this problem. With the help of the offline version of Community Land Model CLM3.0 as a dynamic framework, we use the Ensemble Kalman Filter assimilation algorithm to build a single-site surface water and heat fluxes assimilation system. The algorithm perform an ensemble simulation to estimate initial condition error covariance and observational error covariance for the objective dynamic model and analyze background diagnostic outputs by calculating a weighted mean with observations. Perturbations on surface initial condition, atmospheric forcing data and observations are generated by a random sampling strategy based on the supposition of normal distribution with a priori mean and standard deviation for all variables. Data from three flux observing sites from Ameriflux flux observational network （Chestnut Ridge, ARM SGP Main and Tonzi Ranch） which stand for three different land surface conditions are engaged in parallel experiments to test the system and evaluate the effectiveness of flux assimilation under the framework of land model. Before processing further experiment, an optimal ensemble size was selected by evaluating outputs of latent heat from models with different ensemble size RMSE. The results of parallel experiments showed that direct assimilation of sensible and latent heat fluxes can improve the estimates of total surface sensible and latent heat fluxes in all three types of underlying land surface condition. In ARM SGP Main site, a typical case for cropland ground type, RMSE of sensible heat flux decreased from 67.49W/m2 to 14.07 W/m2 and that of latent heat flux decreased from 70.07 W/m2 to 14.35 W/m2. In Chestnut Ridge site that stands for forestry, RMSE of sensible heat flux dropped from 82.56 W/m2 to 48.56 W/m2 and that of latent heat flux fell from 42.99 W/m2 to 38.92 W/m2, respectively. Tonzi Ranch, a grassland site, RMSE in is also diminished by assimilating in situ observations with decrements of 45.14W/m2 for sensible heat flux and 8.75 W/m2 for latent heat flux. Furthermore, by comparing the results we gained above with mainstream study that focusing on assimilation of surface temperature and humidity to indirectly improve the fluxes prediction, we conclude that under the dynamic framework of community land model the flux outputs from direct assimilation model are better than those from surface state assimilation model. It is noteworthy that the accuracy of observational error estimation will directly affect the assimilation results though errors from initial condition, observation and atmospheric forcing will make contributions simultaneously.