水文集合数据同化系统参数对同化效应的影响研究

Influence of Parameters of Hydrological Ensemble Data Assimilation System on Assimilation Effect

集合数据同化近年来被引入水文研究中,并得到了较为广泛的应用。而集合数和误差是该类算法同化时的重要变量,影响同化的效果和决定计算的消耗。以一个水文模型蒸散发数据同化系统(Evapotranspiration Data Assimilation System,EDAS)为例,借助均方根误差(Root Mean Square Error,RMSE)、皮尔逊相关系数(Pearson Correlation Coefficient,PCC)、平均绝对百分比误差(Mean Absolute Percentage Error,MAPE)等作为评价指标,利用移动步距法研究了集合数、误差的大小变化过程对数据同化结果的影响。研究发现,当集合数大于100时、模型误差方差和观测误差方差分别稳定在0. 05和0. 006时同化结果较为合理。研究成果可为集合同化的参数分析与率定提供借鉴。

Ensemble filtering algorithm is more and more widely used in remote sensing-hydrological model data assimilation in recent years.The size of the ensemble and the value of the errors for the algorithm are the important parameters affecting assimilation,which decides the assimilation effect and computational cost.An evapotranspiration data assimilation system(EDAS)was employed as a case to test the extent to which assimilation effects are affected by assimilation parameters.The assimilation system took the evapotranspiration observation by remote sensing,Distributed Time Variant Gain Model(DTVGM)as the model operator,and the Ensemble Kalman Filter(EnKF)as the assimilation algorithm.By means of the Root Mean Square Error(RMSE),the Pearson Correlation Coefficient(PCC)and the Mean Absolute Percentage Error(MAPE)as the evaluation indexes,the influence of the size of the ensemble and the error values on the data assimilation results is studied by using the moving step method.It is found that when the ensemble size is greater than 100,the model error variance and observation error variance are stable at 0.05 and 0.006 respectively,the assimilation results are reasonable.The research can provide reference for parameter analysis and calibration of the ensemble data assimilation.