The Sensitivity Analysis, Optimization and Uncertainty Assessment of the Land Surface Model Parameters
In order to improve land surface modeling predictions, the land surface models are generally calibrated against measurements. The study addressed the parameter sensitivity analysis, model calibration, the realistic quantification of parameter uncertainty and its effect on the results of Noah land surface model. The LH-OAT method was applied in the sensitivity analysis for the Noah LSM model parameters. Based on the eight important parameters effect on the land surface upward longwave radiation, the shuffled complex evolution metropolis (SCEM-UA) global optimization algorithms is used to automatically infer the posterior distribution of the model parameters. To overcome the computational burden, the optimization has been implemented using parallel computing. The Noah model prediction using the optimal parameters shows that the simulated upward longwave radiation matched measurements fairly well with an R2 value of 0.9842 and Root Mean Squared Error (RMSE) of 5.42W/m2. Results demonstrate that the SCEM-UA algorithm can efficiently evolve the posterior distribution of the parameters for the complex land surface model.
Noah model parameter optimization uncertainty analysis sensitivity analysis
Gaoli Su Qinhuo Liu Fangping Deng Xiaozhou Xin
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications, Chinese Ac State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications, Chinese Ac College of Resources Science and Technology, Beijing Normal University, Beijing, 100875, China
国际会议
长沙
英文
3312-3316
2010-05-11（万方平台首次上网日期，不代表论文的发表时间）