Application of the CNOP‐P Ensemble Prediction (CNOP‐PEP) Method in Evapotranspiration Forecasting Over the Tibetan Plateau to Model Parameter Uncertainties

Abstract

AbstractModel parameter errors are one of the sources of uncertainty when simulating or predicting evapotranspiration (ET) over the Tibetan Plateau (TP). To enhance the ET simulation ability and prediction skill over the TP, the conditional nonlinear optimal parameter perturbation (CNOP‐P) method is used to establish an ensemble prediction method (called the CNOP‐PEP method) to represent uncertainties arising due to the model parameters to implement ensemble prediction experiments. The one‐at‐a‐time (OAT) method and the traditional stochastically perturbed parametrization (SPP) scheme are also employed to implement ensemble prediction experiments for comparison with the CNOP‐PEP method. The numerical results show that all ensemble prediction experiments conducted with the three methods exhibit improved prediction skills compared to the reference ET over the TP. Furthermore, the prediction skill by employing the CNOP‐PEP method is more excellent than those of the OAT and SPP methods when predicting ET over the TP. The physical mechanism analysis shows that the improved evaporation characterization obtained with the ensemble prediction experiments plays a key role in reducing uncertainties when simulating and predicting ET over the TP. The above results indicate that ensemble prediction methods are a helpful tool to improve the simulation ability and prediction skill of the ET over the TP for analyzing model parameters. In addition, the CNOP‐PEP method can supply prime ensemble members to represent uncertainties in model parameters.