Model Forecast Error Correction Based on the Local Dynamical Analog Method: An Example Application to the ENSO Forecast by an Intermediate Coupled Model
Zhaolu Hou, Bin Zuo, Shaoqing Zhang, Fei Huang, Ruiqiang Ding, Wansuo Duan, Jianping Li
Abstract
Abstract Numerical forecasts always have associated errors. Analog correction methods combine numerical simulations with statistical analyses to reduce model forecast errors. However, identifying appropriate analogs remains a challenging task. Here, we use the Local Dynamical Analog (LDA) method to locate analogs and correct model forecast errors. As an example, an El Niño–Southern Oscillation (ENSO) intermediate coupled model forecast error correction experiment confirms that the LDA method locates high quality analogs of states of interest and improves the model forecast performance, which is due to the initial and evolution information included in the LDA method. In addition, the LDA method can be applied using a scalar time series, which reduces the complexity of the dynamical system. The LDA method is a promising method to locate dynamic analogs and can be applied to existing numerical models and forecast results.