A particle filter based target observation method and its application to two types of El Niño events

The optimal observational array for improving the El Niño-Southern Oscillation (ENSO) prediction is investigated by exploring sensitive areas for target observations of two types of El Niño events in the Pacific. A target observation method based on the particle filter and pre-industrial control runs from six coupled model outputs in Coupled Model Intercomparison Project Phase 5 (CMIP5) experiments are used to quantify the relative importance of the initial accuracy of sea surface temperature (SST) in different Pacific areas. The initial accuracy of the tropical Pacific, subtropical Pacific, and extratropical Pacific can influence both types of El Niño predictions. The relative importance of different areas changes along with different lead times of predictions. Tropical Pacific observations are crucial for decreasing the root mean square error of predictions of all lead times. Subtropical and extratropical observations play an important role in reducing the prediction uncertainty, especially when the prediction is made before and throughout the boreal spring. To consider different El Niño types and different start months for predictions, a quantitative frequency method based on frequency distribution is applied to determine the optimal observations of ENSO predictions. The final optimal observational array contains 31 grid points, including 21 grid points in the equatorial Pacific and 10 grid points in the North Pacific, suggesting the importance of the initial SST conditions for ENSO predictions in the tropical Pacific and also in the area outside the tropics. Furthermore, the predictions made by assimilating SST in sensitive areas have better prediction skills in the verification experiment, which can indicate the validity of the optimal observational array designed in this study. This result provided guidance on how to initialize models in predictions of El Niño types.