Dynamic-Statistic Combined Ensemble Prediction and Impact Factors on China’s Summer Precipitation

Abstract. The dynamic-statistic prediction shown excellent performance on monthly and seasonal precipitation prediction in China and has been applied on several dynamical models. In order to further improve the prediction skill of summer precipitation in China, the Unequal-Weighted Ensemble prediction (UWE) based on the dynamic-statistic combined schemes is presented, and its possible impact factors are also analyzed. Results indicate that the UWE has shown promise in improving the prediction skill of summer precipitation in China, on account to the UWE can overcome shortcomings of the structural inadequacy of individual dynamic-statistic prediction, reducing formulation uncertainties, resulting in more stable and accurate predictions. Impact factors analysis indicates that 1) the station-based ensemble prediction with ACC being 0.10–0.11 add PS score being 69.3–70.2, has shown better skills than the grid-based one, as the former produces probability density distribution of precipitation being closer to the observation than the latter. 2) The use of the spatial average removed anomaly correlation coefficient (SACC) may lower the prediction skill and introduce obvious errors on estimating the spatial consistency of prediction anomalies. SACC could be replaced by the revised anomaly correlation coefficient (RACC), which is calculated directly using the precipitation anomalies of each station without subtracting the average precipitation anomaly of all stations. 3) The low dispersal intensity among ensemble samples of UME implies the historical similar error selected by different approach is quite close to each other, making the correction on the model prediction is more reliable. Therefore, the UWE is expected to further improve the accuracy of summer precipitation prediction in China by considering impact factors such as the grid or station-based ensemble approach, the method of calculating the ACC, and the dispersal intensity of ensemble samples in the application and analysis process of UWE.