基于DERF2.0的华南前汛期降水订正

Correction of Precipitation Forecast Predicted by DERF2.0 During the Pre-flood Season in South China

针对我国华南前汛期(4—6月)降水,基于国家气候中心第2代月动力延伸模式(DERF2.0)结果,利用非参数百分位映射方法将模式预测结果转化为概率预报,并进行概率订正。分别选用交叉建模与独立样本建模两种订正方法,并利用偏差、偏差百分率、时间相关系数、均方根误差等统计方法检验订正效果。结果表明:订正方法对预报技巧的改善与起报时间无显著相关,且具有误差稳定性,其订正效果受预报误差影响较小;与订正前模式预测降水落区的范围和平均强度相比,订正后结果与观测更接近;按百分位区间统计的不同强度降水订正预报均有明显改进;预测时段的订正效果与回报时段的订正效果基本一致。

There are two main types of precipitation during the pre-flood season in South China,frontal precipitation in early period and monsoon precipitation in late period.It is related to not only the tropical system,but also the cold air in the middle and high latitudes.The extended range forecasting skills of the precipitation in the pre-flood season which depend on the atmosphere-ocean interaction and the internal changes of the atmosphere are still very low.There are biases in models compared to the observations,which makes it hard to directly use model in operational forecast.Therefore,in order to better apply model forecast data to extended period forecasts,the precipitation biases are corrected during the pre-flood season from 1983 to 2019 produced by the Dynamic Extended Range Forecast Operational System version 2.0(DERF2.0)based on a non-parameter Quantile-Mapping(QM)correction method.Daily precipitation observation data from 261 stations in South China from 1983 to 2019 are selected for evaluation.On the basis of probability forecast of the original model outputs,the model biases are then corrected using monotone cubic spline interpolation combined with the observation.The models are established by cross samples and independent samples to validate the correction method's performance by absolute difference/percentage difference,root mean square error,temporal correlation coefficient and pattern correlation coefficient.It is found that the QM correction method can improve the model forecasting skills by effectively eliminating the systematic deviation of the model.It shows that the improvement of the method remains stable with different lead times and magnitudes of model biases.Further analysis shows that the main locations and average intensities of precipitation show better consistency with observation after correction.The QM correction method can generally capture the trend difference between the model and the observation,and effectively improve the inter-annual variability of model,but it has a poor ability for extreme events.On the other hand,the revised effect of the statistical scheme according to different percentile intervals is also significant.In addition,it shows that the correction performances of prediction are more consistent with the hindcast result.