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首页> 外文期刊>Weather and forecasting >Predicting Summer Rainfall over the Yangtze-Huai Region Based on Time-Scale Decomposition Statistical Downscaling
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Predicting Summer Rainfall over the Yangtze-Huai Region Based on Time-Scale Decomposition Statistical Downscaling

机译：基于时标分解统计降尺度的江淮地区夏季降水预测

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A statistical downscaling scheme based on time-scale decomposition is adapted for summer rainfall prediction over the Yangtze-Huai River region of east China. The predictors are selected from atmospheric circulation variables outputted from the dynamic system models attending the Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction program (DEMETER) or observational datasets. Both the predictand and the predictors are decomposed into interannual and decadal components. Two distinct statistical downscaling models are built for the separated time scales and the predicted results are combined to represent the total prediction. The efficiency of this approach was assessed through comparisons with the models' raw hindcasts as well as that from one parallel statistical downscaling scheme without time-scale decomposition. The results display that the time-scale decomposition scheme leads to significant improvements in the spatial and temporal correlation coefficients (CCs) and the root-mean-square errors (RMSEs) as well. The multiyear averaged spatial CCs reach up to 0.49 for all the individual models and their multimodel ensemble (MME), and the temporal CCs at each station are significantly higher with the coefficients from 0.46 to 0.7. Furthermore, two cases, the years 1998 and 1999, are selected for comparison. The former is a relatively easy predictable case and nearly all models predicted successfully, whereas the latter is a difficult case and nearly all models failed. The results suggest significant improvements for both cases. Thus, the present statistical downscaling scheme with time-scale decomposition may be appropriate for operational predictions.
机译：基于时间尺度分解的统计降尺度方案适用于华东长江淮流地区的夏季降水预测。预测变量是从参加欧洲多模型合奏系统开发的动态系统模型输出的大气循环变量中选择的，该系统用于季节到年际预测程序（DEMETER）或观测数据集。预测变量和预测变量都分解为年际和年代际分量。针对分离的时间尺度建立了两个截然不同的统计缩减模型，并将预测结果进行组合以表示总的预测。通过与模型原始后验的比较以及一种没有时间尺度分解的并行统计缩减方案的比较，评估了该方法的效率。结果表明，时标分解方案可显着改善空间和时间相关系数（CC）和均方根误差（RMSE）。所有单个模型及其多模型集合（MME）的多年平均空间CC均达到0.49，并且每个站点的时间CC均显着较高，系数从0.46到0.7。此外，还选择了1998年和1999年这两个案例进行比较。前者是一个相对容易预测的案例，几乎所有模型都预测成功，而后者是一个困难案例，几乎所有模型都失败了。结果表明这两种情况都有显着改善。因此，具有时间尺度分解的当前统计缩减方案可能适合于操作预测。

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