Seasonal precipitation forecasts over China using monthly large-scale oceanic-atmospheric indices
Seasonal precipitation forecasts over China using monthly large-scale oceanic-atmospheric indices
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DOI：
10.1016/j.jhydrol.2014.08.012
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发表时间：
2014-11
影响因子：
6.4
通讯作者：
Zhaoliang Peng;Quan J. Wang;J. Bennett;P. Pokhrel;Ziru Wang
中科院分区：
文献类型：
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作者：
Zhaoliang Peng;Quan J. Wang;J. Bennett;P. Pokhrel;Ziru Wang
在季节时间尺度上预测降水量仍然是一个艰巨的挑战。在这项研究中，我们评估了一个统计方法，预测中国各地的季节降水的12个重叠的季节。我们使用贝叶斯联合概率建模方法，建立多个概率预报模型，使用8个大尺度海洋-大气指数在1-3个月的滞后时间作为预测因子。然后，我们将多个模型的预测与贝叶斯模型平均值合并，以联合收割机结合各个模型的优势。预测技能和可靠性通过留出一年的交叉验证进行评估。合并后的预报在预报技巧上表现出相当大的季节性和空间变异性。合并后的预报在春季以西部地区最好，在秋季以中南地区最好。相比之下，夏季最潮湿和冬季最干燥时期的预报技能通常较低。当预测提前期从0个月增加到2个月时，积极的预测技能大多保留下来。预测分布可靠地代表预测的不确定性。从西太平洋和印度洋海面温度得出的气候指数往往比厄尔尼诺-南方涛动指数对预报技巧的贡献更大。以北极涛动和北大西洋涛动为代表的大尺度大气环流模式似乎对预报技巧贡献不大。
Forecasting precipitation at the seasonal time scale remains a formidable challenge. In this study, we evaluate a statistical method for forecasting seasonal precipitation across China for 12 overlapping seasons. We use the Bayesian joint probability modelling approach to establish multiple probabilistic forecast models using eight large-scale oceanic-atmospheric indices at lag times of 1–3 months as predictors. We then merge forecasts from the multiple models with Bayesian model averaging to combine the strengths of the individual models. Forecast skill and reliability are assessed through leave-one-year-out cross validation. The merged forecasts exhibit considerable seasonal and spatial variability in forecast skill. The merged forecasts are most skillful over west China in spring periods and over central-south China in autumn periods. In contrast, forecast skill in most wet summer and dry winter periods is generally low. Positive forecast skill is mostly retained when forecast lead time is increased from 0 to 2 months. Forecast distributions are found to reliably represent forecast uncertainty. Climate indices derived from sea surface temperature in the western Pacific and Indian Ocean tend to contribute more to forecast skill than indices of the El Niño-Southern Oscillation. Large-scale atmospheric circulation patterns, represented by the Arctic Oscillation and North Atlantic Oscillation, appear to contribute little to forecast skill.