姚愚, 晏红明. 多模式解释集成方法在云南降水预测中的应用[J]. 云南大学学报(自然科学版), 2020, 42(5): 926-935. doi: 10.7540/j.ynu.20190639
引用本文: 姚愚, 晏红明. 多模式解释集成方法在云南降水预测中的应用[J]. 云南大学学报(自然科学版), 2020, 42(5): 926-935. doi: 10.7540/j.ynu.20190639
YAO Yu, YAN Hong-ming. Application of multi-mode interpretation and integration methods in precipitation prediction in Yunnan[J]. Journal of Yunnan University: Natural Sciences Edition, 2020, 42(5): 926-935. DOI: 10.7540/j.ynu.20190639
Citation: YAO Yu, YAN Hong-ming. Application of multi-mode interpretation and integration methods in precipitation prediction in Yunnan[J]. Journal of Yunnan University: Natural Sciences Edition, 2020, 42(5): 926-935. DOI: 10.7540/j.ynu.20190639

多模式解释集成方法在云南降水预测中的应用

Application of multi-mode interpretation and integration methods in precipitation prediction in Yunnan

  • 摘要: 发展数值预报模式、增强模式产品的解释应用能力,是提高短期气候预测准确率的重要途径. 文章基于国家气候中心提供的3种气候模式产品,开展云南3—9月降水和夏季(6—8月)各月降水的降尺度释用和集成技术方法研究,并利用距平符号一致率(PC)、趋势异常综合(PS)和距平相关系数(ACC)3项指标,对比分析了不同模式和方法对云南降水的预测性能. 结果表明:对模式直接输出结果,各月平均的PC和PS评分NCEP模式最高,EC次之,NCC最低;ACC评分EC最高,NCEP次之,NCC最低;对夏季降水,EC模式PC和PS评分最高,NCC模式则ACC评分最高. 模式降尺度释用结果的PC评分较同一模式直接输出结果以负订正为主,PS和ACC以正订正为主,表明降尺度释用方法对云南月降水距平符号的预测能力不如模式直接输出结果,但对降水空间分布形态和异常级降水的预测与实况更接近. 不同集成方案的预测性能不尽相同,模式集成的PC评分最高,这与模式直接输出结果对降水的PC评分较释用结果偏高有关;优选集成的PS评分和ACC评分最高,且各月评分值较为稳定,表明同时引入模式直接输出与降尺度释用结果的优选集成可发挥二者的优势,改进预测性能.

     

    Abstract: It is an important way to improve the accuracy of short-term climate prediction by developing numerical prediction models and enhancing the interpretation and application ability of model products. Based on three climate model products supplied by National Climate Center, studies are conducted in downscaling interpretation and integration techniques in predicting monthly precipitation from March to September, as well as summer precipitation of Yunnan. By utilizing three indexes such as anomaly sign consistency rate (PC), trend anomaly synthesis (PS) and anomaly correlation coefficient (ACC), prediction performances of different models and methods for precipitation in Yunnan are compared and analyzed. The result shows that for the direct outputs of models, the monthly average scores of PC and PS of NCEP model are the highest, followed by EC and NCC models. As for the ACC scores, EC model is the highest followed by NCEP and NCC models. To summer precipitation, the scores of PC and PS of EC model are the highest and so is the score of the ACC of NCC model. The PC scores of the downscaling interpretation results of models are mostly lower than the corresponding ones of direct output of the original models, while the scores of PS and ACC are higher than the ones of original models. This conclusion shows that the prediction ability of downscaling interpretation method for monthly precipitation anomaly signs in Yunnan raining season is not as good as that of the direct output of the original models, but the prediction of spatial distribution and anomalous level of precipitation is closer to the actual situation. The prediction performances of various integration schemes are different. The PC score of model average integration is the highest, which is related to the higher PC score of precipitation from direct outputs of models than that from the downscaling interpretation of models. The PS and ACC scores of the optimized integration are the highest, and monthly scores are stable, which indicates that optimized integration can take advantage of both the direct outputs of models and downscaling interpretation results of models, which will improve the prediction performance.

     

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