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国内外气候模式对黑龙江省汛期降水预测分析
引用本文:王波,李永生,赵佳莹,班晋.国内外气候模式对黑龙江省汛期降水预测分析[J].中国农学通报,2019,35(15):79-84.
作者姓名:王波  李永生  赵佳莹  班晋
作者单位:黑龙江省气候中心
基金项目:中央引导地方科技发展专项“强厄尔尼诺现象对黑龙江省极端天气气候影响及其中长期预报方法研究”(ZY18C12);黑龙江省气象局项目 “多模式在黑龙江省汛期降水预测中的评估与订正”(HQZD2016003);黑龙江省气象局项目“基于多模式的黑龙江省夏季降水客观预测方法研究” (HQZD2017003)。
摘    要:为了评估季节气候模式及多模式集合对黑龙江省汛期降水的跨季节预测能力,最终提高黑龙江省汛期气候预测准确率。基于1983—2017年中、美、欧三种季节气候模式的资料,将多模式集合预报技术应用于黑龙江省汛期降水预测,采用距平相关系数(ACC)、趋势异常综合检验(Ps)评估、分级评分(Pg)评估和距平符号一致率(Pc)4种定量评估方法全面评估了上述3种季节气候模式及多模式集合对黑龙江省汛期降水的跨季节预测能力,并最终给出适合于黑龙江省汛期降水的客观预测方法。结果表明:各家模式对黑龙江省汛期降水有一定的跨季节预报能力,但对于降水趋势的异常量级预测能力相对较差。各家模式预测评分比较来看,EC模式预测评分相对更好,在预测业务中可以重点考虑;多模式超级集合预测评分高于日常业务质量评分和多模式等权集合平均的预测评分,可以在汛期气候预测中参考。

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收稿时间:2018-05-08

Climate Models in China and Abroad: The Precipitation Forecast in Heilongjiang During Flood Season
Abstract:The paper aims to evaluate the cross-season forecasting ability of seasonal climate model and multimodel ensembles on precipitation during flood season in Heilongjiang, and improve the accuracy rate of climate prediction. Based on the data of 3 seasonal climate models in China, the United States and Europe from 1983 to 2017, by applying the multi- model ensembles to the prediction of precipitation during flood season in Heilongjiang, 4 quantitation methods including anomaly correlation coefficient (ACC), comprehensive testing of trend anomalies (Ps), graded score (Pg) and anomalous symbol agreement rate (Pc), we comprehensively evaluated the ability of the above 3 seasonal climate models and multi-model ensembles to predict crossseason precipitation during flood season, and found an objective forecast method suitable for the precipitation during flood season in Heilongjiang. The results showed that: each model had a certain cross-season forecasting ability to forecast the precipitation in Heilongjiang during flood season, but had a relatively poor forecast ability for the anomalous magnitude of precipitation trend. According to the comparison of the prediction scores of each model, EC model is relatively better and could be applied in the forecasting, multi-model super ensemble is better than the daily business quality score and the multi-model equal weighted average ensemble, which could be used as a reference for climate prediction during flood season.
Keywords:
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