Seasonal Prediction for May Rainfall over Southern China Based on the NCEP CFSv2

IF 1.5 4区 地球科学 Q4 METEOROLOGY & ATMOSPHERIC SCIENCES 热带气象学报 Pub Date : 2022-01-01 DOI:10.46267/j.1006-8775.2022.003
LI Chan-zhu, Yang Song, MO Wei-qiang, Zhang Jin-mei, Wei Wei
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引用次数: 1

Abstract

: In this study, we assess the prediction for May rainfall over southern China (SC) by using the NCEP CFSv2 outputs. Results show that the CFSv2 is able to depict the climatology of May rainfall and associated circulations. However, the model has a poor skill in predicting interannual variation due to its poor performance in capturing related anomalous circulations. In observation, the above-normal SC rainfall is associated with two anomalous anticyclones over the western tropical Pacific and northeastern China, respectively, with a low-pressure convergence in between. In the CFSv2, however, the anomalous circulations exhibit the patterns in response to the El Niño-Southern Oscillation (ENSO), demonstrating that the model overestimates the relationship between May SC rainfall and ENSO. Because of the onset of the South China Sea monsoon, the atmospheric circulation in May over SC is more complex, so the prediction for May SC rainfall is more challenging. In this study, we establish a dynamic-statistical forecast model for May SC rainfall based on the relationship between the interannual variation of rainfall and large-scale ocean-atmosphere variables in the CFSv2. The sea surface temperature anomalies (SSTAs) in the northeastern Pacific and the central-eastern equatorial Pacific, and the 500-hPa geopotential height anomalies over western Siberia in previous April, which exert great influence on the SC rainfall in May, are chosen as predictors. Furthermore, multiple linear regression is employed between the predictors obtained from the CFSv2 and observed May SC rainfall. Both cross validation and independent test show that the hybrid model significantly improve the model's skill in predicting the interannual variation of May SC rainfall by two months in advance.
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基于NCEP CFSv2的中国南方5月降水季节预测
在本研究中,我们利用NCEP CFSv2输出对中国南方5月降水进行了评估。结果表明,CFSv2能较好地描述5月降水和相关环流的气候学特征。然而,由于该模式在捕获相关异常环流方面的性能较差,其预测年际变化的能力较差。观测结果显示,此次超常的南太平洋降水分别与热带太平洋西部和中国东北部的两个异常反气旋有关,其间有低压辐合。然而,在CFSv2中,异常环流表现出对El Niño-Southern涛动(ENSO)的响应模式,表明该模式高估了5月SC降雨与ENSO之间的关系。由于南海季风的开始,5月南海上空的大气环流更加复杂,因此对5月南海降水的预测更具挑战性。本文基于降水年际变化与CFSv2大尺度海洋大气变量的关系,建立了5月SC降水的动态统计预报模型。选择东北太平洋和赤道中东部太平洋海温异常和西伯利亚西部500 hpa位势高度异常作为5月南太平洋降水的预测因子,对5月南太平洋降水有较大影响。此外,CFSv2预测因子与5月SC降水观测值之间采用多元线性回归。交叉验证和独立检验均表明,混合模型将5月SC降水的年际变化预测能力提前两个月显著提高。
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来源期刊
热带气象学报
热带气象学报 METEOROLOGY & ATMOSPHERIC SCIENCES-
CiteScore
1.80
自引率
8.30%
发文量
2793
审稿时长
6-12 weeks
期刊介绍: Information not localized
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