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基于小波变换的郑州降雨量与太阳黑子活动关系分析
引用本文:祁诗阳,沈宸,刘小标,张梦娇,马新明. 基于小波变换的郑州降雨量与太阳黑子活动关系分析[J]. 现代地质, 2023, 37(1): 184-196. DOI: 10.19657/j.geoscience.1000-8527.2022.080
作者姓名:祁诗阳  沈宸  刘小标  张梦娇  马新明
作者单位:1.河南农业大学理学院,河南 郑州 4500022.河南农业大学信息与管理科学学院,河南 郑州 4500463.河南农业大学农学院,河南 郑州 450046
基金项目:河南省高等学校重点科研项目计划“数值模拟研究太阳活动与河南省农作物生长的关系”(20B170004);河南省自然科学基金青年科学基金项目“基于三维聚焦传输模型的太阳高能粒子释放时序问题的数值模拟研究”(222300420190)
摘    要:为探究太阳黑子活动与地区降雨量的关联性,采用连续小波变换方法,分析了1980年以来郑州地区(新郑站点)降雨量和太阳黑子数的数据变化,并对不同时段两者的相关性进行研究;进一步对两者进行交叉小波变换和小波相干谱分析,并根据两者关系通过太阳黑子数观测值对降雨量进行预测。结果表明:(1)不同时段降雨量和太阳黑子数的相关性存在正负差异的现象。降雨量的第一主周期尺度是21 a,在此主周期尺度下得到14 a主周期;太阳黑子数的第一主周期尺度是16 a,在此主周期尺度下得到11 a主周期(与经验值相符)。降雨量与太阳黑子数的主周期相差了3 a,因此导致两者的相关性在不同时段存在正负差异。(2)降雨量与太阳黑子数在1992—2008年的8~12 a时间尺度上关联性显著,且降雨量比太阳黑子数存在规律性的时间滞后,两者在2~4 a和7~10 a的时间尺度上关联性较好;降雨量比太阳黑子数分别在1991—2004年和2006—2013年这两个时间段上呈现规律性的滞后,两者在其他时间段的各时间尺度上关联性不明显。(3)根据延迟年数经验公式,由太阳黑子数观测值对降雨量进行预测,最近的降雨量峰年在2022年附近,与2...

关 键 词:小波变换  降雨量  太阳黑子  太阳活动周期
收稿时间:2022-03-03
修稿时间:2022-10-30

Relationship Between Rainfall and Sunspot Activity Cycle in Zhengzhou Based on Wavelet Analysis
QI Shiyang,SHEN Chen,LIU Xiaobiao,ZHANG Mengjiao,MA Xinming. Relationship Between Rainfall and Sunspot Activity Cycle in Zhengzhou Based on Wavelet Analysis[J]. Geoscience of Geosciences, 2023, 37(1): 184-196. DOI: 10.19657/j.geoscience.1000-8527.2022.080
Authors:QI Shiyang  SHEN Chen  LIU Xiaobiao  ZHANG Mengjiao  MA Xinming
Affiliation:1. College of Science, Henan Agricultural University, Zhengzhou, Henan 450002, China2. College of Information and Management Sciences, Henan Agricultural University, Zhengzhou, Henan 450046, China3. Agricultural College of Henan Agricultural University, Zhengzhou, Henan 450046, China
Abstract:In order to study the correlation-ship of sunspot activity and regional rainfall, we used the continuous wavelet transform method to study the precipitation data of Zhengzhou city (Xinzheng station) since 1980 and the variation of the number of sunspots, and studied the correlation between the two in different periods. Further, rainfall and sunspots were proceed by cross wavelet transform and wavelet coherence spectrum analysis. The rainfall was predicted by sunspots observation number according to the relationship between the two. The results show that there are positive and negative differences in the correlation between the two in different periods. The first main rainfall period scale is a 21-year one, with a 14-year period obtained under this main period scale. Similarly, the first main sunspot period scale is a 16-year one, with a 11-year period obtained under this main period scale, consistent with experience value. The three extra year of rainfall (than sunspots) can explain the positive and negative differences in the curve correlation at different periods. The correlation between rainfall and sunspots is significant on an 8 to 12 year timescale (1992 to 2008) with regular time lag. The rainfall vs. sunspot activity correlation is important on the timescale of 2-4 a and 7-10 a, with regular lag during 1991-2004 and 2006-2013, respectively. There is no obvious correlation between the two in other time scales. Correlation analysis showed that according to the empirical formula for the time delay, rainfall can be predicted from sunspot observations. The results show that the latest peak rainfall is near 2022, close to the 2021 “7·20 heavy rain” in Zhengzhou. The next rainfall valley year is likely around 2028, and the precipita-tion fluctuation or decrease may be greater than before.
Keywords:wavelet transform  precipitation  sunspot  solar activity cycle  
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