大气科学学报2025，Vol.48Issue(4)：626-636,11.DOI:10.13878/j.cnki.dqkxxb.20240204002
基于Liang-Kleeman信息流和小波相干的总云水含量信息熵因果分析
Causal analysis of TWC information entropy using Liang-Kleeman infor-mation flow and wavelet coherence
摘要
Abstract
Cloud microphysical processes are fundamentalto precipitation formation,involving complex interac-tions among cloud particles and their dynamic coupling with the surrounding atmosphere.Understanding the causal relationships underlying the information entropy of cloud microphysical quantities is crucial for elucidating the de-velopment of precipitating cloud systems and improving precipitation forecasting.This study investigates the multi-scale causal relationships between the information entropy of total cloud water content(TWC)and that of rele-vant atmospheric variables,aiming to explore the self-organizational behavior and influencing mechanisms during cloud system evolution.A typical precipitating cloud event over northeastern China was selected for analysis.The degree of self-organization during the development of the cloud system was assessed through the information en-tropy of TWC,calculated based on the spatiotemporal distribution of cloud water content.This metric effectively captures the complexity and uncertainty of microphysical processes,where higher entropy values indicate greater disorder and lower values reflect more organized,potentially stable structures.To examine local coherence charac-teristics across different time scales,wavelet coherence analysis was employed to evaluate the nonlinear and time-varying relationships between TWC entropy and covariate entropies.Wavelet decomposition enabled the breakdown of information entropy time series into multiple scales,facilitating the identification of linear Granger causality relationships via a vector autoregression(VAR)model.The strength and direction of causal interactions were further quantified using the Liang-Kleeman information flow method. Results reveal that the TWC entropy increases initially and decreases as the cloud system matures,with a no-table reduction during its mature stage,indicative of enhanced self-organization.On the 2-hour time scale,bidirec-tional Granger causality was observed between TWC entropy and all covariate entropies,suggesting mutual influ-ence at this temporal resolution.At larger time scales(4 h and 8 h),the entropy of atmospheric precipitable water exerted the most substantial influence on TWC entropy,evidenced by the largest Liang-Kleeman information flow magnitude.Conversely,at shorter time scales(1 h and 2 h),the entropy of upward longwave radiation emerged as the dominnat driver.Radar reflectivity and vertical air velocity entropies also exhibited causal relationships with TWC entropy to varying degrees.In summary,atmospheric precipitable water and upward longwave radiation are key variables influencing changes in TWC information entropy across time scales.These findings offer new in-sights into the self-organization and evolution of precipitating cloud systems,emphasizing the necessity of multi-scale and multi-variable approaches in studying cloud microphysics.Future work should focus on incorporating these casual insights into comprehensive cloud and precipitation models and exploring their applicability across different climatic regions.关键词
总云水含量/信息熵/Liang-Kleeman信息流/线性Granger因果/小波相干Key words
total cloud water content/information entropy/Liang-Kleeman information flow/linear Granger cau-sality/wavelet coherence引用本文复制引用
吴香华,黎亚少,金芯如,任苗苗,王巍巍..基于Liang-Kleeman信息流和小波相干的总云水含量信息熵因果分析[J].大气科学学报,2025,48(4):626-636,11.基金项目
国家自然科学基金项目(42075068 （）
41975176 （）
41975087) （）
国家重点研发计划重点专项(2018YFC1507905) （2018YFC1507905）
