在离散随机过程中时间依赖的信息理论分析:应用到降水可预测性的应用
Juan De Gregorio1, David Sánchez1, Raúl Toral1
1Institute for Cross-Disciplinary Physics and Complex Systems IFISC (UIB-CSIC), Campus Universitat de les Illes Balears, E-07122 Palma de Mallorca, Spain.
Chaos (Woodbury, N.Y.)
|March 12, 2026
概括
这项研究引入了一种新的信息理论方法,用于测量每日降水数据中的记忆. 研究结果表明,降雨模式具有可预测的记忆力,因地区和季节而异.
科学领域:
- 水文学的水文学
- 气象学 天气学
- 信息理论 信息理论
背景情况:
- 准确的降雨预测对于天气预报和随机降雨建模至关重要.
- 了解降水的时间依赖性对于改进这些模型至关重要.
研究的目的:
- 开发一种信息理论方法来量化离散随机过程中的记忆效应.
- 将这种方法应用于连续美国的每日降水记录.
- 建立一个框架来构建降水动态的节的随机描述.
主要方法:
- 利用来自块的可预测性收益来测量高阶时间依赖.
- 采用了基于启动的假设测试和费舍尔的方法来从有限数据中进行强大的内存估计.
- 使用生成的序列验证了对Akaike信息标准和贝叶斯信息标准的估计器.
主要成果:
- 每天降雨的发生是有效地模拟低级马尔科夫链.
- 在降水记忆中观察到显著的区域和季节性变化.
- 在冬季 (西海岸) 和夏季 (东南) 发现了更强的时间相关性.
结论:
- 开发的方法提供了一种可靠的方法来估计降水数据中的内存.
- 这些发现支持使用低级马尔科夫链来描述每日降雨量.
- 该框架有助于解决降水记忆中的空间异质性,以改善预测.
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