预测错误增长:一个动态-随机模型
Eviatar Bach1,2,3, Dan Crisan4, Michael Ghil4,5,6
1Department of Environmental Science and Engineering and Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California 91125, USA.
本研究引入了一种新的非线性随机微分方程 (SDE) 模型,用于数字天气预测 (NWP) 中预测误差的增长. 该模型准确地捕获了平均值和概率错误增长特征.
科学领域:
- 大气科学 大气科学
- 气象学 天气学
- 数据科学是数据科学.
背景情况:
- 数字天气预测 (NWP) 模型历来使用了简单的错误增长模型.
- 现有的模型捕捉了关键属性,但可以通过先进的技术来改进.
研究的目的:
- 为预测错误增长提出一种新的动态-随机标量模型.
- 在非线性随机微分方程 (SDE) 中纳入倍数噪声.
主要方法:
- 开发了一种非线性随机微分方程 (SDE),其中包含了乘法噪声.
- 分析了SDE的属性,包括错误增长曲线和静止分布.
- 将模型与运行NWP错误增长数据相匹配.
主要成果:
- 拟议的SDE模型证明了解决方案的优势和积极性.
- 该模型与NWP错误增长的平均值和概率方面都达成良好一致.
- 该模型的动态-随机方法为错误预测提供了一个强大的框架.
结论:
- 新的动态-随机错误增长模型提供了NWP错误动态的准确表示.
- 这种建模方法在气象学之外,在各种预测科学中都有潜在的应用.
更多相关视频
10:46A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
20:24Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
Published on: January 31, 2014
相关概念视频
Propagation of Uncertainty from Systematic Error
Propagation of Uncertainty from Random Error
Random Error
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
Estimating Population Mean with Unknown Standard Deviation
William S. Gosset (1876–1937) of the...
