Spatio temporal statistical models to improve short term rain forecasts

改善短期降雨预报的时空统计模型

• 批准号: 2605380
• 金额: --
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2605380
• 关键词: Spatio; temporal; statistical; models; improve

The objective of this project is to leverage statistical and physical-dynamical modelling approach with likelihood and Bayesian Inference to improve dynamical predictions of spatial and temporal precipitation patterns. It is expected that this may be achieved by leaning on previous works where sources of data have included radar reflectivity, satellite data or 3-D radar analysis. Improvement of forecast skill and forecast reliability are of key interest in this study.A new approach to radar nowcasting that will be explored in the project is the formulation of numerical solutions of the stochastic advection diffusion equation as a vector autoregressive (VAR) process with a sparse evolution operator. Predictions at high spatial and temporal resolutions involve a trade-off between detailedness of the physical simulation, the number of parameters in statistical models, and computational resources. The scientific contributions of the project will be novel Bayesian methods for forecasting with high-dimensional spatio-temporal statistical models.The project will conduct a comparison of existing nowcasting methods based on simple persistence or advection methods (Prudden et al., 2020), more complicated stochastic partial differential equations (Sigrist et al., 2014), and advanced machine learning methodology such as neural networks (Ayzel et al., 2019).The Integrated Nested Laplace Approximation (INLA, Rue et al., 2009) approach exploits sparse matrix methods and numerical approximations to efficiently calculate high dimensional integrals for fast Bayesian inference. Sparse matrix methods will be used to simulate stochastic advection diffusion equations and INLA can be used to infer Bayesian posterior distributions of statistical model parameters.A truthful representation of the spatial and temporal correlation structure of error patterns in radar nowcasting is a crucial component.To this end, the PhD project will explore parametric approaches based on stochastic partial differential equations (Sigrist et al., 2015), as well as empirical approaches based on empirical copulas (Clark et al.,2004) and analogue techniques.

该项目的目的是利用可能性和贝叶斯推断利用统计和物理动力的建模方法，以改善空间和时间降水模式的动态预测。可以预期，这可以通过依靠以前的数据来源包括雷达反射率，卫星数据或3-D雷达分析来实现。在这项研究中，提高预测技能和预测可靠性是关键的关键兴趣。项目将在项目中探索的一种新方法是将随机对流扩散方程的数值解作为矢量自动进化（VAR）过程，该过程具有稀疏的进化操作员。高空间和时间分辨率的预测涉及物理模拟的详细性，统计模型中的参数数量和计算资源之间的权衡。该项目的科学贡献将是新颖的贝叶斯方法，用于通过高维时空统计模型进行预测。该项目将根据简单的持久性或对流方法进行现有现有的现有方法的比较（Prudden等，2020年）（2020年），更为复杂的部分差分方程（以及更为复杂的部分差异化方法）（以及Sigrist et ne Machine et and Machine et。 Al。，2019）。集成的嵌套拉普拉斯近似（Inla，Rue等，2009）方法利用了稀疏矩阵方法和数值近似值，以有效地计算快速贝叶斯推断的高尺寸积分。稀疏矩阵方法将用于模拟随机对流扩散方程，并且可以使用INLA来推断贝叶斯的后部分布统计模型参数。 2015）以及基于经验的经验方法（Clark等，2004）和模拟技术。