Implications of Multivariate Non-Gaussian Data Assimilation for Multi-scale Weather Prediction
Implications of Multivariate Non-Gaussian Data Assimilation for Multi-scale Weather Prediction
复制标题
多元非高斯数据同化对多尺度天气预报的影响
DOI：
10.1175/mwr-d-21-0228.1
复制
发表时间：
2022
影响因子：
3.2
通讯作者：
Poterjoy, Jonathan
中科院分区：
文献类型：
--
作者：
Poterjoy, Jonathan
天气预报模型目前在概率框架内运作，根据最近对地球大气层的测量结果作出预报。这个框架可以被概念化为一个近似的高斯误差的假设下估计的贝叶斯后验密度的一部分。高斯误差近似是合适的天气尺度的大气流动，经历准线性误差演变所描述的测量的时间尺度，但往往被假设为是不适当的高度非线性，稀疏观测的中尺度过程。目前的研究采用了一个实验性的区域建模系统，以检查高斯先验误差近似的影响，这是通过集合卡尔曼滤波器（EnKFs）生成概率预测。分析是辅助使用最近推出的粒子滤波（PF）的方法，依赖于一个隐式的非参数表示先验概率密度，但增加了计算费用所获得的结果。调查的重点是EnKF和PF的比较进行了为期一个月的实验，使用广泛的域，其特点是发展和众多的热带气旋和热带气旋的通道。实验揭示了虚假的小尺度修正EnKF成员，这是来自不适当的高斯近似的先验主导的对齐不确定性中尺度天气系统。由于使用了定位算子，在PF成员中也发现了类似的行为，但程度要小得多。这一结果是复制和研究使用低维模型，它允许使用大样本估计的贝叶斯后验分布。从这项研究的结果激励使用的数据同化技术，提供了一个更适当的规范多元非高斯先验密度或多尺度处理的对齐错误在dataassimilation.Significance StatementNumerical地球大气预测需要计算机模型，这代表了已知的物理过程，管理大气流动的演变，以及巧妙地使用统计方法，从不完整的测量数据中构建真实大气的完整模型表示。第二个要求是建立在对输入到模型中用于生成预测的变量的误差分布形状的假设上。本研究探讨了使用一种新的技术，避免常见的近似，进入业务天气预报系统的区域天气预报的这些误差假设的保真度。
Weather prediction models currently operate within a probabilistic framework for generating forecasts conditioned on recent measurements of Earth’s atmosphere. This framework can be conceptualized as one that approximates parts of a Bayesian posterior density estimated under assumptions of Gaussian errors. Gaussian error approximations are appropriate for synoptic-scale atmospheric flow, which experiences quasi-linear error evolution over time scales depicted by measurements, but are often hypothesized to be inappropriate for highly nonlinear, sparsely observed mesoscale processes. The current study adopts an experimental regional modeling system to examine the impact of Gaussian prior error approximations, which are adopted by ensemble Kalman filters (EnKFs) to generate probabilistic predictions. The analysis is aided by results obtained using recently introduced particle filter (PF) methodology that relies on an implicit nonparametric representation of prior probability densities—but with added computational expense. The investigation focuses on EnKF and PF comparisons over monthlong experiments performed using an extensive domain, which features the development and passage of numerous extratropical and tropical cyclones. The experiments reveal spurious small-scale corrections in EnKF members, which come about from inappropriate Gaussian approximations for priors dominated by alignment uncertainty in mesoscale weather systems. Similar behavior is found in PF members, owing to the use of a localization operator, but to a much lesser extent. This result is reproduced and studied using a low-dimensional model, which permits the use of large sample estimates of the Bayesian posterior distribution. Findings from this study motivate the use of data assimilation techniques that provide a more appropriate specification of multivariate non-Gaussian prior densities or a multiscale treatment of alignment errors during data assimilation.Significance StatementNumerical predictions of Earth’s atmosphere require computer models, which represent known physical processes governing the evolution of atmospheric flow, and a clever use of statistical methods to construct a complete model representation of the true atmosphere from incomplete measurements. The second requirement is built on assumptions for the shape of error distributions for variables that are input into the model for generating predictions. The present study explores the fidelity of these error assumptions for regional weather forecasting using a novel technique that avoids common approximations that go into operational weather prediction systems.