Stochastic Variation of Parameterized Physical Processes in Idealized Forecast Ensembles: Stochastic Physics

最近的研究表明，数值天气预报误差的一个主要来源是用可分辨变量的相对确定性函数来表示未分辨的亚格子过程。改进集合预报的一项努力涉及扰乱模型的物理参数。为了解决这个问题，PI将研究各种验证措施中的次网格尺度签名，并结合使用它们来改进集合预报。本研究将使用低维构型的理想化模型。研究的重点将放在可分辨变量和未分辨过程的相互作用上，特别注意不同的扰动幅度、物理参数的随机元素的相关尺度以及这些错误在适当的诊断中的可能特征。这项研究的总体目标是提供关于物理参数化扰动结构的知识，并在集合预报中适当地表示它们。这项研究具有为业务天气预报提供有用信息的潜力。

Recently, it has been shown that one major source of errors in numerical weather predictions is generated by using comparatively deterministic functions of resolvable variables to represent the unresolved sub-grid processes. One effort in improving ensemble predictions involves perturbing the physical parameterizations of the model. To tackle the problem, the PI will study the sub-grid scale signatures in various verification measures and use a combination of them to improve the ensemble forecasts. An idealized model with low-dimension configurations will be used for the study. The research focus will be on the interactions of the resolvable variables and unresolved processes with particular attention on the effects of different perturbation amplitudes, correlation scales for the random element of physical parameterizations, and possible signatures of these errors in appropriate diagnostics. The overall goal of the research is to provide knowledge on the structure of perturbations in physical parameterizations and to represent them properly in ensemble forecasts. The research study has the potential of generating useful information for operational weather prediction.