Diverse Planetary Boundary Layer (PBL) Algorithms within Multimodels and the Design of Unified Boundary Layer Modeling for Improved Forecasts

多模型中的多种行星边界层 (PBL) 算法以及用于改进预测的统一边界层建模设计

• 批准号: 0636157
• 负责人: T. Krishnamurti
• 金额: $ 39.12万
• 依托单位: Florida State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-03-01 至 2012-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0636157&HistoricalAwards=false
• 关键词: Diverse; Planetary; Boundary; Layer; PBL

This research addresses improved planetary boundary layer modeling using high resolution global multi-models. As many as five different planetary boundary layer algorithms will be examined. These include 1) Florida State University's K-theory based on mixing length (local closure), 2) India's National Center for Medium Range Weather Forecasting's K-theory with mixing length where stability dependence is strongly dependent on the Bulk Richardson number, 3) a modified gradient approach to a non-local scheme that allows for fluxes down the gradient as well as for convective situations, 4) a non-local scheme based on National Center for Atmospheric Sciences' Community Climate System Model-2, where the surface fluxes are different from the above formulation and 5) a turbulent kinetic energy approach, i.e. a 1.5 order closure scheme. The study is a sequel to recently completed work that address the skills of modeling and forecasting the skills of planetary boundary layer (PBL) fluxes using single models that use single PBL schemes, a unified single model (that synthesizes all such schemes) and a multimodel superensemble that constructs bias-removed forecasts for the PBL fluxes. Benchmark observations are needed for the validation of these forecasts. These are derived from the use of reanalysis data sets that are subjected to rain rate initialization and from a vertical integral (over the troposphere) of the apparent heat sources and moisture sinks. This provides the surface fluxes of heat and moisture as a residual of the problem, since this product is more reliant on observations rather than on models it is a useful proxy for the observed fluxes. The PIs will carry out nearly 100 forecast experiments with each model, with a unified model and with the multimodel superensemble. This will provide them with measures of the errors for these fields at high resolution. This inventory of errors will be followed up with a more detailed analysis on the sources of these errors. This follows a statistical study by the PI, that demonstrates that it is possible to tag areas of model dynamics and physics that contribute to errors (including their location in three dimensions). Such an analysis for the inner details of PBL algorithms will be possible and can provide insights for future model improvements; this has not been done previously. Intellectual Merit: The research is designed as a comprehensive project with its scope encompassing many possible contributions to modeling of the atmospheric planetary boundary layer. The PIs will use a framework for addressing the PBL where finding the source of errors in PBL modeling is a central issue. The data and modeling uncertainties are covered by an ensemble/superensemble approach. The methodology is not limited to forecast modeling. It includes basic science issues on several formulations of PBL physics and ways to find their limitations. Broader Impacts: Planetary Boundary Layer fluxes impact the entire issue of cloud growth and cumulus parameterization. Those, in-turn, impact storm life cycles. This research aims not only to provide somewhat improved models for the PBL fluxes from the construction of unified single and multimodels (the superensemble) but also extends to the tagging of the areas of the PBL physics that contribute to the growth of errors. Given five such diverse local, non-local and turbulent kinetic energy based schemes, the tagging of errors (in three dimensional space) can contribute to further advancement of numerical weather prediction. Two graduate students will be educated and trained under this grant.

本研究采用高分辨率全球多模式改进了行星边界层模式。 多达五种不同的行星边界层算法将被检查。 其中包括1）佛罗里达州立大学基于混合长度的K理论（局部闭合），2）印度国家中期天气预报中心的具有混合长度的K理论，其中稳定性依赖性强烈依赖于Bulk Richardson数，3）非局部方案的修改的梯度方法，其允许梯度下的通量以及对流情况，4）基于美国国家大气科学中心社区气候系统模式2的非局地方案，其中地面通量与上述公式不同; 5）湍流动能方法，即1.5阶闭合方案。这项研究是一个续集最近完成的工作，解决的技能建模和预测的技能的行星边界层（PBL）通量使用单一的模式，使用单一的PBL计划，一个统一的单一模式（综合所有这些计划）和多模式superenergy，构建偏差去除预测的PBL通量。 需要基准观测来验证这些预测。 这些都是来自使用的再分析数据集，受到降雨率初始化和垂直积分（在对流层）的明显热源和水分汇。 这提供了热量和水分的表面通量作为问题的剩余部分，因为该产品更依赖于观测而不是模型，它是观测通量的有用代理。 PI将对每个模型、统一模型和多模型超级模型进行近100次预测实验。 这将为他们提供高分辨率下这些字段的误差测量。 在清点错误之后，将对这些错误的来源进行更详细的分析。 这是在PI的统计研究之后进行的，该研究表明，可以标记导致错误的模型动力学和物理学区域（包括它们在三维中的位置）。 这样的PBL算法的内部细节的分析将是可能的，可以为未来的模型改进提供见解，这是以前没有做过的。智力优势：这项研究被设计为一个综合项目，其范围包括对大气行星边界层建模的许多可能的贡献。 PI将使用一个框架来解决PBL，其中找到PBL建模中的错误来源是一个核心问题。 数据和建模的不确定性涵盖的合奏/superenergyapproach。 该方法不限于预测建模。 它包括PBL物理学的几个公式的基础科学问题和找到它们的局限性的方法。更广泛的影响：行星边界层通量影响云增长和积云参数化的整个问题。 这些反过来又会影响风暴的生命周期。这项研究的目的不仅是通过构建统一的单个和多个模型（超集合）来为PBL通量提供一些改进的模型，而且还扩展到对导致误差增长的PBL物理领域的标记。 给出五个这样不同的本地，非本地和湍流动能为基础的计划，标记的错误（在三维空间），可以有助于数值天气预报的进一步发展。 两名研究生将在这笔赠款下接受教育和培训。