Improving the Representation of Organized Convection in Numerical Weather Prediction (NWP) Models

改进数值天气预报 (NWP) 模型中有组织对流的表示

• 批准号: 0603760
• 负责人: Gary Lackmann
• 金额: $ 32.3万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-10-15 至 2010-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0603760&HistoricalAwards=false
• 关键词: Improving; Representation; Organized; Convection; Numerical

The goal of this research is to improve the convective and microphysical parameterizations that directly impact the motion of mesoscale convective systems (MCSs) and their associated impacts on larger-scale moisture transports and quantitative precipitation forecasts (QPF). Despite continuing advances in numerical weather prediction (NWP), limitations in the ability of numerical models to accurately represent convective systems are widely recognized. Current numerical models have difficulty representing processes such as interactions between convective parameterization (CP) schemes and model grid scale processes; convective-scale momentum, heat, and moisture fluxes; and the motion of MCSs. These problems, in turn, are linked to difficulties in the prediction of convection's impacts upon the larger-scale environment. For example, prior work demonstrated that the inability of operational NWP models to adequately predict the translation speed of MCSs adversely impacts large-scale QPF. The misrepresentation of one or more physical processes likely accounts for operational models' inability to properly forecast MCS movement, but as yet these misrepresented processes have not been elucidated. The PIs' working hypothesis is that two aspects of current CP configurations lead to poor MCS forecasts: (i) the neglect of momentum adjustment in most operational CP schemes, and (ii) the lack of representation of convective organization. These and other factors preclude the development of realistic cold pools in the post-MCS model atmosphere. In part because of these limitations, many NWP efforts are now utilizing explicit convection (EC) model configurations. However, many of the other physical parameterizations in NWP models, including PBL and microphysics schemes, were specifically developed for use in models with CP schemes. Therefore, omitting CP schemes in EC models may actually result in unforeseen consequences, yielding convective forecasts that are still not accurate. Intellectual Merit. The outcomes of the research will be 1) determination of precisely why model runs employing CP schemes often move organized convection too slowly; 2) improvements to CP schemes such that convective organization and translation speed are more realistically represented and better predicted; and, 3) development of a more complete understanding of physical process representation and improved configurations for operational models that do not employ CP schemes (explicit convection). Collectively, these outcomes will be significant because they will markedly improve the forecasting of QPF and the downstream impacts of organized convection. Broader Impacts. Both PIs are committed to an integrated approach that involves undergraduates and graduate students in the research process, includes new research results and tools in courses on forecasting and modeling, and facilitates interaction between students and operational collaborators. A critical aspect of the research is the participation of NOAA Researchers, providing a conduit for the transfer of research results directly into the realm of operational NWP. The following aspects of numerical forecasts are expected to improve: 1) improved QPF downstream of quickly propagating MCSs; 2) improved representation of convectively generated QPF associated with MCSs; and 3) improved near-surface temperature and wind forecasts in the vicinity of propagating convection. Model improvements of this type could provide benefits to daily forecasts.

这项研究的目的是改进直接影响中尺度对流系统(MCSs)运动及其对大尺度水汽输送和定量降水预报(QPF)的影响的对流和微物理参数化。尽管数值天气预报(NWP)不断进步，但数值模式在准确描述对流系统的能力方面的局限性得到了广泛的认识。现有的数值模式很难模拟对流参数化方案与模式网格尺度过程之间的相互作用、对流尺度的动量、热量和水汽通量，以及MCS的运动。这些问题反过来又与难以预测对流对更大范围环境的影响有关。例如，以前的工作表明，可操作的数值预报模式无法充分预测MCS的翻译速度，这对大规模的QPF产生了不利影响。一个或多个物理过程的错误描述可能是操作模型无法正确预测MCS运动的原因，但这些错误描述的过程迄今尚未阐明。PIS的工作假设是，当前CP构型的两个方面导致MCS预报不佳：(I)在大多数业务CP方案中忽略了动量调整，(Ii)缺乏对流组织的代表性。这些因素和其他因素阻碍了后MCS模式大气中现实冷池的发展。部分由于这些限制，许多数值预报工作现在正在使用显式对流(EC)模式配置。然而，数值预报模式中的许多其他物理参数，包括PBL和微物理方案，都是专门为在CP方案的模式中使用而开发的。因此，在EC模式中省略CP方案实际上可能导致不可预见的后果，产生仍然不准确的对流预报。智力上的功绩。研究的结果将是：1)确定使用CP方案的模式运行通常移动有组织对流太慢的确切原因；2)改进CP方案，使对流组织和平移速度得到更真实的描述和更好的预测；以及3)对物理过程表示法有更全面的理解，并改进不使用CP方案的业务模式的配置(显式对流)。总体而言，这些结果将是重要的，因为它们将显著改善QPF的预报和有组织对流的下游影响。更广泛的影响。这两个绩效指标致力于采取一种综合的方法，让本科生和研究生参与研究过程，在预测和建模课程中纳入新的研究成果和工具，并促进学生和业务合作者之间的互动。这项研究的一个关键方面是国家海洋和大气局研究人员的参与，为将研究成果直接转移到业务净水预报领域提供了一条渠道。数值预报的以下几个方面有望得到改进：1)改进了快速传播的MCSs下游的QPF；2)改进了与MCSs相关的对流产生的QPF的表示；以及3)改进了传播对流附近的近地表温度和风的预报。这种类型的模式改进可以为每日预报提供好处。