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.

本研究的目标是改善对流和微物理参数化，直接影响中尺度对流系统（MCSs）的运动及其相关的大尺度水汽输送和定量降水预报（QPF）的影响。 尽管数值天气预报（NWP）不断取得进展，但数值模式准确表示对流系统的能力的局限性已被广泛认识。 目前的数值模式很难代表的过程，如对流参数化（CP）方案和模型网格尺度过程之间的相互作用;对流尺度的动量，热量和水分通量;和MCS的运动。 反过来，这些问题与对流对大尺度环境影响的预测困难有关。 例如，先前的工作表明，业务NWP模型无法充分预测MCS的翻译速度对大规模QPF产生不利影响。 对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尚未得到阐明。 PI的工作假设是，当前CP配置的两个方面导致MCS预报不佳：（i）在大多数业务CP方案中忽略动量调整，以及（ii）缺乏对流组织的代表性。 这些因素和其他因素排除了MCS后模式大气中实际冷池的发展。 部分由于这些限制，许多数值预报工作现在使用显式对流（EC）模式配置。 然而，NWP模式中的许多其他物理参数化，包括PBL和微物理方案，是专门为CP方案的模式而开发的。 因此，在EC模式中省略CP方案实际上可能会导致不可预见的后果，产生对流预报仍然不准确。智力优势。 研究的结果将是：1）准确地确定为什么使用CP方案的模式运行经常使有组织的对流移动得太慢; 2）改进CP方案，使对流组织和平移速度更真实地表示和更好地预测;并且，在本发明中，第三章发展对物理过程表示的更全面的理解，并改进不使用CP的操作模型的配置格式（显式对流）。 总的来说，这些结果将是重要的，因为它们将显着改善QPF的预测和有组织的对流的下游影响。 更广泛的影响。 这两个PI都致力于一个综合的方法，涉及本科生和研究生在研究过程中，包括新的研究成果和工具的课程预测和建模，并促进学生和业务合作者之间的互动。 研究的一个关键方面是NOAA研究人员的参与，为研究成果直接转移到业务NWP领域提供了一个渠道。 预计数值预报的以下方面将得到改进：1）改进快速传播MCS下游的QPF; 2）改进与MCS相关的对流生成QPF的表示; 3）改进传播对流附近的近地面温度和风预报。 这类模型改进可为日常预报带来好处。