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善直接影响中尺度对流系统（MCS）运动的对流和微物理参数化及其对大规模水分传输和定量降水预测（QPF）的相关影响。 尽管数值天气预测（NWP）的持续进展，但数值模型准确代表对流系统的能力的局限性得到了广泛认可。 当前的数值模型很难表示过程，例如对流参数化（CP）方案与模型网格量表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规模环境的影响的困难有关。 例如，先前的工作表明，操作NWP模型无法充分预测MCSS的翻译速度会对大型QPF产生不利影响。 一个或多个物理过程的虚假陈述可能解释了操作模型无法正确预测MCS运动，但尚未阐明这些虚假陈述的过程。 PIS的工作假设是，当前CP配置的两个方面导致MCS预测差：（i）大多数操作CP方案中对动量调整的忽视，以及（ii）对流组织缺乏表示。 这些和其他因素排除了MCS模型大气中逼真的冷池的发展。 部分原因是由于这些局限性，许多NWP努力现在正在利用明确的对流（EC）模型配置。 但是，NWP模型中的许多其他物理参数化（包括PBL和微物理方案）都是专门用于用于具有CP方案模型的。 因此，省略EC模型中的CP方案实际上可能会导致无法预料的后果，从而产生仍然不准确的对流预测。智力优点。 研究的结果将是1）确切的确切确定为什么使用CP方案运行的模型通常过于较慢； 2）改进CP方案，使对流组织和翻译速度更现实地代表和更好地预测； 3）对不采用CP方案的操作模型（显式对流）的物理过程表示和改进的配置进行了更完整的理解。 总的来说，这些结果将是重要的，因为它们将显着改善QPF的预测以及有组织的对流的下游影响。 更广泛的影响。 这两个PI都致力于一种综合方法，该方法涉及本科生和研究生的研究过程，包括有关预测和建模课程的新研究结果和工具，并促进了学生与运营合作者之间的互动。 研究的一个关键方面是NOAA研究人员的参与，为研究结果直接转移到运营NWP领域提供了渠道。 预计数值预测的以下方面将有所改善：1）快速传播MCS的下游改进的QPF； 2）改善了与MCS相关的对流产生的QPF的表示； 3）在传播对流附近提高了近表面温度和风预测。 这种类型的模型改进可以为每日预测带来好处。