Collaborative Research: EAGER--Evaluation of Optimal Mesonetwork Design for Monitoring and Predicting North American Monsoon (NAM) Convection Using Observing System Simulation

合作研究：EAGER——利用观测系统模拟监测和预测北美季风（NAM）对流的最佳中观网络设计评估

• 批准号: 2308410
• 负责人: Tammy Weckwerth
• 金额: $ 3.89万
• 依托单位: University Corporation For Atmospheric Res
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2308410&HistoricalAwards=false
• 关键词: Collaborative; Research; EAGER; Evaluation; Optimal

North American Monsoon (NAM) thunderstorms in the Southwest United States account for nearly 50% of the annual precipitation in this region, yet these phenomena have been relatively understudied and are difficult to predict. The NAM brings a host of hazards in a part of the country experiencing very rapid population growth: life-threatening flash floods, damaging microburst winds, sudden mile-high dust storms that sometimes form in association with thunderstorm outflows, and lightning-triggered wildfires made even more dangerous by the outflows (lightning is responsible for 62% of the wildfires in central Arizona). The timing, location, and intensity of such hazards are challenging to predict, even with state-of-the-art numerical weather prediction (NWP) models having sufficient resolution to explicitly resolve storms. A primary deficiency is that observations of the atmosphere above the surface used to initialize NWP models are currently too widely distributed compared to the spatial scale of thunderstorms; also, these data are available just twice daily, whereas a typical thunderstorm has a lifetime of less than one hour. Therefore, the goal of this project is to determine the optimal distribution and types of much-improved arrays of instruments within both future NSF-supported field campaigns and for a future statewide operational high-resolution “mesonetwork” in Arizona to have the greatest positive impacts on the predictability of the initiation, evolution, and upscale growth of thunderstorms in the NAM. The findings from the study should provide the many current stakeholders of the University of Arizona NWP system with information about the expected value (and cost) of a statewide mesonet for improving the prediction of NAM weather. The project will also inform decisions regarding the optimum instrument deployment strategies to be made for future higher-resolution field campaigns designed to improve understanding and predictability of storms in complex terrain. Lastly, an important result of the effort will be the development of the modeling and data assimilation infrastructure needed to obtain four-dimensional consistent datasets of temperature, moisture, winds and precipitation using the optimally-determined arrays of observations in the future.The project will use a novel application of the Observing System Simulation Experiment (OSSE) methodology to determine the optimal configurations for a future operational Arizona state mesonetwork and the complementary requirements for the design of more densely spaced instrument arrays in future mesoscale field campaigns. OSSE is a modeling experiment used to evaluate the value of observing system when actual observational data are not available. Each new (not currently operational) instrument type can be introduced, along with appropriate error variances, in a systematic manner by using Ensemble Kalman Filter (EnKF) data assimilation to evaluate relative impacts on model predictions. The innovative OSSE approach for optimizing network design has the potential for high reward as it represents a fundamentally different approach from what has been previously used for state-operated mesonet design considerations and large field campaigns, thus making such decisions more cost-effective. The research team has ample peer-reviewed experience conducting OSSEs for the following synthetic observations to be investigated: GPS vertically integrated precipitable water vapor, vertically-resolved measurements of water vapor from MicroPulse Differential (MPD) absorption lidars, winds from Doppler Lidars, and data from rotary-wing Uncrewed Aircraft Systems (UAS) data and 3-hourly soundings. The OSSEs will be conducted within the framework of the University of Arizona WRF modeling system and the ensemble adjustment EnKF available within NCAR’s Data Assimilation Research Testbed (DART). An important benefit of the research is development of the scientific and technical infrastructure needed to create Four Dimensional Dynamically Consistent (4DDC) datasets from the assimilation of the various observing system data, since many of the governing factors in performing the data assimilation will have been addressed during this research. Because the project will develop the 4DDC infrastructure prior to commencement of any future field campaign, scientists will be able to utilize the field 4DDC datasets in their research more efficiently and quickly. Thus, the project represents both a risk reduction effort regarding optimization of network design and the means by which greater use of the data can occur.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

美国西南部的北美季风（NAM）雷暴占该地区年度精度的近50％，但这些现象相对理解，很难预测。 The NAM brings a host of hazards in a part of the country experiencing very rapid population growth: life-threatening flash floods, damaging microburst winds, sudden mile-high dust storms that sometimes form in association with thunderstorm outlets, and lightning-triggered wildfires made even more dangerous by the outlets (lightning is responsible for 62% of the wildfires in central Arizona).即使最先进的数值天气预测（NWP）模型具有足够的解决方案以明确解决风暴，即使最先进的数值天气预测（NWP）模型，也要挑战这种危害的时机，位置和强度。一个主要缺陷是，与雷暴的空间尺度相比，目前，用于初始化NWP模型的表面上方的大气的观察结果太宽。同样，这些数据每天仅提供两次，而典型的雷暴的一生不到一小时。因此，该项目的目的是确定未来NSF支持的现场广告系列中备受推验的乐器阵列的最佳分布和类型，以及在亚利桑那州未来的全州运营高分辨率的“ Mesonetwork”，以对主动性，进化，进化，上升，上升，themerments of theNAMSTRESMS的预测性产生最大的积极影响。该研究的发现应为亚利桑那大学NWP系统的许多当前利益相关者提供有关全州范围内室的预期价值（和成本）的信息，以改善NAM天气的预测。该项目还将为关于未来的高分辨率现场活动制定的最佳仪器部署策略的决策提供信息，旨在提高复杂地形中风暴的理解和可预测性。最后，努力的重要结果将是开发建模和数据同化基础架构，以获取未来最佳确定的观测值的温度，水分，风和精确数据的四维一致数据集，该项目将使用观测系统模拟实验（OSSE）方法的新颖应用程序来确定一个新的态度。在未来的中尺度现场活动中设计更奇特的仪器阵列的互补要求。 OSSE是一个建模实验，用于评估实际观察数据时观察系统的价值。可以通过使用集合Kalman Filter（ENKF）数据同化来评估对模型预测的相对影响，以系统的方式引入每种新的（当前运行）仪器类型，以及适当的误差差异。优化网络设计的创新OSSE方法具有高奖励的潜力，因为它代表了一种与以前用于国家经营的中互联网设计注意事项和大型现场活动的方法根本不同的方法，从而使此类决策更具成本效益。 The research team has ample peer-reviewed experience conducting OSSEs for the following synthetic observations to be investigated: GPS Vertically integrated precipitable water vapor, vertically resolved measurements of water vapor from MicroPulse Differential (MPD) abstraction lidars, winds from Doppler Lidars, and data from rotary-wing Uncrewed Aircraft Systems (UAS) data and 3-hourly sounding. OSSE将在亚利桑那大学WRF大学建模系统的框架内进行，并在NCAR的数据同化研究测试台（DART）中可用的集合调整ENKF进行。该研究的一个重要好处是从同化各种观察者系统数据中创建四个维度一致（4DDC）数据集所需的科学和技术基础架构的发展，因为在这项研究期间，可以解决许多执行数据同化的管理因素。由于该项目将在任何未来的现场活动开始之前开发4DDC基础架构，因此科学家将能够在其研究中更有效，快速地利用4DDC数据集。这既代表了有关网络设计优化的风险降低努力，又代表了可能发生更多使用数据的手段。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，通过评估被认为是珍贵的支持。