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: 2308409
• 负责人: Steven Koch
• 金额: $ 24.38万
• 依托单位: University of Arizona
• 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=2308409&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) 雷暴占该地区年降水量的近 50%，但人们对这些现象的研究相对较少，而且难以预测，NAM 给部分地区带来了许多危害。该国人口增长非常迅速：危及生命的山洪、破坏性的微暴风、有时与雷暴外流一起形成的突然的一英里高的沙尘暴，以及闪电引发的野火，这些都使危险变得更加危险。即使使用具有足够分辨率的最先进的数值天气预报 (NWP) 模型，此类灾害的时间、地点和强度也难以预测。明确解决风暴问题的一个主要缺陷是，与雷暴的空间规模相比，目前用于初始化 NWP 模型的地表大气观测分布过于广泛；而且，这些数据每天只能获得两次，而典型的雷暴则有两次。一生的因此，该项目的目标是确定未来 NSF 支持的野外活动和亚利桑那州未来联邦运营高分辨率“中观网络”中仪器阵列的最佳分布和类型。对 NAM 雷暴的发生、演变和大规模增长的可预测性具有最大的积极影响。该研究的结果应该为亚利桑那大学 NWP 系统的许多当前利益相关者提供有关预期价值（和成本）的信息。 ) 检察官该项目还将为未来更高分辨率的现场活动制定最佳仪器部署策略提供信息，旨在提高对复杂地形风暴的理解和可预测性。我们的努力将是开发建模和数据同化基础设施，以便在未来使用最优确定的观测阵列获得温度、湿度、风和降水的四维一致数据集。该项目将使用观测的新颖应用系统模拟实验 (OSSE) 方法用于确定未来运行的亚利桑那州介观网络的最佳配置以及未来介观尺度现场活动中更密集间隔的仪器阵列设计的补充要求。OSSE 是一种用于评估观测系统价值的建模实验。当实际观测数据不可用时，可以通过使用集成系统卡尔曼滤波器（EnKF）数据同化来评估对模型的相对影响，引入每种新的（当前未运行的）仪器类型以及适当的误差方差。用于优化网络设计的创新 OSSE 方法具有获得高回报的潜力，因为它代表了一种与之前用于国营介网设计考虑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。研究团队拥有针对以下要研究的综合观测进行 OSSE 的同行评审经验样本：GPS 垂直积分可降水蒸气、微脉冲差分 (MPD) 吸收激光雷达对水蒸气的垂直分辨测量、 OSSE 将在亚利桑那大学 WRF 建模系统和 NCAR 数据同化中提供的集合调整 EnKF 的框架内进行。研究测试台 (DART) 该研究的一个重要好处是开发创建四维动态一致 (4DDC) 所需的科学和技术基础设施。由于本研究将解决执行数据同化的许多控制因素，因此该项目将在任何未来的实地活动开始之前开发 4DDC 基础设施。能够在研究中更高效、更快速地利用现场 4DDC 数据集。因此，该项目既代表了在优化网络设计方面的风险降低工作，也代表了如何更好地利用数据。该奖项反映了 NSF 的法定使命和目标。通过使用基金会的智力价值和更广泛的影响审查标准进行评估，该项目被认为值得支持。