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%，但对这些现象的研究相对不足，难以预测。不毛运动给这个国家人口快速增长的部分地区带来了许多危险：危及生命的山洪暴发、破坏性的微爆风、有时与雷暴外流相关的突然的英里高沙尘暴，以及闪电引发的野火，这些野火使其更加危险（亚利桑那州中部62%的野火是由闪电引起的）。即使最先进的数值天气预报（NWP）模型具有足够的分辨率来明确地解决风暴，预测这些灾害的时间、地点和强度也是具有挑战性的。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模式的地表以上大气观测分布过于广泛；此外，这些数据每天只能获得两次，而典型的雷暴的寿命不到一小时。因此，该项目的目标是在未来美国国家科学基金会支持的野外活动和亚利桑那州未来全州范围的高分辨率“中网”中确定优化的仪器阵列的分布和类型，以对NAM雷暴的开始、演变和高级增长的可预测性产生最大的积极影响。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改善不轨运动天气预测的全州中网的预期价值（和成本）的信息。该项目还将为未来更高分辨率的野外活动提供有关最佳仪器部署策略的决策，旨在提高对复杂地形风暴的理解和可预测性。最后，这项工作的一个重要成果将是开发建模和数据同化基础设施，以便在未来使用最佳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。该项目将使用观测系统模拟实验（OSSE）方法的新应用，以确定未来运行亚利桑那州中尺度网络的最佳配置，以及在未来中尺度野外战役中设计更密集间隔的仪器阵列的补充要求。OSSE是在没有实际观测资料的情况下，用来评价观测系统价值的模拟实验。通过使用集成卡尔曼滤波（EnKF）数据同化来评估对模型预测的相对影响，可以以系统的方式引入每种新的（目前未运行的）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方法具有高回报的潜力，因为它代表了一种与以前用于国营中网设计考虑和大型油田活动的根本不同的方法，从而使此类决策更具成本效益。研究团队拥有丰富的同行评议经验，可用于以下综合观测：GPS垂直整合可降水量、微脉冲差分（MPD）吸收激光雷达垂直分辨水汽测量、多普勒激光雷达风向、旋翼无人驾驶飞机系统（UAS）数据和3小时探测数据。osse将在亚利桑那大学WRF建模系统和NCAR数据同化研究试验台（DART）中可用的集成调整EnKF的框架内进行。该研究的一个重要好处是发展了从各种观测系统数据的同化中创建四维动态一致（4DDC）数据集所需的科学和技术基础设施，因为执行数据同化的许多控制因素将在本研究中得到解决。由于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，科学家将能够在他们的研究中更有效、更快速地利用实地4DDC数据集。因此，该项目既代表了在优化网络设计方面降低风险的努力，也代表了可以更好地利用数据的手段。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。