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

美国西南部的北美季风（NAM）雷暴占该地区年降水量的近50%，但这些现象相对而言研究不足，难以预测。 NAM在该国人口增长非常迅速的地区带来了一系列危害：威胁生命的山洪暴发，破坏性的微暴流风，有时与雷暴外流有关的突然一英里高的沙尘暴，以及由外流造成的闪电引发的野火（闪电是亚利桑那州中部62%的野火的原因）。这种灾害的时间，位置和强度是具有挑战性的预测，即使与国家的最先进的数值天气预报（NWP）模型具有足够的分辨率，明确解决风暴。 一个主要的不足是，用于初始化NWP模式的地面以上的大气观测目前分布太广，雷暴的空间尺度相比，这些数据每天只有两次，而一个典型的雷暴有不到一个小时的寿命。 因此，该项目的目标是确定未来NSF支持的实地活动和亚利桑那州未来全州范围内的高分辨率“mesonetwork”的仪器阵列的最佳分布和类型，以最大限度地积极影响雷暴的开始，演变和高档增长的可预测性。 这项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全州中网的预期价值（和成本）的信息，以改善NAM天气的预测。该项目还将为未来更高分辨率的实地活动提供有关最佳仪器部署战略的决策，这些活动旨在提高对复杂地形中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，以获得温度、湿度、风和降水在未来使用最佳确定的观测阵列。该项目将使用观测系统模拟实验（OSSE）的新应用方法，以确定最佳配置为未来的业务亚利桑那州mesonetwork和更密集的间隔仪表阵列的设计在未来的中尺度现场活动的补充要求。 OSSE是一种模拟实验，用于在没有实际观测数据的情况下评估观测系统的价值。 每一种新的（目前尚未运行的）仪器类型都可以采用，沿着适当的误差方差，通过使用Enklman滤波器数据同化系统地评估对模式预测的相对影响。 用于优化网络设计的创新的OSSE方法具有高回报的潜力，因为它代表了与以前用于国营中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。 该研究团队拥有丰富的同行评审经验，对以下待研究的合成观测进行了OSSE：GPS垂直整合的可降水水汽，微脉冲差分（MPD）吸收激光雷达的垂直分辨水汽测量，多普勒激光雷达的风，以及旋翼无人机系统（UAS）数据和3小时测深数据。 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）内可用的集合调整EnKF的框架内进行。 这项研究的一个重要好处是开发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，因为在进行数据同化时的许多控制因素将在这项研究中得到解决。 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，因此科学家将能够更有效、更快速地利用实地4DDC数据集进行研究。 因此，该项目代表了关于网络设计优化的风险降低努力和可以更好地使用数据的方法。该奖项反映了NSF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。

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.