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雷暴占该地区年降水量的近50%，但对这些现象的研究相对较少，难以预测。不结盟运动在美国人口快速增长的地区带来了一系列危险：危及生命的山洪暴发、破坏性的微暴风、有时与雷暴外流相关的一英里高的突如其来的沙尘暴，以及雷电引发的野火，这些外流使野火变得更加危险(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解决风暴，此类灾害的时间、位置和强度也很难预测。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过于分散；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个小时。因此，该项目的目标是确定在未来由国家科学基金会支持的实地活动和未来亚利桑那州业务高分辨率“中间网络”内大大改进的仪器阵列的最佳分布和类型，以对不结盟运动雷暴的开始、演变和高级增长的可预测性产生最大的积极影响。这项研究的结果应该会为亚利桑那大学数值预报系统的许多当前利益攸关方提供关于全州范围内改进不结盟运动天气预报的中层网的期望值(和成本)的信息。该项目还将为今后旨在提高对复杂地形中风暴的了解和预测能力的更高分辨率的实地活动制定最佳仪器部署战略的决定提供信息。最后，这项工作的一个重要成果将是开发所需的模拟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。该项目将使用观测系统模拟实验(OSSE)方法的新应用来确定未来可操作的亚利桑那州中间网络的最佳配置，以及在未来中尺度现场活动中设计更密集间隔的仪器阵列的补充要求。OSSE是一种模拟实验，用于在没有实际观测数据时评估观测系统的价值。通过使用集合卡尔曼滤波(EnKF)数据同化来评估对模式预测的相对影响，可以系统地引入每一种新的(目前尚未使用的)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方法具有获得高额回报的潜力，因为它代表了一种与以前用于国家运营的中间网设计考虑和大型现场活动的根本不同的方法，从而使此类决策更具成本效益。研究小组在开展下列综合观测方面拥有丰富的同行评议经验：全球定位系统垂直积分的可降水量、来自微脉冲差分吸收激光雷达的垂直分辨率水蒸气测量、来自多普勒激光雷达的风，以及来自旋转翼无人驾驶飞机系统数据和每小时3小时的探测数据。OSSES将在亚利桑那大学WRF模拟系统和NCAR数据同化研究试验床(DART)内提供的集合调整EnKF的框架内进行。这项研究的一个重要好处是发展了从各种观测系统数据同化创建四维动态一致(4DDC)数据集所需的科学和技术基础设施，因为在本研究期间将讨论进行数据同化的许多主导因素。由于该项目将在未来任何现场活动开始之前开发4DDC基础设施，因此科学家将能够在他们的研究中更有效和更快地利用现场4DDC数据集。因此，该项目既代表了关于网络设计优化的风险降低努力，也代表了可以更多地利用数据的手段。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.