NNA: RAPID: Atmospheric Measurements from Unmanned Aircraft during SODA - Deployment of miniFlux and Initial Data Analysis

了解北极上层海洋的温度结构对于在气候和天气模式中恰当地模拟海冰的形成和融化是非常重要的。存在(或不存在)对于当地居民的航运、能源勘探和狩猎等各种活动都很重要。因此，在较短的时间尺度上预测冰的存在是非常必要的。此外，海冰作为一个明亮的表面，能够将阳光反射回太空，因此还对气候产生了控制影响，从而突出了在十年时间尺度上准确预测它的必要性。在所有尺度的预报中，一个重要的误差来源是能够预测上层海洋发生混合的程度，以及这种混合如何有助于消除可能改变冰层形成或融化速度的温度和盐度梯度。一个需要了解的重要问题是，我们通常预测得相对较好的大气风在多大程度上通过大气和海洋之间的能量转移对海洋上层的混合做出了贡献。该项目将支持收集必要的关键测量数据，以帮助改进天气和气候模型，以支持各种时间尺度的海冰预测。在这项研究中，将部署一个无人驾驶飞机系统来测量大气温度、风和湿度。这些信息将与来自地面浮标和冰面图像的信息一起使用，以了解秋季冻结期的大气-海洋能量转移。具体地说，这项工作将有助于解决以下问题：海冰的存在在能量转移中的作用以及如何在数值模式中模拟这种作用、支持转移的机制在小空间尺度上的变化程度以及这些在今天是如何处理的？S最新的建模工具，以及模型垂直分辨率在准确捕捉这种能量转移方面的重要性。测量结果将与高分辨率模型进行比较，这些模型将大气、冰和海洋耦合在一起，形成单一的模拟。飞行将于2018年9月和10月从阿拉斯加北部进行，并将与更广泛的努力(北极分层海洋动力学项目，或苏打项目)对接，以了解世界这一地区的上层海洋。此外，这项工作将与正在进行的极地预测年相联系，为能够从这些测量中受益的建模社区提供额外的联系。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。

Understanding the temperature structure of the upper ocean in the Arctic is very important for properly simulating the formation and melt of sea ice in climate and weather models. The presence (or absence) is important for a variety of activities, including shipping, energy exploration and hunting by native populations. Therefore, forecasting the presence of ice at shorter timescales is critically needed. Sea ice additionally has a controlling influence on climate by acting as a bright surface capable of reflecting sunlight back to space, thereby highlighting a need to accurately forecast it on decadal time scales. A significant source of errors in forecasts at all scales is the ability to predict to what extent mixing of the upper ocean occurs and how this mixing helps to eliminate gradients in temperature and salinity that might change the rate of ice formation or melt. An important item to understand is to what extent atmospheric winds, which we generally forecast relatively well, contribute to this upper-oceanic mixing through the transfer of energy between the atmosphere and ocean. This project will support the collection of key measurements necessary to help inform the improvement of weather and climate models to support prediction of sea ice at a variety of time scales. In this study, an unmanned aircraft system will be deployed to provide measurements of atmospheric temperature, winds and humidity. This information will be used together with information from surface buoys and ice imagery to understand atmosphere-ocean energy transfer during the fall freeze-up period. Specifically, this work will help to address questions related to the role of the presence of sea ice in energy transfer and how that role is simulated in numerical models, the extent to which mechanisms supporting transfer vary at small spatial scales and how those are handled in today?s state of the art modeling tools, and the importance of vertical resolution of models in accurately capturing this energy transfer. Measurements will be compared to high-resolution models that couple the atmosphere, ice and ocean together into single simulations. Flights will take place from northern Alaska in September and October of 2018 and will interface with a broader effort (the Stratified Ocean Dynamics of the Arctic, or SODA, project) to understand the upper ocean in this part of the world. Additionally, this work will interface with the ongoing Year of Polar Prediction, providing extra connections to the modeling communities who can benefit from these measurements.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.