Applying Year of Polar Prediction-Southern Hemisphere (YOPP-SH) Targeted Observing Periods to Advance Winter Numerical Weather Forecasting for the U.S. Antarctic Program

该项目旨在促进科学进步，提高美国研究人员和美国南极计划在改进数值天气建模能力方面的国家利益。 这项工作包括利用国际极地预测年-南半球计划的大气建模工作。 这项工作包括2022年的实地活动，其特点是在目标观测期内加强对南极冬季大气的观测。 该项目旨在提高数值天气预报能力，为美国南极计划的研究应用和天气预报提供支持。 该项目的目标包括评估使用特殊观测来改进大气建模，确定新技术是否上级南极大气模拟和天气预测的现有技术，并评估该模型在预测极地云和降水方面的表现。 该项目将通过应用在国际合作中收集的前所未有的数据集并确定其对未来科学和预测的益处来推进该领域。 它还将探索在极地地区大气模型中使用这种信息的新方法，从而推动这一领域的发展。 其好处是增加了知识，改进了天气建模，并提高了南极计划所依赖的模型天气预报能力，从而提高了南极作业的效率和安全性。 该项目还将通过支持一名研究生的教育和专业发展，沿着各种受众互动接触南大洋和南极偏远地区天气建模和预测这一具有挑战性的任务，促进教育和外联。并评估该模型在预测极地云和降水方面的表现。具体而言，在极地预报年-南半球（YOPP-SH）框架内，该项目有四个方面。 首先，该团队将确定从东南极洲到罗斯海的候选目标观测期（TOP）。第二，将利用南极中尺度预报系统（USAP的数值天气预报能力）运行的天气研究和预报模式，将在南极海洋观测站发射的特别YOPP-SH无线电探空仪的数据集纳入模式试验。 这些数据同化实验将应用AMPS框架，以确定观测对影响美国南极计划（USAP）麦克默多和帕尔默站的主要天气事件预测的影响，并确定管理过程。 第三，WRF模式的新数据同化能力-多分辨率增量四维变分法（MRI-4DVAR）-正在这些数据影响实验中进行测试，以确定其在AMPS中实时使用的有效性，从而为未来可能的实施以及研究建模。 第四，在戴维斯和Vernadsky站的TOP期间收集的云和降水观测将用于WRF模拟的分析，以评估模型的弱点。该项目旨在加强WRF对云和降水的处理，以改进参数化。目标是提高AMPS对USAP业务的预测准确性，并为研究界改进WRF模型。该项目将最终促进科学的进步，并提高美国研究人员和USAP在改进数值天气建模能力方面的国家利益。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。

The project aims to promote the progress of science and advance national interests in improved numerical weather modeling capabilities for American researchers and for the United States Antarctic Program. The effort consists of atmospheric modeling work leveraging the international Year of Polar Prediction — Southern Hemisphere program. This effort includes a field campaign component in 2022 that features enhanced observations of the Antarctic atmosphere in winter during Targeted Observing Periods. The project seeks to improve numerical weather prediction capabilities for both research applications and weather forecasting support for the US Antarctic Program. The project goals include evaluating the use of special observations to improve atmospheric modeling, determining whether the new technique is superior to existing techniques for Antarctic atmospheric simulation and weather prediction, and assess how well the model is performing in its prediction of polar clouds and precipitation. The project will advance the field by applying an unprecedented dataset gathered in an international collaboration and determining its benefit for future science and forecasting. It will also advance the field by exploring a new method for the use of such information in atmospheric models for the polar regions. The benefits are increases in knowledge, improved weather modeling, and gains in the model weather forecasting capabilities that the Antarctic Program relies on, allowing for enhanced efficiency and safety for Antarctic operations. The project will also advance education and outreach by supporting the education and professional development of a graduate student along with interactive exposure of diverse audiences to the challenging task of weather modeling and prediction for the remote Southern Ocean and Antarctic regions.This project focuses on the value of special observations for improving Antarctic atmospheric simulation and weather prediction, and evaluate how the model performs in predicting polar clouds and precipitation. Specifically, within the Year of Polar Prediction — Southern Hemisphere (YOPP-SH) framework, this project has four aspects. First, the team will identify candidate Targeted Observing Periods (TOPs) from East Antarctica to the Ross Sea. Second, the dataset of special YOPP-SH radiosonde launches made in the TOPs will be assimilated into model experiments using the Weather Research and Forecasting (WRF) model, which is run in the Antarctic Mesoscale Prediction System (AMPS), the numerical weather prediction capability for USAP. These data assimilation experiments will apply the AMPS framework to determine the impact of the observations on predictions of major weather events affecting the US Antarctic Program (USAP) McMurdo and Palmer Stations, and to identify the governing processes. Third, a new data assimilation capability for the WRF Model— Multi-Resolution Incremental 4DVAR (MRI-4DVAR) —is being tested in these data impact experiments to determine its effectiveness for real-time use in AMPS and thus for possible future implementation as well as for research modeling. Fourth, cloud and precipitation observations collected during the TOPs at Davis and Vernadsky Stations will be applied to analyses of the WRF simulations to assess model weaknesses. This project aims to enhance WRF’s handling of clouds and precipitation to improve parameterization. The goal is to increase the accuracy of AMPS forecasts for USAP operations and to improve the WRF model for the research community. This project will ultimately promote the progress of science and advance national interests in improved numerical weather modeling capabilities for American researchers and for USAP.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.