NNA Track 2: PredictFest: To Build Capacity for Arctic Stakeholders in Need of Multi-Scale Predictions

新北极航行（NNA）是NSF的十大创意之一。NNA项目解决快速变化的北极地区的趋同科学挑战。北极研究需要为国家、更大地区和地球仪的经济、安全和复原力提供信息。NNA授权从地方到国际规模的新的研究伙伴关系，使下一代北极研究人员多样化，加强正规和非正规教育的努力，并在适当的情况下整合知识的共同生产。该奖项通过支持具有明确潜力的规划活动来实现这一目标的一部分，以开发新颖，前沿的研究思路和方法来实现NNA目标。它整合了自然环境和社会系统的各个方面，应对重要的社会挑战，并与当地和土著社区合作，在提高短期天气预报和长期气候、海冰和其他环境预报的准确性方面取得了重大进展。尽管如此，在两周到几个月的范围内，特别是在北极地区，仍然存在预测差距。 这一次季节到季节（S2S）的尺度对于规划生计狩猎、商业捕鱼、灾害应对和风险缓解以及其他活动非常重要。 该项目通过一个协作设计进程和讲习班PredictFest支持改进S2S预测能力的发展，该讲习班使北极利益攸关方参与确定此类预测的需求和应用。 PredictFest参与者将准备研究宣传，阐明利益相关者的规划和决策需求，确定响应这些需求的技术和建模方法，确定相关的连接和应用程序，并制定持续发展计划。PredictFest为北极居民和科学家提供了一个机会，共同开发海冰、降水和其他环境因素的季节性预报应用程序，支持海洋和沿海地区的传统和商业用途。此外，该项目还促进以团队为中心的应用研究，教育和推广方法，并在北极居民和科学家之间建立关系，以支持未来的工作。 科学家和其他北极居民，包括土著组织、社区成员、企业主和公共决策者之间的直接参与，鼓励人们对如何使用季节预报信息以及采取哪些步骤实现既定目标进行新的思考。 该赠款支持一种创新的能力建设方法，利用新兴的S2S平台和科学来确定研究计划，以支持北极利益相关者的各种规划和决策需求。该项目包括三个阶段：（1）利益相关者参与和发展次季节到季节预测重点，（2）组成开发团队和实施PredictFest，以及（3）后评估和合作完善PredictFest输出。 共同学习和共同生产有助于快速识别季节尺度预测可能对支持自然，建筑和社会环境的决策最有益的领域。 在PredictFest期间，每个参与团队都由自然和社会科学家，利益相关者和社区代表以及技术支持人员组成。 重点的主题，海洋环境和近岸海岸界面，产生独特的预测，可用于支持社区生存为中心的海洋哺乳动物，商业渔业，包括固定齿轮的参与者，灾害应对和风险缓解规划。 从预测的角度来看，小组将采取一些方法。 其中包括开发变量之间的新关系，通常使用线性模型或基于树的学习，使用时间序列预测技术模型，如自回归综合移动平均（ARIMA）和模式识别。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

Navigating the New Arctic (NNA) is one of NSF's 10 Big Ideas. NNA projects address convergence scientific challenges in the rapidly changing Arctic. The Arctic research is needed to inform the economy, security and resilience of the Nation, the larger region and the globe. NNA empowers new research partnerships from local to international scales, diversifies the next generation of Arctic researchers, enhances efforts in formal and informal education, and integrates the co-production of knowledge where appropriate. This award fulfills part of that aim by supporting planning activities with clear potential to develop novel, leading edge research ideas and approaches to address NNA goals. It integrates aspects of the natural environment and social systems, addresses important societal challenges, and engages with local and Indigenous communities.Significant progress has been made to improve the accuracy both of short-term weather forecasts and longer-term climate, sea-ice, and other environmental forecasts. Nevertheless, there remains a forecasting gap on the scale from two weeks to a few months, especially in the Arctic. This subseasonal-to-seasonal (S2S) scale is important for planning subsistence hunting, commercial fishing, hazard response and risk mitigation, and other activities. This project supports development of improved S2S prediction capacity through a collaborative design process and workshop, PredictFest, that engages Arctic stakeholders to identify needs and applications for such predictions. PredictFest participants will prepare research pitches that articulate stakeholder planning and decision making needs, identify technical and modelling approaches that respond to those needs, identify relevant connections and applications, and set out a plan for continued development. PredictFest provides an opportunity for Arctic residents and scientists to work together to develop applications for seasonal forecasts of sea ice, precipitation, and other environmental factors, which support traditional and commercial uses of marine and coastal regions. In addition, the project fosters a team-centered approach to applied research, education, and outreach and builds relationships between Arctic residents and scientists to support future work. Direct engagement between scientists and other Arctic residents, including Indigenous organizations, community members, business owners, and public decision makers, encourages new thinking about how seasonal forecast information may be used and what steps may be taken to realize stated goals. This grant supports an innovative new approach to capacity building that leverages emergent S2S platforms and science to identify research programs to support the diverse set of Arctic stakeholder planning and decision-making needs. The project contains three phases: (1) stakeholder engagement and development of sub-seasonal to seasonal prediction focus, 2) composition of development teams and implementation of PredictFest, and 3) post-assessment and collaborative refinement of PredictFest outputs. Co-learning and co-production contribute to the rapid identification of arenas where seasonal scale predictions might be most beneficial to decision making in support of natural, built, and social environments. During PredictFest each participating team is comprised of natural and social scientists, stakeholders and community representatives, as well as technical support staff. The topic of focus, Marine Environments and the Near-shore Coastal Interface, generates distinct predictions that can be used to support community subsistence centered on marine mammals, commercial fisheries including fixed gear participants, and hazard response and risk mitigation planning. From a prediction standpoint a number of approaches will be pursued by the teams. These include developing new relationships between variables typically using linear models or tree-based learning, using time-series forecasting techniques models such as Autoregressive Integrated Moving Average (ARIMA), and pattern recognition.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.