PREEVENTS Track 2: Collaborative Research: Developing a Framework for Seamless Prediction of Sub-Seasonal to Seasonal Extreme Precipitation Events in the United States

预防事件轨道 2：协作研究：开发无缝预测美国次季节到季节性极端降水事件的框架

• 批准号: 1663938
• 负责人: Julie Demuth
• 金额: $ 8.72万
• 依托单位: University Corporation For Atmospheric Res
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1663938&HistoricalAwards=false
• 关键词: PREEVENTS; Track; Collaborative; Research; Developing

PREEVENTS Track 2: Collaborative Research: Developing a Framework for Seamless Prediction of Sub-Seasonal to Seasonal Extreme Precipitation Events in the United StatesExtreme precipitation is a natural hazard that poses risks to life, society, and the economy. Impacts include mortality and morbidity from fast-moving water, contaminated water supplies, and waterborne diseases as well as dam failures, power and transportation disruption, severe erosion, and damage to both natural and agro-ecosystems. These impacts span several sectors including water resource management, energy, infrastructure, transportation, health and safety, and agriculture. However, on the timeframe required by many decision makers for planning, preparing, and resilience-building "subseasonal to seasonal (S2S; 14 to 90 days)" forecasts have poor skill and thus adequate tools for prediction do not exist. Additionally, societal resilience to these events cannot be increased without established, two-way communication pathways between researchers, forecasters, and local or regional decision makers. Therefore, the goal of this project is to enhance scientific understanding of S2S extreme precipitation events, improve their prediction, and increase communication between research and stakeholder communities with regard to such events. The overarching results will be the development of predictive models that have the potential to reduce mortality, morbidity, and damages caused by S2S extreme precipitation events and broadening participation in science by including federal, tribal, and local stakeholders. Three targeted user communities; water resource managers, emergency managers, and tribal environmental professionals will engage throughout the project duration via workshops. The co-production of knowledge will steer the science to focus on useful characteristics that matter most to the people who use and rely on predictions, thus contributing to knowledge-sharing and improving the capability to predict what is meaningful.This project will enhance fundamental understanding of the large-scale dynamics and forcing of S2S extreme precipitation events in the U.S. and improve capability to model and predict such events. This project assembles an expert team of scientists and stakeholders to narrow the prediction gap of S2S extreme precipitation events by answering four scientifically and societally relevant research questions: 1) What are the synoptic patterns associated with, and characteristics of, S2S extreme precipitation events in the contiguous U.S.? 2) Do large-scale modes of climate variability modulate these events? If so, how? 3) How predictable are S2S extreme precipitation events across temporal scales? and 4) How do we create an informative prediction of S2S extreme precipitation events optimized for policymaking and planning? To answer these questions, this project will for the first time, combine observations with novel machine-learning techniques, high-resolution radar composites, dynamical climate models (the National Multi-Model Ensemble and the Coupled Model Intercomparison Project phase 5), and workshops that engage stakeholders in the co-production of knowledge. This project will identify the fundamental weather and climate processes that are tied to S2S extreme precipitation events across the U.S. from scales as small as individual storms to those as large as ocean basins. The prediction skill for S2S extreme precipitation events will be improved through an increased mechanistic understanding of historical events and a quantitative evaluation of model performance for simulating these events and their characteristic patterns. The statistical and co-production frameworks developed in this project will have the flexibility to be applied across meteorological extremes and timescales, in other global regions, with future climate model simulations, and with other stakeholder communities to reduce the impact of and increase resilience to extreme meteorological events.

PREVENTS Track 2：协作研究：开发美国亚季节至季节性极端降水事件的无缝预测框架过度降水是一种对生命、社会和经济构成风险的自然灾害。影响包括快速流动的水、受污染的供水和水媒疾病造成的死亡率和发病率，以及大坝坍塌、电力和交通中断、严重侵蚀以及对自然和农业生态系统的破坏。这些影响涉及多个部门，包括水资源管理、能源、基础设施、交通、卫生和安全以及农业。然而，在许多决策者规划、准备和建立复原力所需的时间框架内，“亚季节性到季节性(S2S；14至90天)”的预测技能很差，因此不存在足够的预测工具。此外，如果研究人员、预报员和当地或区域决策者之间没有建立起双向沟通渠道，社会对这些事件的复原力就无法提高。因此，该项目的目标是加强对S2S极端降水事件的科学理解，改进其预测，并就此类事件加强研究人员和利益攸关方社区之间的沟通。最主要的成果将是开发预测模型，这些模型有可能减少S2S极端降水事件造成的死亡率、发病率和损害，并通过包括联邦、部落和地方利益攸关方扩大对科学的参与。三个目标用户社区：水资源管理人员、应急管理人员和部落环境专业人员将通过研讨会在整个项目期间参与。知识的联合生产将引导科学专注于对使用和依赖预测的人最重要的有用特征，从而有助于知识共享和提高预测有意义的事件的能力。该项目将加强对美国S2S极端降水事件的大规模动力学和强迫的基本了解，并提高对此类事件的建模和预测能力。该项目召集了一个由科学家和利益相关者组成的专家团队，通过回答四个具有科学和社会意义的研究问题来缩小对S2S极端降水事件的预测差距：1)与美国毗邻的S2S极端降水事件有关的天气形势和特征是什么？2)气候变化的大尺度模式是否对这些事件进行了调制？如果是，如何？3)S2S极端降水事件在时间尺度上的可预测性如何？以及4)我们如何创建对S2S极端降水事件的信息性预测，以优化决策和规划？为了回答这些问题，该项目将首次将观测与新颖的机器学习技术、高分辨率雷达合成、动态气候模型(国家多模型集合和耦合模型相互比较项目第五阶段)以及让利益攸关方参与共同产生知识的讲习班结合起来。该项目将确定与美国各地S2S极端降水事件有关的基本天气和气候过程，从单个风暴的小到海洋盆地的大小。通过增加对历史事件的机械性理解和对模拟这些事件及其特征模式的模式性能的定量评估，将提高对S2S极端降水事件的预测技能。该项目制定的统计和联合制作框架将具有灵活性，可应用于极端气象事件和时间尺度、其他全球区域、未来气候模型模拟以及与其他利益攸关方社区，以减少极端气象事件的影响并提高对极端气象事件的复原力。