EAGER: CAS-Climate: AI-driven Probabilistic Technique, Quantile Regression based Artificial Neural Network Model, for Bias Correction and Downscaling of CMIP6 Projections

EAGER：CAS-Climate：人工智能驱动的概率技术、基于分位数回归的人工神经网络模型，用于 CMIP6 投影的偏差校正和缩小

• 批准号: 2151651
• 负责人: Sankarasubraman Arumugam
• 金额: $ 29.95万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-12-15 至 2024-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2151651&HistoricalAwards=false
• 关键词: EAGER; CAS; Climate; AI; driven

Global Climate Models (GCMs) are typically used to develop climate projections to predict extreme events (e.g., droughts, floods). Spatial resolution of GCM projections has improved due to increasing computational power, but is still inadequate for watershed-scale applications where extreme event prediction is needed to enable planning. The research undertaken in this project will develop an AI-based technique to improve hydroclimatic projections at the watershed scale. AI techniques are quite powerful in modeling global climate data and could develop finer spatial and temporal future climatic projections. The potential impact is improved planning for, and resilience to, extreme events at the watershed scale.This research will develop an AI-based probabilistic approach that uses a Quantile Regression based Artificial Neural Network (ANN) (QR-AI) model for bias-corrected and statistically downscaled (BCSD) Coupled Model Intercomparison Projects (CMIP6) projections. Specifically, the research will develop three BCSD data products of CMIP6 projections over the continental U.S. (CONUS): 1) Historical simulations (1950-2014) of precipitation and temperature of GCMs; 2) Near-term (30 year) hindcasts of precipitation and temperature from relevant GCMs and 3) Near-term (30 year) projections of precipitation and temperature for four different Shared Socioeconomic Pathways, which are represented by CO2 emission and mitigation scenarios. Developing BCSD of both hindcasts and historical projections will provide an opportunity to validate the QR-AI methodology by comparing the uncertainty in the estimated climate variables with the observed marginal density of precipitation and temperature over the CONUS. The BCSD CMIP6 products on precipitation and temperature will be developed using the AI method for the entire CONUS and disseminated through the project website. BCSD data will also be archived in figshare and github for dissemination. Additionally, the investigators will work with focused user groups, such as reservoir management and social media, for active dissemination of the developed BCSD products.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.

全球气候模型（GCM）通常用于开发气候预测以预测极端事件（例如，干旱，洪水）。由于计算能力的增加，GCM预测的空间分辨率有所改善，但对于需要极端事件预测以实现计划的流域规模应用程序仍然不足。 该项目中进行的研究将开发一种基于AI的技术，以在分水岭量表上改善氢化气候预测。人工智能技术在建模全球气候数据方面非常有力，并且可以发展出更精细的空间和时间的未来气候预测。潜在的影响是改善了在分水岭规模上的极端事件的计划和韧性。这项研究将开发一种基于AI的概率方法，该方法使用基于分数回归的人工神经网络（ANN）（QR-AAI）模型，用于偏置偏差和统计上的统计学上的降低（BCSD）（BCSD）couppled Model Mode Modemporparison Projections（CMIP6）。具体而言，这项研究将在美国大陆上开发CMIP6预测的三种BCSD数据产品（CONUS）：1）历史模拟（1950-2014）的GCMS的降水和温度； 2）近期（30年）对相关GCM的降水和温度的后广播和3）近期（30年）的降水和温度预测，用于四种不同共享的社会经济途径，这些途径由CO2发射和缓解情况表示。开发后标和历史投影的BCSD将通过将估计气候变量的不确定性与观察到的降水量和温度的边缘密度进行比较，从而为验证QR-AI方法论提供了机会。 BCSD CMIP6产品的降水和温度将使用整个圆锥的AI方法开发，并通过项目网站进行分发。 BCSD数据还将在FigShare和Github中存档，以进行传播。此外，调查人员将与重点用户组（例如水库管理和社交媒体）合作，以积极地传播开发的BCSD产品。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准通过评估来获得支持的。