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) 通常用于制定气候预测，以预测极端事件（例如干旱、洪水）。由于计算能力的提高，GCM 投影的空间分辨率有所提高，但对于需要极端事件预测来实现规划的分水岭规模应用来说仍然不够。 该项目进行的研究将开发一种基于人工智能的技术，以改善流域规模的水文气候预测。人工智能技术在模拟全球气候数据方面非常强大，可以开发更精细的空间和时间未来气候预测。潜在的影响是改善对分水岭规模的极端事件的规划和恢复能力。这项研究将开发一种基于人工智能的概率方法，该方法使用基于分位数回归的人工神经网络（ANN）（QR-AI）模型来消除偏差修正和统计缩小 (BCSD) 耦合模型比较项目 (CMIP6) 预测。具体来说，该研究将开发美国大陆（CONUS）CMIP6预测的三个BCSD数据产品：1）GCM降水和温度的历史模拟（1950-2014年）； 2) 来自相关 GCM 的近期（30 年）降水和温度后报，以及 3）四种不同共享社会经济路径的近期（30 年）降水和温度预测，以二氧化碳排放和减缓情景为代表。开发后报和历史预测的 BCSD 将通过将估计气候变量的不确定性与观测到的美国​​大陆降水和温度边际密度进行比较，为验证 QR-AI 方法提供机会。关于降水和温度的 BCSD CMIP6 产品将使用人工智能方法为整个 CONUS 开发，并通过项目网站传播。 BCSD数据也将存档在figshare和github中以供传播。此外，研究人员将与水库管理和社交媒体等重点用户群体合作，积极传播开发的 BCSD 产品。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评估进行评估，被认为值得支持。影响审查标准。