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预测的空间分辨率得到了提高，但对于需要进行极端事件预测以实现规划的流域尺度应用仍然不够。 该项目的研究将开发一种基于人工智能的技术，以改善流域尺度的水文气候预测。人工智能技术在全球气候数据建模方面非常强大，可以开发更精细的空间和时间未来气候预测。潜在的影响是改善规划，和弹性，极端事件在流域scale.This研究将开发一个基于AI的概率方法，使用基于分位数回归的人工神经网络（ANN）（QR-AI）模型的偏差校正和统计降尺度（BCSD）耦合模型相互比较项目（CMIP 6）预测。具体而言，该研究将开发CMIP 6对美国大陆（CONUS）预测的三种BCSD数据产品：1）历史模拟（1950-2014）降水和温度的大气环流模式; 2）来自相关GCM的降水和温度的近期（30年）后报; 3）近期（30年）四个不同的共享社会经济途径，这是由二氧化碳排放和减缓情景代表的降水和温度的预测。发展BCSD的后报和历史预测将提供一个机会，验证QR-AI方法，通过比较估计的气候变量的不确定性与观测到的边缘密度的降水和温度在CONUS。BCSD CMIP 6关于降水和温度的产品将使用人工智能方法为整个美国大陆开发，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，研究人员还将与重点用户群体（如油藏管理和社交媒体）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。