SBIR Phase I: An Artificial Intelligence-Based Global Seasonal Forecasting System

SBIR 第一阶段：基于人工智能的全球季节性预报系统

• 批准号: 2026025
• 负责人: Maximilian Evans
• 金额: $ 24.18万
• 依托单位: CLIMATEAI INC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026025&HistoricalAwards=false
• 关键词: SBIR; Phase; Artificial; Intelligence; Based

The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be to provide timely and highly localized climate forecasts, plus information such as extreme heat and frost risk, to insurance, energy, and agricultural stakeholders. Climate forecasting at sub-seasonal to seasonal (S2S) timescales is challenging, yet essential for proactive risk management of extreme natural hazards. This project will leverage artificial intelligence and cloud computing to implement a data-intensive approach for revolutionizing global climate forecasting. The project will provide efficient and accurate seasonal forecasts at relatively low computational cost in a user-friendly web environment.This Small Business Innovation Research (SBIR) Phase I project aims to utilize advanced artificial intelligence techniques in order to develop a localized, timely, and reliable climate forecasting system that is industry-focused and crop-specific. In this project, state-of-the-art artificial intelligence techniques will be deployed to advance operational climate forecasting skill at a global scale. While conventional forecasts are trained exclusively on observational data, this project will train models on historical simulations and reanalysis, then evaluate them with observations. In this approach, the training dataset is substantially larger, consequently improving accuracy. This processing at scale is enabled with cloud resources.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.

这项小型企业创新研究（SBIR）I阶段项目的更广泛影响将是提供及时且高度局部的气候预测，以及诸如Extreme Heat和Extreme Heat和Frost风险之类的信息，对保险，能源和农业利益相关者。在季节性到季节性（S2S）时标的气候预测具有挑战性，但对于主动对极端自然危害的风险管理至关重要。 该项目将利用人工智能和云计算来实施革命性的全球气候预测的数据密集型方法。该项目将在用户友好的网络环境中以相对较低的计算成本提供有效，准确的季节性预测。本小型企业创新研究（SBIR）I期项目旨在利用先进的人工智能技术，以开发一种局部，及时，及时，可靠的气候预测系统，该系统是行业范围为中心和作物的。在该项目中，将部署最先进的人工智能技术，以在全球范围内提高运营气候预测技能。尽管常规预测专门接受观察数据的培训，但该项目将在历史模拟和重新分析上培训模型，然后通过观察评估它们。在这种方法中，培训数据集大大更大，因此提高了准确性。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的影响评论标准来评估的。