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

这个小型企业创新研究(SBIR)第一阶段项目的更广泛影响将是向保险、能源和农业利益攸关方提供及时的、高度本地化的气候预报，以及极端高温和霜冻风险等信息。从亚季节到季节性(S2S)时间尺度的气候预测具有挑战性，但对于积极主动地管理极端自然灾害的风险至关重要。该项目将利用人工智能和云计算来实施数据密集型方法，以彻底改变全球气候预测。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高效和准确的季节预报。这个小型企业创新研究(SBIR)第一阶段项目旨在利用先进的人工智能技术，以开发一种针对行业和作物的本地化、及时和可靠的气候预报系统。在这个项目中，将部署最先进的人工智能技术，在全球范围内提升气候预测的业务技能。虽然传统的预报只根据观测数据进行训练，但该项目将根据历史模拟和再分析来训练模型，然后用观测对它们进行评估。在这种方法中，训练数据集明显更大，从而提高了准确率。这种大规模的处理是通过云资源实现的。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影响审查标准进行评估，被认为值得支持。

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.