SBIR Phase I: Subseasonal Forecasting and Climate Risk Analytics Combining Physics and AI

这一小企业创新研究（SBIR）第一阶段项目的更广泛/商业影响在于开发用于亚季节预报、极端天气事件和长期气候变化的天气预报和气候预测工具。预计拟议的技术将影响大量行业，包括农业、保险、物流/供应链和公共部门，最初的重点和市场进入是能源部门。这个市场由大型银行提供资金，拥有根据风险定价的大型保单，需要在短期和长期内分配资源，以满足客户需求并防止服务中断。如果没有这些预测能力，就有可能造成巨大的经济和社会成本。例如，2022年太平洋西北部的热浪造成了89亿美元的损失，并使1,400人丧生。如果提前4周通知，能源公司本可以做好充分的准备，挽救生命，最大限度地减少对有形资产的损害。在过去五年中，对天气事件的次优管理使美国平均每年损失839人的生命和161 B美元（累计7500亿美元），比前五年增加了2.5倍。这个小企业创新研究（SBIR）第一阶段项目旨在建立利用物理信息机器学习创建关键气候参数和极端天气事件的概率模型的可行性。一个概念验证演示集中在一个单一的预测变量，温度，能够预测温度异常提前2-4周，准确率比领先的基于物理的预测北美高30-50%。气候预测模型通过使用未发表的，最先进的物理学机器学习方法和数据蒸馏来提供高分辨率的亚季节预测。该SBIR项目旨在（1）使用尖端的Transformer网络和AI基础模型提高温度预测的准确性，（2）将预测能力扩展到极端天气，如强对流风暴，（三）并通过利用改进的贝叶斯建模来捕获预测的不确定性，从而增强产品的鲁棒性。该奖项反映了NSF的法定使命，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来提供支持。

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project lies in the development of a weather forecasting and climate prediction tool for subseasonal forecasting, extreme weather events, and long-term climatological changes. The proposed technology is expected to impact a significant number of industries, including agriculture, insurance, logistics/supply chains, and the public sector, with an initial focus and market entry in the energy sector. This market is financed by large banks, carries large insurance policies that are priced based on risk, and needs to allocate resources in both the short and long term to meet customer needs and prevent service interruptions. Without these forecasting capabilities, there is a risk of drastic economic and societal costs. For example, the 2022 Pacific Northwest heat wave resulted in $8.9 billion in damages and cost the lives of 1,400 people. With 4 weeks of advanced notice, energy companies could have adequately prepared, saving lives and minimizing the damage to physical assets. The suboptimal management of weather events costs the US an average of 839 lives and $161 B/year for the last five years (cumulative $750B), a 2.5x increase from the previous five years.This Small Business Innovation Research (SBIR) Phase I project aims to establish thefeasibility of utilizing physics-informed machine learning to create probabilistic models of crucial climatological parameters and extreme weather events. A proof-of-concept demonstrationfocused on a single forecast variable, temperature, capable of predicting temperature anomalies 2-4 weeks in advance with 30-50% higher accuracy than the leading physics-based forecast for North America. The climate prediction models operate by using unpublished, state-of-the-art physics-informed machine learning methods and data distillation to provide high-resolution subseasonal forecasts. This SBIR project aims to (1) increase the accuracy of the temperature predictions using cutting-edge transformer networks and AI-foundation models, (2) expand predictive capabilities to extreme weather such as severe convective storms, (3) and enhance the robustness of the product by leveraging improved Bayesian modeling to capture the uncertainty of forecasts.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.