SBIR Phase I: Sub-Decadal Weather and Climate Forecast System to Mitigate Risk for Energy and Natural Resource Applications

SBIR第一阶段项目的更广泛影响是通过多年预测，为环境和自然资源部门的组织减少天气和气候风险。提出的预报系统结合了地球科学和计算机科学，创建了一个高度可解释的预报系统，可针对特定行业的需求进行优化。这些多年期（“十年以下”）预测有助于可再生能源资源评估、水电应用和采矿业。更好的预测可以导致更便宜的能源，更可靠的长期供水管理，以及改善采矿作业的环境可持续性。SBIR第一期项目将推进融合地球科学和机器学习的亚年代际天气和气候预报系统的开发。项目活动包括：评估技术可行性；评估计算可扩展性；确定特定行业的相关投入和产出；并验证输出和可解释性。该算法必须跨越多个时空时间尺度。该奖项反映了美国国家科学基金会的法定使命，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。

The broader impact of this SBIR Phase I project is to reduce weather and climate risk for organizations within the environmental and natural resource sectors through multi-year forecasts. The proposed forecast system uses a combination of Earth science and computer science to create a highly interpretable forecast system that is optimizable for the needs of specific industries. These multi-year ("sub-decadal") forecasts help renewable energy resource assessment, hydropower applications, and the mining industries. Better forecasting can lead to cheaper energy, more reliable long-term water supply management, and the improved environmental sustainability of mining operations. This SBIR Phase I project will advance development of a sub-decadal weather and climate forecast system integrating geoscience and machine learning. Project activities include: assessing the technical feasibility; evaluating the computational scalability; determining the relevant industry-specific inputs and outputs; and validating the output and interpretability. The algorithm must scale across multiple spatial and temporal timescales.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.