SBIR Phase I: Machine learning emulators of weather and hydroclimate models for operational and financial risk assessment

这个小型企业创新研究(SBIR)第一阶段项目的更广泛的影响/商业潜力是，它将提供一个具体的实施，在可再生能源和气候相关风险方面具有实际的商业应用，这是一种混合的、超快的物理信息机器学习技术，模拟基于复杂的基于数值物理的气候/天气模型。从农业到保险，从能源到物流，基于物理学的(水力)气候/天气模拟模型被用于具有最大社会利益的数万亿美元的行业。更快(提高3-5个数量级)、对难以/昂贵或甚至不可能通过经验测量的实际气候/环境参数(如雪水当量)的超局部大规模估计，结合现有的最佳实时观测遥感数据，既可以简化现有的应用程序(更快地预测水电情景)，又可以实现新的能力和产品(例如，实时风暴风险应对或自动化参数保险合同)。拟议的研发工作将展示科学建模，包括气候建模，如何利用科学界花了70多年构建的数值模拟模型中嵌入的知识体系，以及新型人工智能和机器学习模型的高速和自然能力，以处理关于自然环境的新观测数据来源(尤其是遥感数据)。小型企业创新研究(SBIR)第一阶段项目旨在满足可再生能源和保险行业对通过实时观测数据快速、高分辨率(在空间和时间上)估计环境和气候/天气参数的危害概况的需求。该项目旨在提供第一个概念验证，即可以开发一种商业级混合物理信息人工智能技术，以从水气候建模开始估计相关的气候和天气参数。拟议的研发工作将侧重于：1)开发和验证以数值水文气候模拟数据和观测气象数据为基础的生成性深度学习模型；2)确定和确定最佳做法，以确保稳定地培训和更新模型、观测/模拟数据要求和所需的计算资源；以及3)为与可再生能源和保险/风险建模用例相关的用例设计和开发简化的模型访问模式和基于网络的API功能。设想的概念验证是一个在GPU硬件上本地运行的模块化计算系统，它将允许创建物理参数的网格数据集，如雪水当量、降水或水位，以及它们在地理位置和感兴趣的时间范围的相关概率曲线。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/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is that it will provide a concrete implementation with practical commercial applications in renewable energy and climate-related risk of a hybrid, ultrafast physics-informed machine learning technology that emulates complex numerical physics-based climate/weather models. Physics-based (hydro)climate/weather simulation models are used across trillion-dollar industries of utmost societal interest, from agriculture to insurance to energy to logistics. Faster (by 3-5 orders of magnitude), hyperlocal, large-scale estimates of physical climate/environmental parameters that are difficult/expensive or even impossible to measure empirically (such as snow-water equivalent), integrating best-available real-time observational remote-sensing data, can both streamline existing applications (faster hydropower scenario forecasting), as well as enable new capabilities and products (e.g., real-time storm risk response or automated parametric insurance contracts). The proposed R&D effort will illustrate how scientific modeling, including of climate, can leverage both the body of knowledge embedded in numerical simulation models, which the scientific community has spent more than seven decades building, as well as the high speed and natural capability of novel AI and machine learning models to process novel sources of observational data (particularly remote-sensing) on the natural environment. This Small Business Innovation Research (SBIR) Phase I project addresses the need in the renewable energy and insurance industries for fast, high-resolution (in space and time) estimates of the hazard profiles of environmental and climate/weather parameters informed by real-time observational data. The project aims to provide a first proof-of-concept that a commercial-grade hybrid physics-informed AI technology can be developed for estimating relevant climate and weather parameters, starting with hydroclimate modeling. The R&D effort proposed will focus on 1) developing and validating a generative deep learning model trained on numerical hydroclimate simulation data as well as observational meteorological data; 2) identifying and benchmarking best-practices for ensuring stable training and updating of the model, observational/simulation data requirements, and computational resources needed; and 3) designing and developing streamlined model access patterns and web-based API functionality for use cases relevant to renewable energy and insurance/risk modeling use-cases. The envisioned proof-of-concept is a modular computational system running natively on GPU hardware that will allow creating gridded datasets of physical parameters such as snow water equivalent, precipitation, or water level, as well as their associated probability curves for geographical locations and time horizons of interest.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.