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

这个小企业创新研究（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的混合，超快的物理信息机器学习技术，模拟复杂的数值物理为基础的气候/天气模型的实际商业应用。基于物理的（水力）气候/天气模拟模型用于具有最大社会利益的数万亿美元行业，从农业到保险，从能源到物流。对难以/昂贵或甚至无法凭经验测量的物理气候/环境参数（如雪水当量）进行更快（提高3-5个数量级）、超本地化和大规模的估计，并整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（更快地进行水电情景预测），也可以实现新的能力和产品（例如，实时风暴风险响应或自动化参数保险合同）。拟议的研发工作将说明包括气候在内的科学建模如何利用科学界花了70多年时间建立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，以处理自然环境观测数据（特别是遥感）的新来源。该小企业创新研究（SBIR）第一阶段项目解决了可再生能源和保险行业对实时观测数据所提供的环境和气候/天气参数的危害概况的快速，高分辨率（在空间和时间上）估计的需求。该项目旨在提供第一个概念验证，即可以开发商业级混合物理信息AI技术，以估计相关的气候和天气参数，从水文气候建模开始。建议的研发&工作将集中在1）开发和验证基于数值水文气候模拟数据以及观测气象数据的生成式深度学习模型; 2）确定和基准测试最佳实践，以确保模型的稳定训练和更新，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web的API功能。设想的概念验证是在GPU硬件上本地运行的模块化计算系统，该系统将允许创建物理参数的网格数据集，例如雪水当量，降水或水位，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识产权进行评估来支持。优点和更广泛的影响审查标准。

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.