SBIR Phase I: Radar Snow Retrieval

SBIR 第一阶段：雷达除雪

• 批准号: 2232761
• 负责人: Baxter Vieux
• 金额: $ 27.5万
• 依托单位: APPLIED RESEARCH TEAM, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2232761&HistoricalAwards=false
• 关键词: SBIR; Phase; Radar; Snow; Retrieval

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will develop transformative, machine-learning algorithms that will improve water management. Water management is critically important to the social well-being, food supply, and climate resiliency of the local population in the Western United States, yet water managers lack adequate snowpack depth and water content information necessary for the management, storage, and transfer of water for irrigation and consumption. Water deficits are made worse when snowpack depths are in error, potentially resulting in devastating damage to agricultural economies and vulnerable populations. The proposed technology will be able to scale from storm events to seasonal snowpack estimations and provide accurate mappings of snowpack depths and water equivalents for watershed areas needing water management.This Small Business Innovation Research (SBIR) Phase I project develops algorithms for determining snowpack depth and water content. Snow retrieval algorithm development has not kept pace with the deployment of short wavelengths. C- and X-band radars are used as ‘gap-filling’ radars in mountainous valleys. Developing effective algorithms for detection of snow water equivalent is needed for these short wavelength radars. Artificial Intelligence/Machine Learning (AI/ML) and optimization algorithms are expected to improve estimation accuracy compared with point-scale (sensor) observations and across watershed areas relevant to water management. Physics-guided neural networks (PGNNs) can produce physically consistent results and generalize to out of sample scenarios. Application of a PGNN to snow retrievals is expected to perform better than purely data-driven or deterministic algorithms. Anticipated technical results will provide water managers with a cloud-based subscription service updated in real-time, using historical and current radar data to improve operational decision-making.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.

这个小企业创新研究（SBIR）第一阶段项目的更广泛影响/商业潜力将开发变革性的机器学习算法，以改善水资源管理。在美国西部，水资源管理对社会福祉、食物供应和当地人口的气候适应能力至关重要，但水资源管理者缺乏足够的积雪深度和含水量信息，这些信息对于灌溉和消费用水的管理、储存和转移是必要的。当积雪深度错误时，缺水会变得更严重，可能对农业经济和弱势群体造成毁灭性的破坏。拟议的技术将能够从风暴事件扩展到季节性积雪估计，并为需要水管理的流域地区提供积雪深度和水当量的准确映射。这个小企业创新研究（SBIR）第一阶段项目开发了确定积雪深度和含水量的算法。积雪检索算法的发展没有跟上短波长的部署。C波段和x波段雷达在山区山谷中用作“缺口填充”雷达。短波雷达需要开发有效的雪水当量探测算法。与点尺度（传感器）观测相比，人工智能/机器学习（AI/ML）和优化算法有望提高与水管理相关的流域估算精度。物理引导神经网络（pgnn）可以产生物理上一致的结果，并推广到样本外场景。将PGNN应用于积雪检索预计比纯数据驱动或确定性算法表现更好。预期的技术成果将为水务管理者提供实时更新的基于云的订阅服务，利用历史和当前的雷达数据来改进运营决策。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。