Sbir phase i: radar snow retrieval

The National Science Foundation awarded a $275,000 Small Business Innovation Research Phase I Project Grant to Applied Research Team Inc. of Denver, Colorado under the NSF Technology, Innovation, and Partnerships program (CFDA 47.084). The grant will support the development of machine learning algorithms to improve snowpack depth and water content retrieval from C-band and X-band radar data.

Accurate snowpack information is critical for water management in the Western U.S., but current retrieval methods have not kept pace with newer short-wavelength radars increasingly used in mountainous areas. The awardee will develop physics-guided neural networks to produce spatially-consistent snowpack estimates with improved accuracy over deterministic or purely data-driven approaches. Upon completion, water managers will gain access to an updated cloud-based subscription service utilizing historical and current radar data to enhance operational decision making. The project aims to advance technologies supporting solutions to water resource challenges of importance to society.

Abstract

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.