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2232761

Project Grant

Overview

Grant Description
SBIR Phase I: 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.
Awarding / Funding Agency
Place of Performance
Denver, Colorado 80202-6696 United States
Geographic Scope
Single Zip Code
Related Opportunity
None
Applied Research Team was awarded Project Grant 2232761 worth $275,000 from National Science Foundation in May 2023 with work to be completed primarily in Denver Colorado United States. The grant has a duration of 1 year and was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.

SBIR Details

Research Type
SBIR Phase I
Title
SBIR Phase I:Radar Snow Retrieval
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._x000D_ _x000D_ 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._x000D_ _x000D_ 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.
Topic Code
AA
Solicitation Number
NSF 22-551

Status
(Ongoing)

Last Modified 5/4/23

Period of Performance
5/1/23
Start Date
4/30/24
End Date
81.0% Complete

Funding Split
$275.0K
Federal Obligation
$0.0
Non-Federal Obligation
$275.0K
Total Obligated
100.0% Federal Funding
0.0% Non-Federal Funding

Activity Timeline

Interactive chart of timeline of amendments to 2232761

Additional Detail

Award ID FAIN
2232761
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
F9LKLVU743A5
Awardee CAGE
99HW5
Performance District
01
Senators
Michael Bennet
John Hickenlooper
Representative
Diana DeGette

Budget Funding

Federal Account Budget Subfunction Object Class Total Percentage
Research and Related Activities, National Science Foundation (049-0100) General science and basic research Grants, subsidies, and contributions (41.0) $275,000 100%
Modified: 5/4/23