NA23OAR0210574
Project Grant
Overview
Grant Description
Salient Predictions Inc. proposes to develop new technology for accurate subseasonal to seasonal (S2S) water availability forecasts so that communities may manage water resources in preparation for hazardous events like floods and droughts.
Current hydrological models face challenges related to data inputs, as climate change has been presenting weather patterns atypical from historical data. Without quality data inputs, the accuracy of the output from hydrological models suffers.
To overcome these challenges, Salient Predictions proposes to use improved S2S forecasts as the weather input to Variable Infiltration Capacity (VIC) hydrological forecasts, thus providing improved water availability forecasts spanning from 2- to 52-weeks into the future.
Salient's base S2S forecast technology uses machine learning and various oceanic, atmospheric, and land-based variables to make improved predictions of weather up to a year in advance. This core technology evolved from decades of research at Salient Predictions Inc.
Current hydrological models face challenges related to data inputs, as climate change has been presenting weather patterns atypical from historical data. Without quality data inputs, the accuracy of the output from hydrological models suffers.
To overcome these challenges, Salient Predictions proposes to use improved S2S forecasts as the weather input to Variable Infiltration Capacity (VIC) hydrological forecasts, thus providing improved water availability forecasts spanning from 2- to 52-weeks into the future.
Salient's base S2S forecast technology uses machine learning and various oceanic, atmospheric, and land-based variables to make improved predictions of weather up to a year in advance. This core technology evolved from decades of research at Salient Predictions Inc.
Awardee
Funding Goals
18 CLIMATE ADAPTATION AND MITIGATION 19 WEATHER-READY NATION 20 HEALTHY OCEANS 21 RESILIENT COASTAL COMMUNITIES AND ECONOMIES
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Falmouth,
Massachusetts
025402676
United States
Geographic Scope
Single Zip Code
Related Opportunity
Analysis Notes
Infrastructure $161,955 (93%) percent this Project Grant was funded by the 2021 Infrastructure Act.
Salient Predictions was awarded
Project Grant NA23OAR0210574
worth $174,946
from National Oceanic and Atmospheric Administration in September 2023 with work to be completed primarily in Falmouth Massachusetts United States.
The grant
has a duration of 5 months and
was awarded through assistance program 11.021 NOAA Small Business Innovation Research (SBIR) Program.
The Project Grant was awarded through grant opportunity NOAA SBIR FY 2023 Phase I.
SBIR Details
Research Type
SBIR Phase I
Title
Machine Learning and ocean variables for improved predictions of water availability in the US
Abstract
Salient Predictions Inc. proposes to develop new technology for accurate subseasonal to seasonal (S2S) water availability forecasts so that communities may manage water resources in preparation for hazardous events like floods and droughts. Current hydrological models face challenges related to data inputs, as climate change has been presenting weather patterns atypical from historical data. Without quality data inputs, the accuracy of the output from hydrological models suffers. To overcome these challenges, Salient Predictions proposes to use improved S2S forecasts as the weather input to Variable Infiltration Capacity (VIC) hydrological forecasts, thus providing improved water availability forecasts spanning from 2- to 52-weeks into the future. Salient’s base S2S forecast technology uses machine learning and various oceanic, atmospheric, and land-based variables to make improved predictions of weather up to a year in advance. This core technology evolved from decades of research at Woods Hole Oceanographic Institution and the Massachusetts Institute of Technology. Phase I will address challenges related to 1) the spatio-temporal gap between S2S forecast variables and those required to capture more heterogeneous hydrological processes, and 2) the need to include climate change projections into S2S forecasting, as many recent hydrological events are being at least partially attributed to climate change.
Topic Code
9.4
Solicitation Number
None
Status
(Complete)
Last Modified 2/22/24
Period of Performance
9/1/23
Start Date
2/29/24
End Date
Funding Split
$174.9K
Federal Obligation
$0.0
Non-Federal Obligation
$174.9K
Total Obligated
Activity Timeline
Transaction History
Modifications to NA23OAR0210574
Additional Detail
Award ID FAIN
NA23OAR0210574
SAI Number
NA23OAR0210574-002
Award ID URI
None
Awardee Classifications
For-Profit Organization (Other Than Small Business)
Awarding Office
1305N2 DEPT OF COMMERCE NOAA
Funding Office
1333BR OFC OF PROG.PLANNING&INTEGRATION
Awardee UEI
LSE3NRMW8L25
Awardee CAGE
8G4X6
Performance District
MA-09
Senators
Edward Markey
Elizabeth Warren
Elizabeth Warren
Budget Funding
Federal Account | Budget Subfunction | Object Class | Total | Percentage |
---|---|---|---|---|
Operations, Research and Facilities, National Oceanic and Atmospheric Administration, Commerce (013-1450) | Other natural resources | Grants, subsidies, and contributions (41.0) | $174,946 | 100% |
Modified: 2/22/24