NA24OARX021G0054
Project Grant
Overview
Grant Description
Purpose: We propose a high-impact innovation for weather forecasting that integrates global ensemble weather forecasts with an AI-driven post-processing model of extreme weather indices (XTREMECAST).
This innovation will provide the basis for skillful probabilistic forecasts of compound extreme weather events at extended lead times, and the ability to identify weather phenomena not resolved by the global models.
Our proposed calibration methods and extreme event modules are easily adapted to the new AI-based global models, whereby AI global model outputs drive AI-based extreme event models.
The project addresses specific needs for increased resilience of electric utilities in the face of compound extreme weather/climate events, including risks from the increasing penetration of renewable energy.
In addition to extreme weather from severe convective systems and hurricanes, the proposed solution will incorporate forecasts of heat/cold waves, wind and solar droughts, and impacts on natural gas generation efficiency to identify compound events with the potential to push energy systems beyond capacity.
An advanced user interface dashboard based on visual analytics will support interactive visual analysis for more rapid and insightful analysis to support complex, rapidly evolving decision making and direct integration into client-side systems.
Summary of anticipated results, implications of the approach and potential commercial applications: CFAN's clients in the energy and insurance sectors have communicated the need for probabilistic forecasts of compound severe weather events with greater granularity and longer lead times than is currently produced by NOAA and other market providers.
CFAN's proposed innovation responds to this need and will improve electric utility companies' ability to manage the impacts of extreme weather and create more resilient systems through improved load planning.
CFAN's solution will further support decision making in the broader energy sector, including natural gas trading and wind farm operators and investors.
There are also potential applications in insurance, emergency management, logistics, transportation, and construction sectors.
Advanced online decision support tools trained with data-driven and human insights will empower algorithms and experts to continue to learn from and validate each other.
This feedback will support operational adaptation to extreme weather events in a changing climate.
This innovation will provide the basis for skillful probabilistic forecasts of compound extreme weather events at extended lead times, and the ability to identify weather phenomena not resolved by the global models.
Our proposed calibration methods and extreme event modules are easily adapted to the new AI-based global models, whereby AI global model outputs drive AI-based extreme event models.
The project addresses specific needs for increased resilience of electric utilities in the face of compound extreme weather/climate events, including risks from the increasing penetration of renewable energy.
In addition to extreme weather from severe convective systems and hurricanes, the proposed solution will incorporate forecasts of heat/cold waves, wind and solar droughts, and impacts on natural gas generation efficiency to identify compound events with the potential to push energy systems beyond capacity.
An advanced user interface dashboard based on visual analytics will support interactive visual analysis for more rapid and insightful analysis to support complex, rapidly evolving decision making and direct integration into client-side systems.
Summary of anticipated results, implications of the approach and potential commercial applications: CFAN's clients in the energy and insurance sectors have communicated the need for probabilistic forecasts of compound severe weather events with greater granularity and longer lead times than is currently produced by NOAA and other market providers.
CFAN's proposed innovation responds to this need and will improve electric utility companies' ability to manage the impacts of extreme weather and create more resilient systems through improved load planning.
CFAN's solution will further support decision making in the broader energy sector, including natural gas trading and wind farm operators and investors.
There are also potential applications in insurance, emergency management, logistics, transportation, and construction sectors.
Advanced online decision support tools trained with data-driven and human insights will empower algorithms and experts to continue to learn from and validate each other.
This feedback will support operational adaptation to extreme weather events in a changing climate.
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
Reno,
Nevada
895192957
United States
Geographic Scope
Single Zip Code
Related Opportunity
Climate Forecast Applications Network was awarded
Project Grant NA24OARX021G0054
worth $649,493
from National Oceanic and Atmospheric Administration in September 2024 with work to be completed primarily in Reno Nevada United States.
The grant
has a duration of 2 years and
was awarded through assistance program 11.021 NOAA Small Business Innovation Research (SBIR) Program.
The Project Grant was awarded through grant opportunity FY24 NOAA SBIR Phase II.
SBIR Details
Research Type
SBIR Phase II
Title
Applying AI to forecasting compound extreme weather events
Abstract
We propose a high-impact innovation for weather forecasting that integrates global ensemble weather forecasts with an AI-driven post-processing model of extreme weather indices (XtremeCast). This innovation will provide the basis for skillful probabilistic forecasts of compound extreme weather events at extended lead times, and the ability to identify weather phenomena not resolved by the global models. Our proposed calibration methods and extreme event modules are easily adapted to the new AI-based global models, whereby AI global model outputs drive AI-based extreme event models. The project addresses specific needs for increased resilience of electric utilities in the face of compound extreme weather/climate events, including risks from the increasing penetration of renewable energy. In addition to extreme weather from severe convective systems and hurricanes, the proposed solution will incorporate forecasts of heat/cold waves, wind and solar droughts, and impacts on natural gas generation efficiency to identify compound events with the potential to push energy systems beyond capacity. An advanced user interface dashboard based on Visual Analytics will support interactive visual analysis for more rapid and insightful analysis to support complex, rapidly evolving decision making and direct integration into client-side systems. CFAN's clients in the energy and insurance sectors have communicated the need for probabilistic forecasts of compound severe weather events with greater granularity and longer lead times than is currently produced by NOAA and other market providers. CFAN’s proposed innovation responds to this need and will improve electric utility companies’ ability to manage the impacts of extreme weather and create more resilient systems through improved load planning. CFAN’s solution will further support decision making in the broader energy sector, including natural gas trading and wind farm operators and investors. There are also potential applications in insurance, emergency management, logistics, transportation, and construction sectors. Advanced online decision support tools trained with data-driven and human insights will empower algorithms and experts to continue to learn from and validate each other – this feedback will support operational adaptation to extreme weather events in a changing climate.
Topic Code
9.1
Solicitation Number
NOAA-OAR-TPO-2024-2008239
Status
(Ongoing)
Last Modified 12/4/24
Period of Performance
9/1/24
Start Date
8/31/26
End Date
Funding Split
$649.5K
Federal Obligation
$0.0
Non-Federal Obligation
$649.5K
Total Obligated
Activity Timeline
Transaction History
Modifications to NA24OARX021G0054
Additional Detail
Award ID FAIN
NA24OARX021G0054
SAI Number
NA24OARX021G0054-002
Award ID URI
None
Awardee Classifications
Small Business
Awarding Office
1305N2 DEPT OF COMMERCE NOAA
Funding Office
1333BR OFC OF PROG.PLANNING&INTEGRATION
Awardee UEI
CSGXSZSELX13
Awardee CAGE
5HVM3
Performance District
NV-02
Senators
Catherine Cortez Masto
Jacky Rosen
Jacky Rosen
Modified: 12/4/24