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Findings and future recommendations from a review of AI/ML activity across the United States National Weather Service

VLab Forum Members,

The Office of Science and Technology Integration will conduct a seminar on Monday, August 1st, at 12:00 PM (Eastern Time) titled "Findings and future recommendations from a review of AI/ML activity across the United States National Weather Service".  Dr. Paul Roebber will give the talk.  If you would like to attend, please sign up using the link below.

To participate in the seminar, please register for the webinar.

Abstract:

A report was commissioned to summarize current AI/ML activity within the U.S. National Weather Service with a view towards identifying existing obstacles and recommending future directions. Although AI/ML activity is growing quickly, this activity is fragmented and lacks the needed infrastructure for improved coordination and efficiency of effort. Current obstacles to future progress include: lack of workforce training in artificial intelligence and machine learning, lack of curated datasets and software that can be used for development and evaluation of artificial intelligence/machine learning approaches, absence of a centralized clearing house available to weather service personnel for technical expertise/consultation, limited operational compute resources, and lack of a clear end-to-end project pathway that encompasses exploration, development, testbed/proving ground and operational implementation.

Each of these limitations is addressable. Partnering with the NOAA Center for Artificial Intelligence to develop National Weather Service specific training materials, using “learning journey” style materials, is of interest to that group and would help address the current knowledge gap within the weather service. The development of reference software and datasets and the establishment of a consulting team to work on specific projects with operational units will reduce siloed efforts and enhance productivity. By establishing funding vehicles for theme-based projects, and for which there is a sustainable pathway from initial exploration all the way through operational implementation, will help bridge the “valley of death” between research and operations. Agent-based modeling capability with the weather service is currently limited. Given NWS emphasis on Impact-based Decision Support Services (IDSS), agent-based modeling capability should be developed, since this approach can directly link natural and human systems, and can reveal non-intuitive, emergent properties of complex systems like decision support. Collaboration with academic experts in this area, through the above-mentioned sustainable funding pathways, can help to build this expertise.

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VLab Forum Members,

Just a reminder that the Office of Science and Technology Integration will conduct a seminar next Monday, August 1st, at 12:00 PM (Eastern Time) titled "Findings and future recommendations from a review of AI/ML activity across the United States National Weather Service".  Dr. Paul Roebber will give the talk.  If you would like to attend, please sign up using the link below.

To participate in the seminar, please register for the webinar.

Abstract:

A report was commissioned to summarize current AI/ML activity within the U.S. National Weather Service with a view towards identifying existing obstacles and recommending future directions. Although AI/ML activity is growing quickly, this activity is fragmented and lacks the needed infrastructure for improved coordination and efficiency of effort. Current obstacles to future progress include: lack of workforce training in artificial intelligence and machine learning, lack of curated datasets and software that can be used for development and evaluation of artificial intelligence/machine learning approaches, absence of a centralized clearing house available to weather service personnel for technical expertise/consultation, limited operational compute resources, and lack of a clear end-to-end project pathway that encompasses exploration, development, testbed/proving ground and operational implementation.

Each of these limitations is addressable. Partnering with the NOAA Center for Artificial Intelligence to develop National Weather Service specific training materials, using “learning journey” style materials, is of interest to that group and would help address the current knowledge gap within the weather service. The development of reference software and datasets and the establishment of a consulting team to work on specific projects with operational units will reduce siloed efforts and enhance productivity. By establishing funding vehicles for theme-based projects, and for which there is a sustainable pathway from initial exploration all the way through operational implementation, will help bridge the “valley of death” between research and operations. Agent-based modeling capability with the weather service is currently limited. Given NWS emphasis on Impact-based Decision Support Services (IDSS), agent-based modeling capability should be developed, since this approach can directly link natural and human systems, and can reveal non-intuitive, emergent properties of complex systems like decision support. Collaboration with academic experts in this area, through the above-mentioned sustainable funding pathways, can help to build this expertise.

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VLab Forum Members,

Dr. Roebber has shared with us the slides for his talk titled "Findings and future recommendations from a review of AI/ML activity across the United States National Weather Service".  If you are interested in reviewing them as read-ahead material, you can access the PowerPoint presentation using the following link.

Presentation Slides

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VLab Forum Members,

Recall that today at noon (EDT), the Office of Science and Technology Integration will conduct a seminar titled "Findings and future recommendations from a review of AI/ML activity across the United States National Weather Service".  Dr. Paul Roebber will give the talk.  If you would like to attend, please sign up using the link below.

To participate in the seminar, please register for the webinar.

Abstract:

A report was commissioned to summarize current AI/ML activity within the U.S. National Weather Service with a view towards identifying existing obstacles and recommending future directions. Although AI/ML activity is growing quickly, this activity is fragmented and lacks the needed infrastructure for improved coordination and efficiency of effort. Current obstacles to future progress include: lack of workforce training in artificial intelligence and machine learning, lack of curated datasets and software that can be used for development and evaluation of artificial intelligence/machine learning approaches, absence of a centralized clearing house available to weather service personnel for technical expertise/consultation, limited operational compute resources, and lack of a clear end-to-end project pathway that encompasses exploration, development, testbed/proving ground and operational implementation.

Each of these limitations is addressable. Partnering with the NOAA Center for Artificial Intelligence to develop National Weather Service specific training materials, using “learning journey” style materials, is of interest to that group and would help address the current knowledge gap within the weather service. The development of reference software and datasets and the establishment of a consulting team to work on specific projects with operational units will reduce siloed efforts and enhance productivity. By establishing funding vehicles for theme-based projects, and for which there is a sustainable pathway from initial exploration all the way through operational implementation, will help bridge the “valley of death” between research and operations. Agent-based modeling capability with the weather service is currently limited. Given NWS emphasis on Impact-based Decision Support Services (IDSS), agent-based modeling capability should be developed, since this approach can directly link natural and human systems, and can reveal non-intuitive, emergent properties of complex systems like decision support. Collaboration with academic experts in this area, through the above-mentioned sustainable funding pathways, can help to build this expertise.

Add to Your Calendar:

To add this VLab Forum meeting to your calendar, please click on the following button.

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VLab Forum Members,

For those of you who were unable to attend the August 1, 2022 presentation by Dr. Paul Roebber entitled "Findings and future recommendations from a review of AI/ML activity across the United States National Weather Service", I have posted a recording of the talk to YouTube. You can access the video at the following link.

YouTube Recording

Also, there were a number of attendee submitted questions that Dr. Roebber has graciously responded to in the following Q&A document.

Microsoft Word version

PDF version