NOAA Science & Technology Focus Areas: 

Uncrewed Systems • Artificial Intelligence • ‘Omics • Cloud • Citizen Science • Data 

NOAA Artificial Intelligence Strategy

Analytics for Next-Generation Earth Science

National Oceanic and

Atmospheric Administration

U.S. Department of Commerce

February 2020
NOAA’s Artificial Intelligence Strategy

Analytics for Next-Generation Earth Science

APPROVED:

_______________________________

Neil A. Jacobs, Ph.D.

Assistant Secretary of Commerce for Environmental 

 Observation and Prediction

Performing the Duties of Under Secretary of Commerce for 

 Oceans and Atmosphere

_______________________________

Tim Gallaudet, Ph.D., Rear Admiral, U.S. Navy (Ret.)

Assistant Secretary of Commerce for Oceans and Atmosphere /

 Deputy NOAA Administrator

STRATEGY SPONSOR

_______________________________ ________________________________

Stephen Volz, Ph.D. Louis W. Uccellini, Ph.D

Assistant Administrator Assistant Administrator

National Environmental Satellite, Data, National Weather Service

 and Information Service

_______________________________ ________________________________

Nicole LeBoeuf Chris Oliver

Acting Assistant Administrator Assistant Administrator

National Ocean Service for Fisheries

_______________________________ ________________________________

Craig McLean Rear Admiral Michael J. Silah

Assistant Administrator Director

Oceanic and Atmospheric Research Office of Marine and Aviation

 Operations and NOAA Corps

 

 February 2020 

2 NOAA Artificial Intelligence Strategy
NOAA Artificial Intelligence Strategy 3

The National Oceanic and Atmospheric Administration (NOAA) Artificial Intelligence (AI) Strategy 

will dramatically expand the application of artificial intelligence (AI) in every NOAA mission 

area by improving the efficiency, effectiveness, and coordination of AI development and 

usage across the agency. As data exploitation capabilities continue to increase exponentially with 

improved satellite systems and architecture, unmanned systems and commercial data sources, AI 

methods will provide transformative advancements in the quality and timeliness of NOAA science, 

products, and services. The term “AI” is used here to encompass the capabilities identified in the “AI 

Capabilities” graphic on page 4. The reason for developing this strategy is because AI capabilities are 

already demonstrating significant improvements in performance and skill at vastly reduced costs and 

compute time. 

This strategy aligns with the President’s Executive Order on Maintaining American Leadership in 

Artificial Intelligence1 and the 2019 Update to the National Artificial Intelligence Research and 

Development Strategic Plan.2 It also supports provisions in the Weather Research and Forecasting 

Innovation Act of 20173 and its 2019 reauthorization,4 as well as the Commercial Engagement 

Through Ocean Technology (CENOTE) Act of 2018.5 This strategy also supports the November 19, 

2019, presidential memorandum “Memorandum on Ocean Mapping of the United States Exclusive 

Economic Zone and the Shoreline and Nearshore of Alaska.” Lastly, the NOAA AI Strategy supports the 

DOC Strategic Plan for 2018-2022,6 and will accelerate NOAA’s top two priorities to (1) regain world 

leadership in global weather modeling through establishment of the Earth Prediction Innovation 

Center (EPIC), and (2) sustainably grow the economic contributions of our oceans, coasts, and Great 

Lakes (American Blue Economy).7

Background

AI is not new to NOAA. NOAA’s robust experience with AI applications 

across a range of mission areas are already demonstrating significant 

improvements in performance and skill at greatly reduced costs and 

compute time in arenas as diverse as deep-sea exploration, habitat 

characterization, and processing of earth observations. By strengthening 

coordination, operational capabilities, workforce proficiency, and 

multisector partnerships, NOAA’s national and global leadership in AI 

supports science, public safety, and security. 

Specific AI examples include: (1) aerial and underwater surveys from ships 

and autonomous platforms to assess the abundance of marine mammal 

and fish populations, (2) robotics for deep-sea exploration, (3) quality 

control of weather observations, (4) improving physical parameterization 

for weather, ocean, ice modeling, and improving the computational 

performance of numerical models, (5) aiding weather warning generation, 

(6) operation of unmanned systems for bathymetric mapping, habitat 

characterization, hydrologic, oceanographic, atmospheric, fishery, 

ecosystem, and geographic surveys, (7) supporting partners in wildfire 

detection and movement, and (8) using machine learning (ML) for 

reliable and efficient processing, interpretation, and utilization of earth 

observations. Despite this notable progress, the true potential for AI to 

advance NOAA’s mission has not been realized because all NOAA AI activity 

heretofore has originated within individual offices with no institutional 

support. Additionally, some development has been redundant because 

of a lack of awareness across the agency due to the absence of a 

coordinating directive or authority.

Vision

Through the NOAA AI Strategy, expansion of Artificial Intelligence 

is accelerated across the entire agency to make transformative 

improvements in NOAA mission performance and cost effectiveness. 

Purpose

The goals and supporting objectives in this strategy are intended to 

directly improve the understanding, coordination, awareness, and 

application of AI across all of NOAA. By directing organizational and 

process improvements to more rapidly transition research, strengthen 

and expand partnerships, and deliberately develop AI proficiency 

across the NOAA workforce, the guidance below will result in 

transformational advances in every NOAA mission area. A fundamental 

consideration is that the objectives within any goals support and are 

supported by objectives under the other goals in this strategy, thus 

requiring matrixed collaboration during implementation. 

What is Artificial Intelligence?

Artificial Intelligence (AI) refers to computational systems able to perform tasks that normally require human 

intelligence, but with increased efficiency, precision, and objectivity. A subset of AI called machine learning (ML) 

refers to mathematical models able to perform a specific task without using explicit instructions, instead relying on 

patterns and inference. Deep learning (DL) is a subset of ML that utilizes artificial neural networks capable of learning from unstructured or newly added data. The use of labeled training data can further improve the AI predictive 

capability through supervised ML.
4 NOAA Artificial Intelligence Strategy

GOAL 1: Establish Efficient Organizational Structures 

and Processes to Advance AI across NOAA.

When achieved, this goal will provide for the cross-line office 

coordination that has been so far limited in AI development, 

awareness, and application. This, along with prioritization of AI in 

budget formulation, strategic communications, and cloud computing 

applications will exponentially increase the use and utility across all 

of NOAA. 

Objective 1.1. Explore the establishment of a NOAA 

AI Center or similar entity to enable coordination of 

AI research, algorithm development, data acquisition, 

applications, information exchange, and awareness. 

Other functions would be to maintain a portal with 

open source and government applications, host training 

events and workshops, and facilitate new partnerships.

Objective 1.2. Develop technical working groups 

comprised of NOAA line office experts to support the 

NOAA AI Executive Committee’s efforts as needed 

to develop and execute the NOAA AI Strategic and 

Implementation Plans, such as prioritization of AI 

research and transitional requirements, technical 

workshops, specific subject-matter tasks as assigned, 

and metrics to achieve the goals and objectives of 

the plan.

Objective 1.3. Prioritize AI-based approaches in NOAA 

budget formulation guidance, emphasizing the purpose 

to improve performance skill, computational efficiency, 

and cost effectiveness.

Objective 1.4. Include discussion of NOAA AI activity in 

NOAA executive-level engagement and communications 

with key stakeholders, particularly focusing on OMB, 

Congressional members and staff, and counterparts 

from other federal agencies.

Objective 1.5. Leverage and adopt the principles, 

processes, and partnerships articulated in the NOAA 

Cloud Strategy and Roadmap, and Big Data Project 

to improve data accessibility, labeled training data, 

workflow processes using open source tools, and 

cloud computing for AI applications in support of 

the NOAA mission.

NOAA Artificial Intelligence Strategy

Goal 1: Establish an efficient organizational 

structure and processes to advance AI 

across NOAA.

Goal 2: Advance AI research and innovation 

in support of NOAA’s mission.

Goal 3: Accelerate the transition of AI 

research to applications.

Goal 4: Strengthen and expand AI 

partnerships.

Goal 5: Promote AI proficiency in the 

workforce. 

(a) CPC analysis (ground truth); (b) Ensemble mean of eight models: NCEP (global and 

regional) and six international models. Reduced maximum and diffused sharpness of 

fronts due to slightly shifted maps from ensemble members, produced many false alarms; 

(c) AI-based Ensemble composite. Closer to CPC with maintained sharpness and minimal 

alarm rates; (d) the forecast produced by human analyst at HPC.8

AI Capabilities

Example of AI (NN)-based Ensemble: 

Nonlinear Multimodel Ensemble Mean 

Precipitations over ConUS
NOAA Artificial Intelligence Strategy 5

GOAL 2. Advance AI Research and Innovation in 

Support of NOAA’s Mission.

NOAA will advance AI-based environmental research and innovation 

across every mission area by prioritizing and institutionalizing AI 

throughout NOAA’s research processes. The NOAA Research and 

Development Database (NRDD) will be a critical asset to help track 

NOAA research involving AI, with the intent to see the number of AI 

research projects continuously increase.

Objective 2.1. Establish a requirement-based process 

to prioritize AI research to leverage the best available 

assets and expertise in support of the NOAA mission, 

and to continually evaluate the prioritization process to 

meet the rapidly evolving AI technology. 

Objective 2.2. Prioritize AI-based approaches 

and support in NOAA research federal funding 

opportunities (FFO), requests for proposals (RFPs), and 

research grants to promote collaborative AI research 

and maintain an awareness of the rapidly evolving AI 

technology in areas relevant for NOAA mission.

Objective 2.3. Establish an annual research and 

development prize competition series for AI 

applications in environmental science, to include 

separate categories for data processing efficiencies, 

automated detection and classification toolkits, 

improvements in data assimilation and predictive 

modeling, and other organizational efficiencies 

using AI.

Objective 2.4. Evaluate and execute various testbed 

and proving grounds approaches across NOAA to 

expand AI research, develop best practices and 

training data, improve algorithms, and evaluate model 

performance in support of the NOAA mission. NOAA 

testbeds and proving grounds play an important role 

in pre-operational evaluation of new developments 

performed by NOAA and university scientists.

Objective 2.5. Encourage every prospectus for NOAA 

Cooperative Institutes (CIs) and Cooperative Science 

Centers (CSC’s) to develop metrics that track yearly 

increases in the AI research they perform.

GOAL 3. Accelerate the Transition of AI Research to 

Applications.

NOAA will rapidly accelerate the transition of AI-based 

environmental research to NOAA operations (R2O) and private-sector 

commercialization (R2C - together R2X). Because many requirements 

for NOAA’s AI applications are available in private- sector and 

academic research environments, the majority of our AI research 

involves tailoring these applications to our mission. Coordination 

of AI initiatives and activities mentioned in Goal 1, and examples 

from the Earth Prediction Innovation Center (EPIC), will play leading 

roles in transitioning R2O. For successful transitions, we will support 

the growth of a nascent commercial environmental AI sector that 

will increasingly serve as a source for solutions to our operational 

requirements. OAR’s TPO will lead the transition of NOAA tailored R2C.

Objective 3.1. Establish budget and prioritization 

processes to transition to operations AI-based 

environmental research which shows improved skill, 

performance, and computational and cost efficiency.

Objective 3.2. Transition to commercialization that AIbased environmental research in NOAA which shows 

potential for enhancement and marketability in the 

private sector.

Simulated JPSS-1 ATMS channel 21 (183 GHz mid-upper tropospheric water vapor channel) from AI-based radiative transfer 

model (AI-CRTM) trained using CRTM (top), and operational CRTM 

(bottom). CPU time required to simulate 1 day of JPSS-1 ATMS (all 

channels) is reduced from 1.3 hrs using CRTM, to 1 second using 

AI-CRTM.9

CRTM (ATMS Channel 21) 

AI-CRTM (ATMS Channel 21)
6 NOAA Artificial Intelligence Strategy

Objective 3.3. Develop NOAA technical guidelines 

that are updated annually on the best practices and 

standards for the training data, training practices, 

and evaluation of model performance to ensure the 

integrity, reliability, and credibility of scientific products 

generated with AI applications. 

Objective 3.4. Build AI awareness across NOAA 

line offices through NOAA science seminars and 

webinars, internal workshops, and routine internal 

communications venues such as newsletters and 

weekly updates.

Objective 3.5. Complete an annual report of NOAA AI 

research transitions, disseminated broadly across the 

agency and with external partners to be used as a basis 

for investigator performance reviews and incentive 

awards.

GOAL 4. Strengthen and Expand AI Partnerships.

Cooperative partnerships serve as force multipliers to optimize 

resources and effort, and the scientific and technological exchange 

keeps NOAA current in the rapidly evolving field of AI. Partnerships in 

AI-based environmental applications are already creating a community 

of practice that is sparking innovation and has the opportunity to 

accelerate tremendous advances in NOAA’s capabilities.

Objective 4.1. Prioritize AI-based environmental 

research in National Oceanographic Partnership 

Program (NOPP) project proposals and selection.

Objective 4.2. Expand partnerships in AI-based 

environmental research with the academic and 

research community, including CIs and institutions 

that host NOAA EPP/MSI, Nancy Foster, Hollings, John 

Knauss, Pathways, and Margaret Davidson scholars, 

fellows, and interns.

NOAA Northeast Fisheries Science Center uses machine learning to 

deliver higher quality and more timely science for fishery management. In this 

example, automated detection and measurement of commercially important 

species, such as scallops, has reduced processing costs by 98% (from 3 months to 

3 days for more than 2 million images collected during a 15-day survey, with 99% 

scallop-detection accuracy).

NOS and NWS are partnering with researchers at UC Santa Cruz to use AI 

to detect rip currents from coastal imagery. The rip current observations 

are supporting implementation and improvement of the NOAA rip current 

forecast model. (Image credit: NOAA; Model credit: Pang, de Silva and Mori, 

UC Santa Cruz).

Objective 4.3. Support the National Artificial 

Intelligence Research Institutes Program with NSF by 

collaborating with appropriate institutes on AI R&D.

 

Objective 4.4. Increase the number of formal 

cooperative agreements on AI-based environmental 

research and applications with interagency and 

international partners, prioritizing DOD, DOI, DOE, and 

DHS.

Objective 4.5. Formalize new public-private 

partnerships through established mechanisms such as 

Cooperative Research and Development Agreements 

(CRADAs) and Small Businesses Innovative Research 

(SBIR) grants.

Objective 4.6. Provide innovative and substantive 

contributions to the policy and advisory committees 

such as the National Science and Technology Council 

(NSTC) Select Committee on AI, and engage its 

experts in scientific exchange during national 

and international conferences, workshops, and 

other opportunities.

RIP Current Detection
NOAA Artificial Intelligence Strategy 7

Goal 5. Promote AI Proficiency in the Workforce.

NOAA will provide resources to equip our workforce to fully leverage 

the rapid evolving field of AI. This can only be achieved by providing 

continuous, current, creative, and tailored training and learning 

opportunities. NOAA’s existing development programs are well suited 

to be adapted for these, and we will look to partners for new options 

to develop skill, understanding, and expertise.

Objective 5.1. Provide increased online and 

on-scene AI training through line office 

training centers.

Objective 5.2. Focus the assignments in the NOAA 

Rotational Assignment Program (NRAP) to target offices 

where a cross-pollination of AI expertise would raise 

the overall AI proficiency of the workforce.

Objective 5.3. Identify and resource AI-related 

graduate degree, professional development, and 

technical training courses that are available to the 

NOAA workforce.

Objective 5.4. Support and lead collaborative 

events such as conferences, workshops, and external 

rotational assignments targeted to stay current in 

the state of AI technology.

Objective 5.5. Actively encourage graduate

programs, internships and cooperative student training 

programs in AI applications relevant to the NOAA 

mission to improve recruitment, retention, and the 

hiring pool for AI proficient NOAA workforce. 

Objective 5.6. Update individual development plans 

(IDP), position descriptions, performance plans, and 

career paths as a practical approach to build and 

retain NOAA’s workforce proficiency in AI.

Conclusions

To ensure the NOAA AI Strategy realizes transformational advances in 

performance, skill, and efficiency, NOAA is developing an AI Strategic 

Implementation Plan or “Roadmap” that defines detailed action items, 

deadlines, and responsibilities. In the meantime, the NOAA AI Strategy 

is already improving performance significantly in our lifesaving and 

economically impactful missions and setting the course to strengthen 

our renowned environmental science and technology leadership for the 

coming decades. Together with our advances in NOAA’s other science 

and technology focus areas—Unmanned Systems, Cloud Computing, 

and ‘Omics—NOAA’s Artificial Intelligence activities will help the U.S. to 

regain global leadership in numerical weather prediction and sustainably 

expand the American Blue Economy.10
8 NOAA Artificial Intelligence Strategy

References

1. Executive Order on Maintaining American Leadership in National Intelligence, The White House, 11 Feb 2019.

 https://www.whitehouse.gov/presidential-actions/executive-order-maintaining-american-leadership-artificial-intelligence/

2. The 2019 Update to the National Artificial Intelligence Research and Development Strategic Plan, NSTC, 21 June 2019.

 https://www.nitrd.gov/pubs/National-AI-RD-Strategy-2019.pdf

3. The Weather Research and Forecasting Innovation Act of 2017, Public Law 115-25, The White House, 18 April 2017.

 https://www.congress.gov/bill/115th-congress/house-bill/353/text

4. The National Integrated Drought Information System Preauthorization, Public Law 115-423, The White House, 7 Jan 2019.

 https://www.congress.gov/bill/115th-congress/senate-bill/2200

5. The Commercial Engagement Through Ocean Technology (CENOTE) Act of 2018, Public Law 115-394, The White House, 21 Dec 2018.

 https://www.congress.gov/bill/115th-congress/senate-bill/2511/text

6. Department of Commerce Strategic Plan for 2018-2022. 

 https://www.commerce.gov/sites/default/files/us_department_of_commerce_2018-2022_strategic_plan.pdf

7. NOAA FY20 Blue Book. 

 https://www.noaa.gov/sites/default/files/atoms/files/FY2020-BlueBook.pdf

8. V.M. Krasnopolsky, and Y. Lin, 2012. “A Neural Network Nonlinear Multimodel Ensemble to Improve Precipitation Forecasts over Continental US,” Advances 

 in Meteorology, Volume 2012, Article ID 649450, 11 pages doi:10.1155/2012/649450

9. “ Exploring Using Artificial Intelligence (AI) for Remote Sensing, NWP and Situational Awareness (SA)”. S-A Boukabara, E. Maddy, A. Neiss, K. Garrett, E. 

 Jones, K. Ide, N. Shahroudi and K. Kumar. International TOVS Study Conference (ITSC),ITSC-XXI Conference, Darmstadt, Germany, Nov 29th – Dec 5th 2017.

10. The NOAA Artificial Intelligence, Unmanned Systems, ‘Omics, and Draft Cloud Strategies are available at https://nrc.noaa.gov/NOAA-Science-

 Technology-Focus-Areas.

Acknowledgements

The NOAA AI Strategy is led by Timothy Gallaudet, Ph.D., Rear Admiral, U.S. Navy (Ret.), Assistant Secretary of Commerce for Oceans and Atmosphere / 

Deputy NOAA Administrator. Dr. Jamese Sims, on special assignment from the NOAA Office of the Federal Coordinator for Meteorological Services and 

Supporting Research (OFCM), is coordinating the overall process in collaboration with the NOAA AI Executive Committee Chairs, William Michaels from 

the NOAA National Marine Fishers Services, Dr. Sid Boukabara from the NOAA National Environmental Satellite and Data Services (NESDIS), and the 

members of the NOAA AI Executive Committee listed below. NOAA is also grateful for the many generous and thoughtful comments received during the 

Public Comment period. This strategy includes revisions based, in part, on comments received.

National Ocean Service 

Greg Dusek, COOP 

Neil Weston, OCS

NOAA Fisheries

Cisco Werner, OAA

National Weather Service

Hendrik Tolman, OSTI 

Vladimir Krasnopolsky, EMC

Office of Oceanic and Atmospheric Research

Frank Indiviglio, OCIO 

Mashkoor Malik, OER

V. ‘Ram’ Ramaswamy, GFDL

Jebb Stewart, ER/WFO/Charleston, SC

Office of Marine and Aviation Operations

Charly Alexander

John McDonough, PPMD

NOAA Office of the CIO

Ed Kearns