NOAA-OAR-WPO-2022-2006969 NOFO Report.pdf

FY2022 Weather Program Office Research Programs

TABLE OF CONTENTS

I. Funding Opportunity Description 5

A. Program Objective 5 B. Program Priorities 11 C. Program Authority 22

II. Award Information 23 A. Funding Availability 23 B. Project/Award Period 24 C. Type of Funding Instrument 25

III. Eligibility Information 25 A. Eligible Applicants 25 B. Cost Sharing or Matching Requirement 26 C. Other Criteria that Affect Eligibility 26

IV. Application and Submission Information 26 A. Address to Request Application Package 26 B. Content and Form of Application 27 C. Unique Entity Identifier and System for Award Management (SAM) 37 D. Submission Dates and Times 38 E. Intergovernmental Review 39 F. Funding Restrictions 39 G. Other Submission Requirements 40

V. Application Review Information 41 A. Evaluation Criteria 41 B. Review and Selection Process 44 C. Selection Factors 45 D. Anticipated Announcement and Award Dates 46

VI. Award Administration Information 46 A. Award Notices 46 B. Administrative and National Policy Requirements 46 C. Reporting 50

VII. Agency Contacts 51 VIII. Other Information 51

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NOTICE OF FUNDING OPPORTUNITY

EXECUTIVE SUMMARY

Federal Agency Name(s): Oceanic and Atmospheric Research (OAR), National Oceanic and Atmospheric Administration (NOAA), Department of Commerce

Funding Opportunity Title: FY2022 Weather Program Office Research Programs

Announcement Type: Initial

Funding Opportunity Number: NOAA-OAR-WPO-2022-2006969

Catalog of Federal Domestic Assistance (CFDA) Number: 11.459, Weather and Air Quality Research

Dates: For each of the six competitions within this Fiscal Year 2022 funding opportunity, full applications must be received by 5:00 pm Eastern Time (ET) on November 17, 2021.

Applications received after this deadline will not be considered. Pre-proposal Letters of Intent (LOIs) are strongly encouraged for potential applicants to all competitions and must be received by 5:00 pm ET on September 14, 2021, in order to receive a pre-proposal review. Award decisions are expected in May 2022. Financial awards for all competitions will be up to two to three years in length and are recommended to begin either August 1 or September 1, 2022, as described in Section II.B “Project/Award Period”.

Funding Opportunity Description: NOAA’s Weather Program Office (WPO; formerly OWAQ, the Office of Weather and Air Quality) is soliciting proposals for weather, atmospheric composition, and earth system modeling and observations research reflecting multiple science objectives spanning time scales from hours to seasons, and from weather and water observations and earth system modeling to fire weather and social, behavioral, and economic science. There will be six grant competitions from this notification valued at approximately $16,500,000 per year as follows: 1) Fire Weather & Atmospheric Composition (FWAC), 2) Climate Testbed (CTB), 3) Weather Testbeds, 4) Joint Technology Transfer Initiative (JTTI), 5) Subseasonal-to- Seasonal (S2S) , and 6) Verification of the Origins of Rotation in Tornadoes Experiment in the United States (VORTEX-USA).

In alignment with the Weather Forecasting and Innovation Act of 2017 (Public Law 115-25), the funded projects should improve the weather community’s understanding and ultimately its services of weather and water forecasting through engagement with the external scientific community on key science gaps of mutual interest. Through this funding opportunity, NOAA will support new weather, water, climate, earth system, and air quality observing and forecasting applications, including improved analysis techniques, better statistical or dynamic forecast

Notice of Federal Funding Page 2 of 52 models and techniques, and communication of that information to better inform the public.

Forecast model improvements must focus on developing the Unified Forecast System (UFS), with an aim towards addressing forecaster priorities. These priorities were articulated in a recent series of workshops, and the top priorities are listed at https://www.weather.gov/media/sti/Final%20Consolidated%20Forecasters%20requests%20April %202021.pdf.

To achieve success with these objectives, selected projects should focus on advancing science and technology from the research stage to transitionable outputs or prototype products that NOAA or external partners could further develop into practical applications and operations. For the purposes of NOAA-funded projects, the maturity of projects is broadly classified using Readiness Levels (RLs), as adopted by NOAA and other federal agencies. The numerical RL scale from 1 to 9 is designed to track project maturity across the progressive spectrum from research to development to demonstration to deployment. RLs are defined in the context of NOAA’s overall process for transitioning funded research into operations, commercialization, or other applications in NOAA’s Policy on Research and Development Transitions described in NOAA Administrative Order 216-105B and in Section VIII. Additional information can be found in the Procedural Handbook for NOAA’s Policy on Research & Development Transitions at https://www.noaa.gov/organization/administration/nao-216-105b-policy-on-research-and development-transitions, or at the website for the OAR Office of Research, Transition, and Application (ORTA) at https://orta.research.noaa.gov/.

Depending on the program objectives, the individual competitions within this notice of funding opportunity (NOFO) may favor projects at specific stages of maturity as signified by their estimated current RL. Program-specific project maturity considerations for funding are included in Section I.A “Program Objectives.” While all programs in this funding opportunity encourage an acceleration of research toward operationalization and/or other application, no program directly supports an actual research-to-NOAA operations transition (i.e., the RL 8-to-9 transition) itself; the funded projects are, however, expected to work with NOAA operational center representatives to develop strategies if future transitions to NOAA are anticipated.

NOAA, OAR, and WPO encourage applicants and awardees to write their proposals and perform their work in a manner consistent with NOAA’s core values, including those on diversity, inclusion, accessibility, civil rights, and scientific integrity. Applicants and awardees are urged to consider their ability to expand and diversify NOAA capabilities for all Americans in an equitable and just manner. Diversity is defined as a collection of individual attributes that together help organizations achieve objectives. Inclusion is defined as a culture that connects each employee to the organization, and the recognition, appreciation, and use of the talents and skills of employees of all backgrounds. The term “accessibility” means the design, construction, Notice of Federal Funding Page 3 of 52 development, and maintenance of facilities, information and communication technology, programs, and services so that all people, including people with disabilities, can fully and independently use them. Promoting diversity and inclusion improves creativity, productivity, and the vitality of the weather and water research community in which WPO engages.

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FULL ANNOUNCEMENT TEXT

I. Funding Opportunity Description

A. Program Objective

1. Fire Weather & Atmospheric Composition

a. Fire Weather

OAR WPO invites research and development proposals to improve fire weather forecast information and delivery. This program will support new research into the coupled Unified Forecast System (UFS) for both the short-term fire-atmosphere and subseasonal to climate scale modeling systems. OAR will collaborate with NWS, NESDIS, and emergency managers from across the fire weather community to develop new impact-based decision support tools, products, and models, which will improve the ability to provide timely and accurate guidance to safeguard lives and property and manage downstream air quality impacts.

The increase in the frequency of drought and hot-dry-windy conditions over the last several decades, combined with the continued expansion into the wildfire-urban interface region, has led to a marked increase in the number of acres burned by hazardous wildfire. The number of wildfires and the acres burned are projected to increase as the climate warms, with profound changes to certain ecosystems. Wildfires threaten forest and grasslands, housing and communities, crops, aquatic and soil ecosystems, and air quality both near to and far from the fires, and ultimately cost the Nation billions of dollars a year when accounting for the damage to buildings, communities, and the impacts on human health associated with smoke and poor air quality.

Project selections will balance among topics listed below as needed for robust fire modeling forecast system advances. Model development projects must focus on the next generation Unified Forecast System, including both regional (Rapid Refresh Forecast System) and global modeling systems (Global Ensemble Forecast System, v13), and may address fire-weather modeling for short time-scales or seasonal timescales. Although the modeling system may be global, the research focus should be on the United States. Fire weather forecast product development may work with multi-model ensemble systems, including the UFS as one member.

Collaborations with appropriate NOAA Labs and Centers are strongly encouraged.

Engagement with the Earth Prediction Innovation Center (EPIC) and program is also

Notice of Federal Funding Page 5 of 52 encouraged, particularly for priority #1 (FW-1). Projects proposing to engage with EPIC will work with the EPIC contract to develop high-quality, well tested, well documented software using continuous integration, continuous delivery methods. EPIC projects may also perform developments in-cloud HPC and may budget for cloud time in the proposals. See the EPIC website (https://wpo.noaa.gov/Programs/EPIC) for further information on the EPIC program and how to engage.

b. Atmospheric Composition

Air quality has improved significantly in recent decades following passage of the Clean Air Act in 1970. Yet, there are still many areas of the country exposed to unhealthy levels of air pollutants harming human health and damaging sensitive ecosystems. Notable examples during recent years include smoke emissions due to numerous wildfires over large parts of western North America and high surface ozone episodes during heat waves across the eastern United States. To help the nation mitigate these impacts, NOAA works with the Environmental Protection Agency (EPA), state and local air quality agencies, academia, and the private sector to provide atmospheric composition and air quality forecast capabilities for the Nation through the National Air Quality Forecasting Capability (NAQFC).

The current NOAA operational science improvement needs for air quality predictions include: 1) improving emissions modeling (e.g. wildfire smoke and dust, and timely updates of anthropogenic sources), meteorology (e.g. planetary boundary layer (PBL) height, cloudiness, and vertical transport), and initialization of atmospheric composition concentrations (model state) and emissions using coupled data assimilation (DA); 2) incorporating feedbacks of atmospheric composition on atmospheric physics and meteorological DA through coupled modeling and DA; and 3) improving computational efficiency of atmospheric composition transport, gas-phase chemistry, DA and prediction.

Increased collaboration is desired in building the coupled UFS, with shared community atmospheric chemistry components, that NOAA uses for operational air quality applications and for operational weather applications.

Projects focusing on atmospheric composition research and forecasting that are relatively mature and not in the early stages of development or proof-of-concept are appropriate for this funding opportunity. This includes those projects that propose practical outcomes that could be transitioned operationally to NOAA in the next 3 to 5 years. Projects that are most appropriate for this competition generally fall in or near the “demonstration” level of technical maturity, i.e., readiness levels of about 5 through 8 during the duration of the project.

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For additional program information on this competition, please review the supplemental Information Sheet for the Fire Weather and Atmospheric Composition competition in the package associated with this announcement at https://www.grants.gov.

2. VORTEX-USA

VORTEX-Southeast (VORTEX-SE) is a Congressionally authorized research program that has been ongoing since 2015. When examining measures of the threat posed by tornadoes to life and property, the southeastern U.S. has six of the eight most vulnerable states. Some of the vulnerability is due to issues such as a relatively large proportion of the population residing in mobile homes, which are known to be unsafe even in moderate strength tornadoes. Another principal risk in the Southeast is that many tornadoes occur during nighttime hours, associated with over half of the deaths and injuries. Further, tornado casualties are disproportionately in the late fall and winter months, which is a time of year not typically associated with tornadoes according to conventional understanding or public perception; and they often occur under conditions of marginal thermodynamic instability and very strong vertical wind shear (high shear/low Convective Available Potential Energy (CAPE) or HSLC). Hence, there are many issues in the continuum spanning meteorological understanding to public awareness and perception that must be addressed in order to reduce the high societal vulnerability to tornadoes in the Southeast.

The Weather Research and Forecasting Innovation Act of 2017 (Public Law 115-25) authorized the implementation of a tornado warning improvement and extension program, codified in the Consolidated Appropriations Act, 2021, as VORTEX-USA. VORTEX-USA continues the work of the VORTEX-SE program and also expands it by seeking to increase our knowledge of meteorological processes and societal impacts of tornadoes nationwide, while gaining improved understanding of unique regional challenges that heighten tornado vulnerability. VORTEX-USA will integrate research findings into NOAA’s Forecasting a Continuum of Environmental Threats (FACETs) project to ensure that this research effort improves NOAA’s operational forecast and warning capacity and improves societal response to tornado threats and impacts.

For additional program information on VORTEX-USA and this competition, please review the supplemental Information Sheet for the VORTEX-USA competition in the grant package associated with this announcement at https://www.grants.gov.

3. Weather Testbeds

Project proposals submitted to this competition will focus on applied research and

Notice of Federal Funding Page 7 of 52 development, and, in particular, the demonstration and testing of that research in NOAA’s quasi-operational forecasting environment through engagement with one of NOAA’s testbeds (http://www.testbeds.noaa.gov/). The high impact weather focus areas included in this competition include tornadoes, severe wind and hail storms, tropical cyclones, heavy rainfall, winter weather such as heavy snow and ice, and flooding, including coastal, inland, and flash flooding. The three NOAA testbeds relevant to these focus areas and this competition are i) the Hazardous Weather Testbed (HWT), ii) the Hydrometeorology Testbed (HMT), and iii) the Joint Hurricane Testbed (JHT). Projects associated with other NOAA testbeds are not supported in this competition.

The Weather Program Office (WPO) Testbeds Program, a component of the U.S. Weather Research Program (USWRP), supports projects that will transition applied research to NOAA’s operations and services through close collaboration with NOAA testbeds. Close collaboration with one of the testbeds outlined above is critical and therefore required of all projects. The focus is on mature projects that are nearly ready or ready to be tested in a quasi-operational forecasting environment through one of the above testbeds. It is in these testbeds where project outcomes, such as new observing systems, improved data products and analysis techniques, or better statistical or dynamical models and forecast techniques, will be presented to operational forecasters through the testbed and evaluated for potential future implementation in the National Weather Service (NWS) forecast offices at the local, regional, and/or national center levels to improve services to the public. Projects that are most appropriate for the three testbeds generally fall in or near the “demonstration” level of technical maturity, i.e., readiness levels of about 5 through 8 during the duration of the project.

It is expected that NOAA’s funding support of these new forecasting capabilities will speed the transition of this new research and technology into forecasting operations in the next three to five years in order to improve NOAA weather and water services for the public.

Testbed projects that address improvements to NOAA’s existing or near-term future technologies, e.g., numerical models part of NOAA’s Unified Forecast System (UFS) or forecast techniques that NOAA already employs, are most appropriate for this funding opportunity.

For additional testbed program information, please review the supplemental Information Sheet for the Weather Testbeds competition in the package associated with this announcement at https://www.grants.gov.

4. Joint Technology Transfer Initiative

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The U.S. faces a spectrum of high impact environmental hazards that can disrupt people’s lives and the nation’s economy. Through improved forecasting of these events and better communication and preparedness, loss of lives and property damage can be reduced. While NOAA is the sole U.S. government authority for issuing official weather forecasts and warnings, the broader weather enterprise plays an important role in the communication and dissemination of weather information.

The purpose of this competition is to fund further development, testing and evaluation of mature weather research that has potential for improving NOAA’s NWS operational capabilities. The weather research themes which will receive particular emphasis in this competition include:

a. In collaboration with the UFS community (https://ufscommunity.org/) and/or the UFS/R2O Project (https://www.weather.gov/media/sti/UFS-R2O-Project-Proposal- Public.pdf), further develop, test and enhance data assimilation (DA) techniques, improve model component coupling techniques and capabilities, enhance physics suites, improve evaluations and post-processing techniques and tools, and utilize Artificial Intelligence/Machine Learning (AI/ML) for improving the forecasts on short range weather, medium range weather and subseasonal time scales.

b. Further develop innovative methodologies to assess and identify underserved communities and understand their needs, vulnerabilities, and challenges with respect to the communication and dissemination of NWS products and services related to extreme weather and water events.

c. Further develop innovative scientific and technological solutions to improve NWS forecasts, products, services, and Decision Support Services (DSS) for extreme weather and water events in collaboration with NOAA Testbeds and Proving Grounds.

To help achieve one of NOAA’s goals of transitioning mature weather research into operations, the JTTI program emphasizes research associated with Readiness Levels 4 to 8 (see Section VIII). As such, projects that are best suited to submit to JTTI are at an intermediate phase of development, with concepts that have already been proven to work successfully in the local environment, and have potential for further prototype development and testing in a pseudo-operational environment. Applicants are highly encouraged to contact one of the NOAA testbed managers if the project proposal includes a plan to conduct tests in a NOAA testbed, (https://www.testbeds.noaa.gov/). Additionally, in order for NOAA to transition research into operations, applicants must identify a NOAA/NWS operational office (receiving party) that will be responsible for testing and evaluation for feasibility to implement into operations. Ultimately, the implementation decision lies with the NWS.

Applicants are highly encouraged to collaborate with the NOAA/NWS scientists throughout

Notice of Federal Funding Page 9 of 52 the project (see Section V.A.1.(6) and (7)), and to demonstrate collaboration by including a signed NOAA Collaborator Acknowledgment form when submitting a proposal (described in Section IV.B.2.h).

For additional program information on JTTI and this competition, please review the supplemental Information Sheet for the JTTI competition in the grant package associated with this announcement at https://www.grants.gov.

5. Subseasonal to Seasonal

The Office of Oceanic and Atmospheric Research (OAR) seeks to support resiliency of the nation’s economy, infrastructure, and security by fulfilling the subseasonal to seasonal (two weeks out to two years) requirements of the Weather Research and Forecasting Innovation Act of 2017. Through the Weather Program Office, NOAA and OAR will address a spectrum of issues on the subseasonal to seasonal time frame at various stages of research readiness. The subseasonal to seasonal temporal range covers a wide range of phenomena with significant global and regional impacts on economies and human safety. The portfolio will emphasize models and components in NOAA’s community-based Unified Forecast System (UFS), in particular the UFS coupled S2S prototype, and ongoing multi-model ensemble efforts on the subseasonal to seasonal timescale within the North American Multi- Model Ensemble or its related subseasonal ensemble effort. This solicitation coordinates with the Climate Program Office’s Joint Competition to Advance Process Understanding and Representation of Precipitation in Models; some selected projects may coordinate with projects funded under the Climate Variability Program FY22 competition, with potential shared collaborative webinars and meetings.

Projects developing the UFS S2S capability are encouraged to coordinate with developments under the Unified Forecast System Research to Operations (UFS R2O) Project, particularly within the team developing the Global Ensemble Forecast System. Information on this activity may be found at this website: https://vlab.noaa.gov/web/ufs-r2o/mrw-s2s. UFS developments are furthermore encouraged to articulate how they would collaborate with the Earth Prediction Innovation Center (EPIC), leveraging and incorporating EPIC advances to adopt continuous improvement continuous deployment, providing code documentation, incorporating code testing, or utilizing cloud computing.

6. Climate Testbed (CTB)

In partnership with the Climate Prediction Center (CPC) and the Environmental Modeling Center (EMC), WPO is soliciting proposals involving the external community to advance

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NOAA’s operational subseasonal to seasonal prediction capabilities via the Climate Test Bed (CTB). CTB projects should improve NOAA’s operational S2S (two weeks to two years) prediction capability, via:

a. Maturation and implementation of data assimilation (DA) including coupled DA, monitoring products, and data quality control (QC);

b. Development and maturation of GEFS or SFS within the Unified Forecast System (UFS); and

c. Post-processing, diagnostic and verification tools, and innovative statistical techniques (including but not limited to Artificial Intelligence (AI) and Machine Learning (ML)) leading to improvement of S2S operational predictions.

The portfolio will emphasize capabilities that will either improve operational prediction systems or products, or research that intend to transition to operations, with a preference toward activities that enhance precipitation outlooks. Proposed ideas that begin at RL 6–7 (i.e., those in the demonstration stage), with high potential for an eventual transition to operations (the RL 8-to-9 transition) after the two-year funding period will be favored (please see Section VIII for RL definitions). Projects that are projected to end at or below RL 6 may still apply; however, they must consider additional funding sources at the end of the two-year CTB funding period. This solicitation coordinates with the Climate Program Office’s Joint Competition to Advance Process Understanding and Representation of Precipitation in Models; selected projects may coordinate with projects funded under the Climate Variability Program FY22 competition, with potential shared collaborative webinars and meetings.

For additional program information on CTB and this competition, please review the supplemental Information Sheet for the CTB competition in the grant package associated with this announcement at https://www.grants.gov.

B. Program Priorities

NOAA’s highest priorities for each of the six separate competitions funded through this announcement are identified below. Applicants for a given competition below should clearly indicate and address in their proposal one or more of the associated priorities for that specific competition. Proposals not directly associated with one or more of these priorities are discouraged.

1. Fire Weather & Atmospheric Composition

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WPO invites proposals that address one or more of the topics below (including at least one subtopic); the LOI’s and proposals must specify the primary topics to be addressed.

Fire Weather Priorities

FW-1: Fire weather forecast system projects will develop components related to a next generation coupled wildfire-weather Unified Forecast System that leads to improved forecasts of fire behavior and smoke, including:

a. Modeling of conditions (atmospheric and terrestrial) and weather-fuel interactions leading to fire ignition and spread;

b. Evolution of an active fire including interactions between terrestrial and atmospheric conditions;

c. Transport, evolution and interaction of smoke and meteorology;

d. Data assimilation of atmospheric and terrestrial conditions needed for all stages of fire weather forecasts;

e. New physics parameterizations to simulate fire heat, moisture flux, and emission of trace gases and particles;

f. New physics parameterizations to simulate changes in biogeophysical characteristics of the land surface (e.g., roughness and recovery of vegetation canopy) following wildfires that are key for the surface energy budget and land-atmosphere interactions at weather to seasonal scales.

FW-2: Fire Weather forecast system verification and predictability:

a. Develop fire weather system verification datasets, success metrics and tools for any or all stages of fire conditions and evolution;

b. Develop and improve upon fire hazard indices;

c. Perform research to determine environmental conditions most responsible for fire ignition and spread, and therefore information most vital to improving fire forecasting.

FW-3: Social science and decision support services:

a. Identify top priority data, products and tools needed by fire weather communities for effective fire weather impact-based Decision Support Services (IDSS);

b. Identify the most effective information delivery to these communities, taking into account data formats and deterministic vs probabilistic information;

c. Develop or improve upon tools designed to assist with fire weather forecasting and

IDSS;

d. Develop and improve fire weather forecast products, guidance and tools, including probabilistic forecasts in the days to weeks timeframe.

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Atmospheric Composition Priorities

AC-1: Development and evaluation of high-resolution (1–3 km) air quality forecast capabilities consistent with NOAA’s Rapid Refresh Forecast System, including two-way coupled models that capture air quality-weather interactions. In addition, this priority encourages work focusing on representation of local phenomena such as fine-scale processes in coastal regions, over complex terrain, or in urban areas, especially those that take advantage of recent air quality field experiments.

AC-2: Improved spatial and temporal estimates of anthropogenic and natural pollutant emissions, including smoke from wildfires and small fires and other potential sources of model biases, using NOAA satellite remote sensing and other data sources.

AC-3: Explore and quantify the potential value of ensemble model approaches, post processing and artificial intelligence to NOAA’s operational air quality forecasting guidance.

AC-4: Improved model accuracy using data assimilation of remotely-sensed products or in-situ observations, including emissions update through data assimilation.

2. VORTEX-USA

Priorities for VORTEX-USA are established by considering input from a Scientific Steering Committee and through participation of the broader research and operational community in annual workshops and an online community forum. The forum can be joined at this the VORTEX Community website (https://inside.nssl.noaa.gov/vsecommunity/), and the latest version of the Science Plan is available at this URL:

https://inside.nssl.noaa.gov/vsecommunity/wp-content/uploads/sites/34/2020/11/Science_Assessment_Sep2020_formatted_V2.pdf. The VORTEX-USA program priorities, briefly described here, can be found in more detail in the competition Information Sheet in the grant package associated with this announcement at https://www.grants.gov.

VORTEX-USA-1: Studies of the interactions of the co-evolution of vertical draft structure, radar and satellite signatures, and lightning flash character and frequency (e.g., using LDM and/or LMA data) in non-classic (potentially) tornadic storms.

VORTEX-USA-2: Studies that compare the validity of three-dimensional winds obtained through single-Doppler data assimilation versus dual- or multi-Doppler wind synthesis in non-classic (potentially) tornadic storms.

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VORTEX-USA-3: Understanding and reducing societal vulnerability to tornadoes. This topic has three main emphases that are of a strongly interdisciplinary nature: 1) investigating how physical, social, and economic factors interact to contribute to harm, and which intersections in particular contribute to severity of impact in different regional, local and household circumstances; 2) understanding different populations’ capacities and current practices that can be utilized and leveraged to alleviate vulnerabilities and reduce harm from tornadoes in the Southeast and other regions; and 3) understanding the factors and decisions that enhance individual survival of tornadoes under different circumstances.

VORTEX-USA-4: Explore processes relevant to low-level vorticity generation and tornado formation utilizing data from previous VORTEX-SE field campaigns.

VORTEX-USA-5: Explore refined or novel predictors of tornadoes in the Southeast utilizing the sounding archive developed through previous VORTEX-Southeast field campaigns and other appropriate data.

VORTEX-USA-6: Research operational NWS and partners’ forecast and warning decision-making process, and public response. The two main emphases in this topic are 1) investigating forecasters’ interpretations and use of different types of information in decisions about whether, when, and how to issue warnings and other products; and 2) understanding how probabilistic hazard and warning information affects the forecast and warning process and information gaps that contribute to uncertainty and challenges in decision making.

VORTEX-USA-7: Risk communication, risk perception, and information use in protective decision making (with a focus on members of the public and other end-users). This topic has three primary emphases: 1) understanding the interpretations and uses of probabilistic hazard information, warnings and other information by different members of the public in different circumstances; 2) investigating intersections among information, vulnerabilities and capacities, and 3) protective decision making, which needs to take account of sheltering options that exist and the alternatives, especially for vulnerable populations and underserved living in vulnerable structures.

3. Weather Testbeds

WPO, in collaboration with the NWS and other NOAA stakeholders, developed the following priorities for the Testbeds Program for each of the three testbeds funded through this competition. Applicants should clearly indicate in their proposal the specific testbed and

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Hydrometeorology Testbed (HMT) Priorities

HMT-1: Improve probabilistic winter precipitation forecasts. Identify and validate new or improved methods, models, or decision support tools to improve probabilistic winter precipitation forecasts for snowfall amounts, and/or ice accumulation, e.g. convection allowing models (CAMs) out to Day 3 and machine learning forecast tools. Improve the statistical reliability and spatial resolution of these probabilistic forecasts. Other examples of winter forecast challenges are: timing, duration, intensity, rates, position of rain-freezing precipitation-snow line, melt level/snow level, and forecast risk lead time out to day 7.

HMT-2: Improve flash flood monitoring and forecasting. Identify and validate new or improved methods, models, or decision support tools to improve flash flood monitoring and forecasting, e.g. CAMs out to Day 3, machine learning forecast tools, and Weather Parameter in Context Tools. Improve the statistical reliability and spatial resolution of these probabilistic forecasts. Other examples of flash flood forecast challenges are: event timing, rates, duration, multi-day threats, and forecast risk lead time out to day 7.

HMT-3: Enhance forecaster use of probabilistic information. Identify and validate new or improved ways of enhancing forecaster use of probabilistic precipitation or ice accumulation short-range and medium-range forecasts, including 1) best practices for conveying the uncertainty inherent in these forecasts, including the possibility of different types of messaging for different time scales of meteorological phenomena and 2) assessing and communicating the likely impacts associated with these forecasts.

HMT-4: Improve atmospheric forcings for hydrologic models. Identify and validate new or improved methods, data assimilation and models, to evaluate and improve atmospheric forcings for hydrologic models. This could include evaluation of existing NOAA hydrologic models with the improved atmospheric forcings.

HMT-5: Improve characterization of the state of the current environment. Identify and validate new high temporal and spatial resolution in-situ and remotely-sensed observation datasets and dynamically consistent 3-D objective data analysis techniques to provide the best state of the current environment. This would lead to improving the detection, tracking, verification, and analysis of intense rainfall and winter weather phenomena (including precipitation types such as freezing rain, sleet, snow, etc.) and to support warning, nowcasts, and short-term forecasts for flash flood and winter weather hazards. The intention would be to transition these datasets and techniques to be a part of NOAA’s existing operational

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HMT-6: Improve risk communication of probabilistic forecasts. Apply and integrate social and behavioral science methodologies to improve forecasters’ use of forecast data, techniques, and guidance, as well as end-users’ ability to receive, assess, understand, and respond to forecasts and warnings for flash flood and winter weather hazards, including understanding and messaging of probabilistic hazard information.

Hazardous Weather Testbed (HWT) Priorities

HWT-1: Identify and validate concepts and techniques to improve NOAA’s convection-allowing/resolving ensemble forecast system (e.g. Rapid Refresh Forecast System) performance from the rapidly updating, very short-term (next-few hours) through the next few days for tornado, large hail, damaging wind, lightning, and fire weather applications, including sensitivity to factors such as number of ensemble members, data assimilation methodology, experimental observation systems, scale-appropriate initial conditions and lateral boundary condition perturbations, and physics diversity.

HWT-2: Identify and validate innovative post-processing and verification techniques for NOAA’s regional and/or global deterministic models and ensembles across spatial and temporal scales to create skillful and reliable probabilistic thunderstorm and severe hazard threat guidance from the very short term (i.e., next hour) through the Day 8 forecast period in support of the FACETs (Forecasting a Continuum of Environmental Threats) framework.

In addition to developing reliable techniques, projects may include assessment of the limits to predictability for these hazards.

HWT-3: Identify and validate via testbed demonstrations new high temporal and spatial resolution in-situ and remotely-sensed observation datasets and dynamically consistent 3-D objective data analysis techniques to provide the best state of the current environment. This would lead to improving the detection, tracking, and analysis of severe convective phenomena (tornado, hail, strong winds, etc.) and to support warning, nowcasts, and short-term forecasts for severe convective weather hazards.

HWT-4: Apply and integrate relevant social and behavioral science methodologies into the Hazardous Weather Testbed to improve forecasters’ use of convection-allowing/resolving data, techniques, and guidance, as well as end-users’ ability to receive, assess, understand, and respond to forecasts and warnings for severe convective hazards, including understanding and messaging of probabilistic hazard information.

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Joint Hurricane Testbed (JHT) Priorities

JHT-1: Improve operational analysis of the surface wind field in tropical cyclones, including the analysis of the maximum sustained winds and the radius of maximum winds as well as guidance for changes in tropical cyclone size/wind structure and related parameters, including combined sea heights.

JHT-2: Develop guidance on the best flight track strategies and/or supplemental observation strategies that take into account hurricane forecasters’ use of data as well as data assimilation needs for operational models; enhance and improve the interactive capabilities of the real-time flight planning software for forecasters.

JHT-3: Improve tropical cyclone guidance including guidance for the onset, duration, and magnitude of rapid intensification events and over-water rapid weakening events; as well as statistically-based, dynamically-based, and combined statistical/dynamical approaches for real-time guidance on guidance for track and intensity.

JHT-4: Improve guidance for tropical cyclone genesis including the following: techniques or products to support pre-genesis disturbance track, intensity, size, and hazard forecasts;

guidance for tropical cyclone genesis at both the short-range (0-48 hours) and the medium-range (48-168 hours) that exhibits a high probability of detection and a low false alarm rate for, and/or provides probability of, genesis; and techniques to diagnose and predict the formation of tropical cyclones and to forecast track, intensity, and structure prior to tropical cyclone transition.

JHT-5: Apply and integrate relevant social and behavioral science methodologies into the Joint Hurricane Testbed to improve forecasters’ use of forecast data, techniques, and guidance, as well as end-users’ ability to receive, assess, understand, and respond to forecasts and warnings for tropical cyclone hazards, including understanding and messaging of probabilistic hazard information.

JHT-6: Develop tools and/or products to help forecasters better interpret conflicting radar and reconnaissance data in order to more quickly assess the tropical cyclone intensity and structure in real-time. Particularly needed are examinations of how measurements from various sources (e.g., radar, flight-level winds, SFMR, dropsondes) relate to one another and to the tropical cyclone structure and maximum intensity as a function of storm intensity and/or size. Uncertainties in the above relationships are also desired.

4. Joint Technology Transfer Initiative (JTTI)

Notice of Federal Funding Page 17 of 52

WPO, in collaboration with the NWS, developed the following three priorities (each with sub-priorities) for the JTTI Program.

JTTI-1: In collaboration with the UFS community (https://ufscommunity.org/) and/or the UFS/R2O Project (https://vlab.noaa.gov/web/ufs-r2o), further develop, test and enhance data assimilation (DA) techniques, improve model component coupling techniques and capabilities, enhance physics suites, improve evaluations and post-processing techniques and tools, and utilize Artificial Intelligence/Machine Learning (AI/ML) for improving the forecasts on short range weather, medium range weather and subseasonal time scales.

Projects should have a clear evaluation strategy, including potentially contributing to verification capability, with a hierarchical testing approach that can assess performance improvement. Specific priorities include:

JTTI-1(a): Develop, test and enhance coupled data assimilation techniques: Includes advancements in coupled data assimilation techniques focusing on component interfaces, coupled background error, and coupled localization using the framework of the Joint Effort for Data assimilation Integration (JEDI). Priority work will focus on, advancing diagnostic and validation methods of coupled DA leveraging independent data sets for cross-validation, increasing use of observations from both satellite and in situ observing systems, and improving reanalysis for initialization of the coupled model including advancing techniques towards more strongly coupled data assimilation, dealing with coupled model biases, reducing impacts of changes in observing systems, and optimizing the use of historical observations.

JTTI-1(b): Improve model components and coupling techniques: Improve next-generation model components of the coupled UFS as well as model component coupling techniques and capabilities.

JTTI-1(c): Develop and refine ensemble framework for the coupled UFS prototypes:

Improve stochastic physics parameterizations to represent uncertainties in each model component including oceans, sea ice and land, improve computational efficiency in ensemble-based coupled data assimilation schemes, and analyze impacts of different ensemble techniques on weather and subseasonal forecast skill.

JTTI-1(d): Further develop and enhance physics suites: Improvements for the UFS physics, including: (i) Improve and enhance existing Common Community Physics Package (CCPP) physics parameterizations with a focus on scale-aware physics; (ii) improve subgrid-scale turbulence schemes within dynamics, including the 3D turbulence parameterization, and

Notice of Federal Funding Page 18 of 52 improve representation of unresolved processes and multiscale process interactions; and (iii) Improve aerosol-radiation-cloud-precipitation interactions and their respective feedbacks through physics suite improvements and evaluations.

JTTI-1(e): Improve UFS computational efficiency, accuracy, and data utilization using artificial intelligence (AI)/machine learning (ML): Utilize AI/ML tools to improve the quality control of observations, improve data selection, thinning and/or super-obbing, and bias correction, advance coupled data assimilation techniques through developing model emulators that can be integrated with DA, improve and enhance computational efficiency of physical parameterizations, and diagnose and reduce model systematic errors.

JTTI-1(f): Further develop post-processing software for the UFS: Includes novel algorithms and methods for model bias correction and calibration such as machine learning and artificial intelligence, software optimization and compression techniques, visualization techniques, and probabilistic data sets and statistics derived from ensemble systems.

JTTI-1(g): Advance land model processes: Further develop and refine physics, parameter optimization, land-atmosphere coupling, DA and evaluation to advance land processes key to improving numerical predictions from weather to subseasonal timescales in the UFS. The modules of interest include, but are not limited to, those in the current land component in the UFS and should be run and evaluated within the UFS by the end of the project. Projects are strongly encouraged to incorporate or improve critical processes that are currently missing or underperforming in the UFS; isolate and quantify the impacts of the land component on coupled system performance, particularly in forecasting of near-surface temperature, moisture and wind, precipitation, and planetary boundary layer development from weather to subseasonal timescales; and/or assimilate land-relevant observations to improve land initial states.

JTTI-2: Further develop innovative methodologies to assess and identify underserved communities and understand their needs, vulnerabilities, and challenges with respect to the communication and dissemination of NWS products and services related to extreme weather and water events. This may include:

JTTI-2(a): Assessment methodologies: Further develop methods to assess underserved communities' comprehension, reception, and response to forecast communication and/or dissemination of confidence and uncertainty information to improve NWS products and services.

JTTI-2(b): Risk communication improvements: Apply risk communication science to

Notice of Federal Funding Page 19 of 52 recommend improvements to operational NWS products and services, with a particular focus on the improvement of approaches to communicate low probability, high-impact events.

JTTI-3: Further develop innovative scientific and technological solutions to improve NWS forecasts, products, services, and Decision Support Services (DSS) for extreme weather and water events in collaboration with NOAA Testbeds and Proving Grounds (https://www.testbeds.noaa.gov/). This may include:

JTTI-3(a): Development, testing, and evaluation: Further develop, test, and evaluate promising probabilistic tools and technologies to improve the prediction and communication of hazardous weather events (extreme rainfall, extreme temperatures, winter precipitation, extreme wind events, tropical cyclones, fire weather, and severe weather) in improving community response and resilience.

JTTI-3(b): Forecaster assessment of uncertainty: Improve forecaster use of numerical weather prediction (NWP) models, post-processed guidance, tools, and technologies to better assess uncertainty and historical context of hazardous weather events (extreme rainfall, extreme temperatures, winter precipitation, extreme wind events, tropical cyclones, fire weather, and severe weather).

JTTI-3(c): Post-processing/Artificial Intelligence: Further develop tools and techniques to improve the translation of probabilistic NWP into meaningful NWS products and services that convey probabilistic hazard information for hazardous weather events (extreme rainfall, extreme temperatures, winter precipitation, extreme wind events, tropical cyclones, fire weather, and severe weather). This includes AI/ML or other techniques that help diagnose systematic model error, support data mining and feature extraction of NWP, and actionable visualization for the forecaster.

JTTI-3(d): Translation of forecasts to DSS: Further develop, test and evaluate new or improved ways of enhancing forecaster use of probabilistic hazard information to message extreme weather and water events related to hazardous weather events (extreme rainfall, extreme temperatures, winter precipitation, extreme wind events, tropical cyclones, fire weather, and severe weather), with focus on user response and reception. This includes tools and techniques that combine meteorological and non-meteorological inputs to quantitatively assess the probability of impacts.

5. Subseasonal to Seasonal

A particular focus will be on projects that support an increased understanding and predictive

Notice of Federal Funding Page 20 of 52 capability of precipitation or drought within the NOAA UFS modeling suite, particularly as these impact extreme events or precursors for fire weather on the subseasonal to seasonal scale via the following three areas. Proposals for this competition should lie within RLs 2-4;

applicants to this competition should clearly identify and address one or more of the following priorities in their proposal.

S2S-1: Improved data assimilation (DA) for individual Earth components (e.g. cryosphere, ocean, waves, land surface, and atmospheric composition) and the incorporation of new observation types therein for predictions on the subseasonal to seasonal scales while being consistent with coupled DA for other global UFS-based applications. Foci include coupled DA techniques, where observations in one component of the Earth system are allowed to directly impact the state estimation in other components, and subsequent post-processing of derived model output and analysis of DA schema impacting precipitation forecasts within the community-based Joint Effort for DA Integration (JEDI) project, which is central to the UFS DA strategy. Prospective projects are strongly encouraged to leverage existing NOAA, WMO, and publicly available datasets.

S2S-2: Community-based approaches to improve Earth system models via development and evaluation of individual…