Research Themes

GSL’s four research areas-- Organizational Excellence, Earth System Prediction, Advanced Technologies, and Decision Support-- are described in more detail below. These four research areas are the foundation to achieving the GSL Grand Challenge:

Deliver actionable global storm-scale prediction and environmental information through advanced technologies to serve society.

Organizational Excellence

GSL strives to advance its workforce scientifically, technically, and professionally. GSL is committed to increased diversity and inclusion as well as a strong foundation for succession planning and career growth. GSL hosts team-building and training, conducts annual retreats and employee satisfaction surveys, and provides leadership opportunities to early- and mid-career staff to contribute to GSL and develop professionally. GSL also values long-term research continuity across research areas. While GSL’s Grand Challenge is a scientific and technical goal, a healthy organization invested in GSL’s mission will enable the collaboration and innovation needed to achieve this goal. Additionally, GSL invests base funding toward activities that support the GSL mission and collaboration across research areas towards our Grand Challenge. Lastly, GSL is invested in researching new technologies and maintaining an advanced IT infrastructure to provide the technologies needed for innovative research, development, and implementation.

Future plans: Continue to increase staff diversity and inclusivity; make strategic federal hires to fill vacancies and early-to-mid-career scientific positions; integrate GSL’s science strategy across research themes.

Earth System Prediction

GSL is a world leader in the development of storm-scale to global-scale weather prediction models, aligning GSL research with NOAA’s objectives to build a holistic understanding of the Earth system and an integrated environmental modeling system while contributing to the Unified Forecast System. Recent successes are NOAA’s hourly-updating Rapid Refresh (RAP) and the High-Resolution Rapid Refresh (HRRR) high-impact weather prediction models. The RAP and HRRR predict atmospheric variables relevant to severe weather, aviation weather, the wind energy industry, and fire weather. Another recent success was the GSL-led GEFS-Aerosols model-- an atmospheric composition model that integrates weather and air quality-- that recently went into operations. In addition to model development and data assimilation expertise, through the Developmental Testbed Center (DTC), GSL supports community involvement by developing modeling infrastructure, providing support to users and developers of the UFS and Hurricane WRF models, assessing contributed innovations, and organizing events to bring together the research and operational communities.

Future plans: With expertise in physics, model coupling, data assimilation, modeling, and verification, GSL will advance Earth-system capabilities toward a continuous global-to-local storm-scale system.

Advanced technologies

GSL is a world leader in advancing new technologies and methods in computing, modeling, visualization, data access, and information delivery to support NOAA’s Earth system prediction and decision support capabilities. GSL research includes using innovative numerical methods and software design that improve the performance, portability, and scientific accuracy of models running on next-generation exascale computers. The advanced computing efforts in GSL are the foundation of virtually all High Performance Computing methods used in NOAA operations and research. GSL also researches and implements cloud computing capabilities to address the challenges of the end-to-end Unified Forecast System used by NOAA and the research community. Researchers are developing machine learning algorithms to increase the use of observations in data assimilation, improve model prediction capabilities, and gain a better understanding of complex data. GSL has been a leader in closing data gaps for NOAA Operations with MADIS, a database of weather observations from a variety of sources that is used worldwide. GSL researchers leverage the latest server and gaming technologies in SOS Explorer™ Mobile, a free app for smart phones to bring environmental information and education to your hands.

Future plans: Develop advanced technologies to enable the delivery of information and improved Earth-system prediction models.

Decision support

GSL’s history was forged in developing tools that support the weather decision-making process. Decision support tool development began in the early 1980s with the Advanced Weather Interactive Processing System (AWIPS), and is continuing now with incremental deployments of GSL’s Hazard Services system to NWS offices. Hazard Services streamlines NWS watch, warning, and advisory-related services into one interface and can be customized for each office, region, or type of weather. GSL is also advancing the Weather Archive and Visualization Environment (WAVE) project, a web-based multi-purpose system where NWS forecasters create impact-based graphics about weather hazards to deliver via their websites and social media. GSL also works to understand how weather information is used through impact-based forecast assessments and targeted real-time information delivery to benefit decision-making in response to high impact weather events.

Future plans: GSL will build tools and systems that will provide actionable information and enable understanding of impacts.

OAR Strategic Plan goals that apply to GSL Research Themes:

Goal 2: Detect Changes in the Ocean and Atmosphere - Produce, analyze, and interpret observation records to understand the Earth System and inform the public

• 2.2 Identify and address gaps in observation requirements needed to understand causes of variability and change
  • Assess the current suite of observations and modeling capabilities to identify gaps and prioritize needs.
  • Improve understanding, forecasts, applied knowledge, and predictions in regions of significant change and for high-impact events.
• 2.3 Increase ability to access and use Earth system data
  • Leverage technologies and approaches to share relevant information within OAR, across NOAA, and throughout the external community to heighten understanding of the Earth system, the management of its resources, and the effects on society.
  • Engage with stakeholders early and regularly throughout research and development to understand user requirements, needs, and expectations for the interoperability and usability of observational data.
  • Deliver informational products that inform decision making.

Goal 3: Make forecasts better - Improve accuracy, precision and efficiency of forecasts and predictions to save lives and property and support a vibrant economy

• 3.1 Develop interdisciplinary Earth system models
  • Develop models that reflect the Earth system and the intersecting human, ecosystem, and environmental factors as an integrated system.
  • Cutting across disciplines and specializations, encourage the growth of innovative model components and new model applications.
  • Develop and operate next-generation Earth system models using a community-based approach in concert with advances in high-performance computing.
  • Enhance data assimilation and modeling across spatial and time scales.
  • Conduct value-driven assessments of models and sunset models that do not meet current or future NOAA requirements.
• 3.2 Design tools and processes to forecast high-impact weather, water, climate, ocean, and ecosystem events
  • Invest in the development of tools, technologies, and processes to advance models and increase the relevancy of forecasts.
  • Improve the capability to understand observation and forecast uncertainty and better communicate the uncertainty.
• 3.3 Transition science that meets users’ current and future needs
  • Work with NOAA Line Offices and stakeholders to define requirements for future science and operational needs.
  • Integrate social science early to understand societal factors, account for stakeholder behavior, and design tools and products that meet end-user needs and expectations.
  • Drive innovative science - Cultivate and deliver mission-relevant research to lead the environmental science community

Goal 4: Drive innovative science

• 4.1 Reinforce a culture of innovation and adaptability
  • Strengthen processes, governance, and structures that cultivate innovation and the behaviors of innovation.
  • Establish processes for risk acceptance and management across the organization. Create a culture of resilience by fostering an appreciation for risk, failing fast, and creating a structure that is adaptive and flexible.
• 4.2 Invest in high-risk, high-reward science
  • Identify new and innovative science and assess the impacts, risks, and opportunities.
  • Lead research on identified high-risk, high-reward areas to advance NOAA’s mission and guide the environmental community.
• 4.3 Accelerate the delivery of mission-ready, next-generation science
  • Expedite the delivery of mission-ready science, services, and technologies.
  • Prioritize mission-driven science and research agendas, addressing NOAA’s most pressing requirements in a relevant, timely manner.