Ivanka Stajner

NASA is developing an Earth Science Modeling Strategy. This is motivated by recommendation 4.2 from the United States National Academies of Sciences, Engineering, and Medicine, Thriving on Our Changing Planet: A Midterm Assessment of Progress Toward Implementation of the Decadal Survey (2024, https://doi.org/10.17226/27743): “To ensure continued advances in modeling in conjunction with Earth observation: NASA should develop a long-term strategic plan for a strong sustained commitment to Earth system modeling in concert with observations. Success in observation-driven modeling holds the key for maintaining the end-to-end capability that has served NASA well in its effectiveness and service to society.” Moreover, one of the main objectives of NASA’s Earth Science to Action Strategy (https://science.nasa.gov/earth-science/earth-science-to-action/) is to Deliver Trusted Information to Drive Earth Resilience Activities. This Objective will rely on comprehensive Earth system modeling as a key result that will enable NASA to advance and integrate Earth science knowledge to empower humanity to create a more resilient world. In this presentation we will overview the approach being taken to develop the Earth Science Modeling Strategy, within NASA and with the broader community. Some of the key aspects being considered include comprehensive state-of-science modeling representation of the coupled Earth system, from global to local scales, analyses and predictions at different lead times, from short term predictions to climate projections, and using ensembles. Another key facet is data assimilation into Earth system models and improved utilization and demonstration of the value of Earth observations. It is envisioned that bold innovation, new disruptive technologies, including artificial intelligence and machine learning, and utilization of large Earth science datasets will be key enablers for cutting edge research, increased understanding of the Earth system, and improved ability to provide actionable Earth science information for societal applications to meet broad user needs. Modeling underpins NASA’s Earth Science to Action strategy as a key capability for advancing foundational knowledge, Earth system science and applied research, as well as increasing societal value of NASA’s data and information leading to improved public understanding.

The air quality forecasting system is an essential tool widely used by environmental managers to mitigate adverse health effects of air pollutants. This work presents the latest development of the next-generation regional air quality model (AQM) forecast system within the Unified Forecast System (UFS) framework in the National Oceanic and Atmospheric Administration (NOAA). The UFS air quality model incorporates the US Environmental Protection Agency (EPA) Community Multiscale Air Quality (CMAQ) model as its main chemistry component. In this system, CMAQ is integrated as a column model to solve gas and aerosol chemistry, while the transport of chemical species is processed by UFS. The current AQM version 7 (AQMv7) is coupled with an earlier version of CMAQ (version 5.2.1). Here we describe the development of the updated AQMv7 by coupling to a “state-of-the-science” CMAQ version 5.4. The updates include improvements in gas and aerosol chemistry, dry deposition processes, and structural changes to the input/output (I/O) interface, enhancing both computational efficiency and representation of air–surface exchange processes. A simulation was conducted for the period of June–August 2023 to assess the effects of these updates on the forecast performance of ozone (O₃) and fine particulate matter (PM_2.5), two major air pollutants over the continental United States (CONUS). The results show that the updated model demonstrates an enhanced capability in simulating O₃ over the CONUS by reducing the positive bias, leading to a reduction in the mean bias by 3 %–5 % and 8 %–12 % for hourly and the maximum daily 8 h average O₃, respectively. Spatially, the updated model lowers the positive bias of hourly O₃ in most of the 10 EPA regions, particularly within the central and northwest areas, while amplifying the O₃ underestimation over the sites with negative bias. Similarly, the updates induce uniformly lower fine particulate matter (PM_2.5) concentrations across the CONUS domain, reducing the positive bias at some sites over the northeast in August and central Great Plains. The updated model does not improve model performance for PM_2.5 in the vicinity and downwind of fire emission sources, where AQMv7 shows the highest negative bias, thus indicating a focal point of model uncertainty and needed improvement. Despite these challenges, the study highlights the importance of the ongoing refinements for reliable air quality predictions from the UFS-AQM model, which is a planned future update to NOAA's current operational air quality forecast system.

The medium-range forecasting of global weather plays a pivotal role in decision-making processes across various societal and economic sectors. Recent years have witnessed a rapid evolution in machine learning (ML) models applications in weather prediction, demonstrating notably superior performance compared to traditional numerical weather prediction (NWP) models. These cutting-edge models leverage diverse ML architectures, such as Graph Neural Networks (GNNs), Convolutional Neural Networks (CNNs), Fourier Neural Operators (FNOs), and Transformers. Notably, Google DeepMind has pioneered a novel ML-based approach known as GraphCast, enabling direct training from reanalysis data and facilitating global predictions for numerous weather variables in less than a minute. Impressively, GraphCast forecasts show improved accuracy in predicting severe weather events, including phenomena like tropical cyclones, atmospheric rivers, and extreme heat. However, the efficiency of GraphCast relies on high-quality historical weather data for training, typically sourced from ECMWF's ERA5 reanalysis. Concurrently, the National Centers for Environmental Prediction (NCEP) has initiated efforts in collaboration with the research community, focusing on developing Machine Learning Weather Prediction (MLWP). This study assesses the pre-trained GraphCast model, leveraging Global Data Assimilation System (GDAS) data from the operational GFSv16 model as initial states. Additionally, we explore the potential use of GDAS data as an alternative training source for GraphCast. Notably, GDAS data is available at a 0.25-degree latitude-longitude resolution and with a temporal resolution of 6 hours. Our investigation involves a comparative analysis of GraphCast's performance when initiated on GDAS data versus ERA5 and HRES data. Alongside this comparative analysis, we investigate the advantages and limitations of utilizing GDAS data for GraphCast while proposing potential approaches for enhancing future iterations of this study.

Ongoing efforts at the National Oceanic and Atmospheric Administration (NOAA), National Weather Service (NWS) are aimed at increasing the realism of the physical processes represented in both global and regional operational numerical weather prediction systems. These efforts include enhancing the description of atmospheric composition and its impact on the atmosphere by incorporating prognostic aerosols and aerosol radiative feedback in each member of the NOAA Global Ensemble Forecast System (GEFS).Modern Earth system prototypes, built upon the community-based Unified Forecast System (UFS) framework and coupling atmosphere, land, ocean, sea ice, waves, and prognostic aerosols components are being developed and evaluated at the Environmental Modeling Center (EMC) in the process of identifying candidates for the planned operational upgrade of GEFS to version 13.Prognostic aerosols are integrated in GEFS through the coupled UFS-Aerosols component, developed at EMC in collaboration with the National Aeronautics and Space Administration (NASA) Global Modeling and Assimilation Office (GMAO). UFS-Aerosols embeds NASA's 2nd-generation Goddard Chemistry Aerosol Radiation and Transport (GOCART) model and incorporates updates to the dust scheme and anthropogenic and biogenic emissions from NOAA’s Air Resources Laboratory (ARL), along with wildfire emissions provided by NOAA's National Environmental Satellite, Data, and Information Service (NESDIS).The impact of radiative feedback from prognostic aerosols on atmospheric predictions in preliminary experiments with GEFS version 13 prototypes will be reviewed.

National Oceanic and Atmospheric Administration’s (NOAA’s) Environmental Modeling Center (EMC) is a lead developer of operational Numerical Weather Prediction (NWP) systems at the National Weather Service (NWS), which are used for the protection of life and property and the enhancement of the economy. EMC transitions to operations and maintains more than 20 numerical prediction systems that are used by NWS, NOAA, other United States (U.S.) federal agencies, and various other stakeholders. These systems are developed through a close collaboration with academic, federal and commercial sector partners. EMC maintains, enhances and transitions-to-operations numerical forecast systems for weather, ocean, climate, land surface and hydrology, hurricanes, and air quality for the U.S. and global domains. NOAA’s operational predictions are transitioning to the Unified Forecast System (UFS) framework in order to simplify the operational prediction suite of modeling systems. The UFS is being designed as a community-based, comprehensive atmosphere-ocean-sea-ice-wave-aerosol-land coupled Earth modeling system with coupled data assimilation and ensemble capabilities, organized around applications spanning from local to global domains and predictive time scales ranging from sub-hourly analyses to seasonal predictions. Disparate legacy operational applications that have been developed and maintained by EMC in support of various stakeholder requirements are being transitioned to the UFS framework. The transition started several years ago and is planned to continue over the next few years. Fewer resulting applications will consolidate NCEP’s Production Suite that shares a set of common scientific components and technical infrastructure. This streamlined suite is expected to accelerate the transition of research into operations and simplify maintenance of operational systems. This talk describes major development and operational implementation projects at EMC over the last couple of years including for example a new UFS-based hurricane application, recent advances in the use of satellite data and a new verification system. We will present EMC plans for the next few years, within the overall NOAA strategy, and how planned efforts link with other modeling efforts within NOAA, in the broader U.S. and international community.

NOAA is developing the next generation air quality (AQ) prediction system for the United States (U.S.) and global aerosol predictions within the Unified Forecast System (UFS) framework to better represent and forecast impacts of wildfires on AQ and impacts of aerosols globally on weather from hourly to subseasonal scales. A new regional UFS weather model is online coupled with chemistry represented by the EPA’s Community Multiscale AQ (CMAQ) modeling system with Carbon Bond VI and AERO6 mechanisms to form this new UFS-AQM system. Wildfire emissions are specified by satellite-observed hourly Regional Hourly Advanced Baseline Imager (ABI) and Visible Infrared Imaging Radiometer Suite (VIIRS) Emissions (RAVE). Anthropogenic emissions are based on U.S. EPA’s National Emissions Inventories over the contiguous 48 U.S. states and global inventories elsewhere. Lateral boundary conditions for aerosols are provided by NOAA’s Global Ensemble Forecast System which includes the Goddard Chemistry Aerosol Radiation and Transport (GOCART) module. A bias correction post-processing procedure is included in UFS-AQM to improve prediction accuracy. Testing is performed over a large regional domain covering the U.S., and evaluation is done in near-real time and for retrospective periods. Recent examples indicate much improved representation of impacts of wildfires on AQ predictions, especially during Quebec fires in the summer of 2023. Some of the planned refinements for UFS-AQM include better representation of plume rise for wildfire smoke and for point source emissions, increased resolution consistent with the Rapid Refresh Forecast System (RRFS), which is under development, and using aerosol lateral boundary conditions from a 6-way coupled atmosphere - ocean - land - sea-ice - waves - aerosols global UFS system, also under development. Due to challenging computational requirements for UFS-AQM at high resolution, a machine learning emulator is being developed to improve computational efficiency for prediction of chemical transformations and tracer transport. Of most interest for this session, data assimilation capabilities are being developed to constrain initial conditions for pollutant concentrations in UFS-AQM. Observations being assimilated include fine particulate matter (PM2.5) observations from AirNow, VIIRS Aerosol Optical Depth (AOD) retrievals and TROPOspheric Monitoring Instrument (TROPOMI) NO2 retrievals.

Data driven machine learning-based weather prediction (MLWP) models have been under rapid development in recent years. These models leverage autoregressive neural network architectures and are trained using reanalysis data generated by operational centers, demonstrating proficient forecasting abilities. One remarkable advantage of these MLWP models is that, once trained, they take significantly less amount of computational resources to produce forecasts compared to traditional numerical weather prediction (NWP) models while maintaining or surpassing the NWP performance. NCEP has started machine learning development collaborating with the research community for several years. This presentation will provide an overview of the development of MLWP models for the global ensemble forecast system at NCEP Environment Modeling Center (EMC). The model adopts state-of-the-art MLWP models such as GraphCast and leverages the methodologies from FuXi global ensemble system. The development includes developing cascade MLWP models, training the model with GEFSv12 reanalysis data and producing forecasts with the operational GEFSv12 initial states. The model will be validated using two years of GEFSv12 operational forecast data. The ultimate objective is to deliver 15-day forecasts with skill levels comparable to the operational GEFSv12.

The National Oceanic and Atmospheric Administration (NOAA) has developed an advanced regional air quality prediction system (AQPS) within the Unified Forecast System (UFS) framework to improve representations of wildfire emissions and their impacts on air quality predictions. This innovative system integrates the Environmental Protection Agency’s (EPA) Community Multiscale Air Quality (CMAQ) model as a column chemistry model with the UFS-based atmospheric model, operating in an online mode. The calculation of wildfire gas and particulate emissions relies on satellite-derived fire products, high-resolution Regional Hourly Advanced Baseline Imager (ABI) and Visible Infrared Imaging Radiometer Suite (VIIRS) Emissions (RAVE). A period in June and July 2023 with Quebec Canadian wildfires, which severely impacted air quality in the United States (US), was chosen as a case study to assess the predictive capability of the UFS-AQM system. The UFS-AQM predictions of fine particulate (PM_2.5) and ozone (O₃) were evaluated against AirNow observations from June 15 to July 14, 2023. The results indicate a substantial improvement in PM_2.5 predictions when compared to the previous operational forecast. Meanwhile, the system demonstrates a strong ability of predicting O₃ exceedance events during the dissipation phase of the wildfire. Furthermore, the online system shows more realistic predictions of aerosol optical depth (AOD) as compared to the previous operational forecast and satellite retrieval data. Finally, this study outlines a plan for further advancing a comprehensive regional AQPS at NOAA.

Air quality forecasting system is an essential tool widely used by environmental managers to mitigate adverse health effects of air pollutants. This work presents the latest development of the next generation regional air quality model (AQM) forecast system within the Unified Forecast System (UFS) framework in the National Oceanic and Atmospheric Administration (NOAA). The UFS air quality model incorporates the U.S. Environmental Protection Agency (EPA)’s Community Multiscale Air Quality (CMAQ) model as its main chemistry component. In this system, CMAQ is integrated as a column model to solve gas and aerosol chemistry while the transport of chemical species is processed by UFS. The current AQM version 7 (AQMv7) is coupled with an earlier version of CMAQ (version 5.2.1). Here we describe the development of the updated AQMv7 by coupling to a ‘state-of-the-science’ CMAQ version 5.4. The updates include improvements in gas and aerosol chemistry, dry deposition processes, and structural changes to the Input/Output (IO) interface, enhancing both computational efficiency and the representation of air-surface exchange processes. A simulation was conducted for the period of August 2023 to assess the effects of these updates on the forecast performance of ozone (O₃) and fine particulate matter (PM_2.5), two major air pollutants over the continental United States (CONUS). The results show that the updated model demonstrates a significantly enhanced capability in simulating O₃ over the CONUS by reducing the positive bias during both day and night, leading to a reduction of the mean bias by 50 % and 72 % for hourly and the maximum daily 8-hour average O₃, respectively. Spatially, the updated model lowers the positive bias of hourly O₃ in all of the ten EPA regions, particularly within the Great Plains. Similarly, the updates induce uniformly lower fine particulate matter (PM_2.5) concentrations across the CONUS domain, reducing the positive bias in the northeast and central Great Plain and exacerbating the negative bias in the west and south. The updated model does not improve model performance for PM_2.5 in the vicinity of fire emission sources as compared to AQMv7, thus indicating a focal point of model uncertainty and needed improvement. Despite these challenges, the study highlights the importance of the ongoing refinements for reliable air quality predictions from the UFS-AQM model, which is the future replacement of NOAA’s current operational air quality forecast system.