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NASA NTRS2023

LunaNet Position, Navigation, and Timing Services and Signal, Enabling the Future of Lunar Exploration

The International Space Exploration Coordination Group established in 2018 the 3rd edition of the Global Exploration Roadmap (ISECG, 2018) that aims to achieve Mars human surface activities and identifies the exploration of the Moon as a critical intermediate step. A supplement covering updates on surface exploration scenarios was released in 2020 (ISECG, 2020). The Artemis Accords (NASA Artemis, 2020), first signed in October 2020, now includes over two dozen nations, in an agreement on the principles for best practices, including interoperability. September 2022 introduced the National Aeronautics and Space Administration’s (NASA) Moon to Mars Objectives highlighting recurring tenets of collaboration with international and industry partners and interoperability, along with infrastructure objectives for Position, Navigation, and Timing (PNT). The successful Artemis 1 mission paved the way to the ambitious plans to establish a sustainable human presence on the Moon. Just a few months after Artemis 1 launch (NASA, 2022), iSpace HAKUTO-R Mission1 (iSpace, 2022) launched, being the first-ever commercial mission, launched by a commercial launch service provider, aiming to land on the lunar surface. The NASA Artemis programme plans initial crewed landings and traverses in 2025, supported by the Lunar Gateway, followed by regular launches to build the lunar systems (NASA Artemis Plan, 2020), (NASA, 2022). NASA’s contracts with Commercial Lunar Payload Services (CLPS, (NASA, n.d.)) to deliver science and technology to the Moon, with launches starting in November 2023. The European Space Agency (ESA) Argonaut (ESA Argonaut, 2022) programme plans to have recurrent missions to bring payloads to the lunar surface, supporting lunar exploration. These are just a few examples of planned missions that will target the Earth’s natural satellite in the next decade, with forecasts of tens of missions per year (NSR, 2022), (Euroconsult, 2020). The large number of missions and the complexity of landing and operating are expected to demand a change of paradigm from the current Earth-based communication and navigation services. In recent years, several agencies have proposed to deploy cislunar communication and navigation services to support lunar missions (NASA LCRNS, 2022), (ESA Moonlight, 2022), (JAXA, 2022)). All these proposals seek to deploy service-providing satellites in lunar orbit to ease the user missions’ operations. The PNT services objective is to support all types of lunar users (e.g.: orbiters, landers, ascent vehicles, and surface crew and rovers). At the same time, NASA and ESA initiated an effort to define a common framework to ensure interoperability among different service providers: the LunaNet framework. The LunaNet Interoperability Specification (NASA and ESA, 2023) covers communication, PNT, and auxiliary services, by establishing a common set of requirements to ensure interoperability. This conference contribution will present the LunaNet PNT services, focusing on the Lunar Augmented Navigation Service (LANS) that resembles the Global Navigation Satellite System (GNSS) concept on Earth: constellations of satellites broadcasting a radio navigation signal synchronized to a common reference clock, with augmentations to accommodate users’ needs in an environment away from Earth. This includes a description of the high-level LANS concept and the basic principles defined to ensure interoperability. In addition, it will describe the common S-band PNT Augmented Forward Signal (AFS) and common messages to be adopted for compliance with the LunaNet framework, and the justification of the selected approach.

LunaNet↗
NASA NTRS2023

Lunanet Position, Navigation, and Timing Services and Signals, Enabling the Future of Lunar Exploration

The International Space Exploration Coordination Group established in 2018 the 3rd edition of the Global Exploration Roadmap (ISECG, 2018) that aims to achieve Mars human surface activities and identifies the exploration of the Moon as a critical intermediate step. A supplement covering updates on surface exploration scenarios was released in 2020 (ISECG, 2020). The Artemis Accords (NASA Artemis, 2020), first signed in October 2020, now includes over two dozen nations, in an agreement on the principles for best practices, including interoperability. In September 2022 the National Aeronautics and Space Administration (NASA) introduced the Moon to Mars Objectives highlighting recurring tenets of collaboration with international and industry partners and interoperability, along with infrastructure objectives for Position, Navigation, and Timing (PNT). The successful Artemis 1 mission paved the way to the ambitious plans to establish a sustainable human presence on the Moon. Just a few months after Artemis 1 launch (NASA, 2022), iSpace HAKUTO-R Mission1 (iSpace, 2022) launched, being the first-ever mission launched by a commercial launch service provider aiming to land on the lunar surface. The NASA Artemis program plans initial crewed landings and surface traverses in 2025, supported by the Lunar Gateway. Regular launches will follow to build the lunar systems for a sustained presence as presented in the Artemis Plan (NASA Artemis Plan, 2020), (NASA, 2022). NASA’s contracts with commercial providers through the Commercial Lunar Payload Services program (CLPS, (NASA, n.d.)) will deliver science and technology demonstration missions to the Moon starting in November 2023. The European Space Agency (ESA) Argonaut (ESA Argonaut, 2022) program plans to have recurrent missions to bring payloads to the lunar surface, supporting lunar exploration. These are just a few examples of planned missions that will target Earth’s natural satellite in the next decade, with forecasts of tens of missions per year (NSR, 2022), (Euroconsult, 2020). The large number of missions and the complexity of landing and operating are expected to demand a change of paradigm from the current Earth-based communication and navigation services, that may be combined with onboard sensors. In recent years, several agencies have proposed to deploy cislunar communication and navigation services to support lunar missions (NASA LCRNS, 2022), (ESA Moonlight, 2022), (JAXA, 2022)). All these proposals seek to deploy service-providing satellites in lunar orbit to ease the user missions’ operations. The PNT services objective is to support all types of lunar users (e.g.: orbiters, landers, ascent vehicles, surface crew, rovers, and deployed science payloads). At the same time, NASA and ESA initiated an effort to define a common framework to ensure interoperability among different service providers: the LunaNet framework. The LunaNet Interoperability Specification (NASA and ESA, 2023) covers communication, PNT, and auxiliary services, by establishing a common set of requirements to ensure interoperability. This conference contribution will present the LunaNet PNT services, focusing on the Lunar Augmented Navigation Service (LANS) that would be provided by a system that resembles the Global Navigation Satellite System (GNSS) concept on Earth: constellations of satellites broadcasting a radio navigation signal synchronized to a common reference clock, with augmentations to accommodate users’ needs in an environment away from Earth. This paper includes a description of the high-level LANS concept, and the basic principles defined to ensure interoperability. In addition, it will describe the common S-band PNT Augmented Forward Signal (AFS) and common messages to be adopted for compliance with the LunaNet framework, and the justification of the selected approach.

LunaNet↗
NASA NTRSDate not supplied

Medium-Range River Flood Forecasts Using a Long Short-Term Memory Network

River flooding and the impacts are a concern for decision makers throughout the United States. Accurate medium-range forecasts (~3-7 days) are critical for providing advanced outlooks to emergency management officials. Unfortunately, accurately forecasting rainfall-runoff and the subsequent rise and fall within rivers remain a challenge in hydrological modeling. While complex physical modeling systems are the standard for representing the hydrological processes, they are computationally demanding and can require extensive calibration. Further, uncertainties remain in the model parameters and input data. The use of machine learning can reduce some of the computational demand while maintaining high accuracy. Therefore, this project makes use of a Long Short-Term Memory (LSTM) network which explicitly accounts for the time-dependent nature of rainfall-runoff modeling. The developed LSTM was trained to predict river gauge height, or stage height, based on time-lagged input features which include: gauge height to initialize the model, the NASA Short-term Prediction Research and Transition Center’s instance of the Land Information System (SPoRT-LIS) relative soil moisture to describe the rainfall infiltration rate, and 6-hr Multi-Radar Multi-Sensor quantitative precipitation estimate (MRMS QPE). The developed LSTM based system is then used to produce 7-day forecasts with a 6-hr temporal resolution using three different quantitative precipitation forecasts (QPF) from the NWS’s Weather Prediction Center (WPC), the NCEP Global Forecast System (GFS) model and the National Blend of Models (NBM). This trained modeling system has been implemented as an experimental product at over 100 different rivers in collaboration with at multiple National Weather Service (NWS) Forecast Offices and River Forecast Centers (RFC) across the eastern half of the United States. The developed LSTM model achieved average Nash-Sutcliffe efficiency (NSE) 0.89 higher than the equivalent medium-range National Water Model ensemble member forecast over a 7-day forecast. In addition to the initial development and evaluation, this project has continued to expand. While the initial model was developed for precipitation dominated basins, expansion of the project has taken it to basins effected by snow melt. This presentation will provide an overview of the project with focus on recent developments on incorporating snow melt processes into the model.

Andrew T. White↗
NASA NTRS1985

Outside users payload model

The outside users payload model which is a continuation of documents and replaces and supersedes the July 1984 edition is presented. The time period covered by this model is 1985 through 2000. The following sections are included: (1) definition of the scope of the model; (2) discussion of the methodology used; (3) overview of total demand; (4) summary of the estimated market segmentation by launch vehicle; (5) summary of the estimated market segmentation by user type; (6) details of the STS market forecast; (7) summary of transponder trends; (8) model overview by mission category; and (9) detailed mission models. All known non-NASA, non-DOD reimbursable payloads forecast to be flown by non-Soviet-block countries are included in this model with the exception of Spacelab payloads and small self contained payloads. Certain DOD-sponsored or cosponsored payloads are included if they are reimbursable launches.

Source record↗
NASA NTRS2011

Extravehicular Activity Technology Development Status and Forecast

The goal of NASA s current EVA technology effort is to further develop technologies that will be used to demonstrate a robust EVA system that has application for a variety of future missions including microgravity and surface EVA. Overall the objectives will be to reduce system mass, reduce consumables and maintenance, increase EVA hardware robustness and life, increase crew member efficiency and autonomy, and enable rapid vehicle egress and ingress. Over the past several years, NASA realized a tremendous increase in EVA system development as part of the Exploration Technology Development Program and the Constellation Program. The evident demand for efficient and reliable EVA technologies, particularly regenerable technologies was apparent under these former programs and will continue to be needed as future mission opportunities arise. The technological need for EVA in space has been realized over the last several decades by the Gemini, Apollo, Skylab, Space Shuttle, and the International Space Station (ISS) programs. EVAs were critical to the success of these programs. Now with the ISS extension to 2028 in conjunction with a current forecasted need of at least eight EVAs per year, the EVA hardware life and limited availability of the Extravehicular Mobility Units (EMUs) will eventually become a critical issue. The current EMU has successfully served EVA demands by performing critical operations to assemble the ISS and provide repairs of satellites such as the Hubble Space Telescope. However, as the life of ISS and the vision for future mission opportunities are realized, a new EVA systems capability will be needed and the current architectures and technologies under development offer significant improvements over the current flight systems. In addition to ISS, potential mission applications include EVAs for missions to Near Earth Objects (NEO), Phobos, or future surface missions. Surface missions could include either exploration of the Moon or Mars. Providing an EVA capability for these types of missions enables in-space construction of complex vehicles or satellites, hands on exploration of new parts of our solar system, and engages the public through the inspiration of knowing that humans are exploring places that they have never been before. This paper offers insight into what is currently being developed and what the potential opportunities are in the forecast.

Chullen, Cinda↗
DOE OSTI2024

Sensitivity of Fine‐Resolution Urban Heat Island Simulations to Soil Moisture Parameterization

ABSTRACT Urban areas experience the impact of natural disasters, such as heatwaves and flash floods, disparately in different neighbourhoods across a city. The demand for precise urban hydrometeorological and hydroclimatological modelling to examine this disparity, and the interacting challenges posed by climate change and urbanisation, has thus surged. The Weather Research and Forecasting (WRF) model has served such operational and research purposes for decades. Recent advancements in WRF, including enhanced numerical schemes and sophisticated urban atmospheric‐hydrological parameterizations, have empowered the simulation of urban geophysical processes at high resolution (~1 km), but even this resolution misses significant urban microclimate variability. This study applies the large‐eddy simulations (LES) mode within WRF, coupled with single‐layer urban canopy models (SLUCM), to enable even finer‐scale modelling (150 m) of the Urban Heat Island (UHI) effect in the Baltimore metropolitan area. We run nine scenarios to evaluate various methods of initializing soil moisture and various spinup lead times, and to assess the impact of WRF's Mosaic approach in depicting subgrid‐scale processes. We evaluate the scenarios by comparing the WRF simulated land surface temperature (LST) against Landsat LST and the WRF simulated hourly 2‐m air temperatures (AT) with observations from eight weather stations across the domain. Results underscore the paramount influence of the lead spinup time on the spatiotemporal distribution of simulated soil moisture, consequently shaping WRF's efficacy in predicting the UHI. Furthermore, interpolating soil moisture‐related parameters from the parent for child domain initialization yields a notable reduction in mean and root‐mean‐squared errors. This improvement was particularly evident in simulations with the longest spinup time, affirming the importance of carefully designing the initialization of soil moisture for improved urban temperature predictions.

Talebpour, Mahdad↗
DOE OSTI2026

Reimagining How Flood Warnings Can Inform Decision‐Making and Community Actions

Society faces increasingly severe flood hazards, intensifying demand for flood early warning systems (FEWS) that deliver accurate and actionable information. However, most existing FEWS remain prediction‐centric, treating decision‐making as a downstream consumer of hazard forecasts while offering limited support for uncertainty interpretation, risk communication, and real‐world response. This Perspective presents a vision and blueprint for a novel inland FEWS‐decision‐making (FEWS‐DM) framework that repositions decision‐making as an equal partner in the forecasting process—not a passive recipient of its outputs. The framework is built on three tightly coupled, co‐evolving thrusts: Physical Science (T1), which advances flood prediction with quantified uncertainty informed by decision relevance; Human Science (T2), which incorporates psychology, behavior, and cultural and institutional context; and Decision Science (T3), which unifies physical predictions and human factors through principled, utility‐based decision support with end‐to‐end uncertainty management. Rather than treating T1 as a solved problem, FEWS‐DM recognizes that forecast development itself must be shaped by decision needs through continuous bidirectional feedback. We identify key scientific, behavioral, and operational challenges limiting such integration and discuss the enabling role of AI, while emphasizing human‐centered design and community feedback as essential for building trust and improving flood risk management.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1985

Design of an advanced flight planning system

The demand for both fuel conservation and four-dimensional traffic management require that the preflight planning process be designed to account for advances in airborne flight management and weather forecasting. The steps and issues in designing such an advanced flight planning system are presented. Focus is placed on the different optimization options for generating the three-dimensional reference path. For the cruise phase, one can use predefined jet routes, direct routes based on a network of evenly spaced grid points, or a network where the grid points are existing navaid locations. Each choice has its own problem in determining an optimum solution. Finding the reference path is further complicated by choice of cruise altitude levels, use of a time-varying weather field, and requiring a fixed time-of-arrival (four-dimensional problem).

Sorensen, J. A.↗
NASA NTRS2011

Accurate Radiometry from Space: An Essential Tool for Climate Studies

The Earth s climate is undoubtedly changing; however, the time scale, consequences and causal attribution remain the subject of significant debate and uncertainty. Detection of subtle indicators from a background of natural variability requires measurements over a time base of decades. This places severe demands on the instrumentation used, requiring measurements of sufficient accuracy and sensitivity that can allow reliable judgements to be made decades apart. The International System of Units (SI) and the network of National Metrology Institutes were developed to address such requirements. However, ensuring and maintaining SI traceability of sufficient accuracy in instruments orbiting the Earth presents a significant new challenge to the metrology community. This paper highlights some key measurands and applications driving the uncertainty demand of the climate community in the solar reflective domain, e.g. solar irradiances and reflectances/radiances of the Earth. It discusses how meeting these uncertainties facilitate significant improvement in the forecasting abilities of climate models. After discussing the current state of the art, it describes a new satellite mission, called TRUTHS, which enables, for the first time, high-accuracy SI traceability to be established in orbit. The direct use of a primary standard and replication of the terrestrial traceability chain extends the SI into space, in effect realizing a metrology laboratory in space . Keywords: climate change; Earth observation; satellites; radiometry; solar irradiance

Fox, Nigel↗
NASA NTRS2010

Extravehicular Activity (EVA) Technology Development Status and Forecast

Beginning in Fiscal Year (FY) 2011, Extravehicular activity (EVA) technology development became a technology foundational domain under a new program Enabling Technology Development and Demonstration. The goal of the EVA technology effort is to further develop technologies that will be used to demonstrate a robust EVA system that has application for a variety of future missions including microgravity and surface EVA. Overall the objectives will be reduce system mass, reduce consumables and maintenance, increase EVA hardware robustness and life, increase crew member efficiency and autonomy, and enable rapid vehicle egress and ingress. Over the past several years, NASA realized a tremendous increase in EVA system development as part of the Exploration Technology Development Program and the Constellation Program. The evident demand for efficient and reliable EVA technologies, particularly regenerable technologies was apparent under these former programs and will continue to be needed as future mission opportunities arise. The technological need for EVA in space has been realized over the last several decades by the Gemini, Apollo, Skylab, Space Shuttle, and the International Space Station (ISS) programs. EVAs were critical to the success of these programs. Now with the ISS extension to 2028 in conjunction with a current forecasted need of at least eight EVAs per year, the EVA technology life and limited availability of the EMUs will become a critical issue eventually. The current Extravehicular Mobility Unit (EMU) has vastly served EVA demands by performing critical operations to assemble the ISS and provide repairs of satellites such as the Hubble Space Telescope. However, as the life of ISS and the vision for future mission opportunities are realized, a new EVA systems capability could be an option for the future mission applications building off of the technology development over the last several years. Besides ISS, potential mission applications include EVAs for missions to Near Earth Objects (NEO), Phobos, or future surface missions. Surface missions could include either exploration of the Moon or Mars. Providing an EVA capability for these types of missions enables in-space construction of complex vehicles or satellites, hands on exploration of new parts of our solar system, and engages the public through the inspiration of knowing that humans are exploring places that they have never been before. This paper offers insight into what is currently being developed and what the potential opportunities are in the forecast

Chullen, Cinda↗
NASA NTRS2011

Prediction of Weather Impacted Airport Capacity using Ensemble Learning

Ensemble learning with the Bagging Decision Tree (BDT) model was used to assess the impact of weather on airport capacities at selected high-demand airports in the United States. The ensemble bagging decision tree models were developed and validated using the Federal Aviation Administration (FAA) Aviation System Performance Metrics (ASPM) data and weather forecast at these airports. The study examines the performance of BDT, along with traditional single Support Vector Machines (SVM), for airport runway configuration selection and airport arrival rates (AAR) prediction during weather impacts. Testing of these models was accomplished using observed weather, weather forecast, and airport operation information at the chosen airports. The experimental results show that ensemble methods are more accurate than a single SVM classifier. The airport capacity ensemble method presented here can be used as a decision support model that supports air traffic flow management to meet the weather impacted airport capacity in order to reduce costs and increase safety.

Weather impact↗
NASA NTRSDate not supplied

Predictive Workload Model for Air Traffic Controllers during UAM Operations

The effect of airspace factors on air traffic controller (ATC) workload has been an active area of study for almost three decades due to the importance of safety considerations necessary to design and maintain operations. Existing literature has examined several traffic-related (e.g., number of aircraft under control, loss of separation) contributors to ATC workload and proposed mathematical functions to best describe controller response. However, future air traffic continues to increase in complexity with the introduction of urban air mobility (UAM) – or the transportation of humans and cargo using electric vertical takeoff and landing (eVTOL) aircraft. UAM aims to alleviate congestion for existing ground transportation systems and improve mobility within urban centers and other high-demand locations. This shift in the traditional airspace paradigm necessitates an evolved understanding of model use and development for ATC workload prediction. This study aimed to develop an ATC workload forecasting model based on human-in-the-loop (HITL) simulation data for UAM operations at large airports. Data collected from the HITL simulation served as the training and testing data for a Long Short-Term Memory recurrent neural network and enabled time-series forecasting of ATC workload from traffic characteristics. Results demonstrated the potential of LSTM models for forecasting ATC workload 40 minutes into the future and highlighted important considerations for future development.

predictive model↗
NASA NTRSDate not supplied

Predictive Workload Model for Air Traffic Controllers during UAM Operations

The effect of airspace factors on air traffic controller (ATC) workload has been an active area of study for almost three decades due to the importance of safety considerations necessary to design and maintain operations. Existing literature has examined several traffic-related (e.g., number of aircraft under control, loss of separation) contributors to ATC workload and proposed mathematical functions to best describe controller response. However, future air traffic continues to increase in complexity with the introduction of urban air mobility (UAM) – or the transportation of humans and cargo using electric vertical takeoff and landing (eVTOL) aircraft. UAM aims to alleviate congestion for existing ground transportation systems and improve mobility within urban centers and other high-demand locations. This shift in the traditional airspace paradigm necessitates an evolved understanding of model use and development for ATC workload prediction. This study aimed to develop an ATC workload forecasting model based on human-in-the-loop (HITL) simulation data for UAM operations at large airports. Data collected from the HITL simulation served as the training and testing data for a Long Short-Term Memory recurrent neural network and enabled time-series forecasting of ATC workload from traffic characteristics. Results demonstrated the potential of LSTM models for forecasting ATC workload 40 minutes into the future and highlighted important considerations for future development.

predictive model↗
DOE OSTI2024

A Test of Functional Balance Theory for Wetland Biomass Allocation in a Global Change Experiment

Abstract Forecasts of root growth and carbon sequestration under global change are compromised by uncertainty in how plants will allocate biomass between above and belowground pools. Here, we develop a simple model to assess whether functional balance theory can explain a complex biomass allocation response observed in a brackish marsh under experimental warming and elevated CO 2 . Our model shows how treatment‐driven changes in nitrogen supply and demand can explain divergent observations of root growth (i.e., maximum responses under intermediate warming and elevated CO 2 ). The model also reveals a surprising interaction between warming and eutrophication, where enhanced N loading to coastal marshes may reduce adverse impacts of warming on root growth. Our findings provide a mechanistic basis for incorporating biomass allocation into forecast models of marsh evolution. They also provide a general example of using ecological theory to decompose complex net responses observed in multi‐factor global change experiments into constituent processes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1976

Cost/benefit tradeoffs for reducing the energy consumption of the commercial air transportation system. Volume 2: Market and economic analyses

The impact of the most promising fuel conserving options on fuel consumption, passenger demand, operating costs, and airline profits when implemented into the U.S. domestic and international airline fleets is assessed. The potential fuel savings achievable in the U.S. scheduled air transportation system over the forecast period, 1973-1990, are estimated.

Vanabkoude, J. C.↗
NASA NTRSDate not supplied

Improving Access to the GEOS Composition Forecast Model with API Development and Ingestion into Google Earth Engine

The GEOS Composition Forecast (GEOS-CF) model produces forecast and historical estimates of atmospheric composition and meteorology fields, which provide useful insight into air quality issues and events. In a year for which Canadian wildfires created adverse air quality conditions in the eastern United States, access to model fields such as PM2.5 are in high demand. The GEOS-CF team at the NASA Global Modeling and Assimilation Office (GMAO) first developed in-house solutions to improve data access via the CF API, and recently partnered with Google to ingest a collated set of model diagnostics into the Google Earth Engine (GEE) data repository. GEOS-CF model output is also being ingested into AWS storage. Creating these various open access points to GEOS-CF model diagnostics provides the public with an opportunity to easily interact with air quality information. Users are able to use a temporally consistent global grid of air quality fields in machine learning applications, mapping tools, and data informatics. Hosting GEOS-CF forecasts and the historical timeseries of these chemistry and meteorology fields in GEE allows users to create dynamic JavaScript-based air quality applications in the GEE code editor. GEOS-CF users can also access the model output via the GEE Python application programming interface (API), making it easy to perform various analyses with Python. This presentation will show two examples of accessing the GEOS-CF model through GEE. The first is an example application made in the GEE code editor which allows users to view time series plots and downscaled maps of surface level NO2. The second example exhibits using the GEE Python API to create a machine learning model to temporally gap-fill between air quality observations. These examples are an introduction to the many possible benefits of having open access to the GEOS-CF model through multiple platforms.

Callum Wayman↗
NASA NTRSDate not supplied

Improving Access to the GEOS Composition Forecast Model with API Development and Ingestion into Google Earth Engine

The GEOS Composition Forecast (GEOS-CF) model produces forecast and historical estimates of atmospheric composition and meteorology fields, which provide useful insight into air quality issues and events. In a year for which Canadian wildfires created adverse air quality conditions in the eastern United States, access to model fields such as PM2.5 are in high demand. The GEOS-CF team at the NASA Global Modeling and Assimilation Office (GMAO) first developed in-house solutions to improve data access via the CF API, and recently partnered with Google to ingest a collated set of model diagnostics into the Google Earth Engine (GEE) data repository. GEOS-CF model output is also being ingested into AWS storage. Creating these various open access points to GEOS-CF model diagnostics provides the public with an opportunity to easily interact with air quality information. Users are able to use a temporally consistent global grid of air quality fields in machine learning applications, mapping tools, and data informatics. Hosting GEOS-CF forecasts and the historical timeseries of these chemistry and meteorology fields in GEE allows users to create dynamic JavaScript-based air quality applications in the GEE code editor. GEOS-CF users can also access the model output via the GEE Python application programming interface (API), making it easy to perform various analyses with Python. This presentation will show two examples of accessing the GEOS-CF model through GEE. The first is an example application made in the GEE code editor which allows users to view time series plots and downscaled maps of surface level NO 2 . The second example exhibits using the GEE Python API to create a machine learning model to temporally gap-fill between air quality observations. These examples are an introduction to the many possible benefits of having open access to the GEOS-CF model through multiple platforms.

Callum Wayman↗
DOE OSTI2025

A Neural Optimizer With Decision-Focused Learning for Optimal Energy Storage Operation

Here, this article introduces a neural optimizer-based framework for optimizing battery energy storage system (BESS) control for grid services, including demand charge and energy cost reduction. By leveraging decision-focused learning (DFL), the proposed framework ensures seamless integration and adaptation, significantly enhancing control performance. A patch time-series transformer is employed for peak load forecasting, incorporating aleatoric uncertainty quantification to account for forecasting uncertainties within the decision-making process. The framework utilizes a solver-in-the-loop approach to generate optimal BESS actions, which are then used to train the neural optimizer-based agent. By co-optimizing both BESS operational modes and output power within the NN, the system achieves improved performance and robustness. After initial training, the forecasting and control models are jointly fine-tuned to account for forecasting errors, further improving decision precision and efficiency through DFL. Case studies are performed to validate the performance of the framework using multiple real-world datasets, demonstrating superior performance in monthly peak load forecasting compared to state-of-the-art models. In addition, the results are compared against existing decision-making approaches. The results demonstrate a reduction in monthly peak forecasting error by approximately 15% across various performance measures and achieve an optimization gap for BESS operation that is about three times smaller compared to existing methods.

Kim, Hyeonjin [Pacific Northwest National Laborato↗