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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Breaking Down the Computational Barriers to Real-Time Urban Flood Forecasting

Flooding impacts are on the rise globally, and concentrated in urban areas. Currently, there are no operational systems to forecast flooding at spatial resolutions that can facilitate emergency preparedness and response actions mitigating flood impacts. We present a framework for real-time flood modeling and uncertainty quantification that combines the physics of fluid motion with advances in probabilistic methods. The framework overcomes the prohibitive computational demands of high-fidelity modeling in real-time by using a probabilistic learning method relying on surrogate models that are trained prior to a flood event. This shifts the overwhelming burden of computation to the trivial problem of data storage, and enables forecasting of both flood hazard and its uncertainty at scales that are vital for time-critical decision-making before and during extreme events. The framework has the potential to improve flood prediction and analysis and can be extended to other hazard assessments requiring intense high-fidelity computations in real-time.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Distribution System Congestion Management - A Survey of Reliable Integration for Aggregated Resources and Microgrids

Rising penetration of consumer-owned Distribution Grid Resources (DGRs), increasingly managed by third party aggregators and enrolled in grid services and wholesale market programs, can create localized congestion in distribution networks. Managing these constraints is challenging due to a persistent coordination and information gap: utilities are accountable for reliability and have network topology and state visibility, while aggregators control the DGR capability needed to relieve congestion. This survey synthesizes congestion management solutions for distribution systems with high DGR penetration, covering both market-based mechanisms (distribution level markets, locational pricing, flexibility auctions) and non-market-based solutions (network reconfiguration, direct DGR control, demand response, curtailment, etc.). The literature is organized across three decision horizons: long term planning, operational planning, and real-time operation. Special attention is devoted to emerging distribution system operator architectures and coordination frameworks spanning transmission system operators, aggregators, and microgrids. Drawing on recent case studies and implementations, we distill best practices, identify key technical and economic barriers, and outline research directions. The evidence points to a shift toward integrated congestion management that combines market signals with technical controls, enabled by improved monitoring, forecasting, and closed loop control capabilities.

Active Distribution Networks (ADN)↗
DOE OSTI2022

A graph neural network (GNN) approach to basin-scale river network learning: the role of physics-based connectivity and data fusion

Abstract. Rivers and river habitats around the world are under sustained pressure from human activities and the changing global environment. Our ability to quantify and manage the river states in a timely manner is critical for protecting the public safety and natural resources. In recent years, vector-based river network models have enabled modeling of large river basins at increasingly fine resolutions, but are computationally demanding. This work presents a multistage, physics-guided, graph neural network (GNN) approach for basin-scale river network learning and streamflow forecasting. During training, we train a GNN model to approximate outputs of a high-resolution vector-based river network model; we then fine-tune the pretrained GNN model with streamflow observations. We further apply a graph-based, data-fusion step to correct prediction biases. The GNN-based framework is first demonstrated over a snow-dominated watershed in the western United States. A series of experiments are performed to test different training and imputation strategies. Results show that the trained GNN model can effectively serve as a surrogate of the process-based model with high accuracy, with median Kling–Gupta efficiency (KGE) greater than 0.97. Application of the graph-based data fusion further reduces mismatch between the GNN model and observations, with as much as 50 % KGE improvement over some cross-validation gages. To improve scalability, a graph-coarsening procedure is introduced and is demonstrated over a much larger basin. Results show that graph coarsening achieves comparable prediction skills at only a fraction of training cost, thus providing important insights into the degree of physical realism needed for developing large-scale GNN-based river network models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Heuristic Dispatch Based on Price Signals for Behind-the-Meter PV-Battery Systems in the System Advisor Model

The economic potential of a behind-the-meter (BTM) PV-battery system depends greatly on how the battery is dispatched. Different utility rates, system sizes, generation and load profiles can all require different dispatch strategies. This paper presents price signals dispatch, a new algorithm for automated economic dispatch of BTM PV-battery systems, which utilizes 24-hour PV and load forecasts, degradation data, and utility rates. The algorithm is integrated with the System Advisor Model (SAM) tool and is tested with a nonlinear generic electrochemical battery model. Price signals dispatch outperforms SAM's existing algorithms in cases requiring a balance between demand charge management and energy arbitrage, and in cases where battery degradation imposes a significant cost.

batteries↗
NASA NTRS2018

Enhancing the USDA FAS Crop Forecasting System Using SMAP L3 Soil Moisture Observations

One of the U.S. Department of Agriculture-Foreign Agricultural Services (USDA-FAS) mission objectives is to provide current information on global crop supply and demand estimates. Crop growth and development is especially susceptible to the amount of water present in the root-zone portion of the soil profile. Therefore, accurate knowledge of the root-zone soil moisture (RZSM) is an essential for USDA-FAS global crop assessments. This paper focusses on the possibility of enhancing the USDA-FAS's RZSM estimates through the integration of passive-based soil moisture observations derived from the Soil Moisture Active Passive (SMAP) mission into the USDA-FAS Palmer model. Lag-correlation analysis, which explores the agreement between changes in RZSM and crop status indicated that the satellite-based observations can enhance the model-only estimates.

crop growth↗
NASA NTRS2018

A Markov Decision Process Framework for Optimal Airport Reconfiguration

The airport runway configuration is defined as a combination set of runways for arrivals and departures used at a point during operation of the airport. An optimal configuration of these runways depends on a number of factors, including traffic demand, wind magnitude and direction, other adverse weather conditions, and noise restrictions, among others. Based on the current state of these factors and predictions of traffic demand and weather conditions, runway configuration changes are made and coordinated between tower controller, other air traffic control facilities, pilots, and ground personnel. Reconfigurations can be quite disruptive to airport operations; minimizing their frequency and scheduling them well in advance is essential for mitigating some of the added workload for controllers and pilots. Unfortunately, deciding on an appropriate time to change is challenging for human decision makers. Not only do multiple factors need to be evaluated, but the uncertainty in their forecasts must also be considered. Previous optimization methods, such as mixed linear integer programming, have been proposed. Although these methods can reason over a large set of variables, they do not systematically handle the uncertainty associated with weather movement, traffic demands, and other variables. In this work, we introduce a Markov Decision Process (MDP)-based decision making framework which can reason effectively over the inherent uncertainties and make optimal decisions on if/when to change the airport configuration. In a prototype implementation, we present a single runway with three aircraft and utilize knowledge of the forecasted wind speed and direction to determine whether to keep or change the current runway configuration. Our aim through this work is to present a framework for airport reconfiguration which can be scalable to additional aircraft, multiple runways, and various input parameters. This technique will optimize the airport reconfiguration procedure by providing a proactive approach, optimizing not just at the next optimal opportunity for a reconfiguration based on varying atmospheric and traffic conditions in the terminal airspace, but also anticipating future necessary reconfigurations. This will eliminate the inefficiencies of frequent changes currently associated with runway reconfiguration procedures.

runway reconfiguration↗
DOE OSTI2026

Storing Affordability: Battery Storage as an Asset to Reduce Data Center Cost Shifts

This report examines how battery energy storage systems (BESS) can help utilities accommodate large load growth while protecting affordability for existing ratepayers. Rapid growth in electricity demand from artificial intelligence (AI) data centers is straining the U.S. grid. Furthermore, many new data centers are entering rural markets, which could offer economic benefits but may also pose implementation challenges for smaller utilities. At the same time, retail electricity prices are increasing faster than inflation, elevating customer affordability as a key challenge. While data centers have not been the primary driver of increases in residential prices to date, they have pushed wholesale energy and capacity prices higher in several markets. Fundamental utility cost-allocation principles show that data center growth can be rate-positive for existing customers only if new peak demand grows faster than the costs a utility must incur to serve it. Several factors, including a utility’s degree of wholesale market exposure, forecast uncertainty and stranded-asset risk, and tariff design can determine the outcome of load growth on retail rates. Energy storage can make several affordability contributions in the face of this landscape of uncertainty and market volatility, including deferral of higher-cost grid investments through improved utilization of existing assets and flexibility of new large loads, insulation from volatile wholesale prices through peak shaving, and reliability support to address grid risks stemming from the behavior of AI data center loads. Different potential BESS deployment pathways—utility-scale front-of-the-meter systems, aggregated small-scale storage installations, and data center-sited behind-the-meter storage—are compared against each other and against conventional capacity alternatives. This framework is intended as a conceptual resource to utilities, particularly smaller public utilities with rural service territories, who may be considering the role that energy storage can play in insulating existing ratepayers from data center cost shifts.

25 ENERGY STORAGE↗
NASA NTRS1998

Controller Strategies for Managing Air Traffic in High Altitude Arrival Sectors

Substantial increases in the volume of air traffic in the National Airspace System (NAS) are forecast for the next decade, with the number of passengers travelling on U.S. airlines expected to increase by as much as 60%. This increased demand on system capacity will be accompanied by increases in traffic complexity as air traffic service providers routinely accommodate user preferred routing requests. Changes to the NAS to meet these new demands are currently underway, including development of new decision support tools to aid controllers in monitoring and managing air traffic, and increased air-to-air and air-to-ground information exchange. Changes in roles and responsibilities of pilots and controllers in flight path management will accompany these changes in traffic patterns and information technology, however the ultimate responsibility for maintaining aircraft separation will remain with the air traffic controller. A thorough understanding of the methods controllers use to manage air traffic will help ensure that changes to the NAS are implemented in a way that maintains the controller's ability to separate aircraft as the system evolves. This presentation describes the strategies controllers use today to manage arrival traffic in its descent from cruise altitude to the Terminal Radar Approach Control (TRACON) boundary. Factors that increase the complexity of this task include the presence of overflight traffic, varying aircraft performance characteristics, winds aloft, ground speed variations with altitude, the need to merge arrival traffic into a single stream, and, when arrival traffic exceeds airport runway capacity, the added task of metering flow into the TRACON. Because of the limited information available to controllers to manage arrival traffic, their strategies are often driven by the need to reduce the task's complexity, which can result in de-optimized flight paths for individual aircraft (e.g., sub-optimal descent or speed profiles). Understanding these strategies and the cognitive demands that drive them will support a safe transition to a NAS that relies on enhanced technologies. In addition, it could enable system developers to identify opportunities for new automation-based procedures or information displays that could reduce the controller's workload and increase operational efficiency.

Smith, Nancy↗
DOE OSTI2026

How Improved Forecasting Can Increase the Bulk Power System Value of Price-Responsive Electric Vehicle Managed Charging

Personal light-duty vehicle (LDV) electric vehicle managed charging (EVMC) can reduce power system costs by better aligning electric vehicle (EV) charging with locations and times of low energy cost or infrastructure use. The need to coordinate charging demand across thousands to millions of vehicles while preserving mobility service is a barrier to realizing the value of EVMC. Price-responsive dispatch mechanisms like time-of-use rates (TOU) and hourly real-time prices (RTP) are attractive compared to direct load control (DLC) because they only require one-way communications and local controls. However, increasing participation in price responsive mechanisms can induce costly-to-serve spikes in load and otherwise increase, rather than decrease, production costs. We quantify the ability of improved EVMC forecasting to sustain savings from price responsive mechanisms beyond the limit of 14% of LDVs actively participating observed in previous work. Perfect forecasting of price-responsive EV load makes TOU and RTP value-competitive with a low-error DLC formulation with up to 27% (within-week flexibility) to 45% or more (within-session flexibility) of LDVs participating in EVMC in an envisioned New England power system with 84% clean energy. Additional costs of implementing DLC should be no more than tens of dollars per vehicle-year if DLC is to be value-competitive with accurately forecast price-responsive EVMC for double-digit percentage shares of LDVs participating.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Large-scale scenarios of electric vehicle charging with a data-driven model of control

Transportation electrification is forecast to bring millions of new electric vehicles to roads worldwide this decade. Planning to support those vehicles depends on detailed scenarios of their electricity demand in both uncontrolled and controlled or smart charging scenarios. In this work, we present a novel modeling approach to enable rapid generation of demand estimates that represent the impact of controlled charging for large-scale scenarios with millions of individual drivers. To model the effect of load modulation control on aggregate charging profiles, we propose a novel machine learning approach that replaces traditional optimization approaches. We demonstrate its performance modeling workplace charging control under a range of electricity rate schedules, achieving small errors (2.5%–4.5%) while accelerating computations by more than 4000 times. To generate the uncontrolled charging demand for scenarios with residential, workplace, and public charging we use statistical representations of a large data set of real charging sessions. We demonstrate the methodology by generating diverse sets of scenarios for California's charging demand in 2030 which consider multiple charging segments and controls, each run locally in under 50 s. We further demonstrate support for rate design by modeling the large-scale impact of a new, custom rate schedule for workplace charging.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2006

Sensor Management for Applied Research Technologies (SMART)-On Demand Modeling (ODM) Project

NASA requires timely on-demand data and analysis capabilities to enable practical benefits of Earth science observations. However, a significant challenge exists in accessing and integrating data from multiple sensors or platforms to address Earth science problems because of the large data volumes, varying sensor scan characteristics, unique orbital coverage, and the steep learning curve associated with each sensor and data type. The development of sensor web capabilities to autonomously process these data streams (whether real-time or archived) provides an opportunity to overcome these obstacles and facilitate the integration and synthesis of Earth science data and weather model output. A three year project, entitled Sensor Management for Applied Research Technologies (SMART) - On Demand Modeling (ODM), will develop and demonstrate the readiness of Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) capabilities that integrate both Earth observations and forecast model output into new data acquisition and assimilation strategies. The advancement of SWE-enabled systems (i.e., use of SensorML, sensor planning services - SPS, sensor observation services - SOS, sensor alert services - SAS and common observation model protocols) will have practical and efficient uses in the Earth science community for enhanced data set generation, real-time data assimilation with operational applications, and for autonomous sensor tasking for unique data collection.

Goodman, M.↗
NASA NTRS2020

A Review of Distributed Electric Propulsion Concepts for Air Vehicle Technology

The continuous, yet incremental performance gains in high-efficiency gas turbine engines over the last eight decades has enabled passengers to travel long distances at high speeds. Modern commercial jet transports are so common that most passengers traveling by air today take the innovative developments of jet engine technologies for granted. However, the ever-increasing demands for travel in the 21st century has also brought an increased awareness of the energy and environmental concerns associated with aviation. Due to the forecasted limited supply of petroleum-based fuel sources and the global warming potential associated with emissions of traditional aircraft propulsion systems, the need for environmentally-responsible solutions in aircraft technology has been recognized as a priority in the development of future aircraft configurations.

Hyun Dae Kim↗
DOE OSTI2025

Artificial intelligence time series forecasting for feed-forward laser stabilization

Laser plasma accelerators, typically operating at 1–10 Hz repetition rates, have the ability to produce high-quality electron beams in compact, all-optical-driven configurations, with the electron beams uniquely suited for a wide variety of accelerator-based applications. However, fluctuations and drifts in the laser delivery to the meter-scaled and below plasma target (the electron beam source) will translate into electron beam source variations that can limit their utility for demanding applications like light sources or linear colliders. Commercially available active feedback laser stabilization systems are intrinsically bandwidth limited due to their integration with multi-inch corrective mirror mounts which minimizes their effectiveness. In this manuscript, we present a Neural Network time series forecaster that can predict laser position fluctuations of the laser delivery to the final target well ahead of a future laser shot. The Root-Mean-Square-Error (RMSE) of the prediction accuracy was < 2 μ m for a 1 / e 2 beam radius of 34 μ m . Our feed-forward approach serves as a first-step in circumventing the bandwidth limitations imposed by the currently available stabilization systems since it allows for mirrors to be moved into position ahead of time to offset the predicted future position drift. This will help advance laser plasma accelerator research by providing greater robustness and stability needed for its applications.

Berger, Curtis↗
DOE OSTI2025

Ice storage model-predictive control in an office building with PV: scenario, error and sensitivity analysis

Thermal energy storage (TES) can enable more building-sited renewable electricity generation and lower utility bill costs for buildings owners and occupants, especially when there are high demand and variable time-of-use (TOU) charges. A model predictive control (MPC) strategy can offer additional savings over a schedule-based control with added complexity and reliance on forecasts. Here, this study examines savings for medium office buildings with chiller plants in three locations with building-installed solar photovoltaics (PV) to understand the impact of MPC. Control setpoints are fixed by a schedule-based control or optimized by nonlinear MPC. These control setpoints are actuated within EnergyPlus building models to simulate the utility cost of the chiller plant. NLP solutions can be unstable or unrealistic, but our results show that by regularizing the NLP, the solutions can be reasonably followed by the building model. MPC models make simplifications that lead to errors once the controller is participating in and changing the operation of the building. These errors average 9 % across the cases, showing that the most important parts of the system are represented. The no-thermal load costs are computed to show that the optimization can in some cases achieve both the minimum TOU and minimum monthly demand costs by demand management while reducing TOU energy costs by energy arbitrage. The MPC saves 35–66 % in the annual chiller plant operating costs, which is an additional savings above the schedule by 1–33 %. PV and TES are complementary and mostly independent, but a load with PV often results in better performance for the schedule. Our case study and sensitivity analysis show the importance of modeling and optimization for complex rates, but also the circumstances wherein a simpler strategy achieves the same performance with less potential for error.

14 SOLAR ENERGY↗
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↗