Results for “Operational forecasting” — Page 62

NASA NTRS2022

Assimilation of NASA's Airborne Snow Observatory Snow Measurements for Improved Hydrological Modeling: A Case Study Enabled by the Coupled LIS/WRF-Hydro System

The NASA LIS/WRF-Hydro system is a coupled modeling framework that combines the modeling and data assimilation (DA) capabilities of the NASA Land Information System (LIS) with the multi-scale surface hydrological modeling capabilities of the WRF-Hydro model, both of which are widely used in both operations and research. This coupled modeling framework builds on the linkage between land surface models (LSMs), which simulate surface boundary conditions in atmospheric models, and distributed hydrologic models, which simulate horizontal surface and sub-surface flow, adding new land DA capabilities. In the present study, we employ this modeling framework in the Tuolumne River basin in central California. We demonstrate the added value of the assimilation of NASA Airborne Snow Observatory (ASO) snow water equivalent (SWE) estimates in the Tuolumne basin. This analysis is performed in both LIS as an LSM column model and LIS/WRF-Hydro, with hydrologic routing. Results demonstrate that ASO DA in the basin reduced snow bias by as much as 30% from an open-loop (OL) simulation compared to three independent datasets. It also reduces downstream streamflow runoff biases by as much as 40%, and improves streamflow skill scores in both wet and dry years. Analysis of soil moisture and evapotranspiration (ET) also reveals the impacts of hydrologic routing from WRF-Hydro in the simulations, which would otherwise not be resolved in an LSM column model. By demonstrating the beneficial impact of SWE DA on the improving streamflow forecasts, the article outlines the importance of such observational inputs for reservoir operations and related water management applications.

Timothy M. Lahmers↗

NASA NTRS2021

Expanding the application of soil moisture monitoring systems through regression-based transformation

Relative to other geophysical variables, soil moisture (SM) estimates derived from land surface models (LSMs) and land data assimilation systems (LDAS) are difficult to transfer between platforms and applications. This difficulty stems from the highly model-dependent nature of LSM SM estimates and differences in the vertical support of discretized SM values. As a result, operational SM estimates generated by one LSM (or LDAS) cannot generally be directly applied to a hydrologic monitoring or forecast system designed around a second LSM. This lack of transferability is particularly problematic for LDAS applications, where the time, expertise, and computational resources required to generate an operational LDAS analysis cannot be practically duplicated for every LSM-specific application. Here, we develop a set of simple regression tools for translating SM estimates between LSMs and multiple LDAS analyses. Results demonstrate that simple multivariate linear regression - utilizing independent variables based on multi-layer and temporally lagged SM estimates - can significantly improve upon baseline transformation approaches using direct percentile matching. The proposed regression approaches are effective for both the LSM-to-LSM and LDAS-to-LDAS transformation of multi-layer SM percentiles. Application of this approach will expand the utility of existing, high-quality (but LSM-specific) operational sources of SM information like the NASA Soil Moisture Active Passive Level-4 Soil Moisture product.

Soil Moisture↗

DOE OSTI2022

GOOML - Real World Applications of Machine Learning in Geothermal Operations

GOOML (Geothermal Operational Optimization with Machine Learning) is a machine-learning based framework that enables geothermal power plant operators to explore optimization opportunities for their assets in an efficient and robust digital environment. Backed by real-world data sources, thermodynamic constraints and steamfield intelligence, the GOOML environment provides new tools to explore how to best operate steamfields as well as test new scenarios and configurations prior to implementation in the field. To prove the effectiveness of GOOML, we have undertaken optimization experiments using reinforcement learning (RL) to generate operational suggestions using a balance of mass-take targets, sustainability considerations and net generation. Our experiments use the GOOML construct to explore different field parameters and perform multiple reinforcement learning experiments. Like a comprehensive laboratory workbench, we can change out components of a steamfield to perform testing under a variety of conditions (restrict mass, increase pressure, reroute steam, etc.). This flexibility allows us to explore conditions that would require significant infrastructure changes in a real-world setting at a fraction of the cost and time in a digital environment. The results highlight the benefits of using digital twins and advanced data analytics for the geothermal industry.

forecasting↗

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↗

NASA NTRS1975

A cost-effectiveness comparison of existing and Landsat-aided snow water content estimation systems

This study describes how Landsat imagery can be cost-effectively employed to augment an operational hydrologic model. Attention is directed toward the estimation of snow water content, a major predictor variable in the volumetric runoff forecasting model presently used by the California Department of Water Resources. A stratified double sampling scheme is supplemented with qualitative and quantitative analyses of existing operations to develop a comparison between the existing and satellite-aided approaches to snow water content estimation. Results show a decided advantage for the Landsat-aided approach.

Sharp, J. M.↗

NASA NTRS1965

Toward operational weather satellite systems.

Weather satellite system will provide local users with pictures directly from orbit of cloud patterns making possible global cloud cover data daily for worldwide analyses

WEATHER FORECASTING↗

NASA NTRS2021

Commercial Hypersonic Transportation Market Study

In support of NASA’s evaluation of its future research thrusts for its hypersonics program, NASA commissioned independent studies of the market for commercial hypersonic transportation. SAIC and BryceTech (formerly Bryce Space and Technology) were awarded one of these independent studies to assist NASA in better understanding: (1) the passenger demand for high-speed aviation travel; (2) the pressures on the business case for developing and operating a hypersonic aircraft for the commercial aviation market; and (3) the non-technical (i.e., societal) barriers and challenges, including the steps NASA and the Government could take to overcome those barriers and challenges. Our approach to addressing these three tasks for NASA included modeling future demand and future business operations, considering global air transportation at speeds of Mach 2 to Mach 7. The team forecast premium air travel demand through 2060 and assessed the willingness of passengers of different income and wealth levels to pay to save time on flights between 800 city pairs. With the total addressable market defined, we examined industry-level business case viability for several aircraft speed and range cases. Considering operating and manufacturing costs for routes that could be serviced profitably, as well as typical profitability targets for the aviation industry, we quantified the level of RDT&E funding available to support each business case.

hypersonic aircraft↗

NASA NTRS2021

Independent Market Study: Commercial Hypersonic Transportation

In support of NASA’s evaluation of its future research thrusts for its hypersonics program, NASA commissioned independent studies of the market for commercial hypersonic transportation. SAIC and BryceTech (formerly Bryce Space and Technology) were awarded one of these independent studies to assist NASA in better understanding: (1) the passenger demand for high-speed aviation travel; (2) the pressures on the business case for developing and operating a hypersonic aircraft for the commercial aviation market; and (3) the non-technical (i.e., societal) barriers and challenges, including the steps NASA and the Government could take to overcome those barriers and challenges. Our approach to addressing these three tasks for NASA included modeling future demand and future business operations, considering global air transportation at speeds of Mach 2 to Mach 7. The team forecast premium air travel demand through 2060 and assessed the willingness of passengers of different income and wealth levels to pay to save time on flights between 800 city pairs. With the total addressable market defined, we examined industry-level business case viability for several aircraft speed and range cases. Considering operating and manufacturing costs for routes that could be serviced profitably, as well as typical profitability targets for the aviation industry, we quantified the level of RDT&E funding available to support each business case.

hypersonic aircraft↗

NASA NTRS2012

Validation and Verification of Operational Land Analysis Activities at the Air Force Weather Agency

The NASA developed Land Information System (LIS) is the Air Force Weather Agency's (AFWA) operational Land Data Assimilation System (LDAS) combining real time precipitation observations and analyses, global forecast model data, vegetation, terrain, and soil parameters with the community Noah land surface model, along with other hydrology module options, to generate profile analyses of global soil moisture, soil temperature, and other important land surface characteristics. (1) A range of satellite data products and surface observations used to generate the land analysis products (2) Global, 1/4 deg spatial resolution (3) Model analysis generated at 3 hours. AFWA recognizes the importance of operational benchmarking and uncertainty characterization for land surface modeling and is developing standard methods, software, and metrics to verify and/or validate LIS output products. To facilitate this and other needs for land analysis activities at AFWA, the Model Evaluation Toolkit (MET) -- a joint product of the National Center for Atmospheric Research Developmental Testbed Center (NCAR DTC), AFWA, and the user community -- and the Land surface Verification Toolkit (LVT), developed at the Goddard Space Flight Center (GSFC), have been adapted to operational benchmarking needs of AFWA's land characterization activities.

Shaw, Michael↗

NASA NTRS1990

Tropical Pacific moisture variability: Its detection, synoptic structure and consequences in the general circulation

Satellite data analysis tools are developed and implemented for the diagnosis of atmospheric circulation systems over the tropical Pacific Ocean. The tools include statistical multi-variate procedures, a multi-spectral radiative transfer model, and the global spectral forecast model at NMC. Data include in-situ observations; satellite observations from VAS (moisture, infrared and visible) NOAA polar orbiters (including Tiros Operational Satellite System (TOVS) multi-channel sounding data and OLR grids) and scanning multichannel microwave radiometer (SMMR); and European Centre for Medium Weather Forecasts (ECHMWF) analyses. A primary goal is a better understanding of the relation between synoptic structures of the area, particularly tropical plumes, and the general circulation, especially the Hadley circulation. A second goal is the definition of the quantitative structure and behavior of all Pacific tropical synoptic systems. Finally, strategies are examined for extracting new and additional information from existing satellite observations. Although moisture structure is emphasized, thermal patterns are also analyzed. Both horizontal and vertical structures are studied and objective quantitative results are emphasized.

Mcguirk, James P.↗

NASA NTRS1980

An analysis of the Seasat Satellite Data Distribution System

A computerized data distribution network for remote accessing of Seasat generated data is described. The service is intended as an experiment to determine user needs and operational abilities for utilizing on-line satellite generated oceanographic data. Synoptic weather observations are input to the U.S. Fleet Numerical Oceanographic Central for preparation and transfer to a PDP 11/60 central computer, from which all access trunks originate. The data available includes meteorological and sea-state information in the form of analyses and forecasts, and users are being monitored for reactions to the system design, data products, system operation, and performance evaluation. The system provides data on sea level and upper atmospheric pressure, sea surface temperature, wind magnitude and direction, significant wave heights, direction, and periods, and spectral wave data. Transmissions have a maximum rate of 1.1 kbit/sec over the telephone line.

Ferrari, A. J.↗

NASA NTRS2009

An Operational Assessment of the MODIS False Color Composite with the Great Falls, Montana National Weather Service

The close and productive collaborations between the NWS Warning and Forecast Office (WFO) in Great Falls, MT and the Short Term Prediction and Research Transition (SPORT) Center at NASA/Marshall Space Flight Center have provided a unique opportunity for science sharing and technology transfer. In particular, SPoRT has provided a false color composite product derived from MODIS data, which is part of NASA's Earth Observing System. This product is designed to delineate snow and ice covered ground, bare ground and clouds. The Great Falls WFO has been a test bed of the MODIS false color composite as a tool in operations to monitor the development and dissipation of snow cover In particular, preliminary applications have shown that the product can be used to monitor snow cover in remote locations as well as ice in rivers. This information can lead to improved assessments of flooding potential during post event conditions where rapid melting and runoff are anticipated. The potential of this product on future geostationary satellites may substantially contribute to the NWS mission by providing enhanced situational awareness. The operational use of this product has been transitioned at WFO Great Falls through a process of product implementation, discussions with the service hydrologist and forecasters, and post event analysis. A concentrated assessment period from January to March, 2008 was initiated to investigate the impact of the MODIS false color product on WFO Great Falls' operations. This presentation will emphasize the impact the MODIS false color product had in the WFO's situational awareness and how best this information can be used to influence operational decisions.

Loss, GIna↗

DOE OSTI2022

DSO+T: Transactive Energy Coordination Framework (DSO+T Study: Volume 3)

This report describes a transactive energy coordination scheme designed to integrate into existing day-ahead and real-time wholesale energy markets. This scheme was evaluated in the Distribution System Operator with Transactive (DSO+T) study to assess the engineering and economic performance of the transactive energy coordination of a large-scale deployment of distributed energy resources (DER). Transactive agents were developed for a range of DERs (heating, ventilation, and air conditioning units, water heaters, batteries, and electric vehicles) that optimize flexibility over a 48-hour horizon and adjust their strategy in response to changes in real-time prices. A transactive energy coordination scheme, executed by a DSO retail market operator, aggregates these DER bids from participating customers and clears them against a DSO supply curve using a double auction market mechanism. The process of constructing the price-quantity DSO supply curve includes distribution-level transportation constraints (for example, substation congestion limits) and forecast locational marginal price of the DSO’s connected transmission node. The resulting day-ahead and real-time quantities are then bid into a competitive wholesale market operated by an independent system operator. This report also details additional capabilities for proper marketplace simulation such as wholesale price, weather, and load forecasting. The report concludes with a discussion of lessons learned and key design features required to ensure successful operation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS2022

Polarization performance simulation for the GeoXO atmospheric composition instrument: NO2 retrieval impacts

NOAA’s Geostationary Extended Observations (GeoXO) constellation will continue and expand on the capabilities of the current generation of geostationary satellite systems to support US weather, ocean, atmosphere, and climate operations. It is planned to consist of a dedicated atmospheric composition instrument (ACX) to support air quality forecasting and monitoring by providing similar capabilities to missions such as TEMPO (Tropospheric Emission: Monitoring Pollution), currently planned to launch in 2023, and Ozone Monitoring Instrument (OMI), TROPOMI (TROPOspheric Monitoring Instrument), and GEMS (Geostationary Environment Monitoring Spectrometer) currently in operation. As the early phases of ACX development are progressing, design trade-offs are being considered to understand the relationship between instrument design choices and trace gas retrieval impacts. Some of these choices will affect the instrument polarization sensitivity (PS), which can have radiometric impacts on environmental satellite observations. We conducted a study to investigate how such radiometric impacts can affect NO2 retrievals by exploring their sensitivities to time of day, location, and scene type with an ACX instrument model that incorporates PS. The study addresses the basic steps of operational NO2 retrievals: the spectral fitting step and the conversion of slant column to vertical column via the air mass factor (AMF). The spectral fitting step was performed by generating at-sensor radiance from a clear sky scene with a known NO2 amount, the application of an instrument model including both instrument PS and noise, and a physical retrieval. The spectral fitting step was found to mitigate the impacts of instrument PS. The AMF-related step was considered for clear sky and partially cloudy scenes, where instrument PS can lead to errors in interpreting the cloud content, propagating to AMF errors and finally to NO2 retrieval errors. For this step, the NO2 retrieval impacts were small but non-negligible for high NO2 amounts; we estimated that a typical high NO2 amount can cause maximum of 0.25 x 10^15 molecules/cm^2 for a PS of 5%. These simulation capabilities were designed to aid in the development of a GeoXO atmospheric composition instrument that will improve our ability to monitor and understand the Earth’s atmosphere.

trace gases↗

DOE OSTI2022

A Hybrid Reinforcement Learning-MPC Approach for Distribution System Critical Load Restoration

This paper proposes a hybrid control approach for distribution system critical load restoration, combining deep reinforcement learning (RL) and model predictive control (MPC) aiming at maximizing total restored load following an extreme event. RL determines a policy for quantifying operating reserve requirements, thereby hedging against uncertainty, while MPC models grid operations incorporating RL policy actions (i.e., reserve requirements), renewable (wind and solar) power predictions, and load demand forecasts. We formulate the reserve requirement determination problem as a sequential decision-making problem based on the Markov Decision Process (MDP) and design an RL learning environment based on the OpenAI Gym framework and MPC simulation. The RL agent reward and MPC objective function aim to maximize and monotonically increase total restored load and minimize load shedding and renewable power curtailment. The RL algorithm is trained offline using a historical forecast of renewable generation and load demand. The method is tested using a modified IEEE 13-bus distribution test feeder containing wind turbine, photovoltaic, microturbine, and battery. Case studies demonstrated that the proposed method outperforms other policies with static operating reserves.

distribution system↗

DOE OSTI2026

Predictive Model for Starlink Maritime Performance Using Multi-Horizon RandomForest

Low Earth orbit (LEO) satellite systems have become a crucial enabler of broadband access for maritime industries, where traditional networks are unavailable. However, the high mobility of LEO constellations and constantly changing weather conditions result in unpredictable link fluctuations, limiting the ability of maritime platforms to plan bandwidth usage proactively. To the best of our knowledge, no prior work has developed a short-term predictive model for maritime LEO connectivity using real experimental field measurements. This paper proposes a data-driven forecasting model that predicts future downlink throughput using multi-horizon RandomForest regression. The model is trained using real experimental coastal measurement data incorporating recent throughput history, network-layer indicators, and environmental variables. The proposed approach reduces mean absolute error by approximately 31% compared to a persistence baseline for 15-minute horizons. It maintains a measurable improvement at 30 minutes, despite increased stochasticity. These findings confirm that proactive bandwidth awareness is feasible on maritime platforms and can effectively support operational decisions such as adaptive streaming, routing, and resource scheduling. The performance gap between forecasting horizons also highlights the need for expanded offshore datasets to improve prediction robustness under harsher maritime environments.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Incorporating Operational Uncertainties into the Dispatch of an Integrated Solar and Storage System

The economic assessment of hybrid energy systems (HES) pairing battery energy storage systems (BESSs) and photovoltaics (PV) is highly important for advancing their deployment in power systems. This paper presents an innovative assessment framework, including an optimal control policy for dispatch under uncertainty and procedures for exploring control parameters that maximize economic benefits. The proposed dispatch policy consists of two steps using system forecast information. The first step is to determine whether a BESS will be used within an operational scheduling time frame based on the probability of events and their thresholds. Once the dispatch of BESS is triggered, a model predictive control (MPC) is carried out in the second step for scheduling using the expected value of system information. By exercising this policy with different thresholds, one can explore the trade-offs between short-term benefits and battery lifetime, and identify an optimal threshold that maximizes the total economic benefits within the battery lifetime. An evaluation study in a real-world HES project is presented to illustrate the proposed framework. Compared with traditional optimal dispatch algorithms, the proposed method can significantly improve the economic benefits of an HES scheduled under forecast uncertainties.

Ma, Xu↗