Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Operational forecasting”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

A Peak Wind Probability Forecast Tool for Kennedy Space Center and Cape Canaveral Air Force Station

This conference abstract describes the development of a peak wind forecast tool to assist forecasters in determining the probability of violating launch commit criteria (LCC) at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) in east-central Florida. The peak winds are an important forecast element for both the Space Shuttle and Expendable Launch Vehicle (ELV) programs. The LCC define specific peak wind thresholds for each launch operation that cannot be exceeded in order to ensure the safety of the vehicle. The 45th Weather Squadron (45 WS) has found that peak winds are a challenging parameter to forecast, particularly in the cool season months of October through April. Based on the importance of forecasting peak winds, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a short-range peak-wind forecast tool to assist in forecasting LCC violatioas.The tool will include climatologies of the 5-minute mean end peak winds by month, hour, and direction, and probability distributions of the peak winds as a function of the 5-minute mean wind speeds.

Crawford, Winifred↗
NASA NTRS2007

Developing a Peak Wind Probability Forecast Tool for Kennedy Space Center and Cape Canaveral Air Force Station

This conference presentation describes the development of a peak wind forecast tool to assist forecasters in determining the probability of violating launch commit criteria (LCC) at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) in east-central Florida. The peak winds are an important forecast element for both the Space Shuttle and Expendable Launch Vehicle (ELV) programs. The LCC define specific peak wind thresholds for each launch operation that cannot be exceeded in order to ensure the safety of the vehicle. The 45th Weather Squadron (45 WS) has found that peak winds are a challenging parameter to forecast, particularly in the cool season months of October through April. Based on the importance of forecasting peak winds, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a short-range peak-wind forecast tool to assist in forecasting LCC violations. The tool will include climatologies of the 5-minute mean and peak winds by month, hour, and direction, and probability distributions of the peak winds as a function of the 5-minute mean wind speeds.

Lambert, WInifred↗
NASA NTRSDate not supplied

On-Orbit Calibration Assessment of NOAA-21 VIIRS Thermal Emissive Bands and Implications for JPSS-4 VIIRS

The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument onboard the NOAA-21 (N21) satellite has been successfully operating for over three years collecting valuable scientific measurements. A large suite of weather forecasting models and scientific research applications are supported with the VIIRS measurements from N21 combined with those acquired from the VIIRS instrument onboard the S-NPP and NOAA-20 spacecraft. Among the 22 VIIRS spectral bands, there are seven thermal emissive bands (TEB) covering the 3.7 to 12.2 µm spectral range at two different spatial resolutions. The VIIRS TEB detectors are calibrated onboard using a blackbody at a controlled temperature and a space view for background signal measurement. In this paper, we discuss the on-orbit performance of the N21 VIIRS TEB using various parameters such as the detector gain, noise, and offset. This on-orbit assessment provides critical insights into instrument behavior and calibration performance that will inform pre-launch testing and post-launch validation strategies for the upcoming JPSS-4 VIIRS mission scheduled for launch in 2027. The methodologies and lessons learned from characterizing the N21 TEB performance will enable more efficient and comprehensive radiometric assessment of the next VIIRS instrument. Specific focus is placed on identifying performance trends and anomaly signatures observed during the N21 commissioning phase that can enhance the JPSS4 instrument checkout procedures and accelerate the transition to routine on-orbit operations. The established performance baselines and uncertainty estimates will serve as an acceptance criterion for the JPSS-4 commissioning activities, ensuring mission readiness and data continuity for operational users.

Amit Angal↗
DOE OSTI2025

HexWeather: Hexagonal Spatial Data Aggregation for Weather-Driven Grid Resilience Analysis

Extreme weather accounts for over 8 0 % of major U.S. power outages since 2000, highlighting the need for spatial tools that align weather data with the irregular boundaries of electric infrastructure. This paper introduces HexWeather, a modular, resolution-aware framework for aggregating historical and forecasted weather data using Uber's H3 hexagonal spatial indexing system. Unlike traditional methods that rely on state or county-level grids, HexWeather enables weather analysis across custom geographies such as utility service areas where public datasets are often unavailable or misaligned. Using Open-Meteo data, we evaluate how H3 resolution affects anomaly detection, spatial variability, and forecast uncertainty across three scales: state, county, and utility. Results show that while coarse resolutions suffice for broad trend tracking, finer resolutions are essential for identifying localized variability and operational risks. By applying metrics like Z-score standard deviation and interquartile range, HexWeather quantifies the spatial spread of both historical anomalies and forecasted conditions, allowing users to assess resolution adequacy for each analysis. This framework supports rapid weather data reuse, reproducible anomaly detection, and predictive modeling for infrastructure resilience. By bridging spatial misalignment in traditional datasets and enabling retrospective and forward-looking analysis within the same pipeline, HexWeather lays the groundwork for better post event analysis, outage prediction, and resilience planning.

Morris, Jacob [ORNL]↗
NASA NTRS2008

An Enhanced Convective Forecast (ECF) for the New York TRACON Area

In an effort to relieve summer-time congestion in the NY Terminal Radar Approach Control (TRACON) area, the FAA is testing an enhanced convective forecast (ECF) product. The test began in June 2008 and is scheduled to run through early September. The ECF is updated every two hours, right before the Air Traffic Control System Command Center (ATCSCC) national planning telcon. It is intended to be used by traffic managers throughout the National Airspace System (NAS) and airlines dispatchers to supplement information from the Collaborative Convective Forecast Product (CCFP) and the Corridor Integrated Weather System (CIWS). The ECF begins where the current CIWS forecast ends at 2 hours and extends out to 12 hours. Unlike the CCFP it is a detailed deterministic forecast with no aerial coverage limits. It is created by an ENSCO forecaster using a variety of guidance products including, the Weather Research and Forecast (WRF) model. This is the same version of the WRF that ENSCO runs over the Florida peninsula in support of launch operations at the Kennedy Space Center. For this project, the WRF model domain has been shifted to the Northeastern US. Several products from the NASA SPoRT group are also used by the ENSCO forecaster. In this paper we will provide examples of the ECF products and discuss individual cases of traffic management actions using ECF guidance.

Wheeler, Mark↗
DOE OSTI2021

Probabilistic Day-Ahead Forecasting Using an Analog Ensemble Approach for Wind Farm Grid Services

Wind resource assessment and wind power forecasting are used in research and industry to anticipate future power output at scales ranging from individual wind turbines to entire wind farms. Probabilistic day-ahead wind forecasting is useful for anticipating how a wind farm could potentially participate in the day-ahead market by providing upper and lower bounds for expected power generation, thus informing grid operators of its uncertainty. Understanding this uncertainty is part of a larger project focused on building a platform that combines efforts in weather forecasting, aerodynamic and economic modeling to create maximum value of a wind plant to better provide services to the grid. This effort is also known as the Atmosphere to Electrons to Grid (A2E2G) project. One method for producing a probabilistic forecast is through the analog ensemble approach (Delle Monache et al., 2011). This method leverages historical forecasts and their corresponding observations as a training data set from which future forecasts can be made. For some future forecast, the most similar historical forecasts (analogs) are identified on a regular time basis such as once per a 3-hour window. The most similar analogs, based on a metric such as root mean square error (RMSE), are recorded and their corresponding verifying observations are used as an ensemble member for this future forecast. Prior work in this area demonstrates improvements over raw Numerical Weather Prediction (NWP) forecasts and shows skill similar to techniques such as logistic regression and machine learning (Delle Monache et al., 2013; Alessandrini et al., 2015). Here, we take the High-Resolution Rapid Refresh model (HRRR) day-ahead forecast (0-36 hours) to create a probabilistic day-ahead forecast using an analog ensemble approach. The HRRR has an hourly temporal resolution, with a spatial resolution of 3 km. The 12 UTC HRRR model run is downloaded every day for one year from August 2019 - July 2020, with the first 11 months serving as a bank of analogs from which the forecasting algorithm can create a probabilistic forecast. Once downloaded, the original HRRR forecast is temporally interpolated to 5-minutes, aligning with both the temporal resolution of the observations as well as the timescale relevant for day-ahead power forecasts. The forecast is validated at the M2 tower at the Flatirons Campus of the National Renewable Energy Laboratory (NREL) at a typical wind turbine height of 80 m. Variables such as wind speed, wind direction, and turbulence intensity are incorporated into the probabilistic forecast model and weighted according to their relative importance to the forecast. Based on metrics such as mean bias error (MBE), mean absolute error (MAE), and root mean square error, the analog ensemble forecast outperforms the raw HRRR forecast during the testing period of July 2020. Figure 1 illustrates an example day-ahead forecast compared against the verifying observations. The general variability and ramps are captured throughout the day, with potential to further improve the analog ensemble model through machine learning techniques.

numerical weather prediction↗
DOE OSTI2023

Reliability modeling in a predictive maintenance context: A margin-based approach

Current system reliability methods (typically based on fault trees or reliability block diagrams) can effectively propagate reliability data from the asset to the system level in order to identify system critical points. However, employed asset reliability data are an approximated integral representation of the past industrywide operational experience, and they neglect the present asset health status (available, for example, from online monitoring data and diagnostic assessments) and forecasted health projection (when available from prognostic models). Asset health should be informed solely by that specific asset’s current and historical performance data and should not be an approximated integral representation of the past industrywide operational experience (as currently performed by system reliability models through Bayesian updating processes). Sensor data, diagnostic assessments, and prognostic assessments are in fact not considered in plant reliability models used to inform system engineers on the most critical assets. In addition, the propagation of quantitative health data from the asset to the system level is a challenge given the diverse nature and structure of health data elements (e.g., vibration spectra, temperature readings, expected failure time). Ideally, in a predictive maintenance context, system reliability models should support decision making by propagating available health information from the asset to the system level in order to provide a quantitative snapshot of system health and identify the most critical assets. Here, this paper is directly addressing these two goals by proposing a different approach for reliability modeling that relies on asset diagnostic and prognostic assessments, along with monitoring data to measure asset health. The propagation of health data from the asset to the system level is performed through fault tree models not in probability terms, but in terms of margin where margin is the “distance” between the present status and an undesired event (e.g., failure or unacceptable performance). Through a cause-effect lens, while classical reliability models target the effect associated with asset performance, a margin-based approach focuses on the cause of an undesired asset performance (i.e., its health). Hence, thinking of reliability in terms of margins implies decision-making based on causal reasoning. We will show how fault tree models can be solved using a margin language and how this process can effectively assist system engineers to identify the most critical assets.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2023

HPC Forecast: Cloudy and Uncertain

An examination of how the technology landscape has changed and possible future directions for HPC operations and innovation.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2018

Aviation Global Demand Forecast Model Development and ISAAC Studies: UAS - VTOL Cargo Study

This report examines the potential market of cargo ODM operations in the Northern California area. The analysis presented describes how UAS VTOL cargo aircraft (called heron Cargo ODM) could be used in a dual-role purpose to carry cargo and passengers in a large metropolitan area. The purpose of this study is to provide NASA with insights on the potential market for cargo ODM operations.The analysis presented in the report uses various datasets including the American Community Survey (ACS), LODES data and the IHS International Transearc hdatabase.

Mihir Rimjha↗
DOE OSTI2022

Performance and characterization of the SPT-3G digital frequency-domain multiplexed readout system using an improved noise and crosstalk model

The third-generation South Pole Telescope camera (SPT-3G) improves upon its predecessor (SPTpol) by an order of magnitude increase in detectors on the focal plane. The technology used to read out and control these detectors, digital frequency-domain multiplexing (DfMUX), is conceptually the same as used for SPTpol, but extended to accommodate more detectors. A nearly 5× expansion in the readout operating bandwidth has enabled the use of this large focal plane, and SPT-3G performance meets the forecasting targets relevant to its science objectives. However, the electrical dynamics of the higher-bandwidth readout differ from predictions based on models of the SPTpol system due to the higher frequencies used and parasitic impedances associated with new cryogenic electronic architecture. To address this, we present an updated derivation for electrical crosstalk in higher-bandwidth DfMUX systems and identify two previously uncharacterized contributions to readout noise, which become dominant at high bias frequency. The updated crosstalk and noise models successfully describe the measured crosstalk and readout noise performance of SPT-3G. These results also suggest specific changes to warm electronics component values, wire-harness properties, and SQUID parameters, to improve the readout system for future experiments using DfMUX, such as the LiteBIRD space telescope.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2024

Celebrating 10 Years of the Sub-Seasonal to Seasonal Prediction Project and Looking to the Future

The conference clearly demonstrated the increasing interest and growth of the scientific community working on the development and application of sub-seasonal to seasonal prediction since the start of the World Weather Research Programme (WWRP)/World Climate Research Programme (WCRP) sub-seasonal to seasonal (S2S) prediction project in 2013. The conference, which was held at the University of Reading (United Kingdom), was organized into three main themes as briefly summarized below, with eleven invited talks, 74 oral contributed talks, and 101 posters. The conference also included a two-hour breakout session, wherein eight groups discussed the current state and prospect for S2S prediction, and an early career researcher event. A summary of these discussions and recommendations is presented below. The conference web page (https://research.reading.ac.uk/s2s-summit2023/) is archived at the University of Reading. Introductory comments by representatives of the World Meteorological Organization (WMO) WWRP and WCRP emphasized the importance of the weather–climate linkage, targeted by S2S forecasts (from 2 weeks to a season ahead), addressing the challenges of creating “end-to-end” forecasts that encompass the entire climate-services chain from the prediction science and forecast, to the development and issuing of forecast products tailored to informing user-decisions. They also emphasized the efficacy of multi-model ensemble efforts and databases to foster collaborations internationally and between operational centres and academia. Although the WWRP/WCRP S2S project comes to an end in 2023, S2S prediction will remain an important focus for WWRP and WCRP. In WWRP, a new project called SAGE (Sub-seasonal to seasonal predictions for Agriculture and Environment) will start in 2024. Another important legacy of the S2S project will be the maintenance of the S2S database (Vitart et al. 2017) and the establishment of a WMO Lead Center for sub-seasonal prediction multi-model ensemble (LC-SSPMME) which will provide real-time multi-model S2S climate information. In two keynote presentations, Prof. Brian Hoskins (University of Reading) and Dr. Gilbert Brunet (Australian Bureau of Meteorology) discussed the potential of S2S predictability and the ongoing journey for understanding and improving these predictions. This conference was a sequel to the International Conference on Sub-seasonal to Seasonal Prediction (Robertson et al., 2014) which took place in College Park (Maryland, USA) in February 2014 to celebrate the start of the WWRP/WCRP S2S project, and to WCRP and WWRP conferences in Boulder, USA, in 2018 (Merryfield et al., 2020). A significant development compared to the previous S2S conferences was the large number of presentations on research to operation (R2O) and S2S applications and on the use of artificial intelligence and machine learning (AI/ML) methods for S2S prediction. Some of these methods provide empirical S2S forecasts which are competitive with state-of-the-art dynamical models. Other presentations demonstrated that AI/ML can provide alternative calibration of dynamical model outputs to traditional methods. Several talks and posters highlighted the increasing use of AI/ML, including deep learning, in S2S forecast post-processing and using AI to identify higher flow-dependent skill. Finally, some presentations demonstrated the value of AI/ML methods for a better understanding of S2S sources of predictability and attribution of extreme events.

S. J. Woolnough↗
NASA NTRS2017

Comparison of Selected Weather Translation Products

Weather is a primary contributor to the air traffic delays within the National Airspace System (NAS). At present, it is the individual decision makers who use weather information and assess its operational impact in creating effective air traffic management solutions. As a result, the estimation of the impact of forecast weather and the quality of ATM response relies on the skill and experience level of the decision maker. FAA Weather-ATM working groups have developed a Weather-ATM integration framework that consists of weather collection, weather translation, ATM impact conversion and ATM decision support. Some weather translation measures have been developed for hypothetical operations such as decentralized free flight, whereas others are meant to be relevant in current operations. This paper does comparative study of two different weather translation products relevant in current operations and finds that these products have strong correlation with each other. Given inaccuracies in prediction of weather, these differences would not be expected to be of significance in statistical study of a large number of decisions made with a look-ahead time of two hours or more.

Kulkarni, Deepak↗
NASA NTRS1983

A summary and integration of research concerning single pilot IFR operational problems

A review of seven research studies pertaining to Single Pilot IFR (SPIFR) operations was performed. Two studies were based on questionnaire surveys; two based on National Transportation Safety Board (NTSB) reports; two were based on Aviation Safety Reporting System (ASRS) incident reports, and one report used event analysis and statistics to forecast problems. The results obtained in each study were extracted and integrated. Results were synthesized and key issues pertaining to SPIFR operations problems were identified. The research that was recommended by the studies and that addressed the key issues is catalogued for each key issue.

Chapman, G. C.↗
NASA NTRS2024

Optimizing Air Traffic - Integrating Artificial Intelligence and Machine Learning in Flight Path Planning and 3D Airspace Visualization for Air Traffic Control

Air Traffic Control (ATC) systems are vital components of the National Airspace System (NAS). ATC, Airport Traffic Control Towers (ATCT), and Terminal Radar Approach Control (TRACON) are responsible for directing all flights departing from and arriving at airports, managing our nation’s airspace, preventing potential accidents, and ensuring that every flight is accounted for. However, these systems often face challenges in effectively monitoring the skies. Issues such as poor communication between operators, difficulty in performing operations, and the constant need for vigilance frequently burden ATC operators. Additionally, the projected increase in air traffic in the coming years will only exacerbate the stress associated with this role. To address these issues, we propose a system that assists ATC operators in situations such as handovers, emergencies, and routing aircraft to avoid weather hazards. Our solution includes an Artificial Intelligence (AI) and Machine Learning (ML)-based Flight Pathways Planning System (FPPS) designed to find the fastest and most optimal routes for aircraft, taking into account weather conditions, restricted terrain, and Extended-Range Twin-Engine Operational Performance Standards (ETOPS) ratings. The proposed Predictive Weather Planning Model, included in FPPS, adjusts routes based on real-time and forecasted weather conditions. Additionally, our NVIDIA Omniverse 3D Visualization System offers a highly interactive environment for better visualization and a clear view of the airspace. By incorporating these systems, the roles of ATC, ATCT, and TRACON operators will become more manageable and less stressful, equipping them to efficiently handle the growing density of airspace.

Regina Ayoubi↗
NASA NTRS1984

Operational numerical weather prediction on the CYBER 205 at the National Meteorological Center

The Development Division of the National Meteorological Center (NMC), having the responsibility of maintaining and developing the numerical weather forecasting systems of the center, is discussed. Because of the mission of NMC data products must be produced reliably and on time twice daily free of surprises for forecasters. Personnel of Development Division are in a rather unique situation. They must develop new advanced techniques for numerical analysis and prediction utilizing current state-of-the-art techniques, and implement them in an operational fashion without damaging the operations of the center. With the computational speeds and resources now available from the CYBER 205, Development Division Personnel will be able to introduce advanced analysis and prediction techniques into the operational job suite without disrupting the daily schedule. The capabilities of the CYBER 205 are discussed.

Deaven, D.↗
NASA NTRS2014

Impact of Assimilated and Interactive Aerosol on Tropical Cyclogenesis

This article investigates the impact 3 of Saharan dust on the development of tropical cyclones in the Atlantic. A global data assimilation and forecast system, the NASA GEOS-5, is used to assimilate all satellite and conventional data sets used operationally for numerical weather prediction. In addition, this new GEOS-5 version includes assimilation of aerosol optical depth from the Moderate Resolution Imaging Spectroradiometer (MODIS). The analysis so obtained comprises atmospheric quantities and a realistic 3-d aerosol and cloud distribution, consistent with the meteorology and validated against Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and CloudSat data. These improved analyses are used to initialize GEOS-5 forecasts, explicitly accounting for aerosol direct radiative effects and their impact on the atmospheric dynamics. Parallel simulations with/without aerosol radiative effects show that effects of dust on static stability increase with time, becoming highly significant after day 5 and producing an environment less favorable to tropical cyclogenesis.

dust↗
NASA NTRS2012

Operational use of the AIRS Total Column Ozone Retrievals along with the RGB Airmass Product as Part of the GOES-R Proving Ground

The Red, Green, Blue (RGB) Air Mass product has been demonstrated in the GOES ]R Proving Ground as a possible decision aid. Forecasters have been trained on the usefulness of identifying stratospheric intrusions and potential vorticity (PV) anomalies that can lead to explosive cyclogenesis, genesis of mesoscale convective systems (MCSs), or the transition of tropical cyclones to extratropical cyclones. It has also been demonstrated to distinguish different air mass types from warm, low ozone air masses to cool, high ozone air masses and the various interactions with the PV anomalies. To assist the forecasters in understanding the stratospheric contribution to high impact weather systems, the Atmospheric Infrared Sounder (AIRS) Total Column Ozone Retrievals have been made available as an operational tool. These AIRS retrievals provide additional information on the amount of ozone that is associated with the red coloring seen in the RGB Air Mass product. This paper discusses how the AIRS retrievals can be used to quantify the red coloring in RGB Air Mass product. These retrievals can be used to diagnose the depth of the stratospheric intrusions associated with different types of weather systems and provide the forecasters decision aid tools that can improve the quality of forecast products.

Folmer, M.↗
NASA NTRS2008

QuikSCAT follow - on concept study

Global, real-time observations of the speed and direction of winds over the oceans ocean surface vector winds [OSVW]) are high priority measurements for National Oceanic and Atmospheric Administration’s (NOAA’s) weather forecasting, prediction, and hazard warning communities. At present, these data are provided by the experimental National Aeronautics and Space Administration (NASA) QuikSCAT satellite sensor, which is operating well beyond its design lifetime. To continue to meet the Nation’s need for operational OSVW observations beyond QuikSCAT, NOAA tasked the Jet Propulsion Laboratory (JPL) to design and provide costs for a set of QuikSCAT Follow-On mission options. Three scenarios were examined: 1) a QuikSCAT Replacement mission with capabilities commensurate to QuikSCAT, 2) a next-generation Extended Ocean Vector Winds Mission (XOVWM), as recommended in the National Research Council’s decadal survey to provide significantly improved all-weather, allwind, high spatial resolution measurements, and 3) an XOVWM Constellation consisting of two XOVWM observatories to provide improved temporal resolution. In parallel, NOAA asked its users to provide a quantitative assessment of each option’s benefit to NOAA. This report presents the JPL design, risk assessment, and cost for each of three options, together with a summary of the NOAA users’ benefit assessment. The report concludes that though all options are technically feasible for immediate implementation and have a risk posture consistent with a NOAA operational mission, the XOVWM options provide significant observational benefits. While a QuikSCAT Replacement option would continue current operational measurement capabilities, there is a strong and clearly defined operational need for improved capabilities in high winds (e.g., hurricanes or extra-tropical cyclones), heavy precipitation, and near coasts to enable significantly improved severe storm and coastal hazard forecasts, which are provided only by the XOVWM options.

Rodriquez, Ernesto↗