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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

The physical and empirical basis for a specific clear-air turbulence risk index

An improved operational CAT detection and forecasting technique is developed and detailed. This technique is the specific clear air turbulence risk (SCATR) index. This index shows some promising results. The improvements seen using hand analyzed data, as a result of the more realistic representation of the vertical shear of the horizontal wind, are also realized in the data analysis used in the PROFS/CWP application. The SCATR index should improve as database enhancements such as profiler and VAS satellite data, which increase the resolution in space and time, are brought into even more sophisticated objective analysis schemes.

Keller, J. L.↗
NASA NTRS1991

Observing system simulation experiments for the laser atmospheric wind sounder using global spectral model

Fundamental to improving the understanding of the total Earth system are increased and improved observations. In the coming decade several spaceborne instrumented platforms will be constructed and implemented. These platforms will, in large, be housing the NASA Earth Observing System (EOS) instrument suite. One of the proposed instruments is a wind profiling system which is currently referred to as the Laser Atmospheric Wind Sounder (LAWS). This instrument will use a CO2 Doppler lidar wind profiler to give wind measurements with a vertical and horizontal resolution which has yet to be seen globally. The LAWS instrument is now a candidate for launch on a NASA EOS-B platform and is fundamental to increasing our understanding of Earth system science. The LAWS data sets will form an integral component of the temporally continuous data base needed for research of the coupled climate systems. This instrument's observations will aid in giving an improved description of the atmospheric circulation, including the transports of energy, momentum, moisture, trace gases, and aerosols. Also, the wind data will be assimilated and used as the initial state for many global forecast models at various operational centers. Results of system simulation experiments are discussed, and future experiments are described.

Rohaly, Gregg↗
NASA NTRS1991

NASA's program in lidar remote sensing

Several major NASA research efforts in lidar remote sensing are reviewed, with attention given to hardware and key sensor issues along with results and expectations. The discussion covers temperature and pressure measurements, measurements methods and instrumentation, pressure data, wind field measurements, atmospheric backscatter measurement, aerosol and cloud measurements, and water vapor measurement. Consideration is also given to the applicability of lidar measurements to problems of operational weather analysis and forecasting, climate studies, mesoscale and severe storm analysis and forecastig, and studies of atmosphere/surface interface.

Theon, John S.↗
NASA NTRS2010

One-Centimeter Orbits in Near-Real Time: The GPS Experience on OSTM/JASON-2

The advances in Precise Orbit Determination (POD) over the past three decades have been driven in large measure by the increasing demands of satellite altimetry missions. Since the launch of Seasat in 1978, both tracking-system technologies and orbit modeling capabilities have evolved considerably. The latest in a series of precise (TOPEX-class) altimeter missions is the Ocean Surface Topography Mission (OSTM, also Jason-2). GPS-based orbit solutions for this mission are accurate to 1-cm (radial RMS) within 3-5 hrs of real time. These GPS-based orbit products provide the basis for a near-real time sea-surface height product that supports increasingly diverse applications of operational oceanography and climate forecasting.

Ocean Surface Topography Mission (OSTM/Jason-2,)↗
NASA NTRS2012

Instrumentation and First Results of the Reflected Solar Demonstration System for the Climate Absolute Radiance and Refractivity Observatory

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission key goals include enabling observation of high accuracy long-term climate change trends, use of these observations to test and improve climate forecasts, and calibration of operational and research sensors. The spaceborne instrument suites include a reflected solar spectroradiometer, emitted infrared spectroradiometer, and radio occultation receivers. The requirement for the RS instrument is that derived reflectance must be traceable to Sl standards with an absolute uncertainty of <0.3% and the error budget that achieves this requirement is described in previo1L5 work. This work describes the Solar/Lunar Absolute Reflectance Imaging Spectroradiometer (SOLARIS), a calibration demonstration system for RS instrument, and presents initial calibration and characterization methods and results. SOLARIS is an Offner spectrometer with two separate focal planes each with its own entrance aperture and grating covering spectral ranges of 320-640, 600-2300 nm over a full field-of-view of 10 degrees with 0.27 milliradian sampling. Results from laboratory measurements including use of integrating spheres, transfer radiometers and spectral standards combined with field-based solar and lunar acquisitions are presented. These results will be used to assess the accuracy and repeatability of the radiometric and spectral characteristics of SOLARIS, which will be presented against the sensor-level requirements addressed in the CLARREO RS instrument error budget.

McCorkel, Joel↗
NASA NTRS2014

Integrated Medical Model Verification, Validation, and Credibility

The Integrated Medical Model (IMM) was designed to forecast relative changes for a specified set of crew health and mission success risk metrics by using a probabilistic (stochastic process) model based on historical data, cohort data, and subject matter expert opinion. A probabilistic approach is taken since exact (deterministic) results would not appropriately reflect the uncertainty in the IMM inputs. Once the IMM was conceptualized, a plan was needed to rigorously assess input information, framework and code, and output results of the IMM, and ensure that end user requests and requirements were considered during all stages of model development and implementation. METHODS: In 2008, the IMM team developed a comprehensive verification and validation (VV) plan, which specified internal and external review criteria encompassing 1) verification of data and IMM structure to ensure proper implementation of the IMM, 2) several validation techniques to confirm that the simulation capability of the IMM appropriately represents occurrences and consequences of medical conditions during space missions, and 3) credibility processes to develop user confidence in the information derived from the IMM. When the NASA-STD-7009 (7009) was published, the IMM team updated their verification, validation, and credibility (VVC) project plan to meet 7009 requirements and include 7009 tools in reporting VVC status of the IMM. RESULTS: IMM VVC updates are compiled recurrently and include 7009 Compliance and Credibility matrices, IMM VV Plan status, and a synopsis of any changes or updates to the IMM during the reporting period. Reporting tools have evolved over the lifetime of the IMM project to better communicate VVC status. This has included refining original 7009 methodology with augmentation from the NASA-STD-7009 Guidance Document. End user requests and requirements are being satisfied as evidenced by ISS Program acceptance of IMM risk forecasts, transition to an operational model and simulation tool, and completion of service requests from a broad end user consortium including Operations, Science and Technology Planning, and Exploration Planning. CONCLUSIONS: The VVC approach established by the IMM project of combining the IMM VV Plan with 7009 requirements is comprehensive and includes the involvement of end users at every stage in IMM evolution. Methods and techniques used to quantify the VVC status of the IMM have not only received approval from the local NASA community but have also garnered recognition by other federal agencies seeking to develop similar guidelines in the medical modeling community.

Validation↗
NASA NTRS2014

Aggregation Tool to Create Curated Data albums to Support Disaster Recovery and Response

Despite advances in science and technology of prediction and simulation of natural hazards, losses incurred due to natural disasters keep growing every year. Natural disasters cause more economic losses as compared to anthropogenic disasters. Economic losses due to natural hazards are estimated to be around $6-$10 billion dollars annually for the U.S. and this number keeps increasing every year. This increase has been attributed to population growth and migration to more hazard prone locations such as coasts. As this trend continues, in concert with shifts in weather patterns caused by climate change, it is anticipated that losses associated with natural disasters will keep growing substantially. One of challenges disaster response and recovery analysts face is to quickly find, access and utilize a vast variety of relevant geospatial data collected by different federal agencies such as DoD, NASA, NOAA, EPA, USGS etc. Some examples of these data sets include high spatio-temporal resolution multi/hyperspectral satellite imagery, model prediction outputs from weather models, latest radar scans, measurements from an array of sensor networks such as Integrated Ocean Observing System etc. More often analysts may be familiar with limited, but specific datasets and are often unaware of or unfamiliar with a large quantity of other useful resources. Finding airborne or satellite data useful to a natural disaster event often requires a time consuming search through web pages and data archives. Additional information related to damages, deaths, and injuries requires extensive online searches for news reports and official report summaries. An analyst must also sift through vast amounts of potentially useful digital information captured by the general public such as geo-tagged photos, videos and real time damage updates within twitter feeds. Collecting and aggregating these information fragments can provide useful information in assessing damage in real time and help direct recovery efforts. The search process for the analyst could be made much more efficient and productive if a tool could go beyond a typical search engine and provide not just links to web sites but actual links to specific data relevant to the natural disaster, parse unstructured reports for useful information nuggets, as well as gather other related reports, summaries, news stories, and images. This presentation will describe a semantic aggregation tool developed to address similar problem for Earth Science researchers. This tool provides automated curation, and creates "Data Albums" to support case studies. The generated "Data Albums" are compiled collections of information related to a specific science topic or event, containing links to relevant data files (granules) from different instruments; tools and services for visualization and analysis; information about the event contained in news reports, and images or videos to supplement research analysis. An ontology-based relevancy-ranking algorithm drives the curation of relevant data sets for a given event. This tool is now being used to generate a catalog of Hurricane Case Studies at Global Hydrology Resource Center (GHRC), one of NASA's Distribute Active Archive Centers. Another instance of the Data Albums tool is currently being created in collaboration with NASA/MSFC's SPoRT Center, which conducts research on unique NASA products and capabilities that can be transitioned to the operational community to solve forecast problems. This new instance focuses on severe weather to support SPoRT researchers in their model evaluation studies

Ramachandran, Rahul↗
NASA NTRS2015

Integrated Medical Model (IMM) Project Verification, Validation, and Credibility (VVandC)

The Integrated Medical Model (IMM) Project supports end user requests by employing the Integrated Medical Evidence Database (iMED) and IMM tools as well as subject matter expertise within the Project. The iMED houses data used by the IMM. The IMM is designed to forecast relative changes for a specified set of crew health and mission success risk metrics by using a probabilistic model based on historical data, cohort data, and subject matter expert opinion. A stochastic approach is taken because deterministic results would not appropriately reflect the uncertainty in the IMM inputs. Once the IMM was conceptualized, a plan was needed to rigorously assess input information, framework and code, and output results of the IMM, and ensure that end user requests and requirements were considered during all stages of model development and implementation, as well as lay the foundation for external review and application. METHODS: In 2008, the Project team developed a comprehensive verification and validation (VV) plan, which specified internal and external review criteria encompassing 1) verification of data and IMM structure to ensure proper implementation of the IMM, 2) several validation techniques to confirm that the simulation capability of the IMM appropriately represents occurrences and consequences of medical conditions during space missions, and 3) credibility processes to develop user confidence in the information derived from the IMM. When the NASA-STD-7009 (7009) [1] was published, the Project team updated their verification, validation, and credibility (VVC) project plan to meet 7009 requirements and include 7009 tools in reporting VVC status of the IMM. Construction of these tools included meeting documentation and evidence requirements sufficient to meet external review success criteria. RESULTS: IMM Project VVC updates are compiled recurrently and include updates to the 7009 Compliance and Credibility matrices. Reporting tools have evolved over the lifetime of the IMM Project to better communicate VVC status. This has included refining original 7009 methodology with augmentation from the HRP NASA-STD-7009 Guidance Document working group and the NASA-HDBK-7009 [2]. End user requests and requirements are being satisfied as evidenced by ISS Program acceptance of IMM risk forecasts, transition to an operational model and simulation tool, and completion of service requests from a broad end user consortium including operations, science and technology planning, and exploration planning. IMM v4.0 is slated for operational release in the FY015 and current VVC assessments illustrate the expected VVC status prior to the completion of customer lead external review efforts. CONCLUSIONS: The VVC approach established by the IMM Project of incorporating Project-specific recommended practices and guidelines for implementing the 7009 requirements is comprehensive and includes the involvement of end users at every stage in IMM evolution. Methods and techniques used to quantify the VVC status of the IMM Project represented a critical communication tool in providing clear and concise suitability assessments to IMM customers. These processes have not only received approval from the local NASA community but have also garnered recognition by other federal agencies seeking to develop similar guidelines in the medical modeling community.

Walton, M.↗
DOE OSTI2021

Value of Information and Data Management Platform Promoting Modern Data Analysis Utilization. STTR Phase I, Final Technical Report

The immense growth in weather and river sensor data availability along with increases in computational power and algorithmic sophistication have enabled the widespread application of physical, machine learning, and artificial intelligence algorithms to river forecasting. The forecasts produced by widely varying model approaches are inherently limited in different ways, such as by the number and quality of training observations, producing biased predictions that achieve optimal skill only over specific ranges of weather and river conditions. Thus, despite the growing availability and new sources of forecasts, hydropower owners cannot fully leverage the information because they lack an effective mechanism to determine the “best” forecast for given conditions or objectives. Tools are needed to consolidate forecast information, demonstrate skill, and extract optimal forecast guidance from the range of possibilities. Such tools will increase uptake and beneficial use of the growing wealth of forecast information, and lead to improved operating decisions, optimal power generation, and enhanced system value. This research proposes to improve the value of forecasts by developing a broadly accessible verification framework and applying machine learning techniques to identify the ‘best’ forecast from an ensemble of sources given the current weather, river, and operating conditions.

13 HYDRO ENERGY↗
NASA NTRS2014

Toward Improved Land Surface Initialization in Support of Regional WRF Forecasts at the Kenya Meteorological Service (KMS)

SPoRT/SERVIR/RCMRD/KMS Collaboration: Builds off strengths of each organization. SPoRT: Transition of satellite, modeling and verification capabilities; SERVIR‐Africa/RCMRD: International capacity-building expertise; KMS: Operational organization with regional weather forecasting expertise in East Africa. Hypothesis: Improved land‐surface initialization over Eastern Africa can lead to better temperature, moisture, and ultimately precipitation forecasts in NWP models. KMS currently initializes Weather Research and Forecasting (WRF) model with NCEP/Global Forecast System (GFS) model 0.5‐deg initial / boundary condition data. LIS will provide much higher‐resolution land‐surface data at a scale more representative to regional WRF configuration. Future implementation of real‐time NESDIS/VIIRS vegetation fraction to further improve land surface representativeness.

Case, Jonathan L.↗
NASA NTRS2015

Decomposition of Sources of Errors in Seasonal Streamflow Forecasting over the U.S. Sunbelt

Seasonal streamflow forecasts, contingent on climate information, can be utilized to ensure water supply for multiple uses including municipal demands, hydroelectric power generation, and for planning agricultural operations. However, uncertainties in the streamflow forecasts pose significant challenges in their utilization in real-time operations. In this study, we systematically decompose various sources of errors in developing seasonal streamflow forecasts from two Land Surface Models (LSMs) (Noah3.2 and CLM2), which are forced with downscaled and disaggregated climate forecasts. In particular, the study quantifies the relative contributions of the sources of errors from LSMs, climate forecasts, and downscaling/disaggregation techniques in developing seasonal streamflow forecast. For this purpose, three month ahead seasonal precipitation forecasts from the ECHAM4.5 general circulation model (GCM) were statistically downscaled from 2.8deg to 1/8deg spatial resolution using principal component regression (PCR) and then temporally disaggregated from monthly to daily time step using kernel-nearest neighbor (K-NN) approach. For other climatic forcings, excluding precipitation, we considered the North American Land Data Assimilation System version 2 (NLDAS-2) hourly climatology over the years 1979 to 2010. Then the selected LSMs were forced with precipitation forecasts and NLDAS-2 hourly climatology to develop retrospective seasonal streamflow forecasts over a period of 20 years (1991-2010). Finally, the performance of LSMs in forecasting streamflow under different schemes was analyzed to quantify the relative contribution of various sources of errors in developing seasonal streamflow forecast. Our results indicate that the most dominant source of errors during winter and fall seasons is the errors due to ECHAM4.5 precipitation forecasts, while temporal disaggregation scheme contributes to maximum errors during summer season.

Decomposition↗
NASA NTRS2010

Modifications to the Objective Lightning Probability Forecast Tool at Kennedy Space Center/Cape Canaveral Air Force Station, Florida

The 45th Weather Squadron (45 WS) at Cape Canaveral Air Force Station (CCAFS) includes the probability of lightning occurrence in their 24-Hour and Weekly Planning Forecasts, briefed at 0700 EDT for daily operations planning on Kennedy Space Center (KSC) and CCAFS. This forecast is based on subjective analyses of model and observational data and output from an objective tool developed by the Applied Meteorology Unit (AMU). This tool was developed over two phases (Lambert and Wheeler 2005, Lambert 2007). It consists of five equations, one for each warm season month (May-Sep), that calculate the probability of lightning occurrence for the day and a graphical user interface (GUI) to display the output. The Phase I and II equations outperformed previous operational tools by a total of 56%. Based on this success, the 45 WS tasked the AMU with Phase III to improve the tool further.

Crawford, Winifred↗
DOE OSTI2022

A Data Quality-Aware Framework to Reliably Forecast Photovoltaic Generation and Consumer Load for an Improved Resilience of Microgrids

Photovoltaic (PV) power and consumer load forecasting plays a critical role to ensure operational resilience of the electric grid. Most data-driven forecasting algorithms rely heavily on the continuous availability of good quality data for periodic training and validation. When deployed at the grid’s edge, prolonged disruptions to communications during extreme events degrade data quality. Factors such as missing observations, epistemic uncertainties, data drift, and concept drift are manifestations of data quality that impact the generalization of such field-deployed forecasting models. Currently, there exists no mechanism in the literature to dynamically switch between models under varying degrees of data quality as quantified by certain metrics for each factor highlighted above. This paper addresses this shortcoming by conceptually introducing a data qualityaware framework for reliable PV generation and consumer load forecasting. The framework’s design incorporates components of missing values, divergence tests, and continuous monitoring of generalization performance to detect changes in data quality caused by communications disruptions and trigger specific classes of forecasting models grouped under three use cases (UC1- UC3). As a first step towards validating this framework, real data collected from an actual field microgrid system is used to demonstrate the viability of the three use cases. Results show that the performance is the best in UC1 with an unadjusted R-square value of 0.954, followed by 0.939 for UC2 and 0.757 for UC3.

Sundararajan, Aditya↗
NASA NTRS2014

Use of MODIS Cloud Top Pressure to Improve Assimilation Yields of AIRS Radiances in GSI

Improvements to global and regional numerical weather prediction have been demonstrated through assimilation of data from NASA's Atmospheric Infrared Sounder (AIRS). Current operational data assimilation systems use AIRS radiances, but impact on regional forecasts has been much smaller than for global forecasts. Previously, it has been shown that cloud top designation associated with quality control procedures within the Gridpoint Statistical Interpolation (GSI) system used operationally by a number of Joint Center for Satellite Data Assimilation (JCSDA) partners may not provide the best representation of cloud top pressure (CTP). Because this designated CTP determines which channels are cloud-free and, thus, available for assimilation, ensuring the most accurate representation of this value is imperative to obtaining the greatest impact from satellite radiances. This paper examines the assimilation of hyperspectral sounder data used in operational numerical weather prediction by comparing analysis increments and numerical forecasts generated using operational techniques with a research technique that swaps CTP from the Moderate-resolution Imaging Spectroradiometer (MODIS) for the value of CTP calculated from the radiances within GSI.

Zavodsky, Bradley↗
DOE OSTI2020

Stochastic multi-period optimal dispatch of energy storage in unbalanced distribution feeders

This report presents a convex, multi-period, AC-feasible Optimal Power Flow (OPF) framework that robustly dispatches flexible demand-side resources in unbalanced distribution feeders against uncertainty in very-short timescale solar Photo-Voltaic (PV) forecasts. This is valuable for power systems with significant behind-the-meter solar PV generation as their operation is affected by uncertainty from forecasts of demand and solar PV generation. The aim of this work is then to ensure the feasibility and reliability of distribution system operation under high solar PV penetration. We develop and present a novel, robust OPF formulation that accounts for both the nonlinear power flow constraints and the uncertainty in forecasts. This is achieved by linearizing an optimal trajectory and using first-order methods to systematically tighten voltage bounds. Case studies on a realistic distribution feeder shows the effectiveness of a receding-horizon implementation.

25 ENERGY STORAGE↗
NASA NTRS2017

The MSFC Solar Activity Future Estimation (MSAFE) Model

The MSAFE model provides forecasts for the solar indices SSN, F10.7, and Ap. These solar indices are used as inputs to space environment models used in orbital spacecraft operations and space mission analysis. Forecasts from the MSAFE model are provided on the MSFC Natural Environments Branch's solar web page and are updated as new monthly observations become available. The MSAFE prediction routine employs a statistical technique that calculates deviations of past solar cycles from the mean cycle and performs a regression analysis to calculate the deviation from the mean cycle of the solar index at the next future time interval. The forecasts are initiated for a given cycle after about 8 to 9 monthly observations from the start of the cycle are collected. A forecast made at the beginning of cycle 24 using the MSAFE program captured the cycle fairly well with some difficulty in discerning the double peak that occurred at solar cycle maximum.

Suggs, Ron↗
NASA NTRS2017

The MSFC Solar Activity Future Estimation (MSAFE) Model

The MSAFE model provides forecasts for the solar indices SSN, F10.7, and Ap. These solar indices are used as inputs to many space environment models used in orbital spacecraft operations and space mission analysis. Forecasts from the MSAFE model are provided on the MSFC Natural Environments Branch's solar webpage and are updated as new monthly observations come available. The MSAFE prediction routine employs a statistical technique that calculates deviations of past solar cycles from the mean cycle and performs a regression analysis to predict the deviation from the mean cycle of the solar index at the next future time interval. The prediction algorithm is applied recursively to produce monthly smoothed solar index values for the remaining of the cycle. The forecasts are initiated for a given cycle after about 8 to 12 months of observations are collected. A forecast made at the beginning of cycle 24 using the MSAFE program captured the cycle fairly well with some difficulty in discerning the double peak that occurred at solar cycle maximum.

Suggs, Ronnie J.↗