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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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At least 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
NASA NTRS1975

A 30-day forecast experiment with the GISS model and updated sea surface temperatures

The GISS model was used to compute two parallel global 30-day forecasts for the month January 1974. In one forecast, climatological January sea surface temperatures were used, while in the other observed sea temperatures were inserted and updated daily. A comparison of the two forecasts indicated no clear-cut beneficial effect of daily updating of sea surface temperatures. Despite the rapid decay of daily predictability, the model produced a 30-day mean forecast for January 1974 that was generally superior to persistence and climatology when evaluated over either the globe or the Northern Hemisphere, but not over smaller regions.

Spar, J.↗
DOE OSTI2024

Characterizing model uncertainties in simulated coast-to-offshore wind over the northeast U.S. using multi-platform measurements from the TCAP field campaign

Numerical weather prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model, are widely used to provide estimates of the offshore wind energy resource owing to their large spatial coverage compared to available observations. Nevertheless, spatiotemporal distribution of model biases is highly dependent on factors including model configuration, location, and the interplay of multi-scale physical processes. Here, in this study, we focus on the characterization of model uncertainties in simulated coast-to-offshore winds over the northeast U.S., by varying sea surface temperature (SST) forcings, surface layer (SL) and planetary boundary layer (PBL) parameterizations, as well as identifying biases that may be directly passed from initial and boundary conditions. Multiple measurements, including aircraft data collected during the U.S. Department of Energy's Two-Column Aerosol Project (TCAP) experiment, are used to constrain the model results and facilitate quantitative comparisons. Our analysis indicates while SST forcing has notable impacts on simulated air temperature and moisture within PBL, the modeled winds are in general more sensitive to the choices of SL and PBL physics than to SST. The model’s forcing data not only controls the vertical dependence of wind speed errors, but also alters regional variability in wind speed’s spatial correlation. Bias comparisons between ERA5 reanalysis and ensemble simulations revealed significant similarity, particularly in wind speed biases during winter, underscoring their dependency on initial and boundary conditions. Coastal and offshore near-surface wind speed biases tend to exhibit much higher similarity in winter than in summer due to the presence of much stronger and more persistent synoptic wind conditions. This study highlights the importance of accurate atmospheric forcing and parameterization choices in improving wind forecasts and suggests the potential for extrapolating coastal wind biases to offshore locations, aiding wind energy forecasting and informing the Wind Forecast Improvement Project-3 (WFIP3).

17 WIND ENERGY↗
DOE OSTI2021

Active learning strategy for high fidelity short-term data-driven building energy forecasting

The quality of a data-driven model is heavily dependent on the quality of data. Data from building operation often have data bias problems, which means that the data sample is collected in a way that some members of the intended data population are less likely to be included than others. Data-driven energy forecasting models built on such data hence are biased and could lead to large forecasting errors. Active learning—an effective method to defying data bias—is rarely studied or applied in the area of data-driven building energy forecasting modeling. This paper attempts to fill this gap and explores the application of active learning in data-driven building energy forecasting. The developed strategy in this paper efficiently generate informative training data within a time budget and uses block design to passively consider weather disturbances. The developed active learning strategy is applied and evaluated in both virtual and real-building testbeds against traditional data-driven methods. Via these virtual and real-building evaluation cases, we have demonstrated that the data bias problem typically exists in building operation data is resolved by applying the developed active learning strategy. Furthermore, building energy forecasting models trained from data generated from the active learning strategy have shown improved performances in both model accuracy and model extendibility perspectives. The effectiveness of the block design module is also validated to effectively consider the impact of weather conditions on active learning design.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Evaluation of FluSight influenza forecasting in the 2021–22 and 2022–23 seasons with a new target laboratory-confirmed influenza hospitalizations

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021–22 and 2022–23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperform the baseline model across forecast weeks and locations in 2021–22 and 12 out of 18 models in 2022–23. Averaging across all forecast targets, the FluSight ensemble is the 2nd most accurate model measured by WIS in 2021–22 and the 5th most accurate in the 2022–23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degrade over longer forecast horizons. In this work we demonstrate that while the FluSight ensemble was a robust predictor, even ensembles face challenges during periods of rapid change.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2011

A Real-Time MODIS Vegetation Composite for Land Surface Models and Short-Term Forecasting

The NASA Short-term Prediction Research and Transition (SPoRT) Center is producing real-time, 1- km resolution Normalized Difference Vegetation Index (NDVI) gridded composites over a Continental U.S. domain. These composites are updated daily based on swath data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the polar orbiting NASA Aqua and Terra satellites, with a product time lag of about one day. A simple time-weighting algorithm is applied to the NDVI swath data that queries the previous 20 days of data to ensure a continuous grid of data populated at all pixels. The daily composites exhibited good continuity both spatially and temporally during June and July 2010. The composites also nicely depicted high greenness anomalies that resulted from significant rainfall over southwestern Texas, Mexico, and New Mexico during July due to early-season tropical cyclone activity. The SPoRT Center is in the process of computing greenness vegetation fraction (GVF) composites from the MODIS NDVI data at the same spatial and temporal resolution for use in the NASA Land Information System (LIS). The new daily GVF dataset would replace the monthly climatological GVF database (based on Advanced Very High Resolution Radiometer [AVHRR] observations from 1992-93) currently available to the Noah land surface model (LSM) in both LIS and the public version of the Weather Research and Forecasting (WRF) model. The much higher spatial resolution (1 km versus 0.15 degree) and daily updates based on real-time satellite observations have the capability to greatly improve the simulation of the surface energy budget in the Noah LSM within LIS and WRF. Once code is developed in LIS to incorporate the daily updated GVFs, the SPoRT Center will conduct simulation sensitivity experiments to quantify the impacts and improvements realized by the MODIS real-time GVF data. This presentation will describe the methodology used to develop the 1-km MODIS NDVI composites and show sample output from summer 2010, compare the MODIS GVF data to the AVHRR monthly climatology, and illustrate the sensitivity of the Noah LSM within LIS and/or the coupled LIS/WRF system to the new MODIS GVF dataset.

Case, Jonathan L.↗
NASA NTRS2016

Prediction of Weather Impacts on Airport Arrival Meter Fix Capacity

This paper introduces a data driven model for predicting airport arrival capacity with a look-ahead time 2-8 hour forecast. The model is suitable for air traffic flow management by explicitly investigating the impact of convective weather on airport arrival meter fix throughput. Estimation of the arrival airport capacity under arrival meter fix flow constraints due to severe weather is an important part of Air Traffic Management (ATM). Airport arrival capacity can be reduced if one or more airport arrival meter fixes are partially or completely blocked by convective weather. When the predicted airport arrival demands exceed the predicted available airport's arrival capacity for a sustained period, Ground Delay Program (GDP) operations will be triggered by ATM system. Serious imbalances between demand and capacity occur most frequently when the airport capacity is severely degraded due to either bad airport terminal surface weather or inclement convective weather around airport arrival fixes. A model that predicts the weather-impacted airport arrival meter fix throughput may help ATM personnel to plan GDP operations more efficiently. This paper identifies the characteristics of air traffic flow across arrival meter fixes at Newark Liberty International Airport (EWR). The proposed approach, based on machine-learning methods, is developed to predict the weather impacted EWR arrival Meter Fix (MF) throughput. Sector forecast coverage is used to envision the weather impact on airport arrival MF flow, and the validation is accomplished by using Convective Weather Avoidance Model (CWAM) 0.5 to 2-hour and Collaborative Convective Forecast Product (CCFP) 4 to 8-hour look-ahead forecast data for the period of April-September in 2014. Furthermore, the regression tree ensemble learning of random forests approach for translating a sector forecast coverage model to an EWR arrival meter fix throughput model is examined. The results suggest that ATM decision makers in charge of MF flow control and GDP planning may benefit from adopting the airport arrival meter capacity prediction models to estimate the inclement weather impacts.

Wang, Yao X.↗
DOE OSTI2019

Investigating land surface effects on the moisture transport over South America with a moisture tagging model

Land-atmosphere interactions are a critical component of precipitation processes within the Amazon and the La Plata river basins (LPRB) in South America. The possible pathways through which the land surface can affect precipitation are: 1) by changing the amount of moisture available for precipitation (moisture recycling) and 2) by changing the atmospheric thermal structure and consequently affecting circulation patterns. In this study, the Weather Research and Forecasting (WRF) model with embedded water vapor tracers (WVT) is used to disentangle these relative contributions. Using WRF-WVT we track the moisture that originates from the Amazon basin over a 10-year period. It is estimated that Amazon evapotranspiration (ET) contributes to around 30% of the total precipitation over the Amazon and around 20% over the LPRB. Analyzing moisture transport from the Amazon to the LPRB, it is found that warm (cool) surface air temperature over the northwestern Argentine is linked to low level winds and likely to induce northerly (southerly) winds that intensify (weaken) moisture transport by changing continental-scale circulation patterns.

Yang, Zhao↗
DOE OSTI2025

A Scientist-in-the-Loop Data Analytics Framework for Intelligent Simulation Model Tuning and Validation

This project developed a scientist-in-the-loop data analytics framework for intelligent simulation model tuning and validation, targeting the Weather Research and Forecasting (WRF) model and its solar energy variant, WRF-Solar-BNL. Domain experts, such as climate scientists, depend on large-scale numerical simulations for knowledge discovery and decision-making, yet the complexity of parameter tuning and the disconnect between automated optimization and domain expertise pose significant challenges. We extended an interactive visual analytics framework that enables domain experts to observe and intervene in the computational steering process by identifying disagreements between the simulation model, surrogate model, and the expert’s domain knowledge. Using Bayesian Optimization with Gaussian Process Regression as the surrogate model, our system allows users to probe parameter relationships, analyze correlation patterns, and adjust tuning parameters in real time. We developed use cases for solar irradiance forecasting through sustained collaboration with Brookhaven National Laboratory, resolving critical model configuration challenges and achieving meaningful reductions in prediction error. The project supported one PhD student, one MS student, and eight undergraduate students across three Data Science Capstone projects, resulting in one master’s thesis.

Dasgupta, Aritra [New Jersey Institute of Technolo↗
DOE OSTI2016

Forecasting climate change impacts on plant populations over large spatial extents

Plant population models are powerful tools for predicting climate change impacts in one location, but are difficult to apply at landscape scales. Here, we overcome this limitation by taking advantage of two recent advances: remotely sensed, species-specific estimates of plant cover and statistical models developed for spatiotemporal dynamics of animal populations. Using computationally efficient model reparameterizations, we fit a spatiotemporal population model to a 28-year time series of sagebrush (Artemisia spp.) percent cover over a 2.5 × 5 km landscape in southwestern Wyoming while formally accounting for spatial autocorrelation. We include interannual variation in precipitation and temperature as covariates in the model to investigate how climate affects the cover of sagebrush. We then use the model to forecast the future abundance of sagebrush at the landscape scale under projected climate change, generating spatially explicit estimates of sagebrush population trajectories that have, until now, been impossible to produce at this scale. Our broadscale and long-term predictions are rooted in small-scale and short-term population dynamics and provide an alternative to predictions offered by species distribution models that do not include population dynamics. Finally, our approach, which combines several existing techniques in a novel way, demonstrates the use of remote sensing data to model population responses to environmental change that play out at spatial scales far greater than the traditional field study plot.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1976

Monthly mean forecast experiments with the GISS model

The GISS general circulation model was used to compute global monthly mean forecasts for January 1973, 1974, and 1975 from initial conditions on the first day of each month and constant sea surface temperatures. Forecasts were evaluated in terms of global and hemispheric energetics, zonally averaged meridional and vertical profiles, forecast error statistics, and monthly mean synoptic fields. Although it generated a realistic mean meridional structure, the model did not adequately reproduce the observed interannual variations in the large scale monthly mean energetics and zonally averaged circulation. The monthly mean sea level pressure field was not predicted satisfactorily, but annual changes in the Icelandic low were simulated. The impact of temporal sea surface temperature variations on the forecasts was investigated by comparing two parallel forecasts for January 1974, one using climatological ocean temperatures and the other observed daily ocean temperatures. The use of daily updated sea surface temperatures produced no discernible beneficial effect.

Spar, J.↗
NASA NTRS2010

Understanding Solar Eruptions with SDO/HMI Measuring Photospheric Flows, Testing Models, and Steps Towards Forecasting Solar Eruptions

The Solar Dynamics Observatory (SDO) is carrying the first full-disk imaging vector magnetograph, the Helioseismic and Magnetic Imager (HMI), into an inclined geosynchronous orbit. This magnetograph will provide nearly continuous measurements of photospheric vector magnetic fields at cadences of 90 seconds to 12 minutes with 1" resolution, precise pointing, and unfettered by atmospheric seeing. The enormous data stream of 1.5 Terabytes per day from SAO will provide an unprecedented opportunity to understand the mysteries of solar eruptions. These ground-breaking observations will permit the application of a new technique, the differential affine velocity estimator for vector magnetograms (DAVE4VM), to measure photospheric plasma flows in active regions. These measurements will permit, for the first time, accurate assessments of the coronal free energy available for driving CMEs and flares. The details of photospheric plasma flows, particularly along magnetic neutral-lines, are critical to testing models for initiating coronal mass ejections (CMEs) and flares. Assimilating flows and fields into state-of-the art 3D MHD simulations that model the highly stratified solar atmosphere from the convection zone to the corona represents the next step towards achieving NASA's Living with a Star forecasting goals of predicting "when a solar eruption leading to a CME will occur." Our presentation will describe these major science and predictive advances that will be delivered by SDO/HMI.

Schuck, Peter W.↗
NASA NTRS2010

Understanding Solar Eruptions with SDO/HMI Measuring Photospheric Flows, Testing Models, and Steps Towards Forecasting Solar Eruptions

The imminent launch of Solar Dynamics Observatory (SDO) will carry the first full-disk imaging vector magnetograph, the Helioseismic and Magnetic Imager (HMI), into an inclined geosynchronous orbit. This magnetograph will provide nearly continuous measurements of photospheric vector magnetic fields at cadences of 90 seconds to 12 minutes with I" resolution, precise pointing, and unfettered by atmospheric seeing. The enormous data stream of ~1.5 Terabytes per day from SDO will provide an unprecedented opportunity to understand the mysteries of solar eruptions. These ground-breaking observations will permit the application of a new technique, the differential affine velocity estimator for vector magnetograms (DAVE4VM), to measure photospheric plasma flows in active regions. These measurements will permit, for the first time, accurate assessments of the coronal free energy available for driving CMEs and flares. The details of photospheric plasma flows, particularly along magnetic neutral-lines, are critical to testing models for initiating coronal mass ejections (CMEs) and flares. Assimilating flows and fields into state-of-the art 3D MHD simulations that model the highly stratified solar atmosphere from the convection zone to the corona represents the next step towards achieving NASA's Living with a Star forecasting goals of predicting "when a solar eruption leading to a CME will occur." This talk will describe these major science and predictive advances that will be delivered by SDO /HMI.

Schuck, Peter W.↗
DOE OSTI2019

Call Center Arrivals: When to Jointly Forecast Multiple Streams?

We consider call centers that have multiple (potentially inter‐dependent) demand arrival streams. Workforce management of such labor intensive service systems starts with forecasting future arrival demand. We investigate the question of whether and when to jointly forecast future arrivals of the multiple streams. We first develop a general statistical model to simultaneously forecast multi‐stream arrival rates. The model takes into account three types of inter‐stream dependence. We then show with analytical and simulation studies how the forecasting benefits of the multi‐stream forecasting model vary by the type, direction, and strength of inter‐stream dependence. In particular, we find that it is beneficial to simultaneously forecast multi‐stream arrivals (instead of separately forecasting each stream), when there exists inter‐stream lag dependence among daily arrival rates. Empirical studies, using two real call center datasets further demonstrate our findings, and provide operational insights into how one chooses forecasting models for multi‐stream arrivals.

Ye, Han↗
DOE OSTI2018

Process-Based Model Evaluation Using Surface Energy Budget Observations in Central Greenland

Energy exchange at the Greenland ice sheet surface governs surface temperature variability, a factor critical for representing surface melt. Physical processes link driving forces to subsequent surface energy budget responses, including radiative, turbulent, and ground heat fluxes, and ultimately control surface temperature evolution. A reanalysis product (ERA-Interim, ERA-I), operational model (Climate Forecast System version 2, CFSv2), and climate model (Community Earth System Model, CESM) are evaluated using a comprehensive set of surface energy budget observations and process-based relationships obtained at Summit, Greenland. Simulated downwelling longwave radiation is underestimated, which is linked to a deficiency of liquid-bearing clouds. Lower than observed surface albedo, especially in ERA-I, compensates for summer deficiencies in downwelling longwave radiation. In winter, such deficiencies are compensated by an overestimation of the sensible heat flux. Process-based relationships convey that all three models underestimate the response of surface temperature to changes in radiative forcing, primarily due to an overactive ground heat flux response in ERA-I, turbulent heat fluxes in CFSv2, and sensible heat flux in CESM. Cross comparison of three distinct models indicates that the ground heat flux response for ERA-I, CFSv2, and CESM is too high, too low, and comparatively accurate, respectively, signifying the benefit of using an advanced representation of snow properties. Relatively small biases in CESM surface albedo suggest that advances in the representation of cloud microphysics result in more realistic radiative forcing. These results provide insight into model strengths and deficiencies, indicating the importance of representing physical processes when portraying cloud impacts on surface temperature variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

A framework for WRF to WRF-IBM grid nesting to enable multiscale simulations

With advances in computational power, mesoscale models, such as the Weather Research and Forecasting (WRF) model, are often pushed to higher resolutions. As the model’s horizontal resolution is refined, the maximum resolved terrain slope will increase. Because WRF uses a terrain-following coordinate, this increase in resolved terrain slopes introduces additional grid skewness. At high resolutions and over complex terrain, this grid skewness can introduce large numerical errors that require methods, such as the immersed boundary method, to keep the model accurate and stable. Our implementation of the immersed boundary method in the WRF model, WRF-IBM, has proven effective at microscale simulations over complex terrain. WRF-IBM uses a non-conforming grid that extends beneath the model’s terrain. Boundary conditions at the immersed boundary, the terrain, are enforced by introducing a body force term to the governing equations at points directly beneath the immersed boundary. Nesting between a WRF parent grid and a WRF-IBM child grid requires a new framework for initialization and forcing of the child WRF-IBM grid. This framework will enable concurrent multi-scale simulations within the WRF model, improving the accuracy of high-resolution simulations and enabling simulations across a wide range of scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Truly horizontal finite difference scheme in the WRF model FY18 Q1 Update

A main goal of the Wind Forecasting Improvement Project 2 (WFIP2) is to improve performance of the Weather Research and Forecasting (WRF) model over complex terrain. Two methods for improving model performance were outlined in the LLNL WFIP 2 annual operating plan. The first is modification to WRF’s horizontal finite difference scheme to utilize an improved numerical stencil. A second proposed model improvement is use of the immersed boundary method within the WRF model to facilitate improved simulations over complex terrain. In Q1, work focused on implementing an alternative finite difference scheme, with a deliverable of a project update. The alternative finite difference method was implemented for the temperature variable and shown to improve our selected test case, and recommendations are made to continue the work in Q2. In Q2 the focus should be on extending the method to additional model variables, mainly pressure, as well as to real test cases, such as the WFIP 2 cold pool test case. Additionally, use of the immersed boundary method will be pursed in Q2 and Q3, with a second project update due at the end of Q3.

17 WIND ENERGY↗
DOE OSTI2018

Confronting Models with Regional CO 2 Observations

This research addressed an important DOE science area: the role of natural disturbances in carbon cycling. Our project included both (a) modeling and field measurements and (b) strengthened ties between DOE national laboratories and universities which continue after the project was completed. Our focus was on understanding the consequences of drought and emissions (fire and urban) to atmospheric trace gas composition (carbon dioxide, carbon monoxide, and methane), including both concentration and stable isotope composition. Measurements of these atmospheric gases were used to infer the spatial and temporal patterns of both sources and sinks in the carbon cycle. As part of this effort, we maintained two long-term monitoring networks along a geographic gradient in Utah and Colorado that spans montane forests, urban regions, and oil/gas fields. In this research, we expanded on a well-accepted atmospheric model (STILT-WRF) as a tool to evaluate fluxes in CLM/CESM-related models. We used the STILT model to provide a strong linkage between point atmospheric measurements and the surface parameterizations/emissions that are part of the CLM/CESM models. Overall, this project benefited and supported the DOE Long Term Mission and Goals in four distinct ways: (a) testing of carbon cycle models, (b) testing and evaluating model mechanisms whereby factors in CLM influence trace gas composition in CESM, (c) acquiring high-quality, long-term data on concentrations of carbon dioxide in western USA ecosystems, and (d) by reducing uncertainties associated with the representation of climate-carbon feedbacks in Earth System models through the development of new methods for evaluating model performance. One postdoctoral associate (Henrique Duarte) and one Ph.D. student (Darek Malia) were trained with this support. Nine peer-reviewed publications and 14 poster presentations were produced with support from this project. This proposal addresses a specific science area in the RFP: the role of natural disturbances in carbon cycling. In particular, we will focus on the consequences of drought and fire at landscape to regional scales for atmospheric trace gas concentration and stable isotope composition (CO 2 , CO, and CH 4 ). We seek to expand on the well accepted atmospheric Stochastic Time-Inverted Lagrangian Transport (STILT) model coupled to the Weather Research and Forecasting (WRF) model as a tool to evaluate stable isotope fluxes in CLM/CESM-related models through existing, long-term data sets and field campaigns where high-precision trace gas concentration and isotope measurements can be made along gradients and transects. Given the confounding impacts of anthropogenic trace gas emissions on landscape-to-regional atmospheric observations, we will also measure and parameterize isotopic characteristics of CO 2 , CO, and CH 4 from anthropogenic or forest sources in order to allow us the opportunity to partition atmospheric trace gases into natural and anthropogenic sources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Validation of Weather Forecasting Products

Meteorological modeling plays a pivotal role in operational safety and emergency response at the Savannah River Site (SRS). This study focuses on verifying the Regional Atmospheric Modelling System (RAMS) Version 4.3 through Mean Bias Error (MBE) and Root Mean Square Error (RMSE) analyses of temperature, dew point, and wind speed over a decade. Using observed data from SRS, we assessed RAMS' accuracy, revealing seasonal biases and error trends. Results indicate RAMS' strengths in mild weather conditions but challenges during seasonal extremes and wind speed predictions due to measurement disparities. Future research aims to expand verification to other models and parameters, advocating for enhanced forecasting accuracy crucial for safeguarding personnel and community well-being.

42 ENGINEERING↗