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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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Enhancing Remote Sensing Based Yield Forecasting: Application to Winter Wheat in United States

Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. In Becker-Reshef et al. (2010) and Franch et al. (2015) we developed an empirical generalized model for forecasting winter wheat yield. In this study we present a new model based on the extrapolation of the pure wheat signal (100 percent of wheat within the pixel) from MODIS (Moderate-resolution Imaging Spectroradiometer) data at 1-kilometer resolution and using the Difference Vegetation Index (DVI). The model has been applied to monitor the national and state level yield of winter wheat in the United States from 2001 to 2016.

Agriculture↗
NASA NTRS2010

Space Weather Models, Tools and Services at the Community Coordinated Modeling Center

The Community Coordinated Modeling center (CCMC) is a multi-agency partnership to support the research and developmental work necessary to substantially increase space weather modeling capabilities and to facilitate advanced models deployment in forecasting operations. The CCMC conducts unbiased model testing and validation and evaluates model readiness for operational environment. Space weather models and coupled model chains hosted at the CCMC range from the solar corona to the Earth's upper atmosphere. CCMC has developed a number of real-time modeling systems, as well as a large number of modeling and data products tailored to address the space weather needs of NASA's robotic missions. The presentation will demonstrate the rapid progress towards development the system allowing using products derived from space weather models in applications associated with National Space Weather needs. The adaptable Integrated Space Weather Analysis (ISWA) System developed at CCMC for NASA-relevant space weather information combines forecasts based on advanced space weather models hosted at CCMC with concurrent space environment information. The system is also enabling post-impact analysis and flexible dissemination of space weather information.

Kuznetsova, M. M.↗
DOE OSTI2021

Quantifying sensitivity in numerical weather prediction-modeled offshore wind speeds through an ensemble modeling approach

A decade of research has shown that numerical weather prediction (NWP)-modeled wind speeds can be highly sensitive to the inputs and setups within the NWP model. For wind resource characterization applications, this sensitivity is often addressed by constructing a range of setups and selecting the one that best validates against observations. However, this approach is not possible in areas that lack high-quality hub height observations, especially offshore wind areas. In such cases, techniques to quantify and disseminate confidence in NWP-modeled wind speeds in the absence of observations are needed. We address this need in the present study and propose best practices for quantifying the spread in NWP-modeled wind speeds. We implement an ensemble approach in which we consider 24 different setups to the Weather Research and Forecasting (WRF) model. We construct the ensemble by considering variations in WRF version, WRF namelist, atmospheric forcing, and sea surface temperature (SST) forcing. Our analysis finds that the standard deviation produces more consistent estimates compared to the interquartile range and tends to be the more conservative estimator for ensemble variability. We further find that model spread increases closer to the surface and on shorter time scales. In conclusion, we explore methods to attribute total ensemble variability to the different ensemble components (e.g., atmospheric forcing and SST product) and find that contributions by components also vary depending on time scale. We anticipate that the methods and results presented in this paper will provide a reasonable foundation for further research into ensemble-based wind resource data sets.

17 WIND ENERGY↗
DOE OSTI2022

Harmonic Modeling, Data Generation and Analysis of Power Electronics-Interfaced Residential Loads

The share of electronics-based residential load is expected to rise as devices such as variable frequency drives (VFDs), electric vehicle chargers, and inverter-based distributed energy resources (DERs), e.g., photovoltaic (PV) systems become more common. These loads may introduce significant harmonics into power networks that need to be closely studied in order to perform accurate load modeling and forecasting. However, it can be difficult to obtain harmonic-rich voltage and current data - necessary for identifying accurate load models - for residential electrical loads. Recognizing this need, we identify and model a set of electronics-based end-use loads and DERs in an electromagnetic transients program (EMTP) tool for a residence with a single- phase split-phase supply. Further, a procedure is developed to model harmonic interactions between end-use loads connected to the same non-ideal supply voltage in a residential setting. Finally, a harmonic-rich dataset produced via the proposed procedure is utilized to identify frequency coupling matrix (FCM) based load model. Numerical results demonstrate the accuracy of the model, and explore model identifiability with limited data points.

harmonics, power quality, load modeling↗
DOE OSTI2023

Enhanced Geothermal Shot Analysis for the Geothermal Technologies Office

In 2021, the U.S. Department of Energy (DOE) began the Energy Earthshots initiatives to accelerate breakthroughs of reliable clean energy solutions within the next 10 years. In 2022, the National Renewable Energy Laboratory (NREL) was asked by the DOE Geothermal Technologies Office (GTO) to provide analysis for developing Energy Earthshot targets for Enhanced Geothermal Systems (EGS), human-made underground reservoirs that extract thermal energy from the earth for electricity generation and/or heating applications. The Enhanced Geothermal Shot analysis is based on the technology assumptions in the 2019 GTO report GeoVision: Harnessing the Heat Beneath Our Feet. For Earthshot, we updated some of the technology cost and performance assumptions based on recent technology advances and updated the EGS resource potential to include more detailed analysis. We used the updated EGS supply cost curves to forecast the amount of geothermal electricity generation that could be deployed in the US by 2050 using a capacity expansion model. The results were used to develop a cost target for EGS. On September 8th, 2022, the Enhanced Geothermal Shot was announced. Its target - reduce the cost of EGS by 90%, to $45 per megawatt hour by 2035. This paper summarizes the cost and resource assumptions used in the Enhanced Geothermal Shot. It describes the assumptions used in the Regional Energy Deployment System (ReEDS) capacity expansion model to forecast geothermal deployment and discusses the results.

15 GEOTHERMAL ENERGY↗
NASA NTRS2007

An Improved Bulk Microphysical Scheme for Studying Precipitation Processes: Comparisons with Other Schemes

Cloud microphysical processes play an important role in non-hydrostatic high-resolution simulations. Over the past decade both research and operational numerical weather prediction models have started using more complex cloud microphysical schemes that were originally developed for high-resolution cloud-resolving models. An improved bulk microphysical parameterization (adopted from the Goddard microphysics schemes) has recently implemented into the Weather Research and Forecasting (WRF) model. This bulk microphysical scheme has three different options --- 2ICE (cloud ice & snow), 3ICE-graupel (cloud ice, snow & graupel) and 3ICE-hail (cloud ice, snow & hail). High-resolution model simulations are conducted to examine the impact of microphysical schemes on two different weather events (a midlatitude linear convective system and an Atlantic hurricane). In addition, this bulk microphysical parameterization is compared with WIRF's three other bulk microphysical schemes.

Tao, W. K.↗
DOE OSTI2023

Enhanced Geothermal Shot Analysis for the Geothermal Technologies Office: Preprint

In 2021, the U.S. Department of Energy (DOE) began the Energy Earthshots initiatives to accelerate breakthroughs of reliable clean energy solutions within the next 10 years. In 2022, the National Renewable Energy Laboratory (NREL) was asked by the DOE Geothermal Technologies Office (GTO) to provide analysis for developing Energy Earthshot targets for Enhanced Geothermal Systems (EGS), human-made underground reservoirs that extract thermal energy from the earth for electricity generation and/or heating applications. The Enhanced Geothermal Shot analysis is based on the technology assumptions in the 2019 GTO report GeoVision: Harnessing the Heat Beneath Our Feet. For Earthshot, we updated some of the technology cost and performance assumptions based on recent technology advances and updated the EGS resource potential to include more detailed analysis. We used the updated EGS supply cost curves to forecast the amount of geothermal electricity generation that could be deployed in the US by 2050 using a capacity expansion model. The results were used to develop a cost target for EGS. On September 8th, 2022, the Enhanced Geothermal Shot was announced. Its target - reduce the cost of EGS by 90%, to $45 per megawatt hour by 2035. This paper summarizes the cost and resource assumptions used in the Enhanced Geothermal Shot. It describes the assumptions used in the Regional Energy Deployment System (ReEDS) capacity expansion model to forecast geothermal deployment and discusses the results.

analysis↗
DOE OSTI2021

Comparative Analysis of Machine Learning Models for Day-Ahead Photovoltaic Power Production Forecasting

A main challenge for integrating the intermittent photovoltaic (PV) power generation remains the accuracy of day-ahead forecasts and the establishment of robust performing methods. The purpose of this work is to address these technological challenges by evaluating the day-ahead PV production forecasting performance of different machine learning models under different supervised learning regimes and minimal input features. Specifically, the day-ahead forecasting capability of Bayesian neural network (BNN), support vector regression (SVR), and regression tree (RT) models was investigated by employing the same dataset for training and performance verification, thus enabling a valid comparison. The training regime analysis demonstrated that the performance of the investigated models was strongly dependent on the timeframe of the train set, training data sequence, and application of irradiance condition filters. Furthermore, accurate results were obtained utilizing only the measured power output and other calculated parameters for training. Consequently, useful information is provided for establishing a robust day-ahead forecasting methodology that utilizes calculated input parameters and an optimal supervised learning approach. Finally, the obtained results demonstrated that the optimally constructed BNN outperformed all other machine learning models achieving forecasting accuracies lower than 5%.

14 SOLAR ENERGY↗
DOE OSTI2026

How Frequent Will the Rarest Daily Rainfall Records of Hurricane Ida’s Remnants Be in the Future?

Abstract Gaining continued insights into the impact of global warming on the occurrence of hurricane-associated intense record downpours is essential for building climate resilient communities. This study investigates projected future changes in extreme rainfall over the Northeast United States, as represented by extreme daily amounts during Hurricane Ida in 2021. We used historical control simulations of Weather Research and Forecasting (WRF) Model generated from 40 years of weather events (1980–2014, 12 km) forced by the fifth generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis. These simulations are thermodynamically modified (2060–2100) via an imposed warming for the high-emission scenario of shared socioeconomic pathway (SSP585) from a range of general circulation models. Ground observations from the Global Historical Climatology Network (1950–2014) and WRF simulations (historical, 1980–2014, and future, 2060–2100) are integrated into a nonstationary generalized extreme value (GEV) framework to assess the frequency of Ida’s heaviest daily rain rates under the SSP585 scenario. Results show that Ida’s daily maximum rainfall recorded at different observation locations was higher than the single highest September daily maximum observed (1950–2014) for 5 out of 17 stations (∼30% of the stations). Ida-like extreme daily rain rates are projected to be, on average, more than 2 times more likely to occur at the end of the century in the simulations (with some regions as high as 5 times). This work demonstrates that integrating a high-resolution atmospheric model’s present-day and thermodynamically modified future simulations along with ground observations, within a nonstationary statistical framework, is crucial for understanding changing characteristics of extreme weather events. Significance Statement Daily scale extreme precipitation is expected to become more frequent and severe, as evidenced by observations and model simulations. While it is important to investigate how these intensifying heavy rainfall events affect current engineering standards, fewer studies have contextualized how warming impacts the most extreme rainfall from a single storm event relative to historical heavy downpours. In this study, we focused on the daily extreme rainfall associated with the extratropical transition of Hurricane Ida (2021), particularly over the northeastern United States—some of which exceeded the commonly used hydrologic design criteria for a 100-yr storm. Using a high-resolution atmospheric model simulation, we investigated how continued warming may influence the frequency of such daily rain rates. Under a high-emission scenario, these events are projected to become up to 5 times more likely at the end of the twenty-first century.

Dollan, Ishrat J↗
DOE OSTI2020

Simulation of Continental Shallow Cumulus Populations Using an Observation-Constrained Cloud-System Resolving Model

Continental shallow cumulus (ShCu) clouds observed on 30 August 2016 during the Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE) field campaign are simulated by using an observation-constrained cloud-system resolving model. On this day, ShCu forms over Oklahoma and southern Kansas and some of these clouds transition to deeper, precipitating convection during the afternoon. We apply a four-dimensional ensemble-variational (4DEnVar) hybrid technique in the Community Gridpoint Statistical Interpolation (GSI) system to assimilate operational data sets and unique boundary layer measurements including a Raman lidar, radar wind profilers, radiosondes, and surface stations collected by the U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) atmospheric observatory into the Weather Research and Forecasting (WRF) model to ascertain how improved environmental conditions can influence forecasts of ShCu populations and the transition to deeper convection. Independent observations from aircraft, satellite, as well as ARM's remote sensors are used to evaluate model performance in different aspects. Several model experiments are conducted to identify the impact of data assimilation (DA) on the prediction of clouds evolution. The analyses indicate that ShCu populations are more accurately reproduced after DA in terms of cloud initiation time and cloud base height, which can be attributed to an improved representation of the ambient meteorological conditions and the convective boundary layer. Extending the assimilation to 18 UTC (local noon) also improved the simulation of shallow-to-deep transitions of convective clouds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

UNCERTAINTY-AWARE DEEP LEARNING FRAMEWORK FOR FORECASTING COASTAL WATER LEVEL IN VIRGINIA BEACH

Coastal areas like Virginia Beach, USA, are increasingly vulnerable to flooding. To mitigate the impact of flooding, it is crucial for the City of Virginia Beach to have reliable 72-hour-ahead (3 days) forecasts of water levels at key gauge locations. To support this effort, several sensors have been installed throughout the city to monitor water levels and other environmental parameters such as wind speed, precipitation, and atmospheric pressure. Leveraging sensor data from one of these locations, we developed an uncertainty-aware deep learning model to forecast water levels. We employed deep quantile regression (DQR) to quantify variability in the predictions and examined the performance of three different model architectures. In addition to exclusively including historical data, we investigated the improvement wind forecasts provide to the accuracy of 72-hour-ahead water level predictions. The results show a twelvefold improvement in the flood forecast for a real flooding event.

Hasan, Mahmud [Thomas Jefferson National Accelerat↗
NASA NTRSDate not supplied

Sub/seasonal forecasts with a coupled interactive aerosol model in GEOS-S2S

The NASA/Goddard Subseasonal to Seasonal (GEOS-S2S) prediction model includes an interactive aerosol model that allows for prediction of aerosol optical depth (AOD) and PM2.5, as well as improved meteorological forecasts under certain conditions. A model intercomparison of the impact of interactive aerosol is under way, and results from retrospective forecasts with and without interactive aerosol were performed using GEOS-S2S as part of this intercomparison. Results from the GEOS-S2S system will be shown, demonstrating useful skill in AOD and PM2.5 and an example of the impact on a "forecast of opportunity". These results will serve as motivation for the participation of CESM in this interactive aerosol model intercomparison.

S2S↗
NASA NTRS2008

Relativistic Electrons at Geostationary Orbit: Modeling Results

We developed a new prediction model for forecasting relativistic (greater than 2MeV) electrons, which provides a VERY HIGH correlation between predicted and actually measured electron fluxes at geostationary orbit. This model implies the multi-step particle acceleration and is based on numerical integrating two linked continuity equations for primarily accelerated particles and relativistic electrons. The model includes a source and losses, and used solar wind data as only input parameters. We used the coupling function which is a best-fit combination of solar wind/interplanetary magnetic field parameters, responsible for the generation of geomagnetic activity, as a source. The loss function was derived from experimental data. We tested the model for four year period 2004-2007. The correlation coefficient between predicted and actual values of the electron fluxes for whole four year period as well as for each of these years is stable and incredibly high (about 0.9). The high and stable correlation between the computed and actual electron fluxes shows that the reliable forecasting these electrons at geostationary orbit is possible.

Khazanov, George V.↗
NASA NTRS2008

Prediction Model for Relativistic Electrons at Geostationary Orbit

We developed a new prediction model for forecasting relativistic (greater than 2MeV) electrons, which provides a VERY HIGH correlation between predicted and actually measured electron fluxes at geostationary orbit. This model implies the multi-step particle acceleration and is based on numerical integrating two linked continuity equations for primarily accelerated particles and relativistic electrons. The model includes a source and losses, and used solar wind data as only input parameters. We used the coupling function which is a best-fit combination of solar wind/interplanetary magnetic field parameters, responsible for the generation of geomagnetic activity, as a source. The loss function was derived from experimental data. We tested the model for four year period 2004-2007. The correlation coefficient between predicted and actual values of the electron fluxes for whole four year period as well as for each of these years is stable and incredibly high (about 0.9). The high and stable correlation between the computed and actual electron fluxes shows that the reliable forecasting these electrons at geostationary orbit is possible.

Khazanov, George V.↗
NASA NTRS2007

Improving Forecast Skill by Assimilation of Quality-controlled AIRS Temperature Retrievals under Partially Cloudy Conditions

The National Aeronautics and Space Administration (NASA) Atmospheric Infrared Sounder (AIRS) on board the Aqua satellite has been long recognized as an important contributor towards the improvement of weather forecasts. At this time only a small fraction of the total data produced by AIRS is being used by operational weather systems. In fact, in addition to effects of thinning and quality control, the only AIRS data assimilated are radiance observations of channels unaffected by clouds. Observations in mid-lower tropospheric sounding AIRS channels are assimilated primarily under completely clear-sky conditions, thus imposing a very severe limitation on the horizontal distribution of the AIRS-derived information. In this work it is shown that the ability to derive accurate temperature profiles from AIRS observations in partially cloud-contaminated areas can be utilized to further improve the impact of AIRS observations in a global model and forecasting system. The analyses produced by assimilating AIRS temperature profiles obtained under partial cloud cover result in a substantially colder representation of the northern hemisphere lower midtroposphere at higher latitudes. This temperature difference has a strong impact, through hydrostatic adjustment, in the midtropospheric geopotential heights, which causes a different representation of the polar vortex especially over northeastern Siberia and Alaska. The AIRS-induced anomaly propagates through the model's dynamics producing improved 5-day forecasts.

Reale, O.↗
DOE OSTI2015

Evaluation of tropical channel refinement using MPAS‐A aquaplanet simulations

Abstract Climate models with variable‐resolution grids offer a computationally less expensive way to provide more detailed information and increased accuracy by resolving processes that cannot be adequately represented by a coarser grid. This study uses the Model for Prediction Across Scales‐Atmosphere (MPAS‐A), consisting of a nonhydrostatic dynamical core and a subset of Weather Research and Forecasting (WRF) model physics, to investigate the potential benefits of using tropical channel refinement. The simulations are performed with an idealized aquaplanet configuration using 30 and 240 km global grid spacing, and two variable‐resolution grids spanning the same grid spacing range; one with a narrow (20°S–20°N) and one with a wide (30°S–30°N) tropical channel refinement. Increasing resolution in the tropics impacts both the tropical and extratropical circulation. Compared to the 30 km global grid, both refined channel simulations exhibit slightly stronger updrafts inside the Hadley cell resulting in more resolved precipitation. Using a wider tropical refinement leads to a closer correspondence with the global high‐resolution grid. While different grid spacings produce similar cloud size distributions that are consistent with observations, the dependence of precipitation rate on cloud size varies among simulations. The refined channel simulations show improved tropical and extratropical precipitation relative to the global coarse simulation. All simulations show a single precipitation peak centered on the equator. Although the results show that tropical refinement is an effective method for avoiding artifacts due to grid resolution sensitivities seen in earlier studies that only refined a portion of the tropics, some biases remain well inside of the refinement region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

High-Resolution ESM Projections for Energy Applications Over the CONUS

Assessing energy resources under future scenarios requires high-resolution meteorological information that is physically consistent and suitable for regional-scale analysis. While Earth system model (ESM) projections provide valuable large-scale information, their coarse resolution and systematic biases limit direct applicability for energy system modeling and planning. In this study, we develop a high-resolution dynamical downscaling framework based on the Weather Research and Forecasting (WRF) model to translate global-scale ESM data into energy-relevant regional projections over the contiguous United States (CONUS). The framework identifies an optimized WRF configuration through numerical experiments and evaluates raw and bias-corrected ESM initial and boundary conditions, with soil moisture (SM) and soil temperature (ST) bias correction implemented as an integral part of the bias-corrected ESM forcing to improve land-atmosphere coupling prior to WRF dynamical downscaling. Using an optimized WRF configuration at 4-km resolution, we show that raw ESM forcing introduces systematic dry and cold soil biases that propagate into pronounced warm biases in near-surface air temperature and positive biases in solar irradiance, particularly during summer. Applying bias-corrected atmospheric forcing together with bias-corrected SM and ST substantially reduces these downstream biases and improves the surface energy balance and near-surface atmospheric fields. These results demonstrate that bias-aware treatment of initial conditions is critical for producing high-resolution downscaled projections suitable for energy system modeling and planning applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2009

Modeling and Observational Framework for Diagnosing Local Land-Atmosphere Coupling on Diurnal Time Scales

Land-atmosphere interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface temperature and moisture states. The degree of coupling between the land surface and PBL in numerical weather prediction and climate models remains largely unexplored and undiagnosed due to the complex interactions and feedbacks present across a range of scales. Further, uncoupled systems or experiments (e.g., the Project for Intercomparison of Land Parameterization Schemes, PILPS) may lead to inaccurate water and energy cycle process understanding by neglecting feedback processes such as PBL-top entrainment. In this study, a framework for diagnosing local land-atmosphere coupling is presented using a coupled mesoscale model with a suite of PBL and land surface model (LSM) options along with observations during field experiments in the U. S. Southern Great Plains. Specifically, the Weather Research and Forecasting (WRF) model has been coupled to the Land Information System (LIS), which provides a flexible and high-resolution representation and initialization of land surface physics and states. Within this framework, the coupling established by each pairing of the available PBL schemes in WRF with the LSMs in LIS is evaluated in terms of the diurnal temperature and humidity evolution in the mixed layer. The co-evolution of these variables and the convective PBL is sensitive to and, in fact, integrative of the dominant processes that govern the PBL budget, which are synthesized through the use of mixing diagrams. Results show how the sensitivity of land-atmosphere interactions to the specific choice of PBL scheme and LSM varies across surface moisture regimes and can be quantified and evaluated against observations. As such, this methodology provides a potential pathway to study factors controlling local land-atmosphere coupling (LoCo) using the LIS-WRF system, which will serve as a testbed for future experiments to evaluate coupling diagnostics within the community.

Santanello, Joseph A., Jr.↗