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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Can ERA5 Be Used to Study Mesoscale Convective System Climatological Characteristics?

Mesoscale convective systems (MCSs) produce more than half of tropical rainfall and are central to the global hydrologic cycle. As the climate warms, environments favorable for MCSs may become more common; however, limited observational records hamper understanding of how MCSs respond to variations and changes in their environments. Here, we evaluate how well MCSs are represented in ERA5, a widely used global high‐resolution reanalysis product. Using PyFLEXTRKR, which jointly tracks top‐of‐atmosphere infrared brightness temperature and surface precipitation, we identified MCSs in ERA5 and compared them with those identified in satellite observations using the same detection algorithm. This comparison analysis spans 2007–2020 using hourly data at 0.25° horizontal resolution focusing over the tropics. ERA5 reproduces observed brightness‐temperature statistics and captures the geographic distribution and seasonal and diurnal cycles of MCS cold cloud shields. However, ERA5 precipitation exhibits an intensity bias—too much light rain and too little heavy rain—which shifts the rain‐rate distribution and reduces the frequency of MCSs relative to observations. Within MCSs, ERA5 precipitation exhibits the same pattern of bias, yielding a systematic underestimation of MCS precipitation intensity. Consistent with these biases, ERA5 underestimates the contribution of MCS to tropical rainfall by 25%–34% in key regions. Overall, ERA5 is suitable for studying MCS cold cloud‐shield climatology and evolution, but precipitation‐based MCS characteristics (including event‐level precipitation features and the geospatial distribution of MCS precipitation) should be interpreted with caution. These findings clarify which aspects of MCS behavior are robustly represented in ERA5 for climatological applications.

mesoscale convection↗
DOE OSTI2018

Unsupervised Clustering-Based Short-Term Solar Forecasting

Solar forecasting accuracy is highly affected by weather conditions, therefore, weather awareness forecasting models are expected to improve the forecasting performance. However, it may not be available or reliable to classify different forecasting tasks by only using predefined meteorological weather categorization. In this paper, an unsupervised clustering-based (UC-based) solar forecasting method is developed for short-term (1-h-ahead) global horizontal irradiance (GHI) forecasting. This UC-based method consists of three parts: GHI time series unsupervised clustering, pattern recognition, and UC-based forecasting. The daily GHI time series is first clustered by an Optimized Cross-validated ClUsteRing (OCCUR) method, which determines the optimal number of clusters and best clustering results. Then, support vector machine pattern recognition is adopted to recognize the category of a certain day using the first four hours' data in the forecasting stage. GHI forecasts are generated by the most suitable models in different clusters, which are built by a two-layer machine learning based multi-model (M3) forecasting framework. The developed UC-M3 method is validated by using 1-year of data with 13 solar features from three information sources. Numerical results show that 1) UC-based models outperform non-UC (all-in-one) models with the same M3 architecture by approximately 20%; and 2) M3-based models also outperform the single-algorithm machine learning models by approximately 20%.

14 SOLAR ENERGY↗
DOE OSTI2025

Performance of Convection-Permitting and Convection-Parameterized Models in Reproducing the Extreme Precipitation Intensity Relationship with Surface Conditions

Here, this study investigates the warm-season extreme precipitation–temperature scaling relationship in CONUS404, a convection-permitting (4 km) Weather Research and Forecasting (WRF) Model simulation over the conterminous United States for the past four decades, and compares it with the WRF-Thermodynamic Global Warming (WRF-TGW) historical simulation at a coarser resolution (12 km) using parameterized convection. We also analyze the NCEP stage IV and NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) datasets as observational benchmarks. We examine how extreme precipitation intensity (EPI) varies with temperature and saturation deficit over representative regions based on hourly data. The stage IV and IMERG data show a similar pattern of EPI variation with temperature and saturation deficit, except that the EPI peak is lower in IMERG than in stage IV. Under dry and hot conditions, EPI decreases too rapidly with elevated saturation deficit in both CONUS404 and WRF-TGW compared to observations, but the performance of CONUS404 is superior to WRF-TGW. When the near-surface atmosphere is saturated or close to saturated, both CONUS404 and WRF-TGW produce higher peak values of EPI relative to the observational references; IMERG exhibits scaling rates close to the Clausius–Clapeyron (C–C) relationship, while CONUS404, WRF-TGW, and stage IV all demonstrate super-C–C scaling behaviors. Despite marked warming over the past four decades, in both CONUS404 and WRF-TGW, the scaling relationship between EPI and temperature in a saturated atmosphere remains stable and robust. This indicates a strong potential for the EPI–temperature scaling rate under saturation to be used as an emergent constraint in reducing uncertainties of future extreme precipitation projection.

Atmosphere↗
DOE OSTI2020

Soil Water Content (m3 m-3) from Australia, May 2014 - March 2017

Hourly data processed to monthly. Data collected from Cosmic Neutron Probes at variable depth averaging 28 cm that was interpolated to near-surface (0-10 cm). Site located at latitude -17.12 deg N, longitude 145.63 deg E. Data was downloaded from COSMOS data portal and is publicly available. Data from this site is referenced as Australia (1) in Table 1 and Figure 4 of Solander et al., 2020, The pan-tropical soil moisture response to El Nino, Hydrol. Earth Syst. Sci., in press.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Soil Water Content (m3 m-3) from Australia, June 2011 - December 2016

Hourly data processed to monthly. Data collected from Cosmic Neutron Probes at variable depth averaging 38 cm that was interpolated to near-surface (0-10 cm). Site located at latitude -14.16 deg N, longitude 131.39 deg E. Data was downloaded from the The COsmic-ray Soil Moisture Observing System (COSMOS) soil moisture data network and is publicly available. Data from this site located at, referenced as Australia (2) in Table 1 and Figure 4 of Solander et al., 2020, The pan-tropical soil moisture response to El Nino, Hydrol. Earth Syst. Sci., in press.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Soil Water Content (m3 m-3) from Australia, November 2007 - May 2009

Hourly data processed to monthly. Data collected from Eddy Covariance Flux Tower at near-surface (0-10 cm). Site located at latitude -13.08 deg N, longitude 131.12 deg E. Data was downloaded from the FluxNet soil moisture data network and is publicly available. Data from this site located at, referenced as Australia (3) in Table 1 and Figure 4 of Solander et al., 2020, The pan-tropical soil moisture response to El Nino, Hydrol. Earth Syst. Sci., in press.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Soil Water Content (m3 m-3) located in Santarem, Brazil K67, July 2000 - February 2004

Hourly data processed to monthly. Data collected from Eddy Covariance Flux Tower at the near-surface (0-10 cm). Site located at a latitude -3.02 deg N, longitude -54.97 deg E. Data was downloaded from the FluxNet soil moisture data network and is publicly available. Data from this site is referenced as Brazil (3) in Table 1 and Figure 4 of Solander et al., 2020, The pan-tropical soil moisture response to El Nino, Hydrol. Earth Syst. Sci., in press.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Soil Water Content (m3 m-3) from Australia, August 2001 - December 2014

Hourly data processed to monthly. Data collected from Eddy Covariance Flux Tower at near-surface (0-10 cm). Site located at latitude -12.49 deg N, longitude 131.15 deg E. Data was downloaded from the FluxNet soil moisture data network and is publicly available. Data from this site is referenced as Australia (4) in Table 1 and Figure 4 of Solander et al., 2020, The pan-tropical soil moisture response to El Nino, Hydrol. Earth Syst. Sci., in press.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Soil Water Content (m3 m-3), October 2011 - May 2017

Hourly data processed to monthly. Data collected from Cosmic Neutron Probes at variable depth averaging 15 cm that was interpolated to near-surface (0-10 cm). Site located at latitude 0.28 deg N, longitude 36.87 deg E. Data was downloaded from the The COsmic-ray Soil Moisture Observing System (COSMOS) soil moisture data network and is publicly available. Data was made available by the COsmic-ray Soil Moisture Observing SysteM (COSMOS). Data from this site is referenced as Kenya in Table 1 and Figure 4 of Solander et al., 2020, The pan-tropical soil moisture response to El Nino, Hydrol. Earth Syst. Sci., in press.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Impact of Non-Steady State Operation on Cooling Water Consumption at Coal- and Natural Gas-Fired Power Plants

Increased renewable energy penetration in the electricity grid in coming decades will result in more frequent cycling at thermal power plants. Simultaneously, thermal power plants may face water scarcity with declining availability of cooling water. Therefore, to enhance thermal power plants’ resiliency to water shortages it is important to understand how non-steady conditions due to cycling will impact cooling water consumption and withdrawal intensity. Non-steady operation at power plants has been previously shown to decrease power plant thermal efficiency. Energy balance models have also demonstrated that a decrease in thermal efficiency is expected to increase cooling water consumption intensity. Furthermore, past work has used operating hours data to show idling and cycling gaps where cooling systems operate more than corresponding generators. As a result, an increase in cycling behavior may impact cooling water consumption and withdrawal intensity. This study uses data from the Energy Information Administration (EIA) and Environmental Protection Agency (EPA) to quantify the impact of cycling cooling water consumption intensity for recirculating cooling systems and withdrawal intensity for once-through cooling systems using energy balance and statistical approaches. In a novel application of a fixed effects model to study the effect of temperature on cooling water consumption and withdrawal intensity, this study finds temperature was consistently expected to increase consumption intensity and withdrawal intensity. Non-steady state conditions do not increase cooling water consumption intensity with statistical significance across unit types. However, additional validation of cooling water data is required to confirm these observed trends due to the sensitivity of these findings to model form and concerns with data quality of the dependent variables, cooling water consumption and withdrawal.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

A Comparison of Machine Learning Methods to Forecast Tropospheric Ozone Levels in Delhi

Ground-level ozone is a pollutant that is harmful to urban populations, particularly in developing countries where it is present in significant quantities. It greatly increases the risk of heart and lung diseases and harms agricultural crops. This study hypothesized that, as a secondary pollutant, ground-level ozone is amenable to 24 h forecasting based on measurements of weather conditions and primary pollutants such as nitrogen oxides and volatile organic compounds. We developed software to analyze hourly records of 12 air pollutants and 5 weather variables over the course of one year in Delhi, India. To determine the best predictive model, eight machine learning algorithms were tuned, trained, tested, and compared using cross-validation with hourly data for a full year. The algorithms, ranked by R2 values, were XGBoost (0.61), Random Forest (0.61), K-Nearest Neighbor Regression (0.55), Support Vector Regression (0.48), Decision Trees (0.43), AdaBoost (0.39), and linear regression (0.39). When trained by separate seasons across five years, the predictive capabilities of all models increased, with a maximum R 2 of 0.75 during winter. Bidirectional Long Short-Term Memory was the least accurate model for annual training, but had some of the best predictions for seasonal training. Out of five air quality index categories, the XGBoost model was able to predict the correct category 24 h in advance 90% of the time when trained with full-year data. Separated by season, winter is considerably more predictable (97.3%), followed by post-monsoon (92.8%), monsoon (90.3%), and summer (88.9%). These results show the importance of training machine learning methods with season-specific data sets and comparing a large number of methods for specific applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Datasets for Widespread Residential Space Heating Electrification in Texas

In this experiment, we explore long term patterns in electricity demand driven by the dual effects of full electrification of space heating in Texas (by adoption of electric heat pumps), and climate change. We use a predictive model of electricity demand, climate projections, and an open source nodal power system (DC Optimal Power Flow) model of the Electric Reliability Council of Texas (ERCOT) system. Heat pumps are a more energy efficient way of providing space heating and cooling in homes. We attempt to exhaustively investigate the impacts of full residential space heating electrification by adoption of heat pumps for the segment of Texas households that currently rely on fossil fuels (about 40%), while simultaneously incorporating climate change meteorological variables. We explore a range of scenarios of heat pump efficiency and climate uncertainty over a long period of future years (2020-2099). In total, the simulation experiment generates 1,280 simulation years of hourly data. We report and analyze results in form of impacts on residential load, total load, peak load, seasonality of peaking, and reliability measured by occurrence and frequency of loss of load events. While the experiment is for ERCOT, the insights and approach can be applied to other regions. The results from the analysis can inform system planners on a range of potential capacity requirements/ reliability implications and/or risks of full space heating electrification via the adoption of electric heat pumps, given the uncertainty in the scenarios/ climate futures. The dataset includes model output for residential, non residential and total load, and the results from the GO ERCOT model runs for 4 RCP Scenarios (RCP 4.5 Cooler, RCP 4.5 Hotter, RCP 8.5 Cooler, RCP 8.5 Hotter), 4 Heating electrification Scenarios (Base , Standard Efficiency HP, High Efficiency HP, Ultra-High Efficiency HP) over 80 years (2020-2099). The metrological variables at BA scale were weighted weighted using population projections consistent with the SSP3 scenario.

Climate Change↗
NASA NTRS1973

Polar motion and earth tides from Beacon Explorer C

Seventeen months of laser tracking of the Beacon Explorer C spacecraft by a Goddard Space Flight Center laser system were analyzed. The amplitude and phase of the solid-earth and ocean-tide perturbations of the orbit and the variation in latitude of the tracking station were determined. From an analysis of the orbital inclination obtained from 6 hour data spans the tidal perturbations of the orbit were found to have a magnitude equivalent to a second degree Love number, k sub 2, of 0.245 with a phase lag of about 3.2 degrees. These numbers are in complete agreement with values obtained previously from a much shorter data span, although k sub 2, is lower than the value derived from seismic data. This discrepancy is probably due to the influence of the oceans on the satellite result. After removing the tidal perturbations the residuals in inclination were of order 0.04 arcseconds. This implies that the variation in latitude of the station was being determined during the 17 months period with an rms deviation of about 1.4 meters with respect to the smoothed Bureau International de l'Heure values.

Kolenkiewicz, D. E.↗
NASA NTRS1974

A study of remote sensing as applied to regional and small watersheds. Volume 2: Supporting technical details

The Stanford Watershed Model, the Kentucky Watershed Model and OPSET program, and the NASA-IBM system for simulation and analysis of watersheds are described in terms of their applications to the study of remote sensing of water resources. Specific calibration processes and input and output parameters that are instrumental in the simulations are explained for the following kinds of data: (1) hourly precipitation data; (2) daily discharge data; (3) flood hydrographs; (4) temperature and evaporation data; and (5) snowmelt data arrays. The Sensitivity Analysis Task, which provides a method for evaluation of any of the separate simulation runs in the form of performance indices, is also reported. The method is defined and a summary of results is given which indicates the values obtained in the simulation runs performed for Town Creek, Alabama; Alamosa Creek, Colorado; and Pearl River, Louisiana. The results are shown in tabular and plot graph form. For Vol. 1, see N74-27813.

Source record↗
NASA NTRS1976

Test facility for solar-cell reference conditions

A test facility, intended primarily for long-term monitoring of the global insolation and its components and the concurrent solar cell performance under a wide variety of measureable atmospheric and weather conditions, is described. Instruments for the measurement of insolation, cell performance, turbidity, water vapor, and cloud cover are described. Preliminary evaluation of the hourly data base generated over a two-month period for a range of sky conditions from clear to overcast is presented.

Klucher, T. M.↗
NASA NTRS1977

Radio and X-ray observations of NGC 1851 and NGC 1904

The globular clusters NGC 1851 and NGC 1904, have been observed simultaneously at radio and X-ray wavelengths with the aid of the Green Bank interferometer and the proportional counter on OAO Copernicus. The combined radio and X-ray observations provided simultaneous coverage of each subject for a total of about one hour. Data concerning the radio frequency observations of the two globular clusters are presented in a table. The X-ray counts per frame are shown in a graph as a function of time, taking into account a five day interval in December 1975.

Johnson, H. M.↗
NASA NTRS1980

Variation of aircraft noise annoyance

Laboratory and field studies were conducted to determine the basis for increased sensitivity of people to noise during aircraft noise studies. This change in sensitivity could be attributed to either a physiological time-of-day effect (i.e., a circadian rhythm) or simply to the total number of aircraft noise events experienced during a laboratory test period. In order to investigate the time-of-day factor, noise sensitivity measures were obtained from subjects at home with cassette tape recorders/headsets over a 24 hour period. The effect of number of aircraft noise events on noise sensitivity was investigated within a laboratory. In these tests, measures of sensitivity to noise were obtained from subjects before and after their exposure to varying numbers of aircraft noise events. The 24 hour data showed no evidence that noise sensitivity is physiologically cyclical. Consequently, these data can not explain annoyance response variation to aircraft noise tests conducted during the daytime. However, the number of aircraft noise events did influence the subject's noise sensitivity. This effect completely accounts for the systematic increase in noise sensitivity during a laboratory test period.

Dempsey, T. K.↗
NASA NTRS1980

Cabin fire simulator lavatory tests

All tests were conducted in the Douglas Cabin Fire Simulator under in-flight ventilation conditions. All tests were allowed to continue for a period of one hour. Data obtained during these tests included: heat flux and temperatures of the lavatory; cabin temperature variations; gas analyses for O2, CO2, CO, HF, HC1, and HCN; respiration and electrocardiogram data on instrumented animal subjects (rats) exposed in the cabin; and color motion pictures. All tests resulted in a survivable cabin condition; however, occupants of the cabin would have been subjected to noxious fumes.

Schutter, K. J.↗