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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 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2010

A validated methodology for the prediction of heating and cooling energy demand for buildings within the Urban Heat Island: Case-study of London

This paper describes a method for predicting air temperatures within the Urban Heat Island at discreet locations based on input data from one meteorological station for the time the prediction is required and historic measured air temperatures within the city. It uses London as a case-study to describe the method and its applications. The prediction model is based on Artificial Neural Network (ANN) modelling and it is termed the London Site Specific Air Temperature (LSSAT) predictor. The temporal and spatial validity of the model was tested using data measured 8 years later from the original dataset; it was found that site specific hourly air temperature prediction provides acceptable accuracy and improves considerably for average monthly values. It thus is a very reliable tool for use as part of the process of predicting heating and cooling loads for urban buildings. This is illustrated by the computation of Heating Degree Days (HDD) and Cooling Degree Hours (CDH) for a West-East Transect within London. The described method could be used for any city for which historic hourly air temperatures are available for a number of locations; for example air pollution measuring sites, common in many cities, typically measure air temperature on an hourly basis. (author)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2019

Establishing Calibration Standards for Remote Sensing Retrievals of Greenhouse Gases

While in situ greenhouse gas measurements have a concrete calibration standard, establishing a similar standard for remote sensing retrievals remains challenging. As the constellation of greenhouse gas observing satellites grows, such a standard is essential to ensure data are of the quality necessary to support scientific and policy applications. A primary goal of NASA's Atmospheric Carbon and Transport (ACT) - America aircraft campaign is to evaluate column CO2 retrievals from the Orbiting Carbon Observatory 2 (OCO-2) through coordinated underflights. The campaign also includes airborne lidar instruments that measure the amount of CO2 and CH4 below the aircraft. This presentation introduces a system that establishes calibration standards for OCO-2 and lidar retrievals based on in situ data from the ACT-America campaign. The system assimilates the in situ data into NASA's Goddard Earth Observing System (GEOS) to produce high-resolution, two-dimensional transects of CO2 along the flight path which we refer to as curtains. Excluding the ability to sample the entire atmosphere at once, any such analysis must make assumptions about the connection of measurements at different places and times to a given retrieval. We chose to use the GEOS general circulation model forced by meteorology from its data assimilation system because their scientific merits are extensively documented. Furthermore, in areas rich in data, the assimilated curtains approach a field constrained by data alone. Where data are lacking, e.g., the stratosphere, age of air and other transport diagnostics can be used to quantify the uncertainty introduced by the model. Given these uncertainties, we can determine the uncertainties of the curtains and thus our ability to evaluate remote sensing instruments. Here, we demonstrate this for several flights over North America and discuss possible applications to upcoming missions, e.g., GeoCarb.

Weir, B.↗
DOE OSTI2018

National Solar Radiation Database (NSRDB)

The National Solar Radiation Database (NSRDB) is a serially complete collection of meteorological and solar irradiance data sets for the United States and a growing list of international locations for 1998-2023. The NSRDB is updated annually and provides foundational information to support U.S. Department of Energy programs, research, industry and the general public. The NSRDB provides time-series data at 30-minute resolution of resource averaged over surface cells of 0.038 degrees in both latitude and longitude, or nominally 4 km in size. Additionally time series data at 5 minutes for the US and 10 minutes for North, Central and South America at 2 km resolution are produced from the next generation of GOES satellites and made available from 2019. The solar radiation values represent the resource available to solar energy systems. The data was created using cloud properties which are generated using the AVHRR Pathfinder Atmospheres-Extended (PATMOS-x) algorithms developed by the University of Wisconsin. Fast all-sky radiation model for solar applications (FARMS) in conjunction with the cloud properties, and aerosol optical depth (AOD) and precipitable water vapor (PWV) from ancillary source are used to estimate solar irradiance (GHI, DNI, and DHI). The Global Horizontal Irradiance (GHI) is computed for clear skies using the REST2 model. For cloud scenes identified by the cloud mask, FARMS is used to compute GHI and FARMS DNI is used to compute the Direct Normal Irradiance (DNI). The PATMOS-X model uses radiance images in visible and infrared channels from the Geostationary Operational Environmental Satellite (GOES) series of geostationary weather satellites. Ancillary variables needed to run REST2 and FARMS (e.g., aerosol optical depth, precipitable water vapor, and albedo) are derived from NASA's Modern Era-Retrospective Analysis (MERRA-2) dataset. Temperature and wind speed data are also derived from MERRA-2 and provided for use in NREL's System Advisor Model (SAM) to compute PV generation.

aerosol↗
NASA NTRS1983

Analysis of selective chopper radiometer data

Data from SCR-B on Nimbus 5 have been processed to yield global, orbital temperatures at 10, 5, 2, 1, and 0.4 mb for the period January 1977 through April 1978 under the current task. In addition gridded values at 10 deg latitude by 20 deg longitude were prepared by space-time interpolation for the period January 1975 through April 1978. Temperature retrieval was based on regression of radiances against Meteorological Rocket Network data, with regressions recomputed at approximately six-month intervals. This data now completes a consistent time series from April 1970 to April 1978 for all available radiance data from SCR A and SCR B on Nimbus 4 and 5. The processing details for the current period are discussed, but is also applicable to the previous data periods. The accuracy of the temperature retrievals for each 6-month period for the entire eight years is given in the Appendices.

Roe, J.↗
NASA NTRS2012

Global Assimilation of EOS-Aura Data as a Means of Mapping Ozone Distribution in the Lower Stratosphere and Troposphere

Ozone in the lower stratosphere and the troposphere plays an important role in forcing the climate. However, the global ozone distribution in this region is not well known because of the sparse distribution of in-situ data and the poor sensitivity of satellite based observations to the lowermost of the atmosphere. The Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS) instruments on EOS-Aura provide information on the total ozone column and the stratospheric ozone profile. This data has been assimilated into NASA s Global Earth Observing System, Version 5 (GEOS-5) data assimilation system (DAS). We will discuss the results of assimilating three years of OMI and MLS data into GEOS-5. This data was assimilated alongside meteorological observations from both conventional sources and satellite instruments. Previous studies have shown that combining observations from these instruments through the Trajectory Tropospheric Ozone Residual methodology (TTOR) or using data assimilation can yield useful, yet low biased, estimates of the tropospheric ozone budget. We show that the assimilated ozone fields in this updated version of GEOS-5 exhibit an excellent agreement with ozone sonde and High Resolution Dynamics Limb Sounder (HIRDLS) data in the lower stratosphere in terms of spatial and temporal variability as well as integrated ozone abundances. Good representation of small-scale vertical features follows from combining the MLS data with the assimilated meteorological fields. We then demonstrate how this information can be used to calculate the Stratosphere - Troposphere Exchange of ozone and its contribution to the tropospheric ozone column in GEOS-5. Evaluations of tropospheric ozone distributions from the assimilation will be made by comparisons with sonde and other in-situ observations.

Wargan, Krzysztof↗
DOE OSTI2010

Forecasting of preprocessed daily solar radiation time series using neural networks

In this paper, we present an application of Artificial Neural Networks (ANNs) in the renewable energy domain. We particularly look at the Multi-Layer Perceptron (MLP) network which has been the most used of ANNs architectures both in the renewable energy domain and in the time series forecasting. We have used a MLP and an ad hoc time series pre-processing to develop a methodology for the daily prediction of global solar radiation on a horizontal surface. First results are promising with nRMSE {proportional_to} 21% and RMSE {proportional_to} 3.59 MJ/m{sup 2}. The optimized MLP presents predictions similar to or even better than conventional and reference methods such as ARIMA techniques, Bayesian inference, Markov chains and k-Nearest-Neighbors. Moreover we found that the data pre-processing approach proposed can reduce significantly forecasting errors of about 6% compared to conventional prediction methods such as Markov chains or Bayesian inference. The simulator proposed has been obtained using 19 years of available data from the meteorological station of Ajaccio (Corsica Island, France, 41 55'N, 8 44'E, 4 m above mean sea level). The predicted whole methodology has been validated on a 1.175 kWc mono-Si PV power grid. Six prediction methods (ANN, clear sky model, combination..) allow to predict the best daily DC PV power production at horizon d + 1. The cumulated DC PV energy on a 6-months period shows a great agreement between simulated and measured data (R{sup 2} > 0.99 and nRMSE < 2%). (author)

14 SOLAR ENERGY↗
DOE OSTI2026

Repository of HydroSMADE: Hydropower Site-level Monthly Availability Data Ensemble for 1950-2100 at Existing and Potential Global Sites

This repository presents HydroSMADE—Hydropower Site-level Monthly Availability Data Ensemble, a new open dataset that provides monthly hydropower availability for 1,593 existing and 124,333 potential sites worldwide over the period 1950–2100. The dataset is generated by using a global hydrologic model (Xanthos) with explicit representation of hydropower operation. Specifically, HydroSMADE distinguishes between storage and diversion sites, applies optimized operating rules, and incorporates site-specific characteristics such as generation capacity, maximum turbine flow, and reservoir storage. Driven by bias-corrected meteorological inputs, the data is provided for 30 alternative future scenarios. The scenarios consist of the full factorial combination of three standard CMIP6 atmospheric forcing pathways (SSP1-2.6, SSP3-7.0, and SSP5-8.5) and ten CMIP6 General Circulation Models (GCMs): GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, EC-Earth3, CanESM5, MIROC6, CNRM-ESM2-1, UKESM1-0-LL, and CNRM-CM6-1. The repository contains a total of 122 files: a text file (readme.txt) containing a brief description of the included data, a CSV file containing site attributes, and the remaining 120 files (in CSV) containing site-level monthly hydropower availability. Example Jupyter Notebooks to explore the HydroSMADE dataset are available on GitHub at https://github.com/kamal0013/HydroSMADE More details on the methods and technical validation of HydroSMADE are available in the following paper by the same authors: Chowdhury, A. K., Abeshu, G. W., Zhao, M., Wild, T. B., Hassan, N., Ying, Z., Kim, G. J., Matthew, B., Jonathan, L., & Li, H.-Y. (Submitted). Hydropower Site-level Monthly Availability Data Ensemble for 1950-2100 at Existing and Potential Global Sites.

Existing and Potential Sites↗
DOE OSTI2021

Assessment of Model Hub Height Wind Speed Performance Using DOE Lidar Buoy Data

As the offshore wind industry continues to gain momentum both in the U.S. and globally, reanalysis products remain essential tools for the lifecycle of a wind farm. From project siting and production estimates to determining construction and maintenance windows, reanalysis products play an important role. These regional and global long-term models also serve as inputs to higher resolution models. With such wide-ranging impacts on the offshore wind industry, the need for validation of reanalysis products is strong, particularly at typical turbine hub heights. Pacific Northwest National Laboratory (PNNL) operates two AXYS WindSentinelTM buoys for the U.S. Department of Energy (DOE) in order to collect meteorological and oceanographic data, including hub height wind speed, in areas of interest for offshore wind development. This work uses DOE buoy observations off the coasts of New Jersey and Virginia to evaluate the performance of Modern Era Retrospective Analysis for Research and Applications-2 (MERRA-2), the North American Regional Reanalysis (NARR), the European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th generation (ERA5), and the National Oceanic and Atmospheric Administration (NOAA) assimilation system Rapid Refresh (RAP). Biases and degrees of correspondence are determined for each reanalysis product in order to provide insights on the performance and uncertainty for long-term wind resource characterization. With the baseline metrics determined, this work proceeds with an investigation into the sources of large deviations between the modeled and observed hub height wind speeds during the East Coast lidar buoy deployments. Consistent sources of reanalysis model error include stably stratified flow conditions, high wind shear, flow parallel to the coastline, and tropical and winter storms. For the near shore New Jersey location, offshore winds (i.e. blowing from land to water) tend to be associated with model overestimation of observed hub height wind speed, while winds parallel to the coastline are correlated with model underestimation of wind speed. Similarly, near the Virginia buoy, during southerly winds (parallel to the coastline) models underestimate the observed wind speed due to coastal upwelling. Reanalysis model wind speeds underestimate the hub height winds with increasing stable atmospheric stratification. Reanalysis model biases are seen when the observed wind speeds approach or exceed typical turbine cut-out speeds (around 20 m s-1). Large model errors during winter and tropical storm events, high wind speed events, and ramp events are often the result of wind speed timing and magnitude offsets. Overall, ERA5 provides the most successful representation of observed offshore hub height wind speeds at the U.S. East Coast buoy locations and is therefore best suited as an input boundary condition for model case studies. ERA5 performs well for a variety of atmospheric phenomena, including storms and sea breezes, however RAP, the next most successful reanalysis model, performs best in capturing the frequency of ramp events.

17 WIND ENERGY↗
NASA NTRS2019

Impact of Assimilating Adaptively Thinned AIRS Cloud-Cleared Radiances in the GEOS

This work examines the assimilation of AIRS (Atmospheric Infrared Sounder) radiances from two points of view: the thinning strategy and the use of cloud-cleared radiances as opposed to clear-sky. Previous published work by this team, based on a very large set of Observing System Experiments performed with a 2014 3DVAR version of the GEOS (Goddard Earth Observing System), has shown that the assimilation of adaptively thinned AIRS cloud-cleared radiances (CCRs) improves the representation of tropical cyclones (TCs) without damaging the global forecast skill. The simple adaptive methodology is based on denser AIRS coverage in moving domains centered on TCs, and sparser coverage everywhere else. Subsequent experiments showed that the adaptive methodology produces good results also when applied to clear-sky CrIS (Cross-track Infrared Sounder) and IASI (Infrared Atmospheric Sounding Interferometer) radiances. In addition, the results indicate that the density of all hyperspectral data assimilated over meteorologically inactive areas is excessive, probably because of horizontal error correlation, suggesting that the global thinning should be more aggressive. More recent work focused on the polar regions has shown another positive impact of assimilating cloud-cleared AIRS radiances instead of clear-sky. The results show that high latitude atmospheric dynamics is very sensitive to the representation of the lower tropospheric temperature structure over the Arctic region. Specifically, assimilation of CCRs over areas that are data poor and also affected by broken stratus clouds, and as such minimally observed by AIRS clear-sky radiances, changes substantially the temperature structure over the Arctic low troposphere. Ingestion of CCRs over the region propagates, through hydrostatic adjustments, to mid-tropospheric geopotential height, allowing for better prediction of mid-latitude waves. In addition, adaptively thinned CCRs also improve the representation of mesoscale convective cyclones at high latitudes. An example of an Antarctic low is provided. Finally, recent ongoing work with the hybrid 4DenVAR GEOS, investigating the 2017 boreal TC season, has confirmed the previous results: namely that aggressively thinned cloud-cleared radiances improve TC structure with no loss of global skill.

CCR↗
NASA NTRSDate not supplied

CERES Fast Longwave And SHortwave Radiative Flux (FLASHFlux): Research to Operation

The Clouds and Earth’s Radiant Energy System (CERES) project provides satellite-based observations of how Earth’s energy flows are varying in time and space and how clouds and aerosols are affecting the Earth’s radiation budget. Nominally, CERES data products require months of validation and calibration before releasing a climate quality data. The Fast Longwave and Shortwave Radiative Flux (FLASHFlux) data product was developed as to provide key data for applied science research involving the renewable energy and agricultural sectors within a week of observations. FLASHFlux achieves this by using simplified calibration, an operation meteorology product from Global Modeling and Assimilation Office (GMAO), and its own surface parameterizations model. The CERES FLASHFlux provides two data products: 1) an hourly Level 2 Single Scanner Footprint (SSF) data separately for Aqua and Terra observations, and 2) a daily Level 3 Time Interpolated and Spatially Averaged (TISA) 1ox 1ogridded data that combines Aqua and Terra observations. We inter-compared of FLASHFlux Version4A calibration to the climate quality CERES Edition4A data product over time. In addition, we also inter-compared the impact of the fluxes by using the near real-time GMAO GEOS FP-IT instead of GMAO G5-CERES meteorology data. We also compare both SSF overpass and TISA radiative flux products to a set of surface measurement sites that are globally distributed and assess agreement.

P. C. Sawaengphokhai↗
DOE OSTI2020

Developing a simulator-based satellite dataset for using machine learning techniques to derive aerosol-cloud-precipitation interactions in models and observations in a consistent framework

Aerosol-cloud-precipitation interactions (ACPI) remain a major uncertainty in understanding the Earth’s radiation budget and water cycle (including extremes). After decades of active research, various observationally based metrics have been developed to constrain ACPI in Earth System Models (ESMs), but direct comparison of model and data estimates can confound scientific understanding because limitations and uncertainties in sampling and retrieval procedures may combine with model deficiencies in process representations of ACPI to obstruct understanding. Furthermore, conventional ACPI metrics often vary from one regime to another, and the ACPI process representation in ESMs is also typically derived based on only a limited area/regime (even though the parameterization applies globally). To bridge the gap between models and data and to correctly describe ACPI in all regimes, we propose to construct a new CALIPSO-CloudSat merged dataset that is produced by the same algorithms used in satellite simulators in ESMs, and to use machine learning techniques to derive new ACPI metrics that can be accurately estimated by satellites and can provide meaningful constraints on cloud microphysical process representations in ESMs. The dataset will include measured and retrieved variables for aerosol, cloud, and precipitation from CALIPSO and CloudSat, and environmental variables from meteorological reanalysis. The data will be used to train a neural network to construct the ACPI metrics as a function of environmental conditions. The new ACPI formula will be used to constrain the ACPI in the Energy Exascale Earth System Model (E3SM), and to augment/reformulate the ACPI process representation in the E3SM to improve the simulation of the evolution of the atmosphere under different environmental conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Block Island Environmental Monitoring Data (Weather, Waves & Sensor Depth)

This dataset contains meteorological, oceanographic, and sensor-depth data collected near Block Island during the 2016 and 2017 RODEO field seasons to provide environmental context for concurrent acoustic and survey operations. It includes buoy-derived wind and wave time series, in-situ water temperature logger records, and depth (pressure) records from a sensor mounted on the vertical line array.

17 WIND ENERGY↗
NASA NTRS1989

Persistence of polar stratospheric clouds in the southern polar region

Observations of Antarctic polar stratospheric clouds (PSCs) were examined using the 1-micron aerosol extinction ratio data from the SAM II satellite experiment for the years 1979-1982 and 1984-1987. PSCs were sighted between 10 and 25 km and were usually first observed by mid-June. Clouds disappeared earlier at higher altitudes (late August near 24 km, in most cases) and later at lower altitudes (late September or October near 16 km). It was found that PSCs persisted longer in 1985 and 1987 at 18 km and were more frequently observed in September and October 1987 than the other years. Inference of likely PSC formation regions from National Meteorological Center temperature data indicated that clouds would begin forming in late May and usually disappear in September. This analysis confirmed the persistence of colder conditions during the spring of 1987.

Mccormick, M. P.↗
DOE OSTI2020

The Case for Custom TMY's: Examples Using the NSRDB

A typical meteorological year (TMY) data set essentially represents an hourly compilation of median months constructed using multi-year datasets. Although TMY data sets are generated from irradiance data in the horizontal plane, they are used in photovoltaic (PV) modeling for systems inclined to various angles. This paper demonstrates that a POA TMY (plane of array TMY) generated by selecting median months from a multi-year POA irradiance timeseries dataset produces significantly different results than a POA TMY generated by transposing a TMY dataset constructed from horizontal data. In some months the differences can be more than 3%. These results point to the need for generating TMY's using POA irradiance timeseries representing the orientation at which PV panels will be deployed.

14 SOLAR ENERGY↗
NASA NTRS1992

SAGE II stratospheric density and temperature retrieval experiment

This paper describes a stratospheric density and temperature retrieval experiment based on the solar occultation measurement of the Stratospheric Aerosol and Gas Experiment (SAGE II). The entire retrieval analysis involves two inversion steps: the vertical structure inversion, which derives the profile of local atmospheric extinction from SAGE II limb optical depth data, and the species inversion, which inverts the concentration of air molecules, aerosols, ozone, and nitrogen dioxide from the derived atmospheric extinction at five SAGE II short wavelengths (0.385, 0.448, 0.453, 0.525, and 0.600 microns). The derived density profile is then used to infer the temperature distribution, assuming that the atmosphere is in hydrostatic equilibrium and obeys the ideal gas law. The temperature profiles retrieved from the SAGE II observations are compared with near-coincident, in both time and space, French Rayleigh lidar and NASA Wallops Flight Facility rocket datasonde soundings as well as the National Meteorological Center (NMC) data analyses. The results indicate that the mean SAGE II temperature agrees with the mean lidar measurements to within 2 C at altitudes from 30.5 to 52.5 km. The SAGE II and datasonde observations agree to within about 4 C in approximately the same altitude region.

Wang, Pi-Huan↗
DOE OSTI2015

Quantifying Interannual Variability for Photovoltaic Systems in PVWatts

The National Renewable Energy Laboratory's (NREL's) PVWatts is a relatively simple tool used by industry and individuals alike to easily estimate the amount of energy a photovoltaic (PV) system will produce throughout the course of a typical year. PVWatts Version 5 has previously been shown to be able to reasonably represent an operating system's output when provided with concurrent weather data, however this type of data is not available when estimating system output during future time frames. For this purpose PVWatts uses weather data from typical meteorological year (TMY) datasets which are available on the NREL website. The TMY files represent a statistically 'typical' year which by definition excludes anomalous weather patterns and as a result may not provide sufficient quantification of project risk to the financial community. It was therefore desired to quantify the interannual variability associated with TMY files in order to improve the understanding of risk associated with these projects. To begin to understand the interannual variability of a PV project, we simulated two archetypal PV system designs, which are common in the PV industry, in PVWatts using the NSRDB's 1961-1990 historical dataset. This dataset contains measured hourly weather data and spans the thirty years from 1961-1990 for 239 locations in the United States. To note, this historical dataset was used to compose the TMY2 dataset. Using the results of these simulations we computed several statistical metrics which may be of interest to the financial community and normalized the results with respect to the TMY energy prediction at each location, so that these results could be easily translated to similar systems. This report briefly describes the simulation process used and the statistical methodology employed for this project, but otherwise focuses mainly on a sample of our results. A short discussion of these results is also provided. It is our hope that this quantification of the interannual variability of PV systems will provide a starting point for variability considerations in future PV system designs and investigations. however this type of data is not available when estimating system output during future time frames.

14 SOLAR ENERGY↗
NASA NTRS2010

Effects of Lightning and Other Meteorological Factors on Fire Activity in the North American Boreal Forest: Implications for Fire Weather Forecasting

The effects of lightning and other meteorological factors on wildfire activity in the North American boreal forest are statistically analyzed during the fire seasons of 2000-2006 through an integration of the following data sets: the MODerate Resolution Imaging Spectroradiometer (MODIS) level 2 fire products, the 3-hourly 32-kin gridded meteorological data from North American Regional Reanalysis (NARR), and the lightning data collected by the Canadian Lightning Detection Network (CLDN) and the Alaska Lightning Detection Network (ALDN). Positive anomalies of the 500 hPa geopotential height field, convective available potential energy (CAPE), number of cloud-to-ground lightning strikes, and the number of consecutive dry days are found to be statistically important to the seasonal variation of MODIS fire counts in a large portion of Canada and the entirety of Alaska. Analysis of fire occurrence patterns in the eastern and western boreal forest regions shows that dry (in the absence of precipitation) lightning strikes account for only 20% of the total lightning strikes, but are associated with (and likely cause) 40% of the MODIS observed fire counts in these regions. The chance for ignition increases when a threshold of at least 10 dry strikes per NARR grid box and at least 10 consecutive dry days is reached. Due to the orientation of the large-scale pattern, complex differences in fire and lightning occurrence and variability were also found between the eastern and western sub-regions. Locations with a high percentage of dry strikes commonly experience an increased number of fire counts, but the mean number of fire counts per dry strike is more than 50% higher in western boreal forest sub-region, suggesting a geographic and possible topographic influence. While wet lightning events are found to occur with a large range of CAPE values, a high probability for dry lightning occurs only when 500 hPa geopotential heights are above 5700m and CAPE values are near the maximum observed level, underscoring the importance of low-level instability to boreal fire weather forecasts-

Peterson, D.↗