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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Building a landslide hazard indicator with machine learning and land surface models

The U.S. Pacific Northwest has a history of frequent and occasionally deadly landslides caused by various factors. Using a multivariate, machine-learning approach, we combined a Pacific Northwest Landslide Inventory with a 36-year gridded hydrologic dataset from the National Climate Assessment – Land Data Assimilation System to produce a landslide hazard indicator (LHI) on a daily 0.125-degree grid. The LHI identified where and when landslides were most probable over the years 1979–2016, addressing issues of bias and completeness that muddy the analysis of multi-decadal landslide inventories. The seasonal cycle was strong along the west coast, with a peak in the winter, but weaker east of the Cascade Range. This lagging indicator can fill gaps in the observational record to identify the seasonality of landslides over a large spatiotemporal domain and show how landslide hazard has responded to a changing climate.

XGBoost↗
NASA NTRS2006

Terrestrial Water Mass Load Changes from Gravity Recovery and Climate Experiment (GRACE)

Recent studies show that data from the Gravity Recovery and Climate Experiment (GRACE) is promising for basin- to global-scale water cycle research. This study provides varied assessments of errors associated with GRACE water storage estimates. Thirteen monthly GRACE gravity solutions from August 2002 to December 2004 are examined, along with synthesized GRACE gravity fields for the same period that incorporate simulated errors. The synthetic GRACE fields are calculated using numerical climate models and GRACE internal error estimates. We consider the influence of measurement noise, spatial leakage error, and atmospheric and ocean dealiasing (AOD) model error as the major contributors to the error budget. Leakage error arises from the limited range of GRACE spherical harmonics not corrupted by noise. AOD model error is due to imperfect correction for atmosphere and ocean mass redistribution applied during GRACE processing. Four methods of forming water storage estimates from GRACE spherical harmonics (four different basin filters) are applied to both GRACE and synthetic data. Two basin filters use Gaussian smoothing, and the other two are dynamic basin filters which use knowledge of geographical locations where water storage variations are expected. Global maps of measurement noise, leakage error, and AOD model errors are estimated for each basin filter. Dynamic basin filters yield the smallest errors and highest signal-to-noise ratio. Within 12 selected basins, GRACE and synthetic data show similar amplitudes of water storage change. Using 53 river basins, covering most of Earth's land surface excluding Antarctica and Greenland, we document how error changes with basin size, latitude, and shape. Leakage error is most affected by basin size and latitude, and AOD model error is most dependent on basin latitude.

Seo, K.-W.↗
DOE OSTI2012

Estimating future global per capita water availability based on changes in climate and population

Human populations are profoundly affected by water stress, or the lack of sufficient per capita available freshwater. Water stress can result from overuse of available freshwater resources or from a reduction in the amount of available water due to decreases in rainfall and stored water supplies. Analyzing the interrelationship between human populations and water availability is complicated by the uncertainties associated with climate change projections and population projections. We present a simple methodology developed to integrate disparate climate and population data sources and develop first-order per capita water availability projections at the global scale. Simulations from the coupled land-ocean-atmosphere Community Climate System Model version 3 (CCSM3) forced with a range of hypothetical greenhouse gas emissions scenarios are used to project grid-based changes in precipitation minus evapotranspiration as proxies for changes in runoff, or fresh water supply. Population growth changes according to several Intergovernmental Panel on Climate Change (IPCC) storylines are used as proxies for changes in fresh water demand by 2025, 2050 and 2100. These freshwater supply and demand projections are then combined to yield estimates of per capita water availability aggregated by watershed and political unit. Results suggest that important insights might be extracted from the use of the process developed here, notably including the identification of the globe s most vulnerable regions in need of more detailed analysis and the relative importance of population growth versus climate change in in altering future freshwater supplies. However, these are only exemplary insights and, as such, could be considered hypotheses that should be rigorously tested with multiple climate models, multiple observational climate datasets, and more comprehensive population change storylines.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

ARM Standards Policy Committee Report

Data and metadata standards promote the consistent recording of information and are necessary to ensure the stability and high quality of Atmospheric Radiation Measurement (ARM) Climate Research Facility data products for scientific users. Standards also enable automated routines to be developed to examine data, which leads to more efficient operations and assessment of data quality. Although ARM Infrastructure agrees on the utility of data and metadata standards, there is significant confusion over the existing standards and the process for allowing the release of new data products with exceptions to the standards. The ARM Standards Policy Committee was initiated in March 2012 to develop a set of policies and best practices for ARM data and metadata standards.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Coordinating an operational data distribution network for CMIP6 data

The distribution of data contributed to the Coupled Model Intercomparison Project Phase 6 (CMIP6) is via the Earth System Grid Federation (ESGF). The ESGF is a network of internationally distributed sites that together work as a federated data archive. Data records from climate modelling institutes are published to the ESGF and then shared around the world. It is anticipated that CMIP6 will produce approximately 20 PB of data to be published and distributed via the ESGF. In addition to this large volume of data a number of value-added CMIP6 services are required to interact with the ESGF; for example the citation and errata services both interact with the ESGF but are not a core part of its infrastructure. With a number of interacting services and a large volume of data anticipated for CMIP6, the CMIP Data Node Operations Team (CDNOT) was formed. The CDNOT coordinated and implemented a series of CMIP6 preparation data challenges to test all the interacting components in the ESGF CMIP6 software ecosystem. This ensured that when CMIP6 data were released they could be reliably distributed.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2012

Projecting Continental U.S. Water Stress Based on Global Datasets

Human populations may be adversely impacted by water stress, a situation which is commonly defined as a per capita water availability of less than 1700 cubic meters of freshwater per person per year. Water stress may result from either overuse of available freshwater resources or a reduction in the amount of available water due to decreases in rainfall and stored water supplies. Analyzing the interrelationship between human populations and water availability is complicated by the uncertainties associated with climate change projections and population projections. We have developed a simple methodology to integrate disparate climate and population data sources and develop first-order per capita water availability projections at the global scale. Simulations from the coupled land-ocean-atmosphere Community Climate System Model version 3 (CCSM3) forced with a range of hypothetical greenhouse gas emissions scenarios have been used to project grid-based changes in precipitation minus evapotranspiration as proxies for changes in runoff, or fresh water supply. Population growth changes, according to Intergovernmental Panel on Climate Change (IPCC) storylines, have been used as proxies for changes in fresh water demand by 2025, 2050 and 2100. These freshwater supply and demand projections have then been combined to yield estimates of per capita water availability aggregated by U.S. watershed. Results suggest that important insights might be extracted from the use of the process developed here, including the identification of potentially vulnerable areas in need of more detailed analysis. This high-level analysis also illustrates the relative importance of population growth versus climate change in in altering future freshwater supplies. However, these are only exemplary insights and, as such, could be considered hypotheses that should be rigorously tested with multiple climate models, multiple observational climate datasets, and more comprehensive population growth projections.

climate change impacts↗
DOE OSTI2019

Spectral and Microclimate Measurements in Relation to Gold-Standard Methods Field Campaign Report

Arable is an agricultural data and analytics company offering an Internet of things (IOT)-enabled irrigation management tool, weather station, and crop monitor: the Arable Mark. The device synthesizes both climate and plant data to produce actionable insights on natural resource management, mainly commercial agriculture at any scale. Arable collected 11 months of microclimate and radiometric data at the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) observatory to compare measurements of the Arable Mark to the ARM-SGP gold-standard sensors, and for demonstrating the ease of installation and system value to stakeholders. A member from Arable labs installed the device on January 10, 2018, at the ARM facilities and the site managers were in charge of maintaining the sensors in a clean state until the unit’s removal on January 15, 2019. The sampling was positively uneventful, except for a period during the winter when the device stopped posting data for about one week. The verdict was that the equipment required an external solar panel to stay powered through the shorter days of winter. A solar panel was shipped to the SGP, and the unit came back to full charge to continue sending data for the rest of the sampling period. The main goal of the project is to use the data collected by the Arable Mark and build a model that can establish a close relationship with the current methodologies applied to measure ecosystem response to environmental changes. During the sampling period, our team discovered that there was a drift in some of the meteorological data and also in the radiometry, so all efforts are focused into the development of models that would reduce the errors in the reported values.

Southern Great Plains, evapotranspiration, photosy↗
NASA NTRS1986

ISCCP reduced resolution satellite radiance data

The International Satellite Cloud Climatology Project (ISCCP) is the first active project of the World Climate Research Program. It is a multinational data collection project focused on collecting a data set that will improve the ability to predict and/or simulate the radiative effects of clouds on climate. For specified cloud parameters, the goals are to archieve values for 3-hour periods over the whole globe for 5 years at 30 km resolution. The task of collecting and processing radiance data from both geosynchronous and polar orbiting satellites began in July 1983. A diagram was shown illustrating the flow of data from the transmitting satellites to the various receiving institutions that handle it. The various stages of processing were then explained in detail, emphasizing Level B3-normalized, reformatted, reduced raw satellite data. The reduction of data by sampling is an essential step in the flow. By the time the ISCCP data reaches the Global Processing Center at Goddard Institute for Space Studies (GISS), the volume has been reduced by a factor of 1000. The Pilot Climate Data System (PLDS) will provide access to the ISCCP data set. It should prove to be one of the cleanest satellite data sets because it will have been through three filters--that of the operational agency, the Global Processing Center, and the PCDS. The ISCCP data set also includes other correlative data sets delivered in compatible format.

Rossow, W.↗
DOE OSTI2020

A global ensemble of ocean wave climate projections from CMIP5-driven models

This dataset, produced through the Coordinated Ocean Wave Climate Project (COWCLIP) phase 2, represents the first coordinated multivariate ensemble of 21st Century global wind-wave climate projections available (henceforth COWCLIP2.0). COWCLIP2.0 comprises general and extreme statistics of significant wave height (H S ), mean wave period (T m ), and mean wave direction (θ m ) computed over time-slices 1979–2004 and 2081–2100, at different frequency resolutions (monthly, seasonally and annually). The full ensemble comprising 155 global wave climate simulations is obtained from ten CMIP5-based state-of-the-art wave climate studies and provides data derived from alternative wind-wave downscaling methods, and different climate-model forcing and future emissions scenarios. The data has been produced, and processed, under a specific framework for consistency and quality, and follows CMIP5 Data Reference Syntax, Directory structures, and Metadata requirements. Technical comparison of model skill against 26 years of global satellite measurements of significant wave height has been undertaken at global and regional scales. This new dataset provides support for future broad scale coastal hazard and vulnerability assessments and climate adaptation studies in many offshore and coastal engineering applications.

Science & Technology - Other Topics↗
DOE OSTI2015

Constraints on cloud feedback from analysis of ARM observations and models

Over the performance period, the PI and his team performed innovative analyses of surface remote sensing data from the Atmospheric Radiation Measurement (ARM) Program Southern Great Plains, Tropical Western Pacific, and North Slope of Alaska sites to elucidate some of the physical processes underlying cloud feedbacks on anthropogenic climate change. ARM data strongly influenced parameterization evaluation and development in the GISS (Goddard Institute for Space Studies) General Circulation Model (GCM) over this time. In this report we discuss how ARM funding was utilized for data analyses, model evaluation, and cumulus and stratiform cloud parameterization improvements, highlighting especially the areas of low-cloud feedbacks, cloud phase, and convective entrainment and downdrafts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Statistical framework to assess long-term spatio-temporal climate changes: East River mountainous watershed case study

Abstract Evaluation of long-term temporal and spatial climatic change in mountainous regions is a critical challenge because of the interactive effects of multiple land and climatic factors and processes. Here we present the application of the statistical framework to the assessment of changes of climatic conditions, using data from 17 meteorological stations across the East River watershed near Crested Butte, Colorado, USA, and spanning the period from 1966 to 2021. The framework is developed based on (1) a time-series analysis of daily, monthly, and yearly averaged meteorological parameters (temperature, relative humidity, precipitation, wind speed, etc.), (2) evaluation and time series analysis of potential evapotranspiration (ET o ), actual evapotranspiration (ET), aridity index (AI), standard precipitation index (SPI) and standard precipitation-evapotranspiration index (SPEI), and (3) a temporal-spatial climatic zonation of the studied area based on the hierarchical clustering and PCA analysis of the SPEI, because the SPEI can be considered an integrative characteristic of the changes of climatic conditions. The Budyko model, with the application of the Penman–Monteith equation for the estimation of ET o , was used to determine the ET. The time series analysis of the AI is used to identify the periods with energy limited and water limited conditions. Hierarchical clustering of site locations for the three temporal segments of the SPEI showed a significant temporal-spatial shifts, indicating that dynamic climatic processes drive zonation patterns. Therefore, the watershed climatic zonation requires periodic re-evaluation based on the structural time series analysis of meteorological and water balance data.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Evaluating the Global Water and Energy Cycles in Reanalyses Using Ensemble Spread

Reanalyses provide continuous observation based global data over weather and climate time scales through observational assimilation. Ensemble forecasts are a component of modern data assimilation systems, providing information to the analysis process. Storing this spread over the duration of a climate reanalysis will provide insight to the variations of the model that underlies the reanalysis data. Global water and energy cycles (WECs) are a critical aspect of both weather and climate, yet there are some large gaps in the observation of key components as well as their representation in models and reanalyses. NASA’s Water and Energy cycle Studies (NEWS) program is developing a corrected and balanced budget data set that is closed at monthly and regional scales during the 21st century, so far. In this paper, we will evaluate reanalysis WEC components against the initial NEWS collection of observation, but integrate the analysis spread from the reanalysis data assimilation as a new perspective on the evaluation. The initial evaluation will cover a few more recent years using ERA5 10 member spread. As the GMAO’s production of the Modern-Era Retrospective analysis for Research and Applications 21st Century (MERRA-21C) progresses, the effort will include the MERRA-21C 32 member spread. Ultimately, we will explore the potential of the ensemble spread as a measure of uncertainty in the reanalysis.

Michael Bosilovich↗
DOE OSTI2013

Optimizing the Cloud-Aerosol-Radiation Ensemble Modeling System to Improve Future Climate Change Projections at Regional to Local Scales

We have successfully built up, in consistency and completeness, the Cloud-Aerosol-Radiation (CAR) Ensemble Modeling System. Then we have published the CAR general paper to present a general description of the CAR system and illustrate its capabilities for climate modeling applications, especially in the context of estimating climate sensitivity and uncertainty range caused by cloud-aerosol-radiation interactions. For demonstration purpose, the evaluation is based on several CAR standalone experiments driven by CIRC Phase I data or ERA-Interim observational analysis data (ERI) 2004 global data, and coupled climate model experiments by using CWRF, each comparing a limited subset of the full system ensemble with up to 896 members. It is shown that the quantification of radiative forcings and climate impacts strongly depends on the choices of the cloud, aerosol and radiation schemes. The prevailing schemes used in current GCMs are likely insufficient in variety and physically biased in a significant way. There exists large room for improvement by optimally combining radiation transfer with cloud property schemes.

58 GEOSCIENCES↗
DOE OSTI2026

Data and scripts associated with “Non-random processes impacting organic matter chemistry are maximized in mid-order streams”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the publication “Non-random processes impacting organic matter chemistry are maximized in mid-order streams” submitted to Limnology and Oceanography (L&O) by Danczak et al. (in review). This package contains data and scripts used to investigate dissolved organic matter (DOM) molecular chemistry and diversification processes across 47 surface-water sampling sites in the Yakima River Basin, Washington, USA, during an August 2021 sampling campaign. The package contains analyses of ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), geochemical measurements, geospatial attributes, molecular diversity, and meta-metabolome ecological null models needed to reproduce the main manuscript results. The underlying field data were pulled from exising data packages at https://doi.org/10.15485/1892052 (Fulton et al., 2022) and https://doi.org/10.15485/1898914 (Grieger et al., 2022). For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. We thank the following organizations for providing access to field locations for sample collection: the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, the Confederated Tribes and Bands of the Yakama Nation, and the Cowiche Canyon Conservatory. Research was conducted under Washington State Parks and Recreation Commission Scientific Research Permit #210901. We are grateful to the Yakama Nation Tribal Council and Yakama Nation Fisheries for their collaboration in facilitating sample collection and ensuring data usage aligns with their values and worldview. This data package contains an R-Markdown file for analyses and five folders: (1) Data, (2) Geospatial Data, (3) Supplemental_Files, (5) Figures_pdf, (4) and src. The Data folder contains tabular inputs and derived files used in the manuscript analysis. The Geospatial Data folder contains climate and water-balance, hydrologic, land-cover, population/regional water-use, stream, topographic, and stream-order attribute CSV files. The src folder contains scripts used to process data, run analyses, and generate figures. The Figures_pdf folder contains manuscript figure outputs. The Supplemental_Files folder contains supplemental analysis products. All files are .csv, .pdf, .html, .png, .R, .Rmd, .svg, or .tre. This data package is associated with the rcfsa-RC2-SPS_Null_Modeling repository found at https://github.com/river-corridors-sfa/rcfsa-RC2-SPS_Null_Modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Final Report for DOE contract DE-FG02-98ER62609

Several publications document, analyze, and map the paleoclimatic data available for the last glacial maximum (LGM) 21,000 years ago. Data coverage is fairly extensive for North America, Eurasia, and Africa. The data show much lower temperatures for that time in earth history. These data were used to check climate model simulations for the LGM. Simulations forced by the CLIMAP SST for the LGM, where the west/east SST gradient across the Pacific is reduced compared to present, produce a reduction in the strength of the trade winds and a decrease in the west/east slope of the equatorial thermocline that is incompatible with thermocline depths newly inferred from foraminiferal assemblages. Stronger than-present trade winds, and a more realistic simulation of the thermocline slope, are produced when eastern Pacific SSTs are 2°C cooler than western Pacific SSTs. Sensitivity experiments for the model simulations of the LGM were also used to show which aspects of the climate boundary conditions produced various changes in model simulations for the LGM. An experiment which has the LGM ice sheets for the Northern Hemisphere but no Eurasian ice sheet shows that the downstream cooling in Asia is largely dependent on sea-ice growth in the North Atlantic, which is a product of the Laurentide ice sheet. Weaker Asian monsoons develop as a result of this downstream cooling, which is largest for low Laurentide ice sheets of large areal extent.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Estimating Energy Market Schedules Using Historical Price Data: Preprint

The global climate crisis is expected to reshape the energy generation landscape in the coming decades. Increasing integration of non-dispatchable renewable energy resources into energy infrastructures and markets increases uncertainty and creates new opportunities for flexible energy systems. To conduct proper economic evaluation of flexible energy systems, such as integrated energy systems (IES), advancements in modelling of market interactions, such as bidding, is crucial. This work presents a shortcut algorithm which uses two mixed integer linear programs to compute dispatch schedules (e.g., hourly power production targets) that are constrained by the resource's bid information and characteristics (e.g., minimum up and down times) based on historical locational marginal price (LMP) data. This is orders of magnitude less data than required for a market clearing calculation with a full production cost model (PCM). We find the shortcut simulator recapitulates generator dispatch signals for the Prescient PCM with approximately 4% error for the RTS-GMLC test system.

electricity generation↗