Spatiotemporal energy infrastructure datasets for the United States: A review
Not Available
Tavakkoli, Sakineh↗
SEARCH · Search NASA
Results for “spatiotemporal data”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Energy Emissions Accounting Methods Can Determine Whether Direct Air Capture with Storage Achieves Net Removal
The voluntary carbon market within the United States has expanded rapidly in recent years and enabled private companies and other organizations to provide revenue streams to carbon dioxide removal (CDR) technologies. For a CDR technology to participate in the voluntary carbon market (VCM), the emissions associated with constructing and operating the technology must be less than the CO 2 captured from the atmosphere. Assessing the extent to which this is true for direct air capture with storage (DACS), a relatively energy-intensive CDR technology, strongly depends on the accounting method used to assess the emissions intensity of purchased energy. We simulate the hourly weather-dependent operation of sorbent- and solvent-based DACS in California, Louisiana, Texas, and Wyoming, representing a wide range of local weather and electric and natural gas grid compositions. In all cases, the single most important emissions accounting decision is the method used to estimate the emissions intensity of purchased grid electricity, which varies the calculated net removal by −1049% to +108%. All other factors influencing net removal introduce a variation of at most ±14%. No electricity emissions accounting method is universally conservative across all scenarios, and none is objectively more accurate. High-spatiotemporal-resolution, high-quality, publicly available data sets and models for electricity emissions accounting do not currently exist and are urgently needed to enable standardization of emissions accounting methods to more accurately determine the true emissions impacts of DACS and other energy-intensive facilities.
54 ENVIRONMENTAL SCIENCES↗
PubDAS: A PUBlic Distributed Acoustic Sensing Datasets Repository for Geosciences
During the past few years, distributed acoustic sensing (DAS) has become an invaluable tool for recording high-fidelity seismic wavefields with great spatiotemporal resolutions. However, the considerable amount of data generated during DAS experiments limits their distribution with the broader scientific community. Such a bottleneck inherently slows down the pursuit of new scientific discoveries in geosciences. Here, we introduce PubDAS—the first large-scale open-source repository where several DAS datasets from multiple experiments are publicly shared. PubDAS currently hosts eight datasets covering a variety of geological settings (e.g., urban centers, underground mines, and seafloor), spanning from several days to several years, offering both continuous and triggered active source recordings, and totaling up to ~90 TB of data. Here this article describes these datasets, their metadata, and how to access and download them. Some of these datasets have only been shallowly explored, leaving the door open for new discoveries in Earth sciences and beyond.
54 ENVIRONMENTAL SCIENCES↗
A Lagrangian analysis of pockets of open cells over the southeastern Pacific
Abstract. Pockets of open cells (POCs) have been shown to develop within closed-cell stratocumulus (StCu), and a large body of evidence suggests that the development of POCs result from changes in small-scale processes internal to the boundary layer rather than large-scale forcings. Precipitation is widely viewed as a key process important to POC development and maintenance. In this study, GOES-16 satellite observations are used in conjunction with MERRA-2 winds to track and compare the microphysical and environmental evolution of two populations of closed-cell StCu selected by visual inspection over the southeastern Pacific Ocean: one group that transitions to POCs and another comparison group (CLOSED) that does not. The high spatiotemporal resolution of the new GOES-16 data allows for a detailed examination of the temporal evolution of POCs in this region. We find that POCs tend to develop near the coast, last tens of hours, are larger than 104 km2, and often (88 % of cases) do not re-close before they exit the StCu deck. Most POCs are observed to form at night and tend to exit the StCu during the day when the StCu is contracting in area. Relative to the CLOSED trajectories, POCs have systematically larger effective radii, lower cloud drop number concentrations, a comparable conditional in-cloud liquid water path, and a higher frequency of more intense precipitation. Meanwhile, no systematic environmental differences other than boundary layer height are observed between POC and CLOSED trajectories. Interestingly, there are no differences in reanalysis aerosol optical depth between both sets of trajectories, which may lead one to the interpretation that differences in aerosol concentrations are not influencing POC development or resulting in a large number that re-close. However, this largely depends on the reanalysis treatment of aerosol–cloud interactions, and the product used in this study has no explicit handling of these important processes. These results support the consensus view regarding the importance of precipitation on the formation and maintenance of POCs and demonstrate the utility of modern geostationary remote sensing data in evaluating the POC life cycle.
54 ENVIRONMENTAL SCIENCES↗
Application of the Global Precipitation Forecast dataset for Global Landslide Forecasting System
Landslides are extremely damaging, pervasive and cause fatalities and economic impacts. Extreme rainfall events, coupled with inopportune surface conditions, are the primary triggers of landslides around the world. Forecasting landslide events represent an area of open research. A global Landslide Hazard Assessment for Situational Awareness (LHASA) model has been created that provides near real-time dynamic landslide characterization using Integrated Multi-Satellite Retrievals for Global Precipitation Mission (IMERG). However, it does not provide information on prediction of landslides into future. This study considers how global precipitation forecast data compares to satellite rainfall at different spatiotemporal scales and outlines the potential for its use in landslide hazard prediction/forecasting system. NASA Goddard Earth Observing System (GEOS)-Forecast model assimilates new observations every 6 hours, at 00, 06, 12, and 18 UTC. The GEOS-Forecast model is initialized at 00 UTC and is evaluated with IMERG Early, using Multi-Radar Multi-Sensor gauge corrected (MRMS-GC) precipitation product as a reference over contiguous United States (CONUS). Categorical and continuous statistics along with probability density functions and cumulative distribution functions are considered to assess the performance of the precipitation products, with a focus on landslide hotspots. Seasonality appears to influence the performance of both the GEOS-Forecast and IMERG Early product. Moreover, the global comparison between the GEOS-Forecast and IMERG Early is carried out in terms of percentile difference, correlation, and bias maps. For extreme rainfall events in regions such as Mekong, Colombia, and Tajikistan, GEOS-Forecast appears to resolve high rainfall relative to IMERG Early more frequently. Validation over landslide points reveal that for 24hr rainfall accumulations > 100mm, GEOS-Forecast and IMERG coincide. GEOS-Forecast and IMERG Early precipitation matches more closely for tropical cyclones than other types of storms. Overall, the performance varies with respect to location, rainfall intensities, and type of precipitation events.
Sana Khan↗
Midwest Water Resources: Developing an Evapotranspiration Climatology to Analyze Spatiotemporal Water Budget Patterns for Agriculture and Natural Resources Managers in the Midwest
Evapotranspiration (ET) is a climatic variable critical to the hydrologic cycle and is used to evaluate spatiotemporal trends in drought conditions. Although in-situ observations provide accurate ET information, these records are spatially sparse. The United States Department of Agriculture (USDA) Midwest Climate Hub, National Integrated Drought Information System, Minnesota Department of Agriculture, Michigan State University, and the USDA Foreign Agricultural Service have partnered with DEVELOP to gain new insights on spatiotemporal patterns of ET with NASA satellite data. This project evaluates the feasibility of using remotely sensed ET data products to understand trends from 2001 through 2020. Actual ET (aET) data were sourced from NASA’s Terra Moderate Resolution Imaging Spectroradiometer (MODIS), and reference ET (refET) and precipitation data were sourced from the Gridded Surface Meteorological Dataset (gridMET). Spatial and temporal differences in the MODIS and gridMET data sets were resolved such that gridded data could be compared. These data sets were then used to produce monthly Normals maps of mm/8-day of precipitation, aET, and refET. Using precipitation and ET Normals maps, hydrologic state maps were produced by subtracting either refET or aET from precipitation. These hydrologic state maps provide a proxy water balance by summarizing the difference between water entering and leaving the ground surface. Additionally, the ET products were compared in timeseries plots. The ET products from this project provided partners with comparable datasets to assess potential drought and flooding conditions to support Midwest agricultural and natural resource managers in decision-making.
Emma Myrick↗
A Feasibility Study of 4-D Tomography of Soft X-ray Magnetosheath Emissivities Using Multi-Spacecraft Measurements
Upcoming heliophysics missions utilize state-of-the-art wide field-of-view (FOV) imaging technology to measure and investigate the space plasma environment on a global scale. At Earth, remote sensing of soft X-ray emissions, which are generated via the charge exchange interaction between heavy solar wind ions and exospheric neutral atoms, is a promising means to investigate the global magnetosheath structure, its response to varying solar wind conditions, and the spatiotemporal properties of the dayside magnetic reconnection. Data analysis techniques such as optical tomography can provide additional structural and time-varying information from the observed target and thus enhance the mission’s scientific return. In this work, we simulate multiple and simultaneous observations of the dayside magnetosphere using soft X-ray imagers located at long-distance vantage points to reconstruct the time-dependent, three-dimensional (3-D) structure of the magnetosheath using a dynamic tomographic approach. The OpenGCCM MHD model is used to simulate the time-varying response of the magnetosheath to solar wind conditions and, subsequently, generate synthetic soft X-ray images from multiple spacecraft vantage points separated along a common orbit. A detailed analysis is then performed to identify the nominal set of spacecraft that produces the highest fidelity tomographic reconstruction of the magnetopause. This work aims to (i) demonstrate, for the first time, the use of dynamic tomography to retrieve the time-varying magnetosheath structure and (ii) identify a nominal mission design for multi-spacecraft configurations aiming for optical tomography.
3-D/4_d tomography↗
Experiment Based Superposition Thermal Modeling of Laser Powder Bed Fusion
This paper evaluates experiment-based superposition thermal modeling for Laser Powder Bed Fusion (LPBF) with a pulsed laser. An analytical pulsed laser thermal model establishes the modeling procedure. The framework inverts a powder bed’s single pulse temperature response from experimental spatiotemporal Short-Wave Infrared (SWIR) camera data. Superimposing this response along a scan path simulates multi-pulse LPBF. Results show the experimentally informed superposition model rapidly and accurately predicts a layer’s temperature history. The model has applications in correction of thermally driven LPBF errors and in-situ part qualification.
Lough, Cody↗
A high-resolution unified observational data product of mesoscale convective systems and isolated deep convection in the United States for 2004–2017
Deep convection possesses markedly distinct properties at different spatiotemporal scales. We present an original high-resolution (4 km, hourly) unified data product of mesoscale convective systems (MCSs) and isolated deep convection (IDC) in the United States east of the Rocky Mountains and examine their climatological characteristics from 2004 to 2017. The data product is produced by applying an updated Flexible Object Tracker algorithm to hourly satellite brightness temperature, radar reflectivity, and precipitation datasets. Analysis of the data product shows that MCSs are much larger and longer-lasting than IDC, but IDC occurs about 100 times more frequently than MCSs, with a mean convective intensity comparable to that of MCSs. Hence both MCS and IDC are essential contributors to precipitation east of the Rocky Mountains, although their precipitation shows significantly different spatiotemporal characteristics. IDC precipitation concentrates in summer in the Southeast with a peak in the late afternoon, while MCS precipitation is significant in all seasons, especially for spring and summer in the Great Plains. The spatial distribution of MCS precipitation amounts varies by season, while diurnally, MCS precipitation generally peaks during nighttime except in the Southeast. Potential uncertainties and limitations of the data product are also discussed. The data product is useful for investigating the atmospheric environments and physical processes associated with different types of convective systems; quantifying the impacts of convection on hydrology, atmospheric chemistry, and severe weather events; and evaluating and improving the representation of convective processes in weather and climate models.
54 ENVIRONMENTAL SCIENCES↗
Using remotely sensed information to support landslide hazard and exposure assessment throughout the disaster lifecycle
The global coverage and temporal frequency that satellites provide offers a unique opportunity to estimate landslide hazard and exposure throughout the disaster lifecycle, from pre-event planning and forecasting to post-event mapping and impact assessment, and finally to recovery and mitigation. The relevance of satellite-derived data and model products is largely contingent on the spatiotemporal sampling, the hazard characteristics, and the needs from the research or applications community. This work presents an advanced Landslide Hazard Assessment for Situational Awareness (LHASA) framework that brings together satellite and model products with new machine learning techniques and global inventory data to better model landslide hazard and exposure. We present several new ways to map, model, and assess landslide hazard using a range of satellite data. Two new thrusts of this work are to better account for the exacerbating impacts of fires and to provide a multi-day forecast of potential hazard. Together, these additional components blend information from a suite of satellite and model sources to improve early warning of potentially hazardous areas, identify landslide occurrence and impacts in near real-time, and better characterize the spatiotemporal patterns of landslide hazard and exposure more broadly for future awareness and planning. This suite of tools and products is open to the public and provides information to better assess the potential occurrence and impacts of landslides within different regions of the world. This presentation explores both the architecture behind this framework and examples of how the model components and products have been used by different stakeholders around the world.
Thomas Stanley↗
Evaluation of CLARA-A2 and ISCCP-H Cloud Cover Climate Data Records over Europe with ECA&D Ground-Based Measurements
Clouds are of high importance for the climate system but they still remain one of its principal uncertainties. Remote sensing techniques applied to satellite observations have assisted tremendously in the creation of long-term and homogeneous data records; however, satellite data sets need to be validated and compared with other data records, especially ground measurements. In the present study, the spatiotemporal distribution and variability of Total Cloud Cover (TCC) from the Satellite Application Facility on Climate Monitoring (CM SAF) Cloud, Albedo And Surface Radiation dataset from AVHRR data—edition 2 (CLARA-A2) and the International Satellite Cloud Climatology Project H-series (ISCCP-H) is analyzed over Europe. The CLARA-A2 data record has been created using measurements of the Advanced Very High Resolution Radiometer (AVHRR) instrument onboard the polar orbiting NOAA and the EUMETSAT MetOp satellites, whereas the ISCCP-H data were produced by a combination of measurements from geostationary meteorological satellites and the AVHRR instrument on the polar orbiting satellites. An intercomparison of the two data records is performed over their common period, 1984 to 2012. In addition, a comparison of the two satellite data records is made against TCC observations at 22 meteorological stations in Europe, from the European Climate Assessment & Dataset (ECA&D). The results indicate generally larger ISCCP-H TCC with respect to the corresponding CLARA-A2 data, in particular in the Mediterranean. Compared to ECA&D data, both satellite datasets reveal a reasonable performance, with overall mean TCC biases of 2.1 and 5.2% for CLARA-A2 and ISCCP-H, respectively. This, along with the higher correlation coefficients between CLARA-A2 and ECA&D TCC, indicates the better performance of CLARA-A2 TCC data.
Tzallas, Vasileios↗
ORT: a workflow linking genome-scale metabolic models with reactive transport codes
Abstract Motivation Nutrient and contaminant behavior in the subsurface are governed by multiple coupled hydrobiogeochemical processes which occur across different temporal and spatial scales. Accurate description of macroscopic system behavior requires accounting for the effects of microscopic and especially microbial processes. Microbial processes mediate precipitation and dissolution and change aqueous geochemistry, all of which impacts macroscopic system behavior. As ‘omics data describing microbial processes is increasingly affordable and available, novel methods for using this data quickly and effectively for improved ecosystem models are needed. Results We propose a workflow (‘Omics to Reactive Transport—ORT) for utilizing metagenomic and environmental data to describe the effect of microbiological processes in macroscopic reactive transport models. This workflow utilizes and couples two open-source software packages: KBase (a software platform for systems biology) and PFLOTRAN (a reactive transport modeling code). We describe the architecture of ORT and demonstrate an implementation using metagenomic and geochemical data from a river system. Our demonstration uses microbiological drivers of nitrification and denitrification to predict nitrogen cycling patterns which agree with those provided with generalized stoichiometries. While our example uses data from a single measurement, our workflow can be applied to spatiotemporal metagenomic datasets to allow for iterative coupling between KBase and PFLOTRAN. Availability and implementation Interactive models available at https://pflotranmodeling.paf.subsurfaceinsights.com/pflotran-simple-model/. Microbiological data available at NCBI via BioProject ID PRJNA576070. ORT Python code available at https://github.com/subsurfaceinsights/ort-kbase-to-pflotran. KBase narrative available at https://narrative.kbase.us/narrative/71260 or static narrative (no login required) at https://kbase.us/n/71260/258. Supplementary information Supplementary data are available at Bioinformatics online.
54 ENVIRONMENTAL SCIENCES↗
Exploring Synergistic Tools from NASA Satellite Data for Air Quality
Space-borne earth observation has been important to monitor the earth condition and played a critical role in validating other instruments or modeling's outputs. However, the data from satellite earth observation are usually very complex in terms of science contents, formats, and spatiotemporal granularities, making them difficult to use from many aspects. NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), one of the 12 official NASA data centers, archives and distributes rich collections of data from multiple satellite missions and model results. The GES DISC is also the official archive center for data from the Ozone Monitoring Instrument (OMI) aboard NASA's Aura mission since 2004. Recently, the GES DISC has been evolving and improving its data management and services in order to promote NASA data to be easily discovered and accessed, as well as to facilitate interoperability. We'll show in this presentation how to explore and analyze NASA earth observation data for air quality through a suite of user-friendly tools - from Worldview to Giovanni, demonstrating in using this set of tools prepares us to serve the Sentinel 5P TROPOMI to the community.
air quality↗
Explore Air Quality from NASA Satellite Data
Space-borne earth observation has been important to monitor the earth condition and played acritical role in validating other instruments or modeling's outputs. However, the data from satellite earth observation are usually very complex in terms of science contents, formats, and spatiotemporal granularities, making them difficult to use from many aspects. NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), one of the 12 official NASA data centers, archives and distributes rich collections of data from multiple satellite missions and model results. The GES DISC is also the official archive center for data from the Ozone Monitoring Instrument (OMI) aboard NASA's Aura mission since 2004. Recently, the GES DISChas been evolving and improving its data management and services in order to promote NASA data to be easily discovered and accessed, as well as to facilitate interoperability. We'll show in this presentation how to explore and analyze NASA earth observation data for air quality through a suite of user-friendly tools - from Giovanni to ArcGIS, demonstrating in using this set of tools prepares us to serve the Sentinel 5P TROPOMI to the community.
Wei, J.↗
PERSIANN Dynamic Infrared–Rain Rate Model (PDIR) for High-Resolution, Real-Time Satellite Precipitation Estimation
Precipitation measurements with high spatiotemporal resolution are a vital input for hydrometeorological and water resources studies; decision-making in disaster management; and weather, climate, and hydrological forecasting. Moreover, real-time precipitation estimation with high precision is pivotal for the monitoring and managing of catastrophic hydroclimate disasters such as flash floods, which frequently transpire after extreme rainfall. While algorithms that exclusively use satellite infrared data as input are attractive owing to their rich spatiotemporal resolution and near-instantaneous availability, their sole reliance on cloud-top brightness temperature (T b ) readings causes underestimates in wet regions and overestimates in dry regions—this is especially evident over the western contiguous United States (CONUS). We introduce an algorithm, the Precipitation Estimations from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain rate model (PDIR), which utilizes climatological data to construct a dynamic (i.e., laterally shifting) T b –rain rate relationship that has several notable advantages over other quantitative precipitation-estimation algorithms and noteworthy skill over the western CONUS. Validation of PDIR over the western CONUS shows a promising degree of skill, notably at the annual scale, where it performs well in comparison to other satellite-based products. Analysis of two extreme landfalling atmospheric rivers show that solely IR-based PDIR performs reasonably well compared to other IR- and PMW-based satellite rainfall products, marking its potential to be effective in real-time monitoring of extreme storms. This research suggests that IR-based algorithms that contain the spatiotemporal richness and near-instantaneous availability needed for rapid natural hazards response may soon contain the skill needed for hydrologic and water resource applications.
54 ENVIRONMENTAL SCIENCES↗
Deriving high-spatiotemporal-resolution leaf area index for agroecosystems in the U.S. Corn Belt using Planet Labs CubeSat and STAIR fusion data
Not Available
Kimm, Hyungsuk (ORCID:0000000181890874)↗
An Agenda for Multimodal Foundation Models for Earth Observation
Archives of remote sensing (RS) data are increasing swiftly as new sensing modalities with enhanced spatiotemporal resolution become operational. While promising new breakthroughs, the sheer volume of RS archives stretches the limits of human analysts and existing AI tools, as most models are: i) limited to single data modalities; ii) task-specific; iii) heavily reliant on labeled data. The emerging Foundation Models (FMs) have the potential to address these limitations. Trained on vast unlabeled datasets through self-supervised learning, FMs enable generic feature extraction that facilitate specialization to a wide variety of downstream tasks. This paper describes a vision towards an FM for multimodal Earth Observation data (FM4EO), discussing key building blocks and open challenges. We put particular emphasis on multimodal reasoning, a topic underexplored in EO. Our ultimate goal is a practical path toward FM4EO with capacity to unlock breakthroughs in few-shot learning scenarios, multimodal geographic knowledge integration, synthesis, and hypothesis generation.
Ambrozio Dias, Philipe↗
Short-term traffic state prediction from latent structures: Accuracy vs. efficiency
Recently, deep learning models have shown promising performances in many research areas, including traffic states prediction, due to their ability to model complex nonlinear relationships. However, deep learning models also have drawbacks that make them less preferable for certain short-term traffic prediction applications. For example, they require a large amount of data for model training, which is also computationally expensive. Moreover, deep learning models lack interpretability of the results. This paper develops a short-term traffic states forecasting algorithm based on partial least square (PLS) to help enhance real-time decision-making and build better insights into traffic data. The proposed model is capable of predicting short-term traffic states accurately and efficiently by capturing dominant spatiotemporal features and day-to-day variations from collinear and correlated traffic data. Three case studies are developed to demonstrate the proposed model in short-term traffic prediction applications.
99 GENERAL AND MISCELLANEOUS↗