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Form EIA-930 data collection provides a centralized and comprehensive source for hourly operating data about the high-voltage bulk electric power grid in the Lower 48 states. We collect the data from the electricity balancing authorities (BAs) that operate the grid.
Form EIA-930 data collection provides a centralized and comprehensive source for hourly operating data about the high-voltage bulk electric power grid in the Lower 48 states. We collect the data from the electricity balancing authorities (BAs) that operate the grid.
As we plan for a future with higher penetrations of renewables and increasing electrification, it becomes more important to understand how the electricity grid will operate under a variety of weather events. We must also consider that the weather our future grid will experience will be different and possibly more extreme than the historical weather that we have extensive data for. We can use data from global climate models (GCMs) to help understand how our climate may change over the next several decades, but there is often a significant gap between the low-resolution GCM data and the high-resolution weather data required to study power systems under specific weather events. Therefore, our objective in this work is to develop tools that can bridge this gap by using low-resolution GCM data to create realistic high-resolution weather datasets that can be used to study renewable energy generation and electricity demand. To accomplish this objective, we have developed a set of generative machine learning models that can rapidly downscale GCM daily average output data at an approximate grid resolution of 100km to hourly data at an approximate 4 km grid resolution. The models can be used to create high resolution data from nearly any GCM included in the Coupled Model Intercomparison Project (CMIP) Phase 5 or 6. Our methods include all datasets regularly used to study the integration of wind and solar power plants as well as changes in electricity demand due to heating and cooling loads. These models and datasets enable power systems modelers to study climate change-influenced weather events and their impact on the grid. We have downscaled and validated wind, solar, temperature, and humidity data with very promising results. The generative machine learning methods are computationally efficient and produce data that has similar statistical characteristics to current state-of-the-art historical datasets. We have trained initial generative models and produced an initial dataset collectively referred to as Sup3rCC: Super-Resolved Renewable Energy Resource Data with Climate Change Impacts. The data covers a (mostly) historical period from 2015-2025 and a future period from 2050-2059. We have also taken hypothetical high-electrification load data and scaled the heating and cooling loads with respect to the 2050-2059 high-resolution Sup3rCC meteorology. The results show how future levels of renewable energy generation and electrified load may be impacted by climate change, setting the stage for capacity expansion models to consider a dynamic climate through model years.
The NASA Ames wind tunnel customers are one of the first users of the Information Power Grid (IPG) storage system at the NASA Advanced Supercomputing Division. We wanted to be able to store their data on the IPG so that it could be accessed remotely in a secure but timely fashion. In addition, incorporation into the IPG allows future use of grid computational resources, e.g., for post-processing of data, or to do side-by-side CFD validation. In this paper, we describe the integration of grid data access mechanisms with the existing DARWIN web-based system that is used to access wind tunnel test data. We also show that the combined system has reasonable performance: wind tunnel data may be retrieved at 50Mbits/s over a 100 base T network connected to the IPG storage server.
Since the implementation of the GRID and IMGRID computer programs for multivariate spatial analysis in the early 1970's, geographic data analysis subsequently moved from large computers to minicomputers and now to microcomputers with radical reduction in the costs associated with planning analyses. Programs designed to process LANDSAT data to be used as one element in a geographic data base were used once NIMGRID (new IMGRID), a raster oriented geographic information system, was implemented on the microcomputer. Programs for training field selection, supervised and unsupervised classification, and image enhancement were added. Enhancements to the color graphics capabilities of the microsystem allow display of three channels of LANDSAT data in color infrared format. The basic microcomputer hardware needed to perform NIMGRID and most LANDSAT analyses is listed as well as the software available for LANDSAT processing.
The Wendt et al. study aims to incorporate multiple and disparate carbon capture, utilization, and storage (CCUS) decision-making criteria into a systematic, quantitative analytical approach to help identify areas with potentially high suitability to serve as offshore CO2 storage or EOR regions. Spatially-distributed data from publicly-available sources within the Gulf of Mexico (GOM) study area (limited to federal waters; state waters were not evaluated) was compiled using the U.S. Department of Energy's (DOE) National Energy Technology Laboratory (NETL) Cumulative Spatial Impact Layers™ (CSIL) tool to easily aggregate data based on evenly-distributed grids across the study region set at a resolution of approximately 25 square miles (65 square kilometers). The data included in this Microsoft Excel™ workbook provide the aggregated scores for each grid point across the study domain as well the weighting for each criterion under the four scenarios evaluated.
The objective of the North American Land Data Assimilation System (NLDAS) is to provide best available estimates of near-surface meteorological conditions and soil hydrological status for the continental United States. To support the ongoing efforts to develop data assimilation (DA) capabilities for NLDAS, the results of Gravity Recovery and Climate Experiment (GRACE) DA implemented in a manner consistent with NLDAS development are presented. Following previous work, GRACE terrestrial water storage (TWS) anomaly estimates are assimilated into the NASA Catchment land surface model using an ensemble smoother. In contrast to many earlier GRACE DA studies, a gridded GRACE TWS product is assimilated, spatially distributed GRACE error estimates are accounted for, and the impact that GRACE scaling factors have on assimilation is evaluated. Comparisons with quality-controlled in situ observations indicate that GRACE DA has a positive impact on the simulation of unconfined groundwater variability across the majority of the eastern United States and on the simulation of surface and root zone soil moisture across the country. Smaller improvements are seen in the simulation of snow depth, and the impact of GRACE DA on simulated river discharge and evapotranspiration is regionally variable. The use of GRACE scaling factors during assimilation improved DA results in the western United States but led to small degradations in the eastern United States. The study also found comparable performance between the use of gridded and basin averaged GRACE observations in assimilation. Finally, the evaluations presented in the paper indicate that GRACE DA can be helpful in improving the representation of droughts.
Fluid dynamics of turbomachines are complicated due to aerodynamic interactions between rotors and stators. It is necessary to understand the aerodynamics associated with these interactions in order to design turbomachines that are both light and compact as well as reliable and efficient. The current study uses an unsteady, thin-layer Navier-Stokes zonal approach to investigate the unsteady aerodynamics of a multi-stage compressor. Relative motion between rotors and stators is made possible by use of systems of patched and overlaid grids. Results have been computed for a 2 1/2-stage compressor configuration. The numerical data compares well with experimental data for surface pressures and wake data. In addition, the effect of grid refinement on the solution is studied.
Fluid dynamics of turbomachines are complicated due to aerodynamic interactions between rotors and stators. It is necessary to understand the aerodynamics associated with these interactions in order to design turbomachines that are both light and compact as well as reliable and efficient. The current study uses an unsteady, thin-layer Navier-Stokes zonal approach to investigate the unsteady aerodyamics of a multistage compressor. Relative motion between rotors and stators is made possible by use of systems of patched and overlaid grids. Results have been computed for a 2 1/2-stage compressor configuration. The numerical data compares well with experimental data for surface pressures and wake data. In addition, the effect of grid refinement on the solution is studied.
We compare new marine gravity fields derived from satellite altimetry with shipboard measurements over a region of more than 120,000 sq km in the central South Atlantic. Newly declassified satellite data were employed to construct free-air anomaly maps on 0.05 degree grids. An extensive gravity and bathymetry data set from four cruises along the Mid-Atlantic Ridge from 30.5-35.5 deg S provides a benchmark for testing the 2D resolution and accuracy of the satellite measurements where their crosstrack spacing is near their widest. The satellite gravity signal is coherent with bathymetry in this region down to wavelengths of 26 km, compared to 12.5 km for shipboard gravity. Residuals between the shipboard and satellite data sets have a roughly normal distribution. The standard deviation of satellite gravity with respect to shipboard measurements is nearly 7 mGal in a region of 140 mGal total variation, whereas the internal standard deviation at crossovers for GPS-navigated shipboard data is 1.8 mGal. The differences between shipboard and satellite data are too large to use satellite gravity to determine crustal thickness variations within a typical ridge segment.
The recent altimetry data acquired by MOLA over the northern hemisphere of Mars have been combined with the Earth-based radar data obtained between 1971 and 1982, and occultation measurements of the Viking 1 and 2 Orbiters, Mariner 9, and MGS to derive a global model of the shape and topography of Mars. This preliminary model has a horizontal resolution of about 300 km. Vertical accuracy is on average a few hundred meters in the region of the data. Datasets: The altimetry and radar datasets were individually binned in 1.25 degree grids and merged with the occultation data. The Viking and Mariner occultation data in the northern hemisphere were excluded from the combined dataset where MOLA altimetry were available. The laser altimetry provided extensive and almost complete coverage of the northern hemisphere north of latitude 30 while the radar provided longitudinal coverage at several latitude bands between 23N and 23S. South of this region the only data were occultations. The majority of the occultations were obtained from Mariner 9, and the rest from Viking 1 & 2, and MGS. Earlier studies had shown that the Viking and Mariner occultations were on average only accurate to 500 meters. The recent MGS occultations are accurate to a few tens of meters. However, the highest southern latitude reached by the MGS occultations is only about 64S and data near the target region for the Mars 98 lander is limited to a few Viking and Mariner observations of relatively poor quality. In addition to the above datasets the locations of the Viking 1, Viking 2, and Pathfinder landers, obtained from the radio tracking of their signals, were included.
The Global Precipitation Climatology Project (GPCP) is one of a number of long-term, satellite-based, global analyses routinely produced under the auspices of the World Climate Research Program (WCRP) and its Global Energy and Watercycle EXperiment (GEWEX) program. The research quality analyses are produced a few months after real-time through the efforts of scientists at various national agencies and universities in the U.S., Europe and Japan. The primary product is a monthly analysis of surface precipitation that is globally complete and spans the period 1979-present. There are also pentad analyses for the same period and a daily analysis for the 1997-present period. Although generated with somewhat different data sets and analysis schemes, the pentad and daily data sets are forced to agree with the primary monthly analysis on a grid box by grid box basis. The primary input data sets are from low-orbit passive microwave observations, geostationary infrared observations and surface raingauge information. Examples of research with the data sets are discussed, focusing on tropical (25N-25s) rainfall variations and possible long-term changes in the 28-year (1979-2006) monthly dataset. Techniques are used to discriminate among the variations due to ENSO, volcanic events and possible long-term changes for rainfall over both land and ocean. The impact of the two major volcanic eruptions over the past 25 years is estimated to be about a 5% maximum reduction in tropical rainfall during each event. Although the global change of precipitation in the data set is near zero, a small upward linear change over tropical ocean (0.06 mm/day/l0yr) and a slight downward linear change over tropical land (-0.03 mm/day/l0yr) are examined to understand the impact of the inhomogeneity in the data record and the length of the data set. These positive changes correspond to about a 5% increase (ocean) and 3% increase (ocean plus land) during this time period. Relations between variations in surface temperature and precipitation are analyzed on seasonal to inter-decadal time scales. A new, version 3 of GPCP is being planned to incorporate new satellite information (e.g., TRMM) and provide higher spatial and temporal resolution for at least part of the data record. The goals and plans for that GPCP re-processing will be outlined.
Numerical simulations for the flow around the F-16XL configuration as a contribution to the Cranked Arrow Wing Aerodynamic Project International 2 (CAWAPI-2) have been performed. The NASA Langley Tetrahedral Unstructured Software System (TetrUSS) with its USM3D solver was used to perform the unsteady flow field simulations for the subsonic high angle-of-attack case corresponding to flight condition (FC) 25. Two approaches were utilized to capture the unsteady vortex flow over the wing of the F-16XL. The first approach was to use Unsteady Reynolds-Averaged Navier-Stokes (URANS) coupled with standard turbulence closure models. The second approach was to use Detached Eddy Simulation (DES), which creates a hybrid model that attempts to combine the most favorable elements of URANS models and Large Eddy Simulation (LES). Computed surface static pressure profiles are presented and compared with flight data. Time-averaged and instantaneous results obtained on coarse, medium and fine grids are compared with the flight data. The intent of this study is to demonstrate that the DES module within the USM3D solver can be used to provide valuable data in predicting vortex-flow physics on a complex configuration.
NASA Ames Center personnel presented data on stages of code development and corresponding experiments in the application of computational fluid dynamics for aeronautical investigations. Specific subjects included algorithms, grid generation, facilities, instrumentation, and data acquisition. Numerical simulation and flow modelling were described to show the procedure for calibration and validation.
The Global Precipitation Measurement (GPM) mission quarter-degree gridded-text product has a similar file format and a similar purpose as the Tropical Rainfall Measuring Mission (TRMM) 3G68 quarter-degree product. The GPM text-grid format is an hourly summary of surface precipitation retrievals from various GPM instruments and combinations of GPM instruments. The GMI Goddard Profiling (GPROF) retrieval provides the widest swath (800 km) and does the retrieval using the GPM Microwave Imager (GMI). The Ku radar provides the widest radar swath (250 km swath) and also provides continuity with the TRMM Ku Precipitation Radar. GPM's Ku+Ka band matched swath (125 km swath) provides a dual-frequency precipitation retrieval. The "combined" retrieval (125 km swath) provides a multi-instrument precipitation retrieval based on the GMI, the DPR Ku radar, and the DPR Ka radar. While the data are reported in hourly grids, all hours for a day are packaged into a single text file that is g-zipped to reduce file size and to speed up downloading. The data are reported on a 0.25deg x 0.25 deg grid.
The polar format approach to SAR image formation requires data to be interpolated from a warped grid onto a Cartesian lattice. In general, this requires that data be interpolated between varying sampling rates. In this paper, frequency-domain optimality criteria for polar format interpolators are defined and justified, and an approach to designing the corresponding digital filters is described.
Techniques to integrate meteorological data from various satellite sensors to yield a global measure of sea surface wind speed and direction for input to the Navy's operational weather forecast models were investigated. The sensors were launched or will be launched, specifically the GOES visible and infrared imaging sensor, the Nimbus-7 SMMR, and the DMSP SSM/I instrument. An algorithm for the extrapolation to the sea surface of wind directions as derived from successive GOES cloud images was developed. This wind veering algorithm is relatively simple, accounts for the major physical variables, and seems to represent the best solution that can be found with existing data. An algorithm for the interpolation of the scattered observed data to a common geographical grid was implemented. The algorithm is based on a combination of inverse distance weighting and trend surface fitting, and is suited to combing wind data from disparate sources.
Land surface models have facilitated the estimation of soil moisture over a range of spatiotemporal scales. However, limitations in model parameterization and under-representation of anthropogenic processes restrict their ability to estimate local-scale soil moisture variability, especially over irrigated areas. Assimilation of satellite-based soil moisture retrievals into land surface models can be a viable approach to overcome these constraints, specially over highly irrigated countries such as India, where such applications are rare. Additionally, large-scale validation of modeled soil moisture has been limited over India till now due to lack of a representative station network. By assimilating Soil Moisture Active Passive (SMAP)-based estimates into the state-of-the-art Indian Land Data Assimilation System (ILDAS) and combining with a new soil moisture station network of more than 200 stations, this study demonstrates improved soil moisture estimations and capture of irrigation signals over the region. The Noah-MP land surface model is forced by multiple local and global meteorological datasets and Ensemble Kalman Filter (EnKF) is used for assimilation of soil moisture. Comparison of open-loop and data assimilated soil moisture against station soil moisture data shows relative spatial mean improvement of 0.0178 in correlation and 0.0029 m3/m3 in RMSE. Further statistical comparison with in-situ data has also shown better results over most of the stations, as evident from improved correlations and reduced unbiased RMSE after assimilation. Finally, the climatology of soil moisture over the different irrigation fractions reveals that data assimilated outputs over irrigated grid cells tend to have higher soil moisture during dry winter season, demonstrating the ability to capture irrigation signals. These findings quantify the value of data assimilation in improving soil moisture estimates and the ability to capture unmodeled processes such as irrigation, which lays the science groundwork for upcoming space missions such as NASA ISRO Synthetic Aperture Radar (NISAR).