SEARCH · Engineering Papers
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.
The 10-min wind statistics from ground-based Doppler lidar at the RHOD site were calculated using the Sathe et al., 2015, paper in the references.
The 10-min wind statistics from ground-based Doppler lidar at the BLOC site were calculated using the Sathe et al., 2015, paper in the references. This lidar was Halo XR #216 through February 24, 2025, Halo XR #217 from February 24, 2025 through April 17, 2025, and again Halo XR #216 after that.
The 10-min wind statistics from ground-based Doppler lidar at the BARG site were calculated using the Sathe et al., 2015, paper in the references.
The 10-min wind statistics from ground-based Doppler lidar at site H were calculated using the Sathe et al., 2015, paper in the references.
The 10-min wind statistics from ground-based Doppler lidar at site A1 were calculated using a modified version of the Sathe et al., 2015 paper in the references.
The 10-minute wind statistics from the ground-based Doppler lidar at site A1 were calculated using a modified version of the Sathe et al., 2015 paper in the references.
The 10-min wind statistics from ground-based Galion lidar at the RHOD site were calculated using the Sathe et al., 2015, paper in the references.
The 10-min wind statistics from ground-based Doppler lidar at site A1 were calculated using a modified version of the Sathe et al., 2015 paper in the references.
Explore the source record for details and available documents.
In middle latitudes, it is possible for large concentrations of stratospheric air to be brought down to the tropopause through folds or breaks in the tropopause. The exchange of air from the tropopause into higher altitudes is not well understood. Thus, the ITCZ (Intertropical Convergence Zone) experiment, conducted from July 16 through July 31, 1977, included a series of balloon-borne ozone soundings. The results of these soundings are presented and explain in the vertical exchange of air and provide information on the short vertical scales-of-motion. Rocketsonde data was also gathered in the ITCZ experiment in support of a stratospheric scales-of-motion study. The investigation was to determine whether rocketsonde and satellite information currently used yield information on the stratospheric horizontal wave spectrum and its importance with respect to tropospheric and mesospheric interaction and transport.
Explore the source record for details and available documents.
The middle atmosphere circulation is governed by two seasonal basic states in winter and summer, twice a year separated by relatively shortlived reversal periods. These seasonal basic states of circulation and the spring changeover period between them are investigated.
Mean absolute sea level reflects the deviation of the Ocean surface from geoid due to the ocean currents and is an important characteristic of the dynamical state of the ocean. Values of its spatial variations (order of 1 m) are generally much smaller than deviations of the geoid shape from ellipsoid (order of 100 m) that makes the derivation of the absolute mean sea level a difficult task for gravity and satellite altimetry observations. Technique used by Niiler et al. for computation of the absolute mean sea level in the Kuroshio Extension was then developed into more general method and applied by Niiler et al. (2003b) to the global Ocean. The method is based on the consideration of balance of horizontal momentum.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
With a potentially increasing share of the electricity grid relying on wind to provide generating capacity and energy, there is an expanding global need for historically accurate, spatiotemporally continuous, high-resolution wind data. Conventional downscaling methods for generating these data based on numerical weather prediction have a high computational burden and require extensive tuning for historical accuracy. In this work, we present a novel deep learning-based spatiotemporal downscaling method using generative adversarial networks (GANs) for generating historically accurate high-resolution wind resource data from the European Centre for Medium-Range Weather Forecasting Reanalysis version 5 data (ERA5). In contrast to previous approaches, which used coarsened high-resolution data as low-resolution training data, we use true low-resolution simulation outputs. We show that by training a GAN model with ERA5 as the low-resolution input and Wind Integration National Dataset Toolkit (WTK) data as the high-resolution target, we achieved results comparable in historical accuracy and spatiotemporal variability to conventional dynamical downscaling. This GAN-based downscaling method additionally reduces computational costs over dynamical downscaling by two orders of magnitude. We applied this approach to downscale 30 km, hourly ERA5 data to 2 km, 5 min wind data for January 2000 through December 2023 at multiple hub heights over Ukraine, Moldova, and part of Romania. With WTK coverage limited to North America from 2007–2013, this is a significant spatiotemporal generalization. The geographic extent centered on Ukraine was motivated by stakeholders and energy-planning needs to rebuild the Ukrainian power grid in a decentralized manner. This 24-year data record is the first member of the super-resolution for renewable energy resource data with wind from the reanalysis data dataset (Sup3rWind).
In this work we present a novel deep learning-based downscaling method, using generative adversarial networks (GANs), for generating high-resolution wind resource data from ECMWF Reanalysis v5 data (ERA5). We show that by training a GAN model on ERA5, as opposed to coarsened high-resolution data, we achieve results that are competitive with conventional dynamical downscaling. This GAN-based downscaling method additionally reduces computational costs over dynamical downscaling by two orders of magnitude. All GANs are trained on data sampled from CONUS, selected to provide a diverse sampling of terrain conditions, and validated on observational data along with data held out from training. This cross-validation shows low error and high correlations with observations and excellent agreement with hold out data across physical distributions. Our approach is finally used to downscale 30km hourly ERA5 to 2-km 5-minute wind data, for January 2000 through December 2023, at multiple hub heights, over Ukraine, Moldova, and part of Romania. Comparisons against observational data from Meteorological Assimilation Data Ingest System (MADIS) and multiple wind farms show the same level of performance as for CONUS validation. This 24 year data record is the first member of the "super resolution for renewable energy resource data with wind from reanalysis data" dataset (Sup3rWind).