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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Mesoscale-cloud scale simulation of convective response

The importance of mesoscale moisture information in the forecasting of weather events is being studied. A test is being prepared with the March 6, 1982 Vertical Atmospheric Sounder/Atmospheric Variability Experiment (VAS/AVE) case study to be run on the sub-synoptic scale model (SSM). Intracomparison of three carefully designed simulations should isolate the role of mesoscale information in the initial conditions for both moisture and vertical motion. Three simulations will be made for the period 1200Z 6 March to 0000Z 7 March starting with the regular synoptic time data of 1200Z. The distinction between the three cases arises from data manipulation at 1800Z midway through the forecast period. Comparison of results for the 1800Z to 0000Z time period will give the information desired.

Kopp, F. J.↗
NASA NTRS1987

Short-term forecasting of lightning based on the surface wind field at Kennedy Space Center

Cloud-to-ground lightning is related in time and space to surface convergence for 244 days during the summer over a 790 sqkm network. The method uses surface convergence, particularly the average over the area, to identify the potential for new, local thunderstorm growth, and can be used to specify the likely time and location of lightning during the life cycle of the convection. A threshold of 0.0000075/sec change in divergence is used to define a convergence event, and a separation of 30 min between flashes defines a lightning event. Time intervals are found to be on the order of 1 hr from beginning convergence to first flash, and (CH110) 2 hr from beginning convergence to the end of lightning. Major differences between the convergence-lightning relationships based on low-level mean onshore and offshore flow are noted.

Watson, Andrew I.↗
NASA NTRSDate not supplied

Machine Learning Algorithms for Aerosol and Cloud Detection Using CATS on the ISS

Clouds and aerosols are one of the largest uncertainties in understanding and forecasting the Earth’s changing climate system. The type and height of aerosols are important factors in determining the top-of-atmosphere (TOA) radiation budget, either direct reflection of solar radiation back to space and/or absorption of solar radiation. In addition to their impact on the Earth’s climate system, aerosols near the surface from wildfires, man-made pollution events, and dust storms are hazardous to human health. The phase and height of clouds also play a critical role in determining the role of clouds in the Earth’s climate system. Cirrus clouds in the upper troposphere can induce a significant daytime TOA warming effect, while liquid water clouds near the surface cause a large corresponding cooling effect. Lidar measurements provide accurate vertically resolved information about clouds and aerosols, including complex multi-layer scenes where passive sensors are challenged and at night, when passive sensors are unable to measure cloud and aerosol properties. The Cloud-Aerosol Transport System (CATS) is a lidar instrument that operated for 33 months on the International Space Station (ISS) at the 1064 nm wavelength to measure attenuated total backscatter and depolarization ratio. These fundamental measurements are used to derive “vertical feature mask” cloud and aerosol products, including layer top/base heights, layer geometrical thickness, aerosol type, and cloud phase. While space-based lidar systems like CATS provide cloud and aerosol vertical distributions that improve our understanding of the climate system, averaging of the daytime data from these sensors is required, at the expense of spatial resolution, to improve the daytime signal-to noise (SNR) and thus atmospheric layer detection. This presentation shows results from machine learning (ML) techniques that, when applied to CATS data: 1. improve the 1064 nm SNR 2. enable detection of atmospheric features during daytime with a horizontal resolution of 350 m or 5 km (compared to the 60 km required for standard CATS data products) 3. increase the number of atmospheric layers detected in the CATS data. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination, cloud phase, and aerosol typing compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime. The ML tools described in this paper can facilitate the development of smaller, low-cost lidar systems in the future and enable real-time accessibility of lidar data products from future lidar systems for monitoring and forecasting of hazardous events.

John Yorks↗
DOE OSTI2022

Benchmarking of Solar Irradiance Nowcast Performance Derived from All-Sky Imagers

Fluctuations of the incoming solar irradiance impact the power generation from photovoltaic and concentrating solar thermal power plants. Accurate solar nowcasting becomes necessary to detect these sudden changes of generated power and to provide the desired information for optimal exploitation of solar systems. In the framework of the International Energy Agency's Photovoltaic Power Systems Program Task 16, a benchmarking exercise has been conducted relying on a bouquet of solar nowcasting methodologies by all-sky imagers (ASI). In this paper, four ASI systems nowcast the Global Horizontal Irradiance (GHI) with a time forecast ranging from 1 to 20 min during 28 days with variable cloud conditions spanning from September to November 2019 in southern Spain. All ASIs demonstrated their ability to accurately nowcast GHI, with RMSE ranging from 6.9% to 18.1%. Under cloudy conditions, all ASIs' nowcasts outperform the persistence models. Under clear skies, three ASIs are better than persistence. Discrepancies in the observed nowcasting performance become larger at increasing forecast horizons. The findings of this study highlight the feasibility of ASIs to reliably nowcast GHI at different sky conditions, time intervals and horizons. Such nowcasts can be used either to estimate solar power at distant times or detect sudden GHI fluctuations.

all-sky imagers↗
DOE OSTI2025

Advancing Understanding of Deep Convective Anvil Clouds

In this project we use ground-based DOE ARM observations, satellite observations, and Weather Research and Forecasting (WRF) model simulations to arrive at a more complete understanding (or, new theory) of how atmospheric heating profiles in thunderstorm cores drives how quickly air rises in the storms. The rate at which air motion increases or decreases with altitude directly relates to how much cloud mass is ejected laterally into the adjacent atmosphere, which then influences the development and maintenance of “stratiform raining cloud anvils”. Our work has led to new equations connecting the heating in these thunderstorms to these stratiform clouds, with the connection modulated by the stability structure of the atmosphere (i.e., if more stable, then a more rapid change in thunderstorm heating with altitude results in more stratiform cloud, independent of whether over land or ocean, or how organized the thunderstorms are). Improving knowledge of stratiform clouds is important, since they contribute to substantial accumulated rainfall (important to agriculture and local hydrology) as well as radiation, given their large areal extents and lifetime. The radiation impacts likely impact simulated cloud feedback in all Earth System models (ESMs).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Deep learning forecasts the spatiotemporal evolution of fluid-induced microearthquakes

Microearthquakes generated by subsurface fluid injection record the evolving stress state and permeability of reservoirs. Forecasting their spatiotemporal evolution is therefore critical for applications such as enhanced geothermal systems, carbon dioxide sequestration and other geoengineering applications. Here we propose a transformer neural network model that ingests hydraulic stimulation history and prior microearthquake observations to forecast four key quantities: cumulative microearthquake count, cumulative logarithmic seismic moment, and the 50th- and 95th-percentile extents of the microearthquake cloud. Applied to the EGS Collab Experiment 1 dataset, the model achieves R2 > 0.98 for the 1-s forecast horizon and R2 > 0.88 for the 15-s forecast horizon across all targets, and supplies uncertainty estimates through a learned standard deviation term. These accurate, uncertainty-quantified forecasts enable real-time inference of fracture propagation and permeability evolution, demonstrating the strong potential of deep-learning approaches to improve seismic-risk assessment and guide mitigation strategies in future fluid-injection operations.

Chung, Jaehong↗
NASA NTRS2014

Weather Avoidance Guidelines for NASA Global Hawk High-Altitude UAS

NASA operates two Global Hawk unmanned aircraft systems for Earth Science research projects. In particular, they are used in the Hurricane and Severe Storm Sentinel (HS3) project during 2012, 2013, and 2014 to take measurements from the environment around tropical cyclones, and from directly above tropical cyclones. There is concern that strict adherence to the weather avoidance rules used in 2012 may sacrifice the ability to observe important science targets. We have proposed modifications to these weather avoidance rules that we believe will improve the ability to observe science targets without compromising aircraft safety. The previous guidelines, used in 2012, specified: Do not approach thunderstorms within 25 nm during flight at FL500 or below. When flying above FL500: Do not approach reported lightning within 25NM in areas where cloud tops are reported at FL500 or higher. Aircraft should maintain at least 10000 ft vertical separation from reported lightning if cloud tops are below FL500. No over-flight of cumulus tops higher than FL500. No flight into forecast or reported icing conditions. No flight into forecast or reported moderate or severe turbulence Based on past experience with high-altitude flights over tropical cyclones, we have recommended changing this guidance to: Do not approach thunderstorms within 25 nm during flight at FL500 or below. Aircraft should maintain at least 5000 ft vertical separation from significant convective cloud tops except: a) When cloud tops above FL500: In the event of reported significant lightning activity or indicators of significant overshooting tops, do not approach within 10-25 nm, depending on pilot discretion and advice from Mission Scientist. b) When cloud tops are below FL500, maintain 10000 ft separation from reported significant lightning or indicators of significant overshooting tops. No flight into forecasted or reported icing conditions. No flight into forecasted or reported moderate or severe turbulence The key changes have to do with overflight of high convective cloud tops and those producing lightning. Experience shows that most tropical oceanic convection (including that in tropical cyclones) is relatively gentle even if the cloud tops are quite high, and can be safely overflown. Exceptions are convective elements producing elevated lightning flash rates (more than just the occasional flash, which would trigger avoidance under the previous rules) and significant overshooting cloud tops.

Cecil, Daniel J.↗
NASA NTRS2015

Integrated Modeling of Aerosol, Cloud, Precipitation and Land Processes at Satellite-Resolved Scales

With support from NASA's Modeling and Analysis Program, we have recently developed the NASA Unified-Weather Research and Forecasting model (NU-WRF). NU-WRF is an observation-driven integrated modeling system that represents aerosol, cloud, precipitation and land processes at satelliteresolved scales. "Satellite-resolved" scales (roughly 1e25 km), bridge the continuum between local (microscale), regional (mesoscale) and global (synoptic) processes. NU-WRF is a superset of the National Center for Atmospheric Research (NCAR) Advanced Research WRF (ARW) dynamical core model, achieved by fully integrating the GSFC Land Information System (LIS, already coupled to WRF), the WRF/ Chem enabled version of the Goddard Chemistry Aerosols Radiation Transport (GOCART) model, the Goddard Satellite Data Simulation Unit (G-SDSU), and custom boundary/initial condition preprocessors into a single software release, with source code available by agreement with NASA/GSFC. Full coupling between aerosol, cloud, precipitation and land processes is critical for predicting local and regional water and energy cycles.

Satellite-resolved↗
DOE OSTI2020

A New Method for Ice-Ice Aggregation in the Adaptive Habit Model

In this paper, a novel methodology for modeling ice-ice aggregation is presented. This methodology combines a modified hydrodynamic collection algorithm with bulk aggregate characteristic information from an offline simulator that collects ice particles, namely, the Ice Particle and Aggregate Simulator, and has been implemented into the Adaptive Habit Microphysics scheme in the Weather Research and Forecasting Model. Aggregates, or snow, are formed via collection of cloud ice particles, where initial ice characteristics and the resulting geometry determine aggregate characteristics. Upon implementation, idealized squall-line simulations are performed to examine the new methodology in comparison with commonly used bulk microphysics schemes. It is found that the adaptive habit aggregation parameterization develops snow and reduces ice mass and number concentrations compared to other schemes. The development of aggregates through the new methodology cascades into other interesting effects, including enhancements in ice and snow growth, as well as homogeneous freezing. Further microphysical analyses reveal varying sensitivities, where snow processes are most sensitive to the new parameterization, followed by ice, then cloud, rain, and graupel processes. Further, the new scheme results in enhancements in surface precipitation due to the persistence of snow at lower altitudes. This persistence is a result of shape-dependent melting and sublimation, increasing the residence time. Moreover, these low-level enhancements are reflected in increases in radar reflectivity at the surface and its spatial distribution. Finally, the ability to predict snow shape and density allows for the simulation of polarimetric radar quantities, resulting in signature enhancements compared to schemes that do not consider spatial and temporal variations in snow shape and density.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Drop size distribution broadening mechanisms in a bin microphysics Eulerian model

In this study, processes that broaden drop size distributions (DSDs) in Eulerian models with two-moment bin microphysics are analyzed. Numerous tests are performed to isolate the effects of different physical mechanisms that broaden DSDs in two- and three-dimensional Weather Research and Forecasting model simulations of an idealized ice-free cumulus cloud. Additionally, sensitivity of these effects to modifying horizontal and vertical model grid spacings is also examined. As expected, collision-coalescence is a key process broadening the modeled DSDs. In-cloud droplet activation also contributes substantially to DSD broadening, whereas evaporation has only a minor effect and sedimentation has little effect. Cloud dilution (mixing of cloud-free and cloudy air) also broadens the DSDs considerably, whether or not it is accompanied by evaporation. This mechanism involves the reduction of droplet concentration from dilution along the cloud’s lateral edges, leading to locally high supersaturation and enhanced drop growth when this air is subsequently lifted in the updraft. DSD broadening ensues when the DSDs are mixed with those from the cloud core. Decreasing the horizontal and vertical model grid spacings from 100 m to 30 m has limited impact on the DSDs. However, when these physical broadening mechanisms (in-cloud activation, collision-coalescence, dilution, etc.) are turned off, there is a reduction of DSD width by up to ~20-50% when the vertical grid spacing is decreased from 100 m to 30 m, consistent with effects of artificial broadening from vertical numerical diffusion. Nonetheless, this artificial numerical broadening appears to be relatively unimportant overall for DSD broadening when physically based broadening mechanisms in the model are included for this cumulus case.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1967

ATS-I camera experiment successful.

Cloud photographs obtained on board ATS-I SATELLITE with camera equipped with reflecting telescope show feasibility of short-lived weather systems from synchronous altitude

SATELLITE PHOTOGRAPHY↗
NASA NTRS1972

Meteorological support to the West German-United States Barium Ion Cloud Project.

The objective of the Barium Ion Cloud Project was to study a barium ionized cloud released at an altitude of 5 earth radii. Accurate forecasting of weather conditions to prevail during the experiment period was critical to the project success. Good seeing conditions were required at all optical sites during the experiment. All meteorological support was the responsibility of the National Weather Service at Wallops Station, Virginia. Preliminary results confirm the scientists' theories of the magnetic fields and the existence of electric fields in the magnetosphere.

Westfall, R. R.↗
NASA NTRS1985

Hurricane Debby - An illustration of the complementary nature of VAS soundings and cloud and water vapor motion winds

The utility of VISSR Atmospheric Sounder (VAS) temperature and moisture soundings and cloud and water vapor motion winds in defining a storm and its surroundings at subsynoptic scales has been examined using a numerical analysis and prognosis system. It is shown that the VAS temperature and moisture data, which specify temperature and moisture well in cloud-free areas, are complemented by cloud and water vapor motion data generated in the cloudy areas. The cloud and water vapor 'winds' provide thermal gradient information for interpolating the soundings across cloudy regions. The loss of analysis integrity due to the reduction of VAS sounding density in the cloudy regions associated with synoptic activity is ameliorated by using cloud and water vapor motion winds. The improvement in numerical forecasts resulting from the addition of these data to the numerical analysis is recorded.

Le Marshall, J. F.↗
NASA NTRS2015

Spinoff 2015

Topics covered include: 3D Endoscope to Boost Safety, Cut Cost of Surgery; Audio App Brings a Better Night's Sleep Liquid Cooling Technology Increases Exercise Efficiency; Algae-Derived Dietary Ingredients Nourish Animals; Space Grant Research Launches Rehabilitation Chair; Vision Trainer Teaches Focusing Techniques at Home; Aircraft Geared Architecture Reduces Fuel Cost and Noise; Ubiquitous Supercritical Wing Design Cuts Billions in Fuel Costs; Flight Controller Software Protects Lightweight Flexible Aircraft; Cabin Pressure Monitors Notify Pilots to Save Lives; Ionospheric Mapping Software Ensures Accuracy of Pilots' GPS; Water Mapping Technology Rebuilds Lives in Arid Regions; Shock Absorbers Save Structures and Lives during Earthquakes; Software Facilitates Sharing of Water Quality Data Worldwide; Underwater Adhesives Retrofit Pipelines with Advanced Sensors; Laser Imaging Video Camera Sees through Fire, Fog, Smoke; 3D Lasers Increase Efficiency, Safety of Moving Machines; Air Revitalization System Enables Excursions to the Stratosphere; Magnetic Fluids Deliver Better Speaker Sound Quality; Bioreactor Yields Extracts for Skin Cream; Private Astronaut Training Prepares Commercial Crews of Tomorrow; Activity Monitors Help Users Get Optimum Sun Exposure; LEDs Illuminate Bulbs for Better Sleep, Wake Cycles; Charged Particles Kill Pathogens and Round Up Dust; Balance Devices Train Golfers for a Consistent Swing; Landsat Imagery Enables Global Studies of Surface Trends; Ruggedized Spectrometers Are Built for Tough Jobs; Gas Conversion Systems Reclaim Fuel for Industry; Remote Sensing Technologies Mitigate Drought; Satellite Data Inform Forecasts of Crop Growth; Probes Measure Gases for Environmental Research; Cloud Computing Technologies Facilitate Earth Research; Software Cuts Homebuilding Costs, Increases Energy Efficiency; Portable Planetariums Teach Science; Schedule Analysis Software Saves Time for Project Planners; Sound Modeling Simplifies Vehicle Noise Management; Custom 3D Printers Revolutionize Space Supply Chain; Improved Calibration Shows Images' True Colors; Micromachined Parts Advance Medicine, Astrophysics, and More; Metalworking Techniques Unlock a Unique Alloy; Low-Cost Sensors Deliver Nanometer-Accurate Measurements; Electrical Monitoring Devices Save on Time and Cost; Dry Lubricant Smooths the Way for Space Travel, Industry; and Compact Vapor Chamber Cools Critical Components.

Source record↗
NASA NTRS2018

Modeling Key Predictors of Airport Runway Configurations Using Learning Algorithms

Advanced traffic flow management automation will need accurate predictions of airport runway configurations. Terminal area weather and traffic demand are generally considered to be the most significant factors in predicting runway configuration. Weather information is forecasted across multiple features, including wind direction, wind speed, gusts, cloud ceilings, visibility, temperature, and precipitation, among many others. We use machine learning techniques on historical weather and runway data to determine weather features that correlate well with runway configurations. We analyze the predictive capability of weather features using different learning models trained on data from four major U.S. airports: Atlanta (ATL), Washington – Dulles (IAD), New York – Kennedy (JFK), and San Francisco (SFO). Wind direction alone is strongly correlated with runway configurations above all other examined factors, as expected. This correlation is the most significant component of the ~80% prediction accuracy in selecting between the two most frequently used runway configurations. However, individual airports show variations on how well the runway configuration decisions correlate with wind direction. While wind direction was identified as the most significant indicator of configuration decisions in ATL, IAD, and JFK, it did not emerge as such at SFO. Traffic demand was not found to be a strong factor in predicting runway configurations at any of the airports analyzed. In rare instances, when high demand cannot be accommodated within the current configuration, temporary changes are likely to be attributable to demand. However, these occurrences are so limited in number that their overall effect is not sufficient to consider traffic demand as a major indicator of runway configuration at the airports analyzed.

Bilimoria, Karl D.↗
NASA NTRSDate not supplied

Ice Cloud Observations and Science using Submillimeter-Wave Radiometry

Submillimeter-wave radiometers provide vital information on ice clouds and precipitation. These data are key for improving weather forecasting, particularly for disruptive winter storms, and for better understanding climate. Our airborne radiometer will provide data that will allow us to better use data from upcoming space missions with these types of sensors.

Ian Adams↗
DOE OSTI2023

Subsetted model output on height-above-mean-sea-level levels for cloud-related variables from a LASSO-CACTI WRF simulation

Cloud-related model output interpolated to heights-above-mean-sea-level from a Weather Research and Forecasting (WRF) simulation from the Large-Eddy Simulation (LES) Atmospheric Radiation Measurement (ARM) Symbiotic Simulation and Observation (LASSO) deep-convection scenario for the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign. The LASSO-CACTI simulations span grid spacings from 7.5 km to 100 m through the use of four nested domains, labeled d1 through d4. The simulations are of convection near the Sierras de Córdoba mountain range, roughly centered on the ARM Mobile Facility. More information can be found at https://www.arm.gov/capabilities/modeling/lasso. This version of the output is a collection of variables subsetted from the raw WRF output to reduce the file overhead for users not needing the full raw dataset. The subset files are in netCDF format with the height-based variables interpolated to heights based on distance above mean sea level.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Evaluation of the Rapid Refresh Numerical Weather Prediction Model over Arctic Alaska

Abstract Despite a need for accurate weather forecasts for societal and economic interests in the U.S. Arctic, thorough evaluations of operational numerical weather prediction in the region have been limited. In particular, the Rapid Refresh Model (RAP), which plays a key role in short-term forecasting and decision-making, has seen very limited assessment in northern Alaska, with most evaluation efforts focused on lower latitudes. In the present study, we verify forecasts from version 4 of the RAP against radiosonde, surface meteorological, and radiative flux observations from two Arctic sites on the northern Alaskan coastline, with a focus on boundary layer thermodynamic and dynamic biases, model representation of surface inversions, and cloud characteristics. We find persistent seasonal thermodynamic biases near the surface that vary with wind direction, and may be related to the RAP’s handling of sea ice and ocean interactions. These biases seem to have diminished in the latest version of the RAP (version 5), which includes refined handling of sea ice, among other improvements. In addition, we find that despite capturing boundary layer temperature profiles well overall, the RAP struggles to consistently represent strong, shallow surface inversions. Further, while the RAP seems to forecast the presence of clouds accurately in most cases, there are errors in the simulated characteristics of these clouds, which we hypothesize may be related to the RAP’s treatment of mixed-phase clouds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗