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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Exploring Flood Predictability in Taiwan through Coupled Atmospheric–Hydrological and High-Performance Hydrodynamic Models

Effective flood simulation capabilities can tremendously support early warning and disaster prevention. To examine the applicability of a fully physics-based and high-performance flood simulation and forecasting modeling framework for a flood-prone region in Taiwan, we conduct a numerical experiment that couples the Weather Research and Forecasting (WRF) Model, WRF-Hydrological modeling system (WRF-Hydro), and the Two-Dimensional Runoff Inundation Toolkit for Operational Needs (TRITON) to perform integrated rainfall, streamflow, and flood simulations. Furthermore, we first use the coupled WRF and WRF-Hydro (WWH) to predict rainfall and streamflow and then drive TRITON with the predicted streamflow hydrographs to simulate flood depth and inundation area. With the refined spatial resolution and parameterization, this framework can better predict rainfall with reasonable spatial patterns. Although WWH could overestimate the amount of rainfall in some areas, the uncertain rainfall–streamflow predictions produce reasonable flood maps able to pinpoint regions at risk of flooding. In terms of model efficiency, the graphics processing unit–based computation can yield a speed-up factor as high as ∼13 compared to the central processing unit–based computation, promoting the efficacy of the coupled modeling framework in practical real-time flood forecasting.

Coupled models↗
NASA NTRS1996

A technique for determining cloud free vs cloud contaminated pixels in satellite imagery

Since the first earth orbiting satellite sent pictures of the earth back to them, atmospheric scientists have been focused on the possibilities of using that information as both a forecasting tool and as a meteorological research tool. With the latest generation of Geostationary Operational Environmental Satellites (GOES) now entering service, that view of the earth yields views at a frequency and resolution never before available. These satellites have imagers with a five band multi-spectral capability with high spatial resolution. In addition, the sounder has eighteen thermal infrared (IR) channels plus one low-resolution visible band. With a resolution as small as one kilometer, GOES provides scientists with a powerful eye on the atmosphere. Menzel and Purdom (1994) detail both the imager and sounder capability as well as other systems on the GOES satellites. Immediately apparent in the visible channel are the patterns of clouds swirling over both oceans and continents. These clouds range in size from huge planetary systems covering thousands of kilometers to puffy fair weather cumulus clouds on the order of half a kilometer in size. With the IR sensors temperature patterns are observed. High clouds appear very cold, while low stratus field show temperatures near that of the surface. The surface, in turn, generally appears warmer than the clouds. It would seem then a simple manner to determine cloud and surface temperature from the imagery, but such is not the case. While most of the atmospheric constituents are well mixed and homogeneous, water vapor is not. The water molecule, because of its unique structure and vibration modes, affects the transmittance of the atmosphere most notably in the infrared regions. There are regions of the IR spectrum where water vapor acts as a strong absorber, and at others it is nearly transparent. The transparent wavelengths are called windows, and one such window occurs at 11.2 microns. Adjacent to this window at 12.7 microns which is strongly absorbed by water vapor. These two wavelengths form what is known as a split window, the utility of which was used. Using the linearized form of the radiative transfer equation, they were able to use the split window to determine the amount of water vapor present in the atmosphere. Jedlovec developed the physical split-window (PSW) technique which determines the integrated water content (IWC). The PSW method using Visible Infrared Spin Scan Radiometer (VISSR) Atmospheric Sounder (VAS) found on the older versions of the GOES satellites was used. Recently, Jedlovec and colleagues have been attempting to apply the PSW method using full disk IR imagery obtained by the new generation of GOES satellites. IWC is essential for improved analysis and prediction of convective storms which have been observed to develop in regions of both strong and rapidly evolving moisture gradients. It has also been used in the prediction of clouds and precipitation.

Wohlman, Richard A.↗
NASA NTRS2021

Improving Surface PM2.5 Forecasts in the United States Using an Ensemble of Chemical Transport Model Outputs: 2. Bias Correction with Satellite Data for Rural Areas

This work serves as the second of a two-part study to improve surface PM2.5 forecasts in the continental U.S. through the integrated use of multi satellite aerosol optical depth (AOD) products (MODIS Terra/Aqua and VIIRS DT/DB), multichemical transport model (CTM) (GEOS-Chem, WRF-Chem, and CMAQ) outputs, and ground observations. In Part I of the study, an ensemble Kalman filter (KF) technique using three CTM outputs and ground observations was developed to correct forecast bias and generate a single best forecast of PM2.5 for next day over non rural areas that have surface PM2.5 measurements in the proximity of 125 km. Here, with AOD data, we extended the bias correction into rural areas where the closest air quality monitoring station is at least 125–300 km away. First, we ensembled all of satellite AOD products to yield the single best AOD. Second, we corrected daily PM2.5 in rural areas from multiple models through the AOD spatial pattern between these areas and non rural areas, referred to as “extended ground truth” or EGT, for the present day. Lastly, we applied the KF technique to reduce the forecast bias for next day using the EGT. Our results find that the ensemble of bias-corrected daily PM2.5 from three CTMs for both today and next day show the best performance. Together, the two-part study develops a multimodel and multi-AOD bias-correction technique that has the potential to improve PM2.5 forecasts in both rural and non rural areas in near real time, and be readily implemented at state levels.

Huanxin Zhang↗
DOE OSTI2025

A Machine Learning Approach for Hourly Traffic Prediction Used in EV-Charging Sites

Reliable forecasting of hourly traffic volumes on highways is critical for planning and operating electric-vehicle charging infrastructure without overloading the grid. In this work, we develop and evaluate a station-specific machine-learning approach based on NeuralProphet, enhanced with conditional seasonality to better distinguish weekday, weekend, and holiday patterns. For each station, the model automatically retrieves the same calendar day from the prior years as an AR-Net initialization, fits trend and Fourier-based seasonality components, and then applies short-term auto-regressive corrections. We train and test on 2021 and 2022 TMAS data, respectively, and validate performance over the whole year. We chose to demonstrate how the model performs on a typical weekday (3/15/2022), weekend (3/27/2022), and a special holiday (12/25/2022). Our results yield MAPE of 7.4%, 23.6%, and 32.0%, respectively. Over the entire year 2022, the overall MAPE was 17%. This demonstrates that station-specific models with conditional seasonality can achieve accurate, scalable hourly forecasts for EV-charging load planning.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Electron cloud simulations in the Fermilab booster

As part of Fermilab's Proton Improvement Plan-II (PIP-II),the Fermilab Booster synchrotron will operate at a higherintensity, increasing from 4.5 x 10^12 to 6.7 x 10^12 protons per pulse (ppp). A potential challenge for achievinghigh intensity performance arises from rapid transverse instabilities induced by electron cloud (EC). This researchpresents EC simulations using PyECLOUD, which is anadvanced computational tool that incorporates measurements of the secondary electron yield (SEY) from theBooster's combined function magnet material. By systematically varying beam parameters in PyECLOUD, such asbunch structure, SEY, bunch length, and intensity, it becomes possible to forecast the impact of EC effects on thebeam stability of the PIP-II era Booster.

43 PARTICLE ACCELERATORS↗
NASA NTRS2014

Use of MODIS Cloud Top Pressure to Improve Assimilation Yields of AIRS Radiances in GSI

Improvements to global and regional numerical weather prediction have been demonstrated through assimilation of data from NASA's Atmospheric Infrared Sounder (AIRS). Current operational data assimilation systems use AIRS radiances, but impact on regional forecasts has been much smaller than for global forecasts. Previously, it has been shown that cloud top designation associated with quality control procedures within the Gridpoint Statistical Interpolation (GSI) system used operationally by a number of Joint Center for Satellite Data Assimilation (JCSDA) partners may not provide the best representation of cloud top pressure (CTP). Because this designated CTP determines which channels are cloud-free and, thus, available for assimilation, ensuring the most accurate representation of this value is imperative to obtaining the greatest impact from satellite radiances. This paper examines the assimilation of hyperspectral sounder data used in operational numerical weather prediction by comparing analysis increments and numerical forecasts generated using operational techniques with a research technique that swaps CTP from the Moderate-resolution Imaging Spectroradiometer (MODIS) for the value of CTP calculated from the radiances within GSI.

Zavodsky, Bradley↗
NASA NTRS1977

Operational utilization of remotely sensed data

The use of data from environmental satellites and other remote sensing platforms in some of NOAA's operational services are described. Topics discussed include: hurricanes; severe local storms and tornadoes; forest guidance; weather forecasting; hydrology; space program support; oceanography; search and rescue; and wildlife management. Applications which have become routine, and those which are in advanced field test are included. Some applications yield a clear cut economic benefit. In other cases, benefits -- if any -- are obscure. In yet other cases, benefits in one sector may be offset by detriments in another. Illustrative examples are given.

Jones, J. B.↗
NASA NTRS1995

Operational improvements of long-term predicted ephemerides of the Tracking and Data Relay Satellites (TDRSs)

Tracking and Data Relay Satellite (TDRS) orbit determination and prediction are supported by the Flight Dynamics Facility (FDF) of the Goddard Space Flight Center (GSFC) Flight Dynamics Division (FDD). TDRS System (TDRSS)-user satellites require predicted TDRS ephemerides that are up to 10 weeks in length. Previously, long-term ephemerides generated by the FDF included predictions from the White Sands Complex (WSC), which plans and executes TDRS maneuvers. TDRSs typically have monthly stationkeeping maneuvers, and predicted postmaneuver state vectors are received from WSC up to a month in advance. This paper presents the results of an analysis performed in the FDF to investigate more accurate and economical long-term ephemerides for the TDRSs. As a result of this analysis, two new methods for generating long-term TDRS ephemeris predictions have been implemented by the FDF. The Center-of-Box (COB) method models a TDRS as fixed at the center of its stationkeeping box. Using this method, long-term ephemeris updates are made semiannually instead of weekly. The impulse method is used to model more maneuvers. The impulse method yields better short-term accuracy than the COB method, especially for larger stationkeeping boxes. The accuracy of the impulse method depends primarily on the accuracy of maneuver date forecasting.

Kostoff, J. L.↗
NASA NTRS2014

Analysis and Prediction of Weather Impacted Ground Stop Operations

When the air traffic demand is expected to exceed the available airport's capacity for a short period of time, Ground Stop (GS) operations are implemented by Federal Aviation Administration (FAA) Traffic Flow Management (TFM). The GS requires departing aircraft meeting specific criteria to remain on the ground to achieve reduced demands at the constrained destination airport until the end of the GS. This paper provides a high-level overview of the statistical distributions as well as causal factors for the GSs at the major airports in the United States. The GS's character, the weather impact on GSs, GS variations with delays, and the interaction between GSs and Ground Delay Programs (GDPs) at Newark Liberty International Airport (EWR) are investigated. The machine learning methods are used to generate classification models that map the historical airport weather forecast, schedule traffic, and other airport conditions to implemented GS/GDP operations and the models are evaluated using the cross-validations. This modeling approach produced promising results as it yielded an 85% overall classification accuracy to distinguish the implemented GS days from the normal days without GS and GDP operations and a 71% accuracy to differentiate the GS and GDP implemented days from the GDP only days.

Analysis↗
DOE OSTI2024

Redshift evolution and covariances for joint lensing and clustering studies with DESI Y1

ABSTRACT Galaxy–galaxy lensing (GGL) and clustering measurements from the Dark Energy Spectroscopic Instrument Year 1 (DESI Y1) data set promise to yield unprecedented combined-probe tests of cosmology and the galaxy–halo connection. In such analyses, it is essential to identify and characterize all relevant statistical and systematic errors. We forecast the covariances of DESI Y1 GGL + clustering measurements and the systematic bias due to redshift evolution in the lens samples. Focusing on the projected clustering and GGL correlations, we compute a Gaussian analytical covariance, using a suite of N-body and lognormal simulations to characterize the effect of the survey footprint. Using the DESI one percent survey data, we measure the evolution of galaxy bias parameters for the DESI luminous red galaxy (LRG) and bright galaxy survey (BGS) samples. We find mild evolution in the LRGs in $0.4 < z < 0.8$, subdominant to the expected statistical errors. For BGS, we find less evolution for brighter absolute magnitude cuts, at the cost of reduced sample size. We find that for a redshift bin width $\Delta z = 0.1$, evolution effects on DESI Y1 GGL is negligible across all scales, all fiducial selection cuts, all fiducial redshift bins. Galaxy clustering is more sensitive to evolution due to the bias squared scaling. Nevertheless the redshift evolution effect is insignificant for clustering above the 1-halo scale of $0.1h^{-1}$ Mpc. For studies that wish to reliably access smaller scales, additional treatment of redshift evolution is likely needed. This study serves as a reference for GGL and clustering studies using the DESI Y1 sample.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1983

Estimates of the seasonal mean vertical velocity fields of the extratropical Northern Hemisphere

Indirect methods are employed to estimate the wintertime and summertime mean vertical velocity fields of the extratropical Northern Hemisphere and intercomparisons are made, together with comparisons with mean seasonal patterns of cloudiness and precipitation. Twice-daily NMC operational analyses produced general circulation statistics for 11 winters and 12 summers, permitting calculation of the seasonal NMC averages for 6 hr forecasts, solution of the omega equation, integration of continuity equation downward from 100 mb, and solution of the thermodynamic energy equation in the absence of diabatic heating. The methods all yielded similar vertical velocity patterns; however, the magnitude of the vertical velocities could not be calculated with great accuracy. Orography was concluded to have less of an effect in summer than in winter, when winds are stronger.

White, G. H.↗
NASA NTRS2020

Plant responses to rising vapor pressure deficit

Recent decades have been characterized by increasing temperatures worldwide, resulting in an exponential climb in vapor pressure deficit (VPD). VPD has been identified as an increasingly important driver of plant functioning in terrestrial biomes and has been established as a major contributor in recent drought‐induced plant mortality independent of other drivers associated with climate change. Despite this, few studies have isolated the physiological response of plant functioning to high VPD, thus limiting our understanding and ability to predict future impacts on terrestrial ecosystems. An abundance of evidence suggests that stomatal conductance declines under high VPD and transpiration increases in most species up until a given VPD threshold, leading to a cascade of subsequent impacts including reduced photosynthesis and growth, and higher risks of carbon starvation and hydraulic failure. Incorporation of photosynthetic and hydraulic traits in ‘next‐generation’ land‐surface models has the greatest potential for improved prediction of VPD responses at the plant‐ and global‐scale, and will yield more mechanistic simulations of plant responses to a changing climate. By providing a fully integrated framework and evaluation of the impacts of high VPD on plant function, improvements in forecasting and long‐term projections of climate impacts can be made.

mortality↗
NASA NTRS2004

SIAM-SERVIR: An Environmental Monitoring and Decision Support System for Mesoamerica

In 2002/2003 NASA, the World Bank and the United States Agency for International Development (USAID) joined with the Central American Commission for Environment and Development (CCAD) to develop an advanced decision support system for Mesoamerica (named SERVIR) as part of the Mesoamerican Environmental Information System (SIAM). Mesoamerica, composed of the seven Central American countries and the five southernmost states of Mexico, make up only a small fraction of the world's land surface. However, the region is home to seven to eight percent of the planet's biodiversity (14 biosphere reserves, 31 Ramsar sites, 8 world heritage sites, 589 protected areas) and 45 million people including more than 50 different ethnic groups. Today Mesoamerica's biological and cultural diversity is severely threatened by extensive deforestation, illegal logging, water pollution, and uncontrolled slash and burn agriculture. Additionally, Mesoamerica's distinct geology and geography result in disproportionate vulnerability to natural disasters such as earthquakes, hurricanes, drought, and volcanic eruptions. NASA Marshall Space Flight Center, together with the University of Alabama in Huntsville (UAH) and the SIAM-SERVIR partners are developing state-of-the-art decision support tools for environmental monitoring as well as disaster prevention and mitigation in Mesoamerica. These partners are contributing expertise in space-based observation with information management technologies and intimate knowledge of local ecosystems to create a system that is being used by scientists, educators, and policy makers to monitor and forecast ecological changes, respond to natural disasters and better understand both natural and human induced effects. In its first year of development and operation, the SIAM-SERVIR project has already yielded valuable information on Central American fires, weather conditions, and the first ever real-time data on red tides. This paper presents the progress thus far in the development of SIAM-SERVIR and the plans for the future.

Irwin, D. E.↗
NASA NTRS2005

SIAM-SERVIR: An Environmental Monitoring and Decision Support System for Mesoamerica

In 2002/2003 NASA, the World Bank and the United States Agency for International Development (USAID) joined with the Central American Commission for Environment and Development (CCAD) to develop an advanced decision support system for Mesoamerica (named SERVIR) as part of the Mesoamerican Environmental Information System (SIAM). Mesoamerica - composed of the seven Central American countries and the five southernmost states of Mexico - make up only a small fraction of the world s land surface. However, the region is home to seven to eight percent of the planet s biodiversity (14 biosphere reserves, 31 Ramsar sites, 8 world heritage sites, 589 protected areas) and 45 million people including more than 50 different ethnic groups. Today Mesoamerica s biological and cultural diversity is severely threatened by extensive deforestation, illegal logging, water pollution, and uncontrolled slash and burn agriculture. Additionally, Mesoamerica's distinct geology and geography result in disproportionate vulnerability to natural disasters such as earthquakes, hurricanes, drought, and volcanic eruptions. NASA Marshall Space Flight Center, together with the University of Alabama in Huntsville (UAH) and the SIAM-SERVIR partners are developing state-of-the-art decision support tools for environmental monitoring as well as disaster prevention and mitigation in Mesoamerica. These partners are contributing expertise in space-based observation with information management technologies and intimate knowledge of local ecosystems to create a system that is being used by scientists, educators, and policy makers to monitor and forecast ecological changes, respond to natural disasters and better understand both natural and human induced effects. In its first year of development and operation, the SIAM-SERVIR project has already yielded valuable information on Central American fires, weather conditions, and the first ever real-time data on red tides. This paper presents the progress thus far in the development of SIAM-SERVIR and the plans for the future.

Irwin, Daniel E.↗
NASA NTRS1985

Calculation and use of an environment's characteristic software metric set

Since both cost/quality and production environments differ, this study presents an approach for customizing a characteristic set of software metrics to an environment. The approach is applied in the Software Engineering Laboratory (SEL), a NASA Goddard production environment, to 49 candidate process and product metrics of 652 modules from six (51,000 to 112,000 lines) projects. For this particular environment, the method yielded the characteristic metric set (source lines, fault correction effort per executable statement, design effort, code effort, number of I/O parameters, number of versions). The uses examined for a characteristic metric set include forecasting the effort for development, modification, and fault correction of modules based on historical data.

Basili, Victor R.↗
NASA NTRS2005

Space Based Ornithology: On the Wings of Migration and Biophysics

The study of bird migration on a global scale is one of the compelling and challenging problems of modern biology with major implications for human health and conservation biology. Migration and conservation efforts cross national boundaries and are subject to numerous international agreements and treaties. Space based technology offers new opportunities to shed understanding on the distribution and migration of organisms on the planet and their sensitivity to human disturbances and environmental changes. Migration is an incredibly diverse and complex behavior. A broad outline of space based research must address three fundamental questions: (1) where could birds be, i.e. what is their fundamental niche constrained by their biophysical limits? (2) where do we actually find birds, i.e. what is their realizable niche as modified by local or regional abiotic and biotic factors, and (3) how do they get there (and how do we know?), that is what are their migration patterns and associated mechanisms? Our working hypothesis is that individual organism biophysical models of energy and water balance, driven by satellite measurements of spatio-temporal gradients in climate and habitat, will help us to explain the variability in avian species richness and distribution. Dynamic state variable modeling provides one tool for studying bird migration across multiple scales and can be linked to mechanistic models describing the time and energy budget states of migrating birds. Such models yield an understanding of how a migratory flyway and its component habitats function as a whole and link stop-over ecology with biological conservation and management. Further these models provide an ecological forecasting tool for science and application users to address what are the possible consequences of loss of wetlands, flooding, drought or other natural disasters such as hurricanes on avian biodiversity and bird migration.

Smith, James A.↗
NASA NTRS1980

AgRISTARS: Foreign commodity production forecasting. Minutes of the annual formal project manager's review, including preliminary technical review reports of FY80 experiments

The U.S./Canada wheat/barley exploratory experiment is discussed with emphasis on labeling, machine processing using P1A, and the crop calendar. Classification and the simulated aggregation test used in the U.S. corn/soybean exploratory experiment are also considered. Topics covered regarding the foreign commodity production forecasting project include: (1) the acquisition, handling, and processing of both U.S. and foreign agricultural data, as well as meteorological data. The accuracy assessment methodology, multicrop sampling and aggregation technology development, frame development, the yield project interface, and classification for area estimation are also examined.

Source record↗
NASA NTRSDate not supplied

Non-Gaussian Ensemble Filtering and Adaptive Inflation for Soil Moisture Data Assimilation

The rank histogram filter (RHF) and the ensemble Kalman filter (EnKF) are assessed for soil moisture estimation using perfect model (identical twin) synthetic data assimilation experiments. The primary motivation is to gauge the impact on analysis quality attributable to the consideration of non-Gaussian forecast error distributions. Using the NASA Catchment land surface model, the two filters are compared at 18 globally distributed single-catchment locations for a 10-yr experiment period. It is shown that both filters yield adequate estimates of soil moisture, with the RHF having a small but significant performance advantage. Most notably, the RHF consistently increases the normalized information contribution (NIC) score of the mean absolute bias by 0.05 over that of the EnKF for surface, root-zone, and profile soil moisture. The RHF also increases the NIC score for the anomaly correlation of surface soil moisture by 0.02 over that of the EnKF (at a 5% significance level). Results additionally demonstrate that the performance of both filters is somewhat improved when the ensemble priors are adaptively inflated to offset the negative effects of systematic errors.

Rolf Reichle↗