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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
NASA NTRS2014

Reply to Comment by Laprise on 'the Added Value to Global Model Projections of Climate Change by Dynamical Downscaling: a Case Study over the Continental U.S. Using the GISS-ModelE2 and WRF Models'

In his comment, Laprise raises several points that we agree merit consideration. His primary critique is that our study [Racherla et al., 2012] tested the ability of the WRF regional climate model to reproduce historical temperature and precipitation change relative to the driving global climate model (GCM) using only a single simulation rather than an ensemble. He asserts that the observed changes are smaller than the internal variability in the climate system (i.e., not statistically significant) and that thus a single simulation should not necessarily be able to capture the observations. Laprise points out that the statistical signal is reduced for a multi-decadal trend such as the one we analyzed in comparison with mean climatology and cites two studies showing that for particular climate parameters it can take any years for a signal to be discerned over internal variability. He states that The results of theexperiment as designed were strongly influenced by the presence of internal variability and sampling errors,which masked the rather small climate changes that may have occurred as a consequence of changes inforcing during the period considered. While Laprise discusses statistics in general terms at some length, for the actual climate trends examined in our study, he offers no evidence that the forced signal was smallcompared with internal variability. The two studies he cites [de Ela et al., 2013; Maraun, 2013] do not provide convincing evidence as they concern climate variables averaged over different times and areas. One in fact examines extreme precipitation events, which by definition are rare and thus have a lower significance level. We accept the general point that it is important to consider internal variability, and as noted in our paper we agree that an ensemble of simulations is in principle an optimal, though computationally expensive, approach. While we did not present the statistical significance of the observations in our original paper, we have now evaluated those for the regional temperature trends used in our study to evaluate the added value of WRF and thus can analyze data as to the magnitude of the trends with respect to internal variability.

climate↗
NASA NTRS2017

On the Dependence of Cloud Feedbacks on Physical Parameterizations in WRF Aquaplanet Simulations

We investigate the effects of physical parameterizations on cloud feedback uncertainty in response to climate change. For this purpose, we construct an ensemble of eight aquaplanet simulations using the Weather Research and Forecasting (WRF) model. In each WRF‐derived simulation, we replace only one parameterization at a time while all other parameters remain identical. By doing so, we aim to (i) reproduce cloud feedback uncertainty from state‐of‐the‐art climate models and (ii) understand how parametrizations impact cloud feedbacks. Our results demonstrate that this ensemble of WRF simulations, which differ only in physical parameterizations, replicates the range of cloud feedback uncertainty found in state‐of‐the‐art climate models. We show that microphysics and convective parameterizations govern the magnitude and sign of cloud feedbacks, mostly due to tropical low‐level clouds in subsidence regimes. Finally, this study highlights the advantages of using WRF to analyze cloud feedback mechanisms owing to its plug‐and‐play parameterization capability.

Cesana, Gregory↗
NASA NTRS1991

A multicomputer simulation of the Galileo spacecraft command and data subsystem

A detailed simulation of the command and data subsystem of the Galileo spacecraft on a distributed memory multicomputer is described. The simulation is based on an ensemble of Inmos Transputers for simulating, to the bit level, the execution of instruction sequences for the six RCA 1802 microcomputers and the intricate bus traffic between them and other components of the spacecraft. Expressions were developed to estimate the performance of the simulator on a distributed system given the processor clock speed, memory access time, and communication characteristics.

Zipse, John E.↗
NASA NTRS2002

On Simulating the Mid-western-us Drought of 1988 with a GCM

The primary cause of the midwestern North American drought in the summer of 1988 has been identified to be the La Nina SST anomalies. Yet with the SST anomalies prescribed, this drought has not been simulated satisfactorily by any general circulation model. Seven simulation-experiments, each containing an ensemble of 4-sets of simulations, were conducted with the GEOS GCM for both 1987 and 1988. All simulations started from January 1 and continued through the end of August. In the first baseline case, Case 1, only the SST anomalies and some vegetation parameters were prescribed, while everything else (such as soil moisture, snow-cover, and clouds) was interactive. The GCM did produce some of the circulation features of a drought over North America, but they could only be identified on the planetary scales. The 1988 minus 1987 precipitation differences show that the GCM was successful in simulating reduced precipitation in the mid-west, but the accompanying circulation anomalies were not well simulated, leading one to infer that the GCM has simulated the drought for the wrong reason. To isolate the causes for this unremarkable circulation, analyzed winds and soil moisture were prescribed in Case 2 and Case 3 as continuous updates by direct replacement of the GCM-predicted fields. These cases show that a large number of simulation biases emanate from wind biases that are carried into the North American region from surroundings regions. Inclusion of soil moisture also helps to ameliorate the strong feedback, perhaps even stronger than that of the real atmosphere, between soil moisture and precipitation. Case 2 simulated one type of surface temperature anomaly pattern, whereas Case 3 with the prescribed soil moisture produced another.

Sud, Y. C.↗
NASA NTRS2004

Estimating the instability of a composite clock

A composite clock created from a local clock ensemble is known by its time offsets from the ensemble clocks. By a geometrical argument, estimate for the instability of the composite clock are calculated from the instabilities of the ensemble clocks, individually and against the composite clock. The method is illustrated by examples using simulated and real ensembles.

timescale↗
NASA NTRS1992

Role of a cumulus parameterization scheme in simulating atmospheric circulation and rainfall in the nine-layer Goddard Laboratory for Atmospheres General Circulation Model

The influence of a cumulus convection scheme on the simulated atmospheric circulation and hydrologic cycle is investigated by means of a coarse version of the GCM. Two sets of integrations, each containing an ensemble of three summer simulations, were produced. The ensemble sets of control and experiment simulations are compared and differentially analyzed to determine the influence of a cumulus convection scheme on the simulated circulation and hydrologic cycle. The results show that cumulus parameterization has a very significant influence on the simulation circulation and precipitation. The upper-level condensation heating over the ITCZ is much smaller for the experiment simulations as compared to the control simulations; correspondingly, the Hadley and Walker cells for the control simulations are also weaker and are accompanied by a weaker Ferrel cell in the Southern Hemisphere. Overall, the difference fields show that experiment simulations (without cumulus convection) produce a cooler and less energetic atmosphere.

Sud, Y. C.↗
NASA NTRS2012

A Comparison of Perturbed Initial Conditions and Multiphysics Ensembles in a Severe Weather Episode in Spain

Ensembles of numerical model forecasts are of interest to operational early warning forecasters as the spread of the ensemble provides an indication of the uncertainty of the alerts, and the mean value is deemed to outperform the forecasts of the individual models. This paper explores two ensembles on a severe weather episode in Spain, aiming to ascertain the relative usefulness of each one. One ensemble uses sensible choices of physical parameterizations (precipitation microphysics, land surface physics, and cumulus physics) while the other follows a perturbed initial conditions approach. The results show that, depending on the parameterizations, large differences can be expected in terms of storm location, spatial structure of the precipitation field, and rain intensity. It is also found that the spread of the perturbed initial conditions ensemble is smaller than the dispersion due to physical parameterizations. This confirms that in severe weather situations operational forecasts should address moist physics deficiencies to realize the full benefits of the ensemble approach, in addition to optimizing initial conditions. The results also provide insights into differences in simulations arising from ensembles of weather models using several combinations of different physical parameterizations.

severe weather↗
NASA NTRS2016

An Assessment of Multimodel Simulations for the Variability of Western North Pacific Tropical Cyclones and Its Association with ENSO

An assessment of simulations of the interannual variability of tropical cyclones (TCs) over the western North Pacific (WNP) and its association with El Niño-Southern Oscillation (ENSO), as well as a subsequent diagnosis for possible causes of model biases generated from simulated large-scale climate conditions, are documented in the paper. The model experiments are carried out by the Hurricane Work Group under the U.S. Climate Variability and Predictability Research Program (CLIVAR) using five global climate models (GCMs) with a total of 16 ensemble members forced by the observed sea surface temperature and spanning the 28-yr period from 1982 to 2009. The results show GISS and GFDL model ensemble means best simulate the interannual variability of TCs, and the multimodel ensemble mean (MME) follows. Also, the MME has the closest climate mean annual number of WNP TCs and the smallest root-mean-square error to the observation. Most GCMs can simulate the interannual variability of WNP TCs well, with stronger TC activities during two types of El Niño-namely, eastern Pacific (EP) and central Pacific (CP) El Niño-and weaker activity during La Niña. However, none of the models capture the differences in TC activity between EP and CP El Niño as are shown in observations. The inability of models to distinguish the differences in TC activities between the two types of El Niño events may be due to the bias of the models in response to the shift of tropical heating associated with CP El Niño.

climate models↗
NASA NTRS1992

Infrared, microwave, and spaceborne radar simulations of a deep convective system using a 3-D cloud ensemble method

A 3D cloud model is used to simulate the storm structure, and the results are linked to microwave and infrared radiative transfer models for simulation of aircraft observations. Spaceborne radar data are also simulated along the aircraft flight track. The cloud and radiative model simulations are studied and compared with aircraft observations. The initial results indicate that the 3D cloud model is capable of simulating the major features of observed storm systems when given a representative atmospheric sounding to initialize the convective systems. The simulations of infrared and microwave radiances provide reasonably good comparisons with the observations.

Yeh, H.-Y. M.↗
DOE OSTI2024

The Influence of ENSO Diversity on Future Atlantic Tropical Cyclone Activity

El Niño–Southern Oscillation (ENSO) influences seasonal Atlantic tropical cyclone (TC) activity by impacting environmental conditions important for TC genesis. However, the influence of future climate change on the teleconnection between ENSO and Atlantic TCs is uncertain, as climate change is expected to impact both ENSO and the mean climate state. We used the Weather Research and Forecasting Model on a tropical channel domain to simulate 5-member ensembles of Atlantic TC seasons in historical and future climates under different ENSO conditions. Experiments were forced with idealized sea surface temperature configurations based on the Community Earth System Model (CESM) Large Ensemble representing: a monthly varying climatology, eastern Pacific El Niño, central Pacific El Niño, and La Niña. The historical simulations produced fewer Atlantic TCs during eastern Pacific El Niño compared to central Pacific El Niño, consistent with observations and other modeling studies. For each ENSO state, the future simulations produced a similar teleconnection with Atlantic TCs as in the historical simulations. Specifically, La Niña continues to enhance Atlantic TC activity, and El Niño continues to suppress Atlantic TCs, with greater suppression during eastern Pacific El Niño compared to central Pacific El Niño. In addition, we found a decrease in the Atlantic TC frequency in the future relative to historical regardless of ENSO state, which was associated with a future increase in northern tropical Atlantic vertical wind shear and a future decrease in the zonal tropical Pacific sea surface temperature (SST) gradient, corresponding to a more El Niño–like mean climate state. Our results indicate that ENSO will remain useful for seasonal Atlantic TC prediction in the future.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2022

Are Soybean Models Ready for Climate Change Food Impact Assessments?

An accurate estimation of crop yield under climate change scenarios is essential to quantify our ability to feed a growing population and develop agronomic adaptations to meet future food demand. A coordinated evaluation of yield simulations from process-based eco-physiological models for climate change impact assessment is still missing for soybean, the most widely grown grain legume and the main source of protein in our food chain. In this first soybean multi-model study, we used ten prominent models capable of simulating soybean yield under varying temperature and atmospheric CO 2 concentration [CO 2 ] to quantify the uncertainty in soybean yield simulations in response to these factors. Models were first parametrized with high quality measured data from five contrasting environments. We found considerable variability among models in simulated yield responses to increasing temperature and [CO 2 ]. For example, under a + 3 °C temperature rise in our coolest location in Argentina, some models simulated that yield would reduce as much as 24%, while others simulated yield increases up to 29%. In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 °C temperature rise to no effect on yield. Similarly, when increasing [CO 2 ] from 360 to 540 ppm, the models simulated a yield increase that ranged from 6% to 31%. Model calibration did not reduce variability across models but had an unexpected effect on modifying yield responses to temperature for some of the models. The high uncertainty in model responses indicates the limited applicability of individual models for climate change food projections. However, the ensemble mean of simulations across models was an effective tool to reduce the high uncertainty in soybean yield simulations associated with individual models and their parametrization. Ensemble, ensemble mean yield responses to temperature and [CO 2 ] were similar to those reported from the literature. Our study is the first demonstration of the benefits achieved from using an ensemble of grain legume models for climate change food projections, and highlights that further soybean model development with experiments under elevated [CO 2 ] and temperature is needed to reduce the uncertainty from the individual models.

Agricultural Model Inter-comparison and Improvemen↗
NASA NTRS2006

The Effects of Lightning NO(x) Production during the July 21 EULINOX Storm studied with a 3-D Cloud-scale Chemical Transport Model

The July 21,1998 thunderstonn observed during the European Lightning Nitrogen Oxides Project (EULINOX) project was simulated using the three-dimensional Goddard Cumulus Ensemble (GCE) model. The simulation successfully reproduced a number of observed storm features including the splitting of the original cell into a southern cell which developed supercell characteristics, and a northern cell which became multicellular. Output from the GCE simulation was used to drive an offline cloud-scale chemical transport model which calculates tracer transport and includes a parameterization of lightning NO(x) production which uses observed flash rates as input. Estimates of lightning NO(x) production were deduced by assuming various values of production per intracloud and production per cloud-to-ground flash and comparing the results with in-cloud aircraft observations. The assumption that both types of flashes produce 360 moles of NO per flash on average compared most favorably with column mass and probability distribution functions calculated from observations. This assumed production per flash corresponds to a global annual lightning NOx source of 7 Tg N per yr. Chemical reactions were included in the model to evaluate the impact of lightning NO(x), on ozone. During the storm, the inclusion of lightning NOx in the model results in a small loss of ozone (on average less than 4 ppbv) at all model levels. Simulations of the chemical environment in the 24 hours following the storm show on average a small increase in the net production of ozone at most levels resulting from lightning NO(x), maximizing at approximately 5 ppbv per day at 5.5 km. Between 8 and 10.5 km, lightning NO(x) causes decreased net ozone production.

Ott, Lesley E.↗
NASA NTRS1994

Measurement uncertainty and feasibility study of a flush airdata system for a hypersonic flight experiment

Presented is a feasibility and error analysis for a hypersonic flush airdata system on a hypersonic flight experiment (HYFLITE). HYFLITE heating loads make intrusive airdata measurement impractical. Although this analysis is specifically for the HYFLITE vehicle and trajectory, the problems analyzed are generally applicable to hypersonic vehicles. A layout of the flush-port matrix is shown. Surface pressures are related airdata parameters using a simple aerodynamic model. The model is linearized using small perturbations and inverted using nonlinear least-squares. Effects of various error sources on the overall uncertainty are evaluated using an error simulation. Error sources modeled include boundarylayer/viscous interactions, pneumatic lag, thermal transpiration in the sensor pressure tubing, misalignment in the matrix layout, thermal warping of the vehicle nose, sampling resolution, and transducer error. Using simulated pressure data for input to the estimation algorithm, effects caused by various error sources are analyzed by comparing estimator outputs with the original trajectory. To obtain ensemble averages the simulation is run repeatedly and output statistics are compiled. Output errors resulting from the various error sources are presented as a function of Mach number. Final uncertainties with all modeled error sources included are presented as a function of Mach number.

Whitmore, Stephen A.↗
DOE OSTI2025

Multiscale Machine-Learned Modeling Infrastructure

The Multiscale Machine-Learned Modeling Infrastructure (MuMMI) is a multiscale workflow management infrastructure that can concurrently orchestrate thousands of molecular dynamics (MD) simulations operating at different time and/or length scales, spanning nanoseconds to seconds and nanometers to micrometers. MuMMI uses machine learning (backed by biology experiments) to guide a massive ensemble of MD simulations that capture biologically relevant time and length scales with unprecedented resolution. MuMMI supports multiple MD codes such as GROMACS and ddcMD and can be fully deployed using the HPC package manager Spack. MuMMI has been used in many publications to run hundreds of thousands simulations, leading to significant biology breakthroughs.

Di Natale, Francesco [Lawrence Livermore National ↗
NASA NTRS2012

A Modeling Study of the Spring 2011 Extreme US Weather Activity

The spring of 2011 was characterized by record-breaking tornadic activity with substantial loss of life and destruction of property. While a waning La Nina and other atmospheric teleconnections have been implicated in the development of these extreme weather events, a quantitative assessment of their causes is still lacking. This study uses high resolution (1/4 lat/lon) GEOS-5 AGCM experiments to quantify the role of SSTs and soil moisture in the development of the extreme weather activity with a focus on April - the month of peak tornadic activity. The simulations, consisting of 22-member ensembles of three-month long simulations (initialized March 1st) reproduce the main features of the observed large-scale changes including the below-normal temperature and above-normal precipitation in the Central US, and the hot and dry conditions to the south. Various sensitivity experiments are conducted to separate the roles of the SST, soil moisture and the initial atmospheric conditions in the development and predictability of the atmospheric conditions (wind shear, moisture, etc.) favoring the severe weather activity and flooding.

Schubert, S.↗
NASA NTRS2015

Multimodel Ensembles of Wheat Growth: More Models are Better than One

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

wheat↗
DOE OSTI2024

Quantum Simulations of Radiation Damage in a Molecular Polyethylene Analog

Abstract An atomic‐level understanding of radiation‐induced damage in simple polymers like polyethylene is essential for determining how these chemical changes can alter the physical and mechanical properties of important technological materials such as plastics. Ensembles of quantum simulations of radiation damage in a polyethylene analog are performed using the Density Functional Tight Binding method to help bind its radiolysis and subsequent degradation as a function of radiation dose. Chemical degradation products are categorized with a graph theory approach, and occurrence rates of unsaturated carbon bond formation, crosslinking, cycle formation, chain scission reactions, and out‐gassing products are computed. Statistical correlations between product pairs show significant correlations between chain scission reactions, unsaturated carbon bond formation, and out‐gassing products, though these correlations decrease with increasing atom recoil energy. The results present relatively simple chemical descriptors as possible indications of network rearrangements in the middle range of excitation energies. Ultimately, the work provides a computational framework for determining the coupling between nonequilibrium chemistry in polymers and potential changes to macro‐scale properties that can aid in the interpretation of future radiation damage experiments on plastic materials.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
NASA NTRS2015

Multimodel Ensembles of Wheat Growth: Many Models are Better than One

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop model scan give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 2438 for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

model intercomparison↗