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At least 217 records · Page 12

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NASA NTRS2015

Message Latency Characterization of a Distributed Live, Virtual, Constructive Simulation Environment

A distributed test environment incorporating Live, Virtual, Constructive, (LVC) concepts was developed to execute standalone and integrated simulations and flight-tests that support unmanned aircraft research for NASA’s Unmanned Aircraft Systems (UAS) in the National Airspace System (NAS) Project. The LVC components form the core infrastructure that supports simulation of UAS operations by integrating live and virtual aircraft in a realistic air traffic environment. This LVC infrastructure enables efficient testing by leveraging the use of existing distributed assets. The LVC concepts used for the UAS in the NAS project include live aircraft, flight simulators, and virtual air traffic control assets operating at facilities distributed across multiple NASA Centers. With a distributed network, however, there is a concern that message latency could impact the realism of a simulation and its data. The latencies associated with sending data among these distributed facilities were, therefore, measured to ensure that they fall within acceptable parameters. Several live and virtual test assets were integrated into the LVC infrastructure including NASA Armstrong’s Ikhana MQ-9 unmanned aircraft, NASA Glenn’s S3-B manned aircraft, and the B747 flight simulator at NASA Ames. Average latencies from 100 to 150 milliseconds were observed between the LVC System running at NASA Ames and each of the participating NASA Centers under a light-to-moderate (fifty aircraft) traffic sample.

Murphy, James R.↗

NASA NTRS2019

Strong Dependence of Atmospheric Feedbacks on Mixed-Phase Microphysics and Aerosol-Cloud Interactions in HadGEM3

We analyze the atmospheric processes that explain the large changes in radiative feed-backs between the two latest climate configurations of the Hadley Centre Global Environmental model. We use a large set of atmosphere-only climate-change simulations (amip and amip-p4K) to separate the contributions to the differences in feedback parameter from all the atmospheric model developments between the two latest model configurations. We show that the differences are mostly driven by changes in the shortwave cloud radiative feedback in the midlatitudes, mainly over the Southern Ocean. Two new schemes explain most of the differences: the introduction of a new aerosol scheme; and the development of a new mixed-phase cloud scheme. Both schemes reduce the strength of the pre-existing shortwave negative cloud feedback in the midlatitudes. The new aerosol scheme dampens a strong aerosol-cloud interaction, and it also suppresses a negative clear-sky shortwave feedback. The mixed-phase scheme increases the amount of cloud liquid water path (LWP) in the present-day, thereby reducing the radiative effciency of the increase of LWP in the warmer climate. It also enhances a strong, pre-existing, positive cloud fraction feedback. We assess the realism of the changes by comparing present-day simulations against observations, and discuss avenues that could help constrain the relevant processes.

HadGEM3↗

NASA NTRS2017

Realistic 3D Radiative Modeling of Turbulent Structure of Moderate-Mass Stars and Sun

Understanding the turbulent dynamics of the Sun and stars is a critical element for interpreting observed processes and phenomena on different scales and for predicting extreme events such as flares and super flares. High-‐resolution observations of the Sun and high-‐fidelity radiative MHD numerical simulations have substantially advanced our understanding of solar and stellar local dynamics and magnetism from the upper convection zone to the atmosphere and corona. However, global modeling of the Sun with such a high degree of realism is currently not affordable due to the extremely high computational cost of resolving the scales in the convection zone. The physics of the deep solar dynamics can be effectively addressed through modeling more massive solar-‐type stars where the convection zone is shallower and the convective overturning time is much shorter than those on the Sun. We present recent 3D realistic simulation results of moderate-‐mass stars and discuss links between solar and stellar dynamics, such as the multiscale structure of granulation, convective overshoot, and others. In particular, these simulations have provided better understanding of the dynamics of the tachocline (the overshoot layer at the bottom of the convection zone) and have explained long-‐ standing results from helioseismology.

Realistic↗

NASA NTRS2019

Integrating Remote Sensing Observations with NASA's GEOS-5 Modeling Framework in Support of Retrospective Analyses and Seasonal Prediction of Biosphere-Atmosphere CO2 Flux

The absorption of nearly half of all anthropogenic carbon dioxide (CO2) emissions by terrestrial and marine ecosystems has played a critical role in mitigating climate change. However, a persistent lack of understanding of these uptake processes impedes attribution, and thereby introduces large uncertainties into projections of the trajectory of carbon and climate in coming decades. A better understanding and quantification of the seasonal and interannual variability of carbon flux between the atmosphere and ocean and terrestrial biospheres is critically needed to improve predictions from Earth system models. We propose to bring together a diverse suite of remote sensing observations, mechanistic models, and NASA's Goddard Earth Observing System, version 5 (GEOS-5) model to address two objective 1) quantify the processes controlling the temporal variability of biosphere-atmosphere CO2 flux on seasonal and interannual timescales from 1989 to 2018 and 2) evaluating the predictability of the components of carbon flux on seasonal timescales. Because GEOS-5 has been developed to incorporate a variety of Earth system observations and includes a complete and physically consistent depiction of the atmosphere, land, and ocean carbon cycles, it represents a unique and ideal synthesis framework for this effort. By expanding the realism of processes that could be included in GEOS-5 seasonal forecasts, this effort supports NASA's goals toward an integrated Earth system modeling and prediction framework.We propose to integrate a number of observations into the GEOS-5 modeling system to refine retrospective estimates of both terrestrial and marine carbon flux and to test hypotheses regarding the effects of climate drivers on terrestrial carbon flux. This will include assimilating several satellite ocean color products in the NASA Ocean Biogeochemistry Model (NOBM) to improve the representation of ocean biology. We will also utilize remote sensing datasets to increase the temporal resolution of global land-use change estimates and fire emissions to better constrain the temporal variability of the terrestrial carbon cycle. This information will be incorporated into two NASA terrestrial biosphere models Catchment-CN, a dynamic vegetation model that is integrated within GEOS-5 and the Ecosystem Demography (ED) model, a core modeling resource supporting the Global Ecosystem Dynamics Investigation (GEDI) mission to map biomass. Fluxes will be evaluated through 1) comparison to top-down flux estimates inferred from surface CO2 observations and 2) comparison against available aircraft and satellite observations.By combining predictions of net ecosystem exchange and air-sea CO2 flux from land and ocean models with statistical predictions of land use change, fire, and fossil fuel emissions, we propose to create the first dynamical CO2 forecast on seasonal timescales. Because of the experimental nature of this endeavor, an important project objective will be to evaluate the limits to the predictability of different carbon cycle processes over lead times of 1-12 months using different satellite and surface data streams.

Ott, Lesley↗

NASA NTRS2020

An Evaluation of Clouds and Radiation in a Large-Scale Atmospheric Model Using a Cloud Vertical Structure Classification

We revisit the concept of the cloud vertical structure (CVS) classes we have previously employed to classify the planet's cloudiness (Oreopoulos et al., 2017). The CVS classification reflects simple combinations of simultaneous cloud occurrence in the three standard layers traditionally used to separate low, middle, and high clouds and was applied to a dataset derived from active lidar and cloud radar observations. This classification is now introduced in an atmospheric global climate model, specifically a version of NASA's GEOS-5, in order to evaluate the realism of its cloudiness and of the radiative effects associated with the various CVS classes. Such classes can be defined in GEOS-5 thanks to a sub column cloud generator paired with the model's radiative transfer algorithm, and their associated radiative effects can be evaluated against observations. We find that the model produces 50% more clear skies than observations in relative terms and produces isolated high clouds that are slightly less frequent than in observations, but optically thicker, yielding excessive planetary and surface cooling. Low clouds are also brighter than in observations, but underestimates of the frequency of occurrence (by ~20% in relative terms) help restore radiative agreement with observations. Overall the model better reproduces the longwave radiative effects of the various CVS classes because cloud vertical location is substantially constrained in the CVS framework.

Lee, Dongmin↗

NASA NTRS2020

Land-Focused Changes in the Updated GEOS FP System (Version 5.25)

Many of the changes imposed in the January 2020 upgrade from Version 5.22 to 5.25 of the Goddard Earth Observing System (GEOS) Forward Processing (FP) analysis system were designed to increase the realism of simulated land variables. The changes, which consist of both land model parameter updates and improvements to the physical treatments employed for various land processes, have generally positive or neutral impacts on the character of the FP product, as documented here.

Koster, Randal D.↗

NASA NTRS2020

A Digital Twin Feasibility Study (Part II):Non-Deterministic Predictions of Fatigue Life Using In-Situ Diagnostics and Prognostics

The Digital Twin (DT) concept has the potential to revolutionize the way systems and their components are designed, managed, maintained, and operated across a vast number of fields from engineering to healthcare. The focus of this work is the implementation of DT for the health management of fatigue critical structures. This paper is the second part of a two-part series. The first of the series demonstrated the use of multi-scale, initiation-to-failure crack growth modeling to form non-deterministic predictions of fatigue life. In this second part, a general method for reducing uncertainty in fatigue life predictions is presented that couples in-situ diagnostics and prognostics in a probabilistic framework. Monte Carlo methods and high-fidelity finite element models are used to (i) generate probabilistic estimates of crack state throughout the life of the same geometrically complex test specimen and (ii) predict fatigue life with decreasing uncertainty as more of these diagnoses are obtained. The ability to predict accurately and in the presence of uncertainty is demonstrated, suggesting that the proposed DT method is feasible for fatigue life prognosis and should be pursued further with a focus on increasing application realism.

Patrick E Leser↗

NASA NTRS2020

Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions

Traditional watershed modeling often overlooks the role of vegetation dynamics. There is also little quantitative evidence to suggest that increased physical realism of vegetation dynamics in process-based models improves hydrology and water quality predictions simultaneously. In this study, we applied a modified Soil and Water Assessment Tool (SWAT) to quantify the extent of improvements that the assimilation of remotely sensed Leaf Area Index (LAI) would convey to streamflow, soil moisture, and nitrate load simulations across a 16,860 km2 agricultural watershedin the midwestern United States. We modified the SWAT source code to automatically override the model’s built-in semiempirical LAI with spatially distributed and temporally continuous estimates from Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to a “basic” traditional model with limited spatial information, our LAI assimilation model (i) significantly improved daily streamflow simulations during medium-to-low flow conditions, (ii) provided realistic spatial distributions of growing season soil moisture, and (iii) substantially reproduced the long-term observed variability of daily nitrate loads. Further analysis revealed that the overestimation or underestimation of LAI imparted a proportional cascading effect on how the model partitions hydrologic fluxes and nutrient pools. As such, assimilation of MODIS LAI data corrected the model’sLAI overestimation tendency, which led to a proportionally increased rootzone soil moisture and decreased plant nitrogen uptake. With these new findings, our study fills the existing knowledge gap regarding vegetation dynamics in watershed modeling and confirms that assimilation of MODIS LAI data in watershed models can effectively improve both hydrology and water quality predictions.

Adnan Rajib↗

NASA NTRSDate not supplied

Evaluating Subgrid-Scale Properties of Low Clouds over the Beaufort Sea in Arctic System Reanalysis using ARISE Airborne In Situ Observations

Arctic low clouds influence the evolution of the Arctic system through their effects on radiative fluxes, boundary layer mixing, stability, turbulence, humidity, and precipitation. Unfortunately, atmospheric models and retrospective analysis (reanalysis) products struggle to accurately simulate the occurrence and properties of low clouds in the Arctic. To better understand the magnitude and possible causes of this problem, we analyze the disagreements between the Arctic System Reanalysis version 2 (ASR) and data taken during the September 2014 Arctic Radiation-IceBridge Sea and Ice Experiment (ARISE) airborne campaign conducted over the Beaufort Sea. Previous analysis of grid scale-averaged meteorological variables revealed a lack of cloud water produced in ASR, related to warm/dry biases, a failure to produce sufficient cloud water in conditions that are observed to be favorable for large cloud water values, and sampling issues with the ARISE flight paths. However, ARISE collected meteorological observations at one second intervals, offering spatial data resolution comparable to the subgrid scale of ASR. Subgrid scale properties and processes must be parameterized in conventional atmospheric models and reanalyses such as ASR, and often rely on assumed distributions of meteorological variables about the grid box mean state. To enable an examination of the realism of ASR’s subgrid scale properties, we use high resolution data from ARISE determine the “subgrid scale” distributions of cloud water and thermodynamic variables in the real atmosphere. Preliminary results show that the subgrid scale distribution of thermodynamic variables about the grid scale mean resembles the triangular distribution assumed in many bulk microphysical parameterization schemes, but also with tails resembling Gaussian distributions. The main goal is to understand the partitioning of the total available atmospheric water into water vapor and cloud water. Models typically covert some percentage of total water in a grid box to cloud water based on the estimated amount of supersaturation from the assumed vapor distribution. The ARISE measurements can reveal the relationship between subgrid scale supersaturation and the partition between cloud water and water vapor.

Jason Brant Dodson↗

NASA NTRS2020

An Observation‐Driven Approach to Improve Vegetation Phenology in a Global Land Surface Model

An empirical model calibration approach is presented that aims to approximate missing biosphere processes in a global land surface model without the need for substantial model structural changes. The strategy is implemented here using the NASA Catchment‐CN land surface model and Moderate Resolution Imaging Spectroradiometer (MODIS) observations of the fraction of absorbed photosynthetically active radiation (FPAR). Existing plant functional types (PFTs) of the Catchment‐CN model are divided into three subtypes, based on the bias between the model‐simulated and MODIS‐observed FPAR. Separate sets of vegetation parameters for each subtype are then calibrated at a small number of grid cells with homogeneous, single‐PFT land cover, using MODIS FPAR reference observations from 2003 to 2009. The effectiveness of the empirical approach at improving the realism of modeled vegetation dynamics is investigated with two global model simulations for the period 2010–2016, one using the newly calibrated parameter values and the other using the original values. Globally, the calibrated parameters reduce the root mean square error (RMSE) of the modeled FPAR with respect to MODIS by 0.029 (∼10%) on average. In some regions, substantially larger RMSE reductions are achieved. RMSE reductions are primarily driven by model bias reductions, with neutral effects on the temporal correlation skill. While the empirical approach is suitable for achieving consistent model improvements, it is shown to be sensitive to the characteristics of the model error, specifically a dominance of the bias component in the case of Catchment‐CN. Ultimately, more fundamental model structural changes may be required to achieve better improvements.

J. Kolassa↗

NASA NTRSDate not supplied

Effects of Rotation on Internal Structure and Dynamics of Main-Sequence Stars

Current state-of-the-art computer simulations allow us to build 3D dynamical and radiative models of stars from physical first principles with a high degree of realism. The radiative 3D dynamical stellar models obtained with the StellarBox code take into account the effects of turbulence, stellar abundances, a realistic equation of state, and radiative energy transport. In this talk, I will discuss the effects of rotation on the turbulent dynamics and surface structure for a 1.47Msun star for rotational periods of 1 and 14 days. The simulations are performed with the computational domain at various latitudes. The models reproduce stellar granulation, the subsurface shear layer, structural changes in convection, and the tachocline, which is the interface between the inner radiative zone and the outer convection zone and plays a crucial role in stellar variability. In particular, the model results reveal the formation of differential rotation and meridional circulation.

SMD↗

NASA NTRS2021

Establishing Fault Tolerance for a Class of Systems by Experiment

A long-standing problem in system verification is establishing fault tolerance at the ultra-high level by experiment. It is considered impossible because of system complexity and the enormous number of trials needed. This paper considers the problem for a class of digital systems that use redundancy to achieve reliability. The class is the systems that operate for a period of time without maintenance followed by a maintenance check that replaces components identified as faulty. The paper considers simulating a natural life test where a natural life test observes a number of operating periods. If the system does not fail during the test, it can be said to have a certain reliability at a certain confidence level. The approach in this paper is to make the simulated life test more efficient while maintaining realism by integrating structural arguments, information on fault occurrence, and fault injection in the lab. The major result of this paper is constructing a global fault model using the failure rate of the components and proving theorems about the model that tell how many, what kind, when, and where to inject faults. A simple example illustrates applying the theorems.

design of experiments↗

NASA NTRSDate not supplied

Dynamics and Structure of Main-Sequence Stars with Shallow Convection Zones

A dramatic increase in observational data from NASA’s Kepler, K2, and TESS missions and supporting ground-based observatories has opened new opportunities to investigate the internal structure, dynamics, and evolution of stars and their atmospheres. We present 3D radiative MHD simulations for several main-sequence stars with masses from 1.4 to 1.5M sun. The simulations are performed using the "StellarBox" code developed for modeling stellar turbulent convection and atmospheres with a high degree of realism. This presentation discusses similarities and differences between 3D realistic-type and 1D mixing-length models with regard to structural, thermodynamic, and turbulent property variations from the radiative zone to the convection zone and photosphere.

SMD↗

NASA NTRSDate not supplied

3D Radiative MHD Modeling of the Solar Atmospheric Dynamics and Structure

Dramatic dynamical phenomena accompanied by strong thermodynamic and magnetic structuring are the primary drivers of great interest in studying the solar atmosphere with high spatial and temporal resolutions. Using current computational capabilities, it has become possible to model the magnetized solar plasma in different regimes with a high degree of realism. To study the fine structuring of the solar atmosphere and dynamics, we use 3D MHD radiative models covering all layers from the upper convection zone to the corona. Realistic 3D radiative MHD modeling of the solar magnetoconvection and atmosphere allows us to generate synthetic observables that directly link the physical properties of the solar plasma to spectroscopic observables. In the presentation, we discuss qualitative and quantitative changes of the atmospheric structure and dynamics at different layers of the solar atmosphere, properties of acoustic and surface gravity waves, sources of local heating in the chromosphere-corona transition region, formation of shocks, and high-frequency oscillations in the corona, as well as manifestation of these phenomena in the modeled observables.

SMD↗

NASA NTRS2022

Effects of Increasing the Category Resolution of the Sea Ice Thickness Distribution in a Coupled Climate Model on Arctic and Antarctic Sea Ice Mean State

Many modern sea ice models used in global climate models represent the subgrid-scale heterogeneity in sea ice thickness with an ice thickness distribution (ITD), which improves model realism by representing the significant impact of the high spatial heterogeneity of sea ice thickness on thermodynamic and dynamic processes. Most models default to five thickness categories. However, little has been done to explore the effects of the resolution of this distribution (number of categories) on sea-ice feedbacks in a coupled model framework and resulting representation of the sea ice mean state. Here, we explore this using sensitivity experiments in CESM2 with the standard 5 ice thickness categories and 15 ice thickness categories. Increasing the resolution of the ITD in a run with preindustrial climate forcing results in substantially thicker Arctic sea ice year-round. Analyses show that this is a result of the ITD influence on ice strength. With 15 ITD categories, weaker ice occurs for the same average thickness, resulting in a higher fraction of ridged sea ice. In contrast, the higher resolution of thin ice categories results in enhanced heat conduction and bottom growth and leads to only somewhat increased winter Antarctic sea ice volume. The spatial resolution of the ICESat-2 satellite mission provides a new opportunity to compare model outputs with observations of seasonal evolution of the ITD in the Arctic (ICESat-2; 2018–2021). Comparisons highlight significant differences from the ITD modeled with both runs over this period, likely pointing to underlying issues contributing to the representation of average thickness.

Madison M. Smith↗

NASA NTRSDate not supplied

Regional Impacts of Ice Sheet Surface Representation Improvements in an Earth System Model

Ice sheet surface conditions and their subsequent impact on surface mass balance play an important role in ice sheet dynamics and ice sheet interaction with the overlying atmosphere and surrounding ocean. The NASA Global Modeling and Assimilation Office’s (GMAO) Goddard Earth Observing System (GEOS) ecosystem of models and reanalyses model ice sheet and glacier surface mass balance with a moderately complex snow and ice module, including prognostic surface albedo evolution, fractional snow cover, and snowpack hydrology and meltwater retention. With this configuration, Greenland near surface air temperatures and ice sheet melt are well represented in MERRA-2, GMAO’s current atmospheric reanalysis; however, the similarity to observations in some regions is due, in part, to compensating biases in surface energy budget. Recent improvements to GEOS’ cloud microphysics and longwave radiation parameterizations reduced biases in surface net longwave radiation and exacerbated surface net shortwave radiation biases, effectively increasing near surface temperatures and melting, particularly during the summer months. Here, we demonstrate the Earth system response to an improved representation of the ice sheet surface focused on minimizing the surface energy biases and increasing spatial and temporal representativeness. New changes to the surface albedo parameterization, including the introduction of a prognostic aerosol darkening scheme, help mitigate shortwave biases, while modifications to surface roughness characteristics improve near surface sensible heat fluxes. Focusing on the Arctic and Greenland, we examine the combined impact on these changes on regional weather conditions and ice sheet surface mass balance using a high resolution atmospheric only experiment, and we use a coupled model configuration to assess the impact on oceanic conditions and subseasonal-to-seasonal prediction. Overall, these improvements increase modeled ice sheet surface realism and provide a strong basis for more accurate ice sheet mass balance in GMAO’s future reanalyses and forecasting systems.

Lauren C Andrews↗

NASA NTRSDate not supplied

Regional Impacts of Ice Sheet Surface Representation Improvements in an Earth System Model

Lauren C. Andrews↗

NASA NTRSDate not supplied

From Formal Errors Towards Realistic Uncertainties

Evaluation of uncertainties of geodetic parameter estimates is the problem that is not yet solved in a satisfactory way. A direct evaluation of the uncertainties derived from a least square solution is labeled "formal" and is usually biased, sometimes up to an order of magnitude. Customary, the use of formal errors for scientific analysis is discouraged. We claim that the root of the problem is neglecting off-diagonal elements in the variance-covariance matrix of the noise in the data. A careful reconstruction of the full variance-covariance matrix, including the off-diagonal terms greatly improves realism of uncertainty estimates derived from least squares. We processed the dataset of VLBI group delays and built a prior variance-covariance of the atmosphere-driven noise based on analysis of the output of NASA high-resolution numerical weather models. We found that the uncertainties of parameter estimates derived from this least square solution that uses such variance-covariance matrices become much closer to realistic errors. We consider approaches for implementation of this method in routine data analysis of space geodesy data.

Leonid Petrov↗