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Comparison between Computational and Experimental Wall-Shear Stress and Pressure Measurements in a Supersonic Wind Tunnel
We study the performance of two selected RANS models (SST-$k$-$\omega$ and Baldwin-Lomax) to simulate the mean wall-shear and pressure data obtained from a distributed-roughness-related experimental campaign conducted at Ahmic Aerospace, LLC, located in Ohio, US. A structured compressible Navier-Stokes in-house solver, DPLR, is used to conduct the RANS simulations. As the final objective is to perform the studies under high-enthalpy flow conditions, such as inside the AHF facility at NASA-Ames Research Center (ARC), the current investigation focuses on the low-enthalpy cases using Ahmic's high-Reynolds-number supersonic wind tunnel as the initial step to simplify the flow physics. Furthermore, the wind tunnel simulations are conducted with perfectly-smooth walls to further simplify the problem and perform the initial testing of different solver-based RANS models. The Baldwin-Lomax model is observed to be more accurate than the SST model. However, when these RANS models and their roughness augmentation models are utilized to study their accuracy with respect to the experimental datasets, where the roughness effects are activated, both models perform well for a low roughness height case. However, as the roughness height increases, the SST model more accurately predicts the wall data (shear and pressure) than the Baldwin-Lomax model. Therefore, this present work has built a solid foundation for selecting the best RANS model to study the wall data under high-enthalpy flow conditions at the AHF facility soon.
The relationship between Uncertainty Quantification and Observations in Ice Sheet Modeling
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Using NASA Earth Observations to Monitor and Model Juniper Woodland Mortality in Grand Canyon National Park
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Update on GSFC Modeling, Information Systems, and Observations
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Enhancing Assimilation of MW and Radar Observation in the NWP Models
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Quantifying Spatial and Temporal Extents of Plasmaspheric Hiss Through Modeling of POES/MetOp Electron Observations
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Exploiting Earth Observations to Enable Groundwater Modeling in the Data-Sparse Region of Goulbi Maradi, Niger
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Bounding uncertainty in volumetric geometric models for terrestrial lidar observations of ecosystems
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Evaluation of Radiatively Active Frozen Hydrometeors Mass in CMIP6 Global Climate Models Using CloudSat‐CALIPSO Observations
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Drivers of Variability in Atmospheric Evaporative Demand: Multiscale Spectral Analysis Based on Observations and Physically Based Modeling
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Land–Atmosphere Coupling at the U.S. Southern Great Plains: A Comparison on Local Convective Regimes between ARM Observations, Reanalysis, and Climate Model Simulations
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Marine Boundary Layer Cloud Condensation Nuclei Bias Over the Southern Ocean: Comparisons Between the Community Atmosphere Model 6 and Field Observations
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Atmospheric Carbon Cycle Dynamics Over the Above Domain: an Integrated Analysis Using Aircraft Observations (Arctic-CA) and Model Simulations (GEOS)
The Arctic Carbon Atmospheric Profiles (Arctic-CAP) project conducted six airborne surveys of Alaska and northwestern Canada between April and November 2017 to capture the spatial and temporal gradients of northern high-latitude carbon dioxide (CO2), methane (CH4) and carbon monoxide (CO) as part of NASA's Arctic-Boreal Vulnerability Experiment (ABoVE). The Arctic-CAP sampling strategy involved acquiring vertical profiles of CO2, CH4 and CO from the surface to 5 km altitude at 25 sites around the ABoVE domain on a 4- to 6-week time interval. We observed vertical gradients of CO2, CH4 and CO that vary by eco-region and duration of the sampling period, which spanned the majority of the seasonal cycle. All Arctic-CAP measurements were compared to a global simulation using the Goddard Earth Observing System (GEOS) modeling system. Comparisons with GEOS simulations of atmospheric CO2, CH4 and CO highlight the potential of these multi-species observations to inform improvements in surface flux estimates and the representation of atmospheric transport. GEOS simulations provide estimates of the near surface average CO2 and CH4 enhancements that are well correlated with aircraft observations (R=0.74 and R=0.60 respectively), suggesting that GEOS has reasonable fidelity over this complex and heterogeneous region. This model-data comparison over the ABoVE domain reveals that while current state-of-the-art models and flux estimates are able to capture broadscale spatial and temporal patterns in near-surface CO2 and CH4 concentrations, more work is needed to resolve fine-scale flux features that are observed. The study also provides a framework for benchmarking a global model at regional scales, which is needed to use climate models as tools to investigate high-latitude carbon-climate feedbacks.
Modeling a Coronal Mass Ejection from an Extended Filament Channel. I. Eruption and Early Evolution
We present observations and modeling of the magnetic field configuration, morphology, and dynamics of a large-scale, high-latitude filament eruption observed by the Solar Dynamics Observatory. We analyze the 2015 July 9–10 filament eruption and the evolution of the resulting coronal mass ejection (CME) through the solar corona. The slow streamer-blowout CME leaves behind an elongated post-eruption arcade above the extended polarity inversion line that is only poorly visible in extreme ultraviolet (EUV) disk observations and does not resemble a typical bright flare-loop system. Magnetohydrodynamic (MHD) simulation results from our data-inspired modeling of this eruption compare favorably with the EUV and white-light coronagraph observations. We estimate the reconnection flux from the simulation’s flare-arcade growth and examine the magnetic-field orientation and evolution of the erupting prominence, highlighting the transition from an erupting sheared-arcade filament channel into a streamer-blowout flux-rope CME. Our results represent the first numerical modeling of a global-scale filament eruption where multiple ambiguous and complex observational signatures in EUV and white light can be fully understood and explained with the MHD simulation. In this context, our findings also suggest that the so-called “stealth CME” classification, as a driver of unexpected or “problem” geomagnetic storms, belongs more to a continuum of observable/non-observable signatures than to separate or distinct eruption processes.
An optimist's guide to supernovae
There are some hints that a satisfactory agreement between observations and models is developing in several areas of supernova research. The light curves and spectra of Type I supernovae look so much like the models that they may well have their origin in the synthesis of Ni-56 in the explosion of a compact star. Type II supernovae are found where massive stars form, have light curves that correspond to 10 to the 51st ergs suddenly deposited inside a red supergiant, and may in fact be the explosions of massive stars. Some young supernova remnants, such as Cas A, show abundance patterns that bear a striking resemblance to those seen deep inside models of massive stars on the verge of destruction. Observations of old supernova remnants provide energy estimate of 10 to the 51st ergs - just the amount needed for the models of the outbursts.
Space Weather Nowcasting of Atmospheric Ionizing Radiation for Aviation Safety
There is a growing concern for the health and safety of commercial aircrew and passengers due to their exposure to ionizing radiation with high linear energy transfer (LET), particularly at high latitudes. The International Commission of Radiobiological Protection (ICRP), the EPA, and the FAA consider the crews of commercial aircraft as radiation workers. During solar energetic particle (SEP) events, radiation exposure can exceed annual limits, and the number of serious health effects is expected to be quite high if precautions are not taken. There is a need for a capability to monitor the real-time, global background radiations levels, from galactic cosmic rays (GCR), at commercial airline altitudes and to provide analytical input for airline operations decisions for altering flight paths and altitudes for the mitigation and reduction of radiation exposure levels during a SEP event. The Nowcast of Atmospheric Ionizing Radiation for Aviation Safety (NAIRAS) model is new initiative to provide a global, real-time radiation dosimetry package for archiving and assessing the biologically harmful radiation exposure levels at commercial airline altitudes. The NAIRAS model brings to bear the best available suite of Sun-Earth observations and models for simulating the atmospheric ionizing radiation environment. Observations are utilized from ground (neutron monitors), from the atmosphere (the METO analysis), and from space (NASA/ACE and NOAA/GOES). Atmospheric observations provide the overhead shielding information and the ground- and space-based observations provide boundary conditions on the GCR and SEP energy flux distributions for transport and dosimetry simulations. Dose rates are calculated using the parametric AIR (Atmospheric Ionizing Radiation) model and the physics-based HZETRN (High Charge and Energy Transport) code. Empirical models of the near-Earth radiation environment (GCR/SEP energy flux distributions and geomagnetic cut-off rigidity) are benchmarked against the physics-based CMIT (Coupled Magnetosphere- Ionosphere-Thermosphere) and SEP-trajectory models.
GEOS Atmospheric Model: Challenges at Exascale
The Goddard Earth Observing System (GEOS) model at NASA's Global Modeling and Assimilation Office (GMAO) is used to simulate the multi-scale variability of the Earth's weather and climate, and is used primarily to assimilate conventional and satellite-based observations for weather forecasting and reanalysis. In addition, assimilations coupled to an ocean model are used for longer-term forecasting (e.g., El Nino) on seasonal to interannual times-scales. The GMAO's research activities, including system development, focus on numerous time and space scales, as detailed on the GMAO website, where they are tabbed under five major themes: Weather Analysis and Prediction; Seasonal-Decadal Analysis and Prediction; Reanalysis; Global Mesoscale Modeling, and Observing System Science. A brief description of the GEOS systems can also be found at the GMAO website. GEOS executes as a collection of earth system components connected through the Earth System Modeling Framework (ESMF). The ESMF layer is supplemented with the MAPL (Modeling, Analysis, and Prediction Layer) software toolkit developed at the GMAO, which facilitates the organization of the computational components into a hierarchical architecture. GEOS systems run in parallel using a horizontal decomposition of the Earth's sphere into processing elements (PEs). Communication between PEs is primarily through a message passing framework, using the message passing interface (MPI), and through explicit use of node-level shared memory access via the SHMEM (Symmetric Hierarchical Memory access) protocol. Production GEOS weather prediction systems currently run at 12.5-kilometer horizontal resolution with 72 vertical levels decomposed into PEs associated with 5,400 MPI processes. Research GEOS systems run at resolutions as fine as 1.5 kilometers globally using as many as 30,000 MPI processes. Looking forward, these systems can be expected to see a 2 times increase in horizontal resolution every two to three years, as well as less frequent increases in vertical resolution. Coupling these resolution changes with increases in complexity, the computational demands on the GEOS production and research systems should easily increase 100-fold over the next five years. Currently, our 12.5 kilometer weather prediction system narrowly meets the time-to-solution demands of a near-real-time production system. Work is now in progress to take advantage of a hybrid MPI-OpenMP parallelism strategy, in an attempt to achieve a modest two-fold speed-up to accommodate an immediate demand due to increased scientific complexity and an increase in vertical resolution. Pursuing demands that require a 10- to 100-fold increases or more, however, would require a detailed exploration of the computational profile of GEOS, as well as targeted solutions using more advanced high-performance computing technologies. Increased computing demands of 100-fold will be required within five years based on anticipated changes in the GEOS production systems, increases of 1000-fold can be anticipated over the next ten years.