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All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Comprehensive Assessment of Models and Events Using Library Tools (CAMEL) Framework: Time Series Comparisons

The Comprehensive Assessment of Models and Events using Library Tools (CAMEL) framework leverages existing Community Coordinated Modeling Center services: Run on Request post processing tools that generate model time series outputs and the new Community Coordinated Modeling Center Metadata Registry that describes simulation runs using Space Physics Archive Search and Extract metadata. The new CAMEL visualization tool compares the modeled time series with observational data and computes a suite of skill scores such as Prediction Efficiency, Root Mean Square Error, and Symmetric Signed Percentage Bias. Model data pairs used for skill calculations are obtained considering a user selected maximum difference between the time of observation and the nearest model output. The system renders available data for all locations and time periods selected using interactive visualizations that allow the user to zoom, pan, and pick data values along traces. Skill scores are reported for each selected event or aggregated over all events for all participating model runs. Separately, scores are reported for all locations (satellites or stations) and for each location individually. We are building on past experiences with model data comparisons of magnetosphere and ionosphere model outputs from GEM2008, GEMCEDAR Electrodynamics Thermosphere Ionosphere, and the SWPC Operational Space Weather Model challenges. The CAMEL visualization tool is demonstrated using three validation studies: (a) Wang Sheeley Arge heliosphere simulations compared against OMNI solar wind data, (b) ground magnetic perturbations from several magnetosphere and ionosphere electrodynamics models as observed by magnetometers, and (c) electron fluxes from several ring current simulations compared to Radiation Belt Storm Probes Helium Oxygen Proton Electron instrument measurements, integrated over different energy ranges.

Rastätter, Lutz↗
NASA NTRS2012

Comparison of Dst Forecast Models for Intense Geomagnetic Storms

We have compared six disturbance storm time (Dst) forecast models using 63 intense geomagnetic storms (Dst <=100 nT) that occurred from 1998 to 2006. For comparison, we estimated linear correlation coefficients and RMS errors between the observed Dst data and the predicted Dst during the geomagnetic storm period as well as the difference of the value of minimum Dst (Delta Dst(sub min)) and the difference in the absolute value of Dst minimum time (Delta t(sub Dst)) between the observed and the predicted. As a result, we found that the model by Temerin and Li gives the best prediction for all parameters when all 63 events are considered. The model gives the average values: the linear correlation coefficient of 0.94, the RMS error of 14.8 nT, the Delta Dst(sub min) of 7.7 nT, and the absolute value of Delta t(sub Dst) of 1.5 hour. For further comparison, we classified the storm events into two groups according to the magnitude of Dst. We found that the model of Temerin and Lee is better than the other models for the events having 100 <= Dst < 200 nT, and three recent models (the model of Wang et al., the model of Temerin and Li, and the model of Boynton et al.) are better than the other three models for the events having Dst <= 200 nT.

Geomagnetic Storms↗
NASA NTRS1974

Comparison of the trapped electron models AE-4 and AE-5 with AE-2 and AE-3

Models of geomagnetically trapped electrons are compared to estimate mission flux changes. The differences in the model environments were caused by a change in both the existing environment and the available data from which the models were derived. Different data analysis techniques have also influenced the models. This report compares the dependence of the flux to each of the relevant variables. To show the effects of the model differences, included are a comparison of orbital flux integrations for a range of orbits.

Hilberg, R. H.↗
NASA NTRS1981

An analysis of the cloud fields simulated by the GLAS climate model

The cloud field produced by the climate model was analyzed. The following items are discussed: (1) reasonable cloudiness production by the model; (2) comparison of the amount of model generated cumulus cloudiness to super saturation cloudiness; and (3) the size and effect of cumulus clouds on radiation balance. It is shown that the scattered, small scale cumulus clouds occur more frequently than the horizontally wide spread supersaturation clouds in the low latitudes and mid troposphere.

Moeng, C. H.↗
NASA NTRS1982

Crop weather models of corn and soybeans for Agrophysical Units (APU's) in Iowa using monthly meteorological predictors

Models based on multiple regression were developed to estimate corn and soybean yield from weather data for agrophysical units (APU) in Iowa. The predictor variables are derived from monthly average temperature and monthly total precipitation data at meteorological stations in the cooperative network. The models are similar in form to the previous models developed for crop reporting districts (CRD). The trends and derived variables were the same and the approach to select the significant predictors was similar to that used in developing the CRD models. The APU's were selected to be more homogeneous with respect crop to production than the CRDs. The APU models are quite similar to the CRD models, similar explained variation and number of predictor variables. The APU models are to be independently evaluated and compared to the previously evaluated CRD models. That comparison should indicate the preferred model area for this application, i.e., APU or CRD.

Leduc, S.↗
NASA NTRS1982

Crop weather models of barley and spring wheat yield for agrophysical units in North Dakota

Models based on multiple regression were developed to estimate barley yield and spring wheat yield from weather data for Agrophysical units(APU) in North Dakota. The predictor variables are derived from monthly average temperature and monthly total precipitation data at meteorological stations in the cooperative network. The models are similar in form to the previous models developed for Crop Reporting Districts (CRD). The trends and derived variables were the same and the approach to select the significant predictors was similar to that used in developing the CRD models. The APU models show sight improvements in some of the statistics of the models, e.g., explained variation. These models are to be independently evaluated and compared to the previously evaluated CRD models. The comparison will indicate the preferred model area for this application, i.e., APU or CRD.

Leduc, S.↗
NASA NTRS1991

Near-wall modeling of the dissipation rate equation

Near-wall modeling of the dissipation rate equation is investigated and its asymptotic behavior is studied in detail using a k-epsilon model. It is found that all existing modeled dissipation rate equations predict an incorrect behavior for the dissipation rate near a wall. An improvement is proposed and the resulting near-wall dissipation rate distribution is found to be similar to that given by numerical simulation data. To further validate the improved k-epsilon model, it is used to calculate flat-plate turbulent boundary-layer flows at high- as well as low-turbulence Reynolds numbers, and the results are compared with measurements, numerical simulation data, and the calculations of three different two-equation models. These comparisons show that all the models tested give essentially the same flow properties away from the wall; significant differences only occur in a region very close to the wall. In this region, the calculations of the improved k-epsilon model are in better agreement with measurements and numerical simulation data. In particular, the modeled distribution of the dissipation rate is significantly improved and a maximum is predicted at the wall instead of away from the wall. Furthermore, the improved k-epsilon model is found to be the most asymptotically consistent among the four different two-equation models examined.

So, R. M. C.↗
NASA NTRS2006

Microwave Signatures of Snow on Sea Ice: Modeling

Accurate knowledge of snow-depth distribution over sea ice is critical for polar climate studies. Current snow-depth-over-sea-ice retrieval algorithms do not sufficiently account for variations in snow and ice physical properties that can affect the accuracy of retrievals. For this reason, airborne microwave observations were coordinated with ground-based measurements of snow depth and snow properties in the vicinity of Barrow, AK, in March 2003. In this paper, the effects of snowpack properties and ice conditions on microwave signatures are examined using detailed surface-based measurements and airborne observations in conjunction with a thermal microwave-emission model. A comparison of the Microwave Emission Model of Layered Snowpacks (MEMLS) simulations with detailed snowpack and ice data from stakes along the Elson Lagoon and the Beaufort Sea and ra- 'diometer data taken from low-level flights using a Polarimetric Scanning Radiometer (PSR-A) shows that MEMLS can be used to simulate snow on sea ice and is a useful tool for understanding the limitations of the snow-depth algorithm. Analysis of radiance data taken over the Elson Lagoon and the Beaufort Sea using MEMLS suggests that the radiometric differences between the two locations are due to the differences in sea-ice emissivity. Furthermore, measured brightness temperatures suggest that the current snow-depth retrieval algorithm is sufficient for areas of smooth first-year sea ice, whereas new algorithm coefficients are needed for rough first-year sea ice. Snowpack grain size and density remain an unresolved issue for snow-depth retrievals using passive-microwave radiances.

Powell, D. C.↗
NASA NTRS2016

Estimating Model Prediction Error: Should You Treat Predictions as Fixed or Random?

Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEP fixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEP uncertain( X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEP uncertain (X) can be estimated using a random effects ANOVA. It is argued that MSEP uncertain (X) is the more informative uncertainty criterion, because it is specific to each prediction situation.

model structure uncertainty↗
NASA NTRS2021

VESIcal Part I: An open-source thermodynamic model engine for mixed volatile (H2O-CO2) solubility in silicate melts

Thermodynamics has been fundamental to the interpretation of geologic data and modeling of geologic systems for decades. However, more recent advancements in computational capabilities and a marked increase in researchers’ accessibility to computing tools has outpaced the functionality and extensibility of currently available modeling tools. Here we present VESIcal (Volatile Equilibria and Saturation Identification calculator): the first comprehensive modeling tool for H 2 O, CO 2 , and mixed (H 2 O-CO 2 ) solubility in silicate melts that: a) allows users access to seven commonly used models, plus easy inter-comparison between models; b) provides universal functionality for all models (e.g., functions for calculating saturation pressures, degassing paths, etc.); c) can process large datasets (1,000’s of samples) automatically; d) can output computed data into an excel spreadsheet for simple post-modeling analysis; e) integrates advanced plotting capabilities directly within the tool; and f) provides all of these within the framework of a python library, making the tool extensible by the user and allowing any of the model functions to be incorporated into any other code capable of calling python. The tool is presented within this manuscript, which is a Jupyter notebook containing worked examples accessible to python users with a range of skill levels. The basic functions of VESIcal can also be access via a web app (https://vesical.anvil.app). The VESIcal python library is open-source and available for download at https://github.com/kaylai/VESIcal.

K. Iacovino↗
NASA NTRS2022

VESIcal: An Open-source Thermodynamic Model Engine for Mixed Volatile (H2O-CO2) Solubility in Silicate Melts

Modeling the solubility of volatiles in silicate melts is fundamental to the interpretation of volcanic systems and has implications for magma dynamics, eruption style, and material transport between the mantle, crust, and atmosphere. Recent advancements in computational capabilities and access to computing tools has outpaced the functionality and extensibility of previously available modeling platforms. Here we present VESIcal (Volatile Equilibria and Saturation Index calculator), the first comprehensive modeling tool for H2O, CO2, and mixed (H2O-CO2) solubility in silicate melts that: a) allows users access to seven popular models, with easy inter-comparison between models; b) provides universal functionality for all models (e.g., functions for calculating saturation pressures, degassing paths, etc.); c) can process large datasets (1,000’s of samples) automatically; d) can output computed data into an Excel spreadsheet or CSV file for post-modeling analysis; e) integrates plotting capabilities directly within the tool; and f) provides all of this within the framework of a python library, making the tool extensible by the user and allowing any of the model functions to be incorporated into any other code capable of calling python.Here we will provide a demonstration of VESIcal and its capabilities with applications to various volcanic processes affected by volatiles. VESIcal represents the first tool capable of directly comparing multiple solubility models and equations of state. We find that commonly used models predict surprisingly different volatile solubilities, particularly for pure CO2 or mixed CO2-H2O fluids. Even for melt compositions that are well represented in the calibration datasets of multiple models (e.g., MORBs), calculated solubilities for pure CO2 and pure H2O can deviate from one another by factors of >2 leading to 2x deviations in calculated saturation pressures (e.g., 5 to 10 kbar). The solubility of CO2 predicted by different rhyolitic models also differs substantially, overwhelming other sources of uncertainty such as analytical errors on measurements of volatile contents or uncertainties in crustal density profiles. This highlights the importance of model choice when drawing geological conclusions based on volatiles in magmas.

Kayla Iacovino↗
NASA NTRS2012

Community Coordinated Modeling Center: Addressing Needs of Operational Space Weather Forecasting

Models are key elements of space weather forecasting. The Community Coordinated Modeling Center (CCMC, http://ccmc.gsfc.nasa.gov) hosts a broad range of state-of-the-art space weather models and enables access to complex models through an unmatched automated web-based runs-on-request system. Model output comparisons with observational data carried out by a large number of CCMC users open an unprecedented mechanism for extensive model testing and broad community feedback on model performance. The CCMC also evaluates model's prediction ability as an unbiased broker and supports operational model selections. The CCMC is organizing and leading a series of community-wide projects aiming to evaluate the current state of space weather modeling, to address challenges of model-data comparisons, and to define metrics for various user s needs and requirements. Many of CCMC models are continuously running in real-time. Over the years the CCMC acquired the unique experience in developing and maintaining real-time systems. CCMC staff expertise and trusted relations with model owners enable to keep up to date with rapid advances in model development. The information gleaned from the real-time calculations is tailored to specific mission needs. Model forecasts combined with data streams from NASA and other missions are integrated into an innovative configurable data analysis and dissemination system (http://iswa.gsfc.nasa.gov) that is accessible world-wide. The talk will review the latest progress and discuss opportunities for addressing operational space weather needs in innovative and collaborative ways.

Kuznetsova, M.↗
NASA NTRS2019

Earth Global Reference Atmospheric Model (GRAM) Overview and Comparison to Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)

Earth-GRAM (Earth-Global Reference Atmospheric Model) Overview: Provides monthly statistics at any point in the atmosphere; Monthly, geographic, altitude variation; Current Version - Earth-GRAM 2016, C++; Output Includes - pressure, density, temperature, horizontal and vertical winds, speed of sound, atmospheric constituents; Used by engineering community to create atmospheric dispersions at a rapid runtime; Not a forecast model. MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, Version 2) Background: Developed by NASA Goddard Modelling and Assimilation Office; Horizontal Resolution: 0.625 degrees by 0.5 degrees longitude-by-latitude grid (NCEP (National Centers for Environmental Prediction) reanalysis I vs. 2.5 degrees by 2.5 degrees currently used in Earth-GRAM); Vertical resolution: 72 model layers or interpolated to 42 pressure levels to 0.1 hectopascals (hPa) (NCEP reanalysis I, surface to 10hPa at 17 pressure levels).

White, Patrick W.↗
NASA NTRS1982

Comparison of gas dynamic model with steady solar wind flow around Venus

A gas dynamic model for solar wind flow around Venus has been compared with Pioneer Venus orbiter plasma analyzer measurements from times when the solar wind flow seemed steadiest. The comparisons were made near the terminator. When the observed and model bow shock locations are matched, the model agrees fairly well with the observed parameters: the components of the flow velocity and magnetic field, and the proton number density and isotropic temperature. However, the Mach numbers required to fit the observed bow shock locations are less than 2/3 those estimated from the measured parameters of the free-stream flow, because the bow shock locations (near the terminator) are farther from Venus than expected. Sometimes the measured flow speeds appear to be retarded near the ionopause.

Mihalov, J. D.↗
NASA NTRS2022

Using Atmospheric Trace Gas Vertical Profiles to Evaluate Model Fluxes: A Case Study of Arctic-Cap Observations and Geos Simulations for the ABoVE Domain

Accurate estimates of carbon–climate feedbacks require an independent means for evaluating surface flux models at regional scales. The altitude-integrated enhancement (AIE) derived from the Arctic Carbon Atmospheric Profiles (Arctic-CAP) project demonstrates the utility of this bulk quantity for surface flux model evaluation. This bulk quantity leverages background mole fraction values from the middle free troposphere, is agnostic to uncertainties in boundary layer height, and can be derived from model estimates of mole fractions and vertical gradients. To demonstrate the utility of the bulk quantity, six airborne profiling surveys of atmospheric carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO) throughout Alaska and northwestern Canada between April and November 2017 were completed 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. All Arctic-CAP measurements were compared to a global simulation using the Goddard Earth Observing System (GEOS) modeling system. Comparisons of the AIE bulk quantity from aircraft observations and GEOS simulations of atmospheric CO2, CH4, and CO highlight the fidelity of the modeled surface fluxes. The model–data comparison over the ABoVE domain reveals that while current state-of-the-art models and flux estimates are able to capture broad-scale spatial and temporal patterns in near-surface CO2 and CH4 concentrations, more work is needed to resolve fine-scale flux features that are captured in CO observations

CO2↗
NASA NTRS1999

Coronal Physics and the Chandra Emission Line Project

With the launch of the Chandra X-ray Observatory, high resolution X-ray spectroscopy of cosmic sources has begun. Early, deep observations of three stellar coronal sources will provide not only invaluable calibration data, but will also give us benchmarks for plasma spectral modeling codes. These codes are to interpret data from stellar coronae, galaxies and clusters of galaxies. supernova remnants and other astrophysical sources, but they have been called into question in recent years as problems with understanding moderate resolution ASCA and EUVE data have arisen. The Emission Line Project is a collaborative effort to improve the models, with Phase 1 being the comparison of models with observed spectra of Capella, Procyon, and HR, 1099. Goals of these comparisons are (1) to determine and verify accurate and robust diagnostics and (2) to identify and prioritize issues in fundamental spectroscopy which will require further theoretical and/or laboratory work. A critical issue in exploiting the coronal data for these purposes is to understand the extent to which common simplifying assumptions (coronal equilibrium, time-independence, negligible optical depth) apply. We will discuss recent advances in our understanding of stellar coronae in this context.

Brickhouse, Nancy↗
NASA NTRS2000

Coronal Physics and the Chandra Emission Line Project

With the launch of the Chandra X-ray Observatory, high resolution X-ray spectroscopy of cosmic sources has begun. Early, deep observations of three stellar coronal sources Capella, Procyon, and HR 1099 are providing not only invaluable calibration data, but also benchmarks for plasma spectral models. These models are needed to interpret data from stellar coronae, galaxies and clusters of galaxies, supernova, remnants and other astrophysical sources. They have been called into question in recent years as problems with understanding low resolution ASCA and moderate resolution Extreme Ultraviolet Explorer Satellite (EUVE) data have arisen. The Emission Line Project is a collaborative effort, to improve the models, with Phase I being the comparison of models with observed spectra of Capella, Procyon, and HR 1099. Goals of these comparisons are (1) to determine and verify accurate and robust diagnostics and (2) to identify and prioritize issues in fundamental spectroscopy which will require further theoretical and/or laboratory work. A critical issue in exploiting the coronal data for these purposes is to understand the extent, to which common simplifying assumptions (coronal equilibrium, negligible optical depth) apply. We will discuss recent, advances in our understanding of stellar coronae, in this context.

Brickhouse, N. S.↗