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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

The longitude dependence of the dayside F region trough - A detailed model-observation comparison

The nighttime main F-region trough extends into the sunlit afternoon sector. This trough feature exhibits both a strong magnetic-activity dependence and a longitude (UT) dependence. Whalen (1987), using IGY ionosonde data, showed that both of these effects are readly extracted from f0F2 observations. This study shows that the longitude effect is the same as that contained in the Utah State University time-dependent ionospheric model. It arises from the offset of the geomagnetic axis from the geographic axis. The magnetic-activity dependency is associated with the westward convection in the afternoon sector. It is also contained in the ionospheric model via the empirical magnetospheric convection model.

Sojka, J. J.↗
NASA NTRS2004

CO2 and CO Simulations and Their Source Signature Indicated by CO/CO2

Three years (2000-2002) atmospheric CO2 and CO fields are simulated by a Chemistry Transport Model driven by the assimilated meteorological fields from GEOS-4. The simulated CO2 and CO are evaluated by measurements from surface (CMDL), satellite (MOPITT/CO), and aircraft. The model-observation comparisons indicate reasonable agreement in both source and remote regions, and in the lower and upper troposphere. The simulation also captures the seasonality of CO2 and CO variations. The ratios of CO/CO2 are analyzed over different representative regions to identify the source signature, since the anthropogenic CO comes fiom the same combustion processes as CO2. This work enables us to improve satellite inversion estimates of CO2 sources and sinks by simultaneously using satellite CO measurement.

Kawa, Randy↗
NASA NTRS2010

Dst Index in the 2008 GEM Modeling Challenge - Model Performance for Moderate and Strong Magnetic Storms

The GEM 2008 modeling challenge efforts are expanding beyond comparing in-situ measurements in the magnetosphere and ionosphere to include the computation of indices to be compared. The Dst index measures the largest deviations of the horizontal magnetic field at 4 equatorial magnetometers from the quiet-time background field and is commonly used to track the strength of the magnetic disturbance of the magnetosphere during storms. Models can calculate a proxy Dst index in various ways, including using the Dessler-Parker Sckopke relation and the energy of the ring current and Biot-Savart integration of electric currents in the magnetosphere. The GEM modeling challenge investigates 4 space weather events and we compare models available at CCMC against each other and the observed values of Ost. Models used include SWMF/BATSRUS, OpenGGCM, LFM, GUMICS (3D magnetosphere MHD models), Fok-RC, CRCM, RAM-SCB (kinetic drift models of the ring current), WINDMI (magnetosphere-ionosphere electric circuit model), and predictions based on an impulse response function (IRF) model and analytic coupling functions with inputs of solar wind data. In addition to the analysis of model-observation comparisons we look at the way Dst is computed in global magnetosphere models. The default value of Dst computed by the SWMF model is for Bz the Earth's center. In addition to this, we present results obtained at different locations on the Earth's surface. We choose equatorial locations at local noon, dusk (18:00 hours), midnight and dawn (6:00 hours). The different virtual observatory locations reveal the variation around the earth-centered Dst value resulting from the distribution of electric currents in the magnetosphere during different phases of a storm.

Rastaetter, Lutz↗
NASA NTRSDate not supplied

Impact of Size Distribution Assumption on PM2.5 Simulation

Aerosol particles, especially those with diameter that is smaller than 2.5um, i.e. PM2.5, have detrimental impact on public health. As a regulated criteria air pollutant, PM2.5 is monitored by ground networks using various instruments. At the same time aerosol assimilation modeling system is developed to simulate the aerosol distribution with continuous global coverage. In this study we compared the PM2.5 simulated by NASA’s MERRA-2 modeling system with the measurements made at US Embassy sites around the globe on diurnal cycle, monthly average, and interannual variabilities. We focused the comparison in the places that are significantly impacted by the desert dust. Simulations based on both aerodynamic and geometric diameters are compared with available measurements. Aerodynamic diameter assumption shows better model-observation agreements over several dust belt stations.

Qian Tan↗
NASA NTRS2022

Understanding Model-Observation Discrepancies in Satellite Retrievals of Atmospheric Temperature Using GISS ModelE

We examine multiple factors in the representation of satellite-retrieved atmospheric temperature diagnostics in historical simulations of climate change during the satellite era (specifically 1979–2021) using GISS ModelE contributions to the Coupled Model Intercomparison Project (Phase 6) (CMIP6). The tropospheric and stratospheric trends in these diagnostics are affected by greenhouse gases (notably carbon dioxide and ozone), coupling with the ocean, volcanic aerosols, solar activity and compositional and dynamic feedbacks. We explore the impacts of internal variability, changing forcing specifications, composition interactivity, the quality of the stratospheric circulation, vertical resolution, and possible impacts of the mis-specification of volcanic aerosol optical depths. Overall temperature trends throughout the satellite period are well captured, but discrepancies at all levels exist and have multiple distinct causes. We find that stratospheric comparisons (using Stratospheric Sounding Unit (SSU) retrievals and successor instruments) are most affected by variations in the representation of ozone depletion and feedbacks, followed by the volcanic signals. Tropospheric skill (using the Microwave Sounding Unit (MSU) retrievals) is affected by the trends in ocean heat uptake and tropospheric aerosols, but also by the representation of stratospheric processes through the impact of the Brewer-Dobson circulation on the height of the tropical tropopause. We demonstrate that no single factor is the dominant cause of the discrepancies and that almost all observations lie within the broad envelope of structural uncertainty.

satellite retrievals of atmospheric temperature↗
NASA NTRS2004

Multivariate Error Covariance Estimates by Monte-Carlo Simulation for Assimilation Studies in the Pacific Ocean

One of the most difficult aspects of ocean state estimation is the prescription of the model forecast error covariances. The paucity of ocean observations limits our ability to estimate the covariance structures from model-observation differences. In most practical applications, simple covariances are usually prescribed. Rarely are cross-covariances between different model variables used. Here a comparison is made between a univariate Optimal Interpolation (UOI) scheme and a multivariate OI algorithm (MvOI) in the assimilation of ocean temperature. In the UOI case only temperature is updated using a Gaussian covariance function and in the MvOI salinity, zonal and meridional velocities as well as temperature, are updated using an empirically estimated multivariate covariance matrix. Earlier studies have shown that a univariate OI has a detrimental effect on the salinity and velocity fields of the model. Apparently, in a sequential framework it is important to analyze temperature and salinity together. For the MvOI an estimation of the model error statistics is made by Monte-Carlo techniques from an ensemble of model integrations. An important advantage of using an ensemble of ocean states is that it provides a natural way to estimate cross-covariances between the fields of different physical variables constituting the model state vector, at the same time incorporating the model's dynamical and thermodynamical constraints as well as the effects of physical boundaries. Only temperature observations from the Tropical Atmosphere-Ocean array have been assimilated in this study. In order to investigate the efficacy of the multivariate scheme two data assimilation experiments are validated with a large independent set of recently published subsurface observations of salinity, zonal velocity and temperature. For reference, a third control run with no data assimilation is used to check how the data assimilation affects systematic model errors. While the performance of the UOI and MvOI is similar with respect to the temperature field, the salinity and velocity fields are greatly improved when multivariate correction is used, as evident from the analyses of the rms differences of these fields and independent observations. The MvOI assimilation is found to improve upon the control run in generating the water masses with properties close to the observed, while the UOI failed to maintain the temperature and salinity structure.

Borovikov, Anna↗
NASA NTRS2013

PROcess Based Diagnostics PROBE

Many of the aspects of the climate system that are of the greatest interest (e.g., the sensitivity of the system to external forcings) are emergent properties that arise via the complex interplay between disparate processes. This is also true for climate models most diagnostics are not a function of an isolated portion of source code, but rather are affected by multiple components and procedures. Thus any model-observation mismatch is hard to attribute to any specific piece of code or imperfection in a specific model assumption. An alternative approach is to identify diagnostics that are more closely tied to specific processes -- implying that if a mismatch is found, it should be much easier to identify and address specific algorithmic choices that will improve the simulation. However, this approach requires looking at model output and observational data in a more sophisticated way than the more traditional production of monthly or annual mean quantities. The data must instead be filtered in time and space for examples of the specific process being targeted.We are developing a data analysis environment called PROcess-Based Explorer (PROBE) that seeks to enable efficient and systematic computation of process-based diagnostics on very large sets of data. In this environment, investigators can define arbitrarily complex filters and then seamlessly perform computations in parallel on the filtered output from their model. The same analysis can be performed on additional related data sets (e.g., reanalyses) thereby enabling routine comparisons between model and observational data. PROBE also incorporates workflow technology to automatically update computed diagnostics for subsequent executions of a model. In this presentation, we will discuss the design and current status of PROBE as well as share results from some preliminary use cases.

PROBE↗
NASA NTRS2013

Evaluation of Precipitation Simulated by Seven SCMs against the ARM Observations at the SGP Site

This study evaluates the performances of seven single-column models (SCMs) by comparing simulated surface precipitation with observations at the Atmospheric Radiation Measurement Program Southern Great Plains (SGP) site from January 1999 to December 2001. Results show that although most SCMs can reproduce the observed precipitation reasonably well, there are significant and interesting differences in their details. In the cold season, the model-observation differences in the frequency and mean intensity of rain events tend to compensate each other for most SCMs. In the warm season, most SCMs produce more rain events in daytime than in nighttime, whereas the observations have more rain events in nighttime. The mean intensities of rain events in these SCMs are much stronger in daytime, but weaker in nighttime, than the observations. The higher frequency of rain events during warm-season daytime in most SCMs is related to the fact that most SCMs produce a spurious precipitation peak around the regime of weak vertical motions but rich in moisture content. The models also show distinct biases between nighttime and daytime in simulating significant rain events. In nighttime, all the SCMs have a lower frequency of moderate-to-strong rain events than the observations for both seasons. In daytime, most SCMs have a higher frequency of moderate-to-strong rain events than the observations, especially in the warm season. Further analysis reveals distinct meteorological backgrounds for large underestimation and overestimation events. The former occur in the strong ascending regimes with negative low-level horizontal heat and moisture advection, whereas the latter occur in the weak or moderate ascending regimes with positive low-level horizontal heat and moisture advection.

Model comparison↗
NASA NTRS2018

Southeast Atmosphere Studies: Learning from Model-Observation Syntheses

Concentrations of atmospheric trace species in the United States have changed dramatically over the past several decades in response to pollution control strategies, shifts in domestic energy policy and economics, and economic development (and resulting emission changes) elsewhere in the world. Reliable projections of the future atmosphere require models to not only accurately describe current atmospheric concentrations, but to do so by representing chemical, physical and biological processes with conceptual and quantitative fidelity. Only through incorporation of the processes controlling emissions and chemical mechanisms that represent the key transformations among reactive molecules can models reliably project the impacts of future policy, energy and climate scenarios. Efforts to properly identify and implement the fundamental and controlling mechanisms in atmospheric models benefit from intensive observation periods, during which collocated measurements of diverse, speciated chemicals in both the gas and condensed phases are obtained. The Southeast Atmosphere Studies (SAS, including SENEX, SOAS, NOMADSS and SEAC4RS) conducted during the summer of 2013 provided an unprecedented opportunity for the atmospheric modeling community to come together to evaluate, diagnose and improve the representation of fundamental climate and air quality processes in models of varying temporal and spatial scales. This paper is aimed at discussing progress in evaluating, diagnosing and improving air quality and climate modeling using comparisons to SAS observations as a guide to thinking about improvements to mechanisms and parameterizations in models. The effort focused primarily on model representation of fundamental atmospheric processes that are essential to the formation of ozone, secondary organic aerosol (SOA) and other trace species in the troposphere, with the ultimate goal of understanding the radiative impacts of these species in the southeast and elsewhere. Here we address questions surrounding four key themes: gas-phase chemistry, aerosol chemistry, regional climate and chemistry interactions, and natural and anthropogenic emissions. We expect this review to serve as a guidance for future modeling efforts.

atmospheric chemistry↗
NASA NTRS2018

The Central Role of Air Quality Observations in NASA's GEOS Composition Forecasting Model

The NASA GEOS composition forecast model (GEOS-CF) provides global, high-resolution (25 km) air quality forecasts in near-real time. This system combines the operational GEOS-5 weather forecasting model with the state-of-the-science GEOS-Chem chemistry module to provide detailed chemical analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). The resolution of the forecasts is the highest compared to current, publicly-available global composition forecasts.Air quality observations are an indispensable tool to evaluate the model's ability to capture the strong temporal and spatial gradients of air pollutants across the globe. We show how comparisons against near-real time observations available through OpenAQ (www.openaq.org) demonstrate the model's overall success in reproducing surface concentrations of ozone, nitrogen dioxide, and PM2.5. This analysis also helps identifying current limitations of the model, for example over South America. The model-observation mismatches are most likely caused by uncertainties in the emissions data. Using the example of Rio de Janeiro, we show how the model skill can be improved by using local, high-resolution emission inventories in combination with air quality data.

Keller, Christoph A.↗