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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Evaluation of Near-Tropopause Ozone Distributions in the Global Modeling Initiative Combined Stratosphere/Troposphere Model with Ozonesonde Data

The NASA Global Modeling Initiative has developed a combined stratosphere/troposphere chemistry and transport model which fully represents the processes governing atmospheric composition near the tropopause. We evaluate model ozone distributions near the tropopause, using two high vertical resolution monthly mean ozone profile climatologies constructed with ozonesonde data, one by averaging on pressure levels and the other relative to the thermal tropopause. Model ozone is high biased at the SH tropical and NH midlatitude tropopause by approx. 45% in a 4 deg. latitude x 5 deg. longitude model simulation. Increasing the resolution to 2 deg. x 2.5 deg. increases the NH tropopause high bias to approx. 60%, but decreases the tropical tropopause bias to approx. 30%, an effect of a better-resolved residual circulation. The tropopause ozone biases appear not to be due to an overly vigorous residual circulation or excessive stratosphere/troposphere exchange, but are more likely due to insufficient vertical resolution or excessive vertical diffusion near the tropopause. In the upper troposphere and lower stratosphere, model/measurement intercomparisons are strongly affected by the averaging technique. NH and tropical mean model lower stratospheric biases are less than 20%. In the upper troposphere, the 2 deg. x 2.5 deg. simulation exhibits mean high biases of approx. 20% and approx. 35% during April in the tropics and NH midlatitudes, respectively, compared to the pressure averaged climatology. However, relative-to-tropopause averaging produces upper troposphere high biases of approx. 30% and 70% in the tropics and NH midlatitudes. This is because relative-to-tropopause averaging better preserves large cross-tropopause O3 gradients, which are seen in the daily sonde data, but not in daily model profiles. The relative annual cycle of ozone near the tropopause is reproduced very well in the model Northern Hemisphere midlatitudes. In the tropics, the model amplitude of the near tropopause annual cycle is weak. This is likely due to the annual amplitude of mean vertical upwelling near the tropopause, which analysis suggests is approx. 30% weaker than in the real atmosphere.

Considine, David B.↗
NASA NTRS2014

Building a Database for a Quantitative Model

A database can greatly benefit a quantitative analysis. The defining characteristic of a quantitative risk, or reliability, model is the use of failure estimate data. Models can easily contain a thousand Basic Events, relying on hundreds of individual data sources. Obviously, entering so much data by hand will eventually lead to errors. Not so obviously entering data this way does not aid linking the Basic Events to the data sources. The best way to organize large amounts of data on a computer is with a database. But a model does not require a large, enterprise-level database with dedicated developers and administrators. A database built in Excel can be quite sufficient. A simple spreadsheet database can link every Basic Event to the individual data source selected for them. This database can also contain the manipulations appropriate for how the data is used in the model. These manipulations include stressing factors based on use and maintenance cycles, dormancy, unique failure modes, the modeling of multiple items as a single "Super component" Basic Event, and Bayesian Updating based on flight and testing experience. A simple, unique metadata field in both the model and database provides a link from any Basic Event in the model to its data source and all relevant calculations. The credibility for the entire model often rests on the credibility and traceability of the data.

Kahn, C. Joseph↗
DOE OSTI2026

Cooperative Agreement To Analyze variabiLity, change and predictabilitY in the earth SysTem (CATALYST)

CATALYST proposes to perform foundational coordinated research in a team-oriented collaborative effort aimed at advancing a robust understanding of modes of Earth system variability and change using models, observations and process studies. The proposed research will address the DOE/BER mission by exploring the limits to predictability, identifying fundamental underlying mechanisms, quantifying interactions among modes of variability, and discovering tipping points in the Earth system to understand the current and future impacts of these phenomena on regional and global climate. Four fundamental gaps are identified in our knowledge of the Earth system: 1) What are the limits to predictability on various timescales? 2) What are the interactions among modes of Earth system variability? 3) How may modes of Earth system variability change in response to changes in external forcing, and what are the tipping points involved with those changes? 4) How are high impact events connected to modes of Earth system variability and how may they change in the future? Related to those gaps in our knowledge, we formulate four research objectives to address those gaps using a combination of Earth system models (ESMs) and machine learning (ML) methods. Research Objective 1 (RO1) addresses the first gap above and proposes to understand modes of variability and their limits of predictability on subseasonal to decadal timescales using ESMs and ML. Research Objective 2 (RO2) addresses the second gap and proposes to use a hierarchy of models to understand relevant processes and feedbacks related to how modes of variability interact with each other. Research Objective 3 (RO3) is designed to study the third gap and proposes to examine the role of external forcings in changes of modes of Earth system variability and their interactions, and the likelihood and predictability of tipping points and irreversible changes. Research Objective 4 (RO4) will address the fourth gap and proposes to use high resolution ESMs, regionally refined models (RRMs), and ML methods to investigate the relationships between high impact events (e.g. flash droughts and precipitation extremes, atmospheric rivers (ARs), tropical cyclones (TCs), storm surge/sea level rise), the synoptic systems that produce them, and their changes related to modes of Earth system variability. The research will involve the use of the Community Earth System Model (CESM), Energy Exascale Earth System Model (E3SM), CMIP multi-model data sets, a hierarchy of simpler models, and numerous observational data sets. In the course of the proposed research, CATALYST will contribute to metrics and diagnostics that will be integrated in Coordinated Model Evaluation Capabilities (CMEC), particularly with regards to the Quasi-biennial Oscillation (QBO) and its interactions with the Madden-Julian Oscillation (MJO), high atmospheric pressure blocking, and new precipitation metrics.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1973

Quantitative magnetospheric models derived from spacecraft magnetometer data

Quantitative models of the external magnetospheric field were derived by making least-squares fits to magnetic field measurements from four IMP satellites. The data were fit to a power series expansion in the solar magnetic coordinates and the solar wind-dipole tilt angle, and thus the models contain the effects of seasonal north-south asymmetries. The expansions are divergence-free, but unlike the usual scalar potential expansions, the models contain a nonzero curl representing currents distributed within the magnetosphere. Characteristics of four models are presented, representing different degrees of magnetic disturbance as determined by the range of Kp values. The latitude at the earth separating open polar cap field lines from field lines closing on the dayside is about 5 deg lower than that determined by previous theoretically-derived models. At times of high Kp, additional high latitude field lines are drawn back into the tail.

Mead, G. D.↗
NASA NTRS2006

NASA Earth Sciences Data Support System and Services for the Northern Eurasia Earth Science Partnership Initiative

The presentation describes the recently awarded ACCESS project to provide data management of NASA remote sensing data for the Northern Eurasia Earth Science Partnership Initiative (NEESPI). The project targets integration of remote sensing data from MODIS, and other NASA instruments on board US-satellites (with potential expansion to data from non-US satellites), customized data products from climatology data sets (e.g., ISCCP, ISLSCP) and model data (e.g., NCEP/NCAR) into a single, well-architected data management system. It will utilize two existing components developed by the Goddard Earth Sciences Data & Information Services Center (GES DISC) at the NASA Goddard Space Flight Center: (1) online archiving and distribution system, that allows collection, processing and ingest of data from various sources into the online archive, and (2) user-friendly intelligent web-based online visualization and analysis system, also known as Giovanni. The former includes various kinds of data preparation for seamless interoperability between measurements by different instruments. The latter provides convenient access to various geophysical parameters measured in the Northern Eurasia region without any need to learn complicated remote sensing data formats, or retrieve and process large volumes of NASA data. Initial implementation of this data management system will concentrate on atmospheric data and surface data aggregated to coarse resolution to support collaborative environment and climate change studies and modeling, while at later stages, data from NASA and non-NASA satellites at higher resolution will be integrated into the system.

Leptoukh, Gregory↗
NASA NTRS2003

Estimation of Model Error Variances During Data Assimilation

Data assimilation is all about understanding the error characteristics of the data and models that are used in the assimilation process. Reliable error estimates are needed to implement observational quality control, bias correction of observations and model fields, and intelligent data selection. Meaningful covariance specifications are obviously required for the analysis as well, since the impact of any single observation strongly depends on the assumed structure of the background errors. Operational atmospheric data assimilation systems still rely primarily on climatological background error covariances. To obtain error estimates that reflect both the character of the flow and the current state of the observing system, it is necessary to solve three problems: (1) how to account for the short-term evolution of errors in the initial conditions; (2) how to estimate the additional component of error caused by model defects; and (3) how to compute the error reduction in the analysis due to observational information. Various approaches are now available that provide approximate solutions to the first and third of these problems. However, the useful accuracy of these solutions very much depends on the size and character of the model errors and the ability to account for them. Model errors represent the real-world forcing of the error evolution in a data assimilation system. Clearly, meaningful model error estimates and/or statistics must be based on information external to the model itself. The most obvious information source is observational, and since the volume of available geophysical data is growing rapidly, there is some hope that a purely statistical approach to model error estimation can be viable. This requires that the observation errors themselves are well understood and quantifiable. We will discuss some of these challenges and present a new sequential scheme for estimating model error variances from observations in the context of an atmospheric data assimilation system.

Dee, Dick↗
NASA NTRS2017

Enabling NLDAS-2 Anomaly Analysis Using Giovanni

A newly implemented feature in Giovanni (GES DISC Interactive Online Visualization and Analysis Interface) allows users to explore and visualize anomaly data from the NLDAS-2 Primary Forcing and Noah model data sets. For a given measurement and location, an anomaly describes how conditions for a particular time period compare to normal conditions, based on long-term averages. Analyzing anomalies is important for monitoring droughts, determining weather trends, and studying land surface processes relevant for meteorology, hydrology, and climate. Using Giovanni to analyze anomalies for NLDAS-2 data allows for these studies to be efficiently conducted for the central North American region. Phase 2 of NLDAS (NLDAS-2) currently runs at an 1/8th degree resolution, in near-real time, with data sets extending back to January 1979. NLDAS-2 provides data for soil moisture, precipitation, temperature, and other hydrology measurements. Hourly, monthly, and 30-year (1980-2009) monthly climatology data are available for several land surface models and forcing data sets. The Giovanni anomaly tool calculates monthly anomalies, for a given user-defined variable, as the difference between the NLDAS-2 monthly climatology data and the monthly data. The resulting anomaly describes how a chosen month compares to the 30-year monthly average. The presentation will demonstrate the capabilities and usefulness of Giovanni's anomaly tool, detail the recently added NLDAS-2 variables for which anomalies are available, and show how users can access the data.

drough↗
NASA NTRSDate not supplied

Analysis of Atmospheric Conditions Responsible for an Ozone Exceedance Event in Southeast Virginia on June 15, 2022

On June 15, 2022, the Virginia Department of Environmental Quality monitoring site at Suffolk/Holland, a rural site in southeast Virginia, recorded its first ozone exceedance since 2016. An ozone exceedance day occurs when the daily maximum 8-hour average surface ozone concentration is greater than 70 ppb. On this day, it was 75 ppb. This event is also noteworthy due to the rapid increase in surface ozone between 6 am and 9 am EDT as well as the hourly maximum ozone concentration of 82 ppb that was measured at 1 pm EDT. In this analysis, we utilize various observational and model data to determine the atmospheric conditions responsible for this ozone exceedance event. The analysis is conducted in two parts: (1) an evaluation of the accuracy of the GEOS-CF and WRF-Chem model forecasts and (2) an investigation of the mechanisms responsible for the high surface ozone at the Virginia DEQ Suffolk/Holland monitoring site. Comparisons of model forecasted ozone time-height cross sections with measured ozone by lidars from the Tropospheric Ozone Lidar Network (TOLNet) at NASA Langley and NASA Goddard reveal that both models forecasted ozone reasonably well near the surface. Model and observational data reveal that an upper-level ridge was present over much of the eastern United States, with a broad anticyclonic circulation near the surface, resulting in north-northeasterly flow over southeast Virginia. Back trajectory calculations using the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) and model data suggest that a low-level plume rich in ozone and precursor species originating from the New York City and New Jersey region was transported along the east coast between June 14 and June 15, arriving in southeast Virginia around 6 am EDT on June 15. In the early morning hours, this plume mixed down to the surface, elevating the background ozone mixing ratio as well as the mixing ratios of several precursor species. Other potential contributions are also explored and discussed. Lastly, the authors note that this work represents the analysis of the authors and not the Virginia Department of Environmental Quality.

Daniel B Phoenix↗
DOE OSTI2024

Heterogeneous Data Type Mixture Model

This code performs estimation of a mixture model involving heterogeneous data types. The resulting model can be used to calculate likelihoods of underlying model components.

Kaplan, AlanD↗
NASA NTRS1975

A quantitative magnetospheric model derived from spacecraft magnetometer data

The model is derived by making least squares fits to magnetic field measurements from four Imp satellites. It includes four sets of coefficients, representing different degrees of magnetic disturbance as determined by the range of Kp values. The data are fit to a power series expansion in the solar magnetic coordinates and the solar wind-dipole tilt angle, and thus the effects of seasonal north-south asymmetries are contained. The expansion is divergence-free, but unlike the usual scalar potential expansion, the model contains a nonzero curl representing currents distributed within the magnetosphere. The latitude at the earth separating open polar cap field lines from field lines closing on the day side is about 5 deg lower than that determined by previous theoretically derived models. At times of high Kp, additional high-latitude field lines extend back into the tail. Near solstice, the separation latitude can be as low as 75 deg in the winter hemisphere. The average northward component of the external field is much smaller than that predicted by theoretical models; this finding indicates the important effects of distributed currents in the magnetosphere.

Mead, G. D.↗