Engineering topics

Bryan N Duncan

Publications and source records attributed to Bryan N Duncan.

7 records

NASA NTRS2021

Description of the NASA GEOS Composition Forecast Modeling System GEOS-CF v1.0

The Goddard Earth Observing System composition forecast (GEOS-CF) system is a high-resolution (0.25 degree) global constituent prediction system from NASA’s Global Modeling and Assimilation Office (GMAO). GEOS-CF offers a new tool for atmospheric chemistry research, with the goal to supplement NASA’s broad range of space-based and in-situ observation sand to support flight campaign planning, support of satellite observations, and air quality research. GEOS-CF expands on the GEOS weather and aerosol modeling system by introducing the GEOS-Chem chemistry module to provide analyses and 5-day forecasts of atmospheric constituents including ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), and fine particulate matter (PM2.5). The chemistry module integrated in GEOS-CF is identical to the offline GEOS-Chem model and readily benefits from the innovations provided by the GEOS-Chem community.Evaluation of GEOS-CF against satellite, ozone sonde and surface observations show realistic simulated concentrations of O3, NO2, and CO, with normalized mean biases of -0.1 to -0.3, normalized root mean square errors (NRMSE) between 0.1-0.4, and correlations between 0.3-0.8. Comparisons against surface observations highlight the successful representation of air pollutants under a variety of meteorological conditions, yet also highlight current limitations, such as an over prediction of summertime ozone over the Southeast United States. GEOS-CFv1.0 generally overestimates aerosols by 20-50% due to known issues in GEOS-Chem v12.0.1 that have been addressed in later versions.The 5-day hourly forecasts have skill scores comparable to the analysis. Model skills can be improved significantly by applying a bias-correction to the surface model output using a machine-learning approach.

GEOS-CF↗

NASA NTRS2020

A Machine Learning Examination of Hydroxyl Radical Differences Among Model Simulations for CCMI-1

Hydroxyl radical (OH) plays critical roles within the troposphere, such as determining the lifetime of methane (CH4), yet is challenging to model due to its fast cycling and dependence on a multitude of sources and sinks. As a result, the reasons for variations in OH and the resulting CH4 lifetime (TCH4), both between models and in time, are difficult to diagnose. We apply a neural network (NN) approach to address this issue within a group of models that participated in the Chemistry-Climate Model Initiative (CCMI). Analysis of the historical specified dynamics simulations performed for CCMI indicates that the primary drivers of TCH4 differences among ten models are the flux of UV light to the troposphere (indicated by the photolysis frequency JO1D) due mostly to clouds, mixing ratio of tropospheric ozone (O3), the abundance of nitrogen oxides (NOx≡NO+NO2), and details of the various chemical mechanisms that drive OH. Water vapor, carbon monoxide (CO), the ratio of NO:NOx, and formaldehyde (HCHO) explain moderate differences in TCH4, while isoprene, CH4, the photolysis frequency of NO2 by visible light (JNO2), overhead O3 column, and temperature account for little-to-no model variation in CH4. We also apply the NNs to analysis of temporal trends in OH from 1980 to 2015. All models that participated in the specified dynamics historical simulation for CCMI demonstrate a decline in CH4 during the analysed timeframe. The significant contributors to this trend, in order of importance, are tropospheric O3, JO1D, NOx, and H2O, with CO also causing substantial interannual variability in OH burden. Finally, the identified trends in TCH4 are compared to calculated trends in the tropospheric mean OH concentration from previous work, based on analysis of observations. The comparison reveals a robust result for the effect of rising water vapor on OH and CH4, imparting an increasing and decreasing trend of about 0.5% decade(exp -1), respectively. The responses due to NOx, O3 column, and temperature are also in reasonably good agreement between the two studies, though a discrepancy in the CH4 response highlights a need for further examination of the CH4 feedback on the abundance of OH.

Julie M Nicely↗

NASA NTRS2019

Space-Based Observations for Understanding Changes in the Arctic-Boreal Zone

Observations taken over the last few decades indicate that dramatic changes are occurring in the ArcticBoreal Zone (ABZ), which are having significant impacts on ABZ inhabitants, infrastructure, flora and fauna, and economies. While suitable for detecting overall change, the current capability is inadequate for systematic monitoring and for improving process based and large scale understanding of the integrated components of the ABZ, which includes the cryosphere, biosphere, hydrosphere, and atmosphere. Such knowledge will lead to improvements in Earth system models, enabling more accurate prediction of future changes and development of informed adaptation and mitigation strategies. In this article, we review the strengths and limitations of current space based observational capabilities for several important ABZ components and make recommendations for improving upon these current capabilities. We recommend an interdisciplinary and stepwise approach to develop a comprehensive ABZ Observing Network (ABZON), beginning with an initial focus on observing networks designed to gain process based understanding for individual ABZ components and systems that can then serve as the building blocks for a comprehensive ABZON.

Bryan N Duncan↗

NASA NTRS2019

Earth Observations and Integrative Models in Support of Food and Water Security

Global food production depends upon many factors that Earth observing satellites routinely measure about water, energy, weather, and ecosystems. Increasingly sophisticated, publicly-available satellite data products can improve efficiencies in resource management and provide earlier indication of environmental disruption. Satellite remote sensing provides a consistent, long-term record that can be used effectively to detect large-scale features over time, such as a developing drought. Accuracy and capabilities have increased along with the range of Earth observations and derived products that can support food security decisions with actionable information. This paper highlights major capabilities facilitated by satellite observations and physical models that have been developed and validated using remotely-sensed observations. Although we primarily focus on variables relevant to agriculture, we also include a brief description of the growing use of Earth observations in support of aquaculture and fisheries.

Water Resources↗

NASA NTRS2016

The Description and Validation of a Computationally-Efficient CH4-CO-OH (ECCOH) Module for 3D Model Applications

We present the Efficient CH4-CO-OH Module (ECCOH) that allows for the simulation of the methane, carbon monoxide and hydroxyl radical (CH4-CO-OH cycle, within a chemistry climate model, carbon cycle model, or earth system model. The computational efficiency of the module allows many multi-decadal, sensitivity simulations of the CH4-CO-OH cycle, which primarily determines the global tropospheric oxidizing capacity. This capability is important for capturing the nonlinear feedbacks of the CH4-CO-OH system and understanding the perturbations to relatively long-lived methane and the concomitant impacts on climate. We implemented the ECCOH module into the NASA GEOS-5 Atmospheric Global Circulation Model (AGCM), performed multiple sensitivity simulations of the CH4-CO-OH system over two decades, and evaluated the model output with surface and satellite datasets of methane and CO. The favorable comparison of output from the ECCOH module (as configured in the GEOS-5 AGCM) with observations demonstrates the fidelity of the module for use in scientific research.

Remote Sensing↗

NASA NTRS2014

Model Evaluation of Methods for Estimating Surface Emissions and Chemical Lifetimes from Satellite Data

Column densities from satellite retrievals can provide valuable information for estimating emissions and chemical lifetimes objectively across the globe. To better understand the uncertainties associated with these estimates, we test four methods using simulated column densities from a point source: a box model approach, a 2D Gaussian fit, an Inverse Radius fit and an Exponentially-Modified Gaussian fit. The model results were simulated using the WRF and CAMx models for the year 2005, for a single point source outside Atlanta in Georgia, USA with specified emissions and three chemical scenarios: no chemical reactions, 12 h chemical lifetime and 1 h chemical lifetime. No other sources were included in the simulations. We find that the box model provides reliable estimates irrespective of plume speed and plume direction, if the plume speed and the chemical lifetime are known accurately. The 2D Gaussian fit was found to be sensitive to plume speed and direction, and requires omnidirectional dispersion in order to have a decent fit. However, the 2D Gaussian fit is only an approximate fit to the data, and the discrepancies mean that the results are dependent on the geographical domain used for the optimization. An Inverse Radius fit is introduced to correct this issue, which is found to provide improved emissions and lifetime estimates. The Exponentially-Modified Gaussian fit also gave improved estimates. It is however dependent on accurate plume rotation such that reported chemical lifetimes with this method could be significantly underestimated.

Emission inventory↗

NASA NTRS2014

Satellite Data of Atmospheric Pollution for U.S. Air Quality Applications: Examples of Applications, Summary of Data End-user Resources, Answers to Faqs, and Common Mistakes to Avoid

Satellite data of atmospheric pollutants are becoming more widely used in the decision-making and environmental management activities of public, private sector and non-profit organizations. They are employed for estimating emissions, tracking pollutant plumes, supporting air quality forecasting activities, providing evidence for "exceptional event" declarations, monitoring regional long-term trends, and evaluating air quality model output. However, many air quality managers are not taking full advantage of the data for these applications nor has the full potential of satellite data for air quality applications been realized. A key barrier is the inherent difficulties associated with accessing, processing, and properly interpreting observational data. A degree of technical skill is required on the part of the data end-user, which is often problematic for air quality agencies with limited resources. Therefore, we 1) review the primary uses of satellite data for air quality applications, 2) provide some background information on satellite capabilities for measuring pollutants, 3) discuss the many resources available to the end-user for accessing, processing, and visualizing the data, and 4) provide answers to common questions in plain language.

remote sensing↗