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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Quantifying the benefits of improved satellite remote-sensing observations for inverse modeling of NOx and NMVOC emissions

This study aims to demonstrate the benefits of using novel high spatiotemporal retrieval products from newer satellites for top-down emission estimates of nitrogen oxides (NO x ) and non-methane volatile organic compounds (NMVOCs) for the summer of 2019 over the contiguous United States. Recent satellite retrievals have not only advanced spatiotemporal resolution but also greatly reduced error and uncertainty due to reduced noise in the retrievals compared to spaceborne sensors launched in the past. We applied inverse modeling techniques using tropospheric nitrogen dioxide (NO 2 ) and formaldehyde (H-CHO) column retrieval products from the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) in conjunction with the Weather Research Forecast and Community Multiscale Air Quality Modeling system (WRF-CMAQ). In order to provide a better representation of background chemical composition and avoid misalignment of emission adjustment, we applied monthly scaling factors for ozone (O 3 ) and CO boundary concentrations in addition to the inclusion of lightning and aviation emissions. Satellite-constrained NO x and NMVOCs posterior emissions showed a mitigated discrepancy between observed and modeled columns. The improvement in the model performance was greater when using TROPOMI, primarily benefiting from reduced errors/biases of the satellite retrievals that enabled us to explore corresponding changes in O 3 concentrations and production sensitivity regimes using the ratio of H-CHO and NO 2 .

remote sensing↗
DOE OSTI2023

Simulating organic aerosol in Delhi with WRF-Chem using the volatility-basis-set approach: exploring model uncertainty with a Gaussian process emulator

The nature and origin of organic aerosol in the atmosphere remain unclear. The gas–particle partitioning of semi-volatile organic compounds (SVOCs) that constitute primary organic aerosols (POAs) and the multigenerational chemical aging of SVOCs are particularly poorly understood. The volatility basis set (VBS) approach, implemented in air quality models such as WRF-Chem (Weather Research and Forecasting model with Chemistry), can be a useful tool to describe emissions of POA and its chemical evolution. However, the evaluation of model uncertainty and the optimal model parameterization may be expensive to probe using only WRF-Chem simulations. Gaussian process emulators, trained on simulations from relatively few WRF-Chem simulations, are capable of reproducing model results and estimating the sources of model uncertainty within a defined range of model parameters. In this study, a WRF-Chem VBS parameterization is proposed; we then generate a perturbed parameter ensemble of 111 model runs, perturbing 10 parameters of the WRF-Chem model relating to organic aerosol emissions and the VBS oxidation reactions. This allowed us to cover the model's uncertainty space and to compare outputs from each run to aerosol mass spectrometer observations of organic aerosol concentrations and O:C ratios measured in New Delhi, India. The simulations spanned the organic aerosol concentrations measured with the aerosol mass spectrometer (AMS). However, they also highlighted potential structural errors in the model that may be related to unsuitable diurnal cycles in the emissions and/or failure to adequately represent the dynamics of the planetary boundary layer. While the structural errors prevented us from clearly identifying an optimized VBS approach in WRF-Chem, we were able to apply the emulator in the following two periods: the full period (1–29 May) and a subperiod period of 14:00–16:00 h LT (local time) on 1–29 May. The combination of emulator analysis and model evaluation metrics allowed us to identify plausible parameter combinations for the analyzed periods. We demonstrate that the methodology presented in this study can be used to determine the model uncertainty and to identify the appropriate parameter combination for the VBS approach and hence to provide valuable information to improve our understanding of OA production.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

FVCOM-ICM4.0

FVCOM-ICM is a water quality model that simulates the influence of nutrients and carbon pollution on biogeochemical oceanographic processes such as phytoplankton primary productivity, nutrient consumption, mineralization, decay and settling of particulate organic matter and computes resulting dissolved oxygen (DO) concentrations and pH levels. An “offline” approach of coupling Finite Volume Coastal Ocean Model (FVCOM) hydrodynamic solutions to CE-QUAL-ICM biogeochemical model was used. FVCOM and CE-QUAL-ICM are public domain Fortran Codes that were developed independently by US Army Corps of Engineers and University of Massachusetts. PNNL developed FVCOM-ICM that has the ability to conduct water quality calculations using a previously computed hydrodynamic solution. The biogeochemical calculations using CE-QUAL-ICM kinetics are conducted over the same finite-volume mesh and in the FVCOM framework.

Khangaonkar, Tarang↗
NASA NTRSDate not supplied

Aero-Propulsive Modeling for eVTOL Aircraft Using Wind Tunnel Testing with Multisine Inputs

A novel approach for modeling the aero-propulsive characteristics of an electric vertical takeoff and landing (eVTOL) aircraft was developed and demonstrated in wind tunnel testing. The approach was applied to the NASA LA-8 tandem tilt-wing eVTOL aircraft, using an efficient, hybrid experiment design composed of a static I-optimal response surface design for slowly-varying test variables, and dynamic orthogonal phase-optimized multisine excitations for the control surfaces and electric propulsors. Both the static and dynamic experiment designs were executed simultaneously to collect informative data for model identification. Statistically-weighted multivariate orthogonal function modeling was used to combine local modeling results computed in the frequency domain using data collected with dynamic excitations operating on the control effectors to form an aggregate aero-propulsive model. The final identified model exhibited good predictive capability when compared to validation data acquired separately from the data used to develop the model. The required test time using these new techniques was reduced by at least a factor of five compared to previous static wind tunnel testing for the LA-8 aircraft, while providing more informative data, greater parameterization flexibility, and high-quality models.

system identification↗
DOE OSTI2022

A machine learning approach for clinker quality prediction and nonlinear model predictive control design for a rotary cement kiln

Abstract Cement manufacturing is energy‐intensive (5Gj/t) and comprises a significant portion of the energy footprint of concrete systems. Incorporating modern monitoring, simulation and control systems will allow lower energy use, lower environmental impact, and lower costs of this widely used construction material. One of the goals of the CESMII roadmap project on the Smart Manufacturing of Cement included developing an analytical process model for clinker quality that includes the chemistry of the kiln feed and accounts for critical process variables. This predictive model will be used in nonlinear model predictive control system designed to significantly reduce process energy use while maintaining or improving product quality. In the cement manufacturing plant used in this study, the kiln feed (meal) is tested every 12 h and used to estimate the mineral composition of the cement kiln output (clinker) using the stoichiometry‐based Bogue's model and the expertise of the plant operators. During kiln operation, kiln output (clinker) is sampled and tested every 2 h to measure its chemical and mineral composition. The predicted and measured values of the clinker composition are used by the plant operators to adjust the kiln input stream and the production process characteristics to maintain stable operation and uniform product quality. However, the time delay between prediction and testing, along with inaccuracies inherent in the Bogue's model have made any process changes designed to minimize energy use problematic, especially in‐light of potential clinker quality issues that process changes often pose. A new analytical model that integrates quality information and process operation information has been developed from data collected from 2 years of production from an operating cement facility. To make the model fuel‐type‐independent, consumed heat energy was computed in the model instead of fuel type and amount. A Feedforward Network was trained and tailored from collected data. Many data‐based simulations were conducted to quantitatively evaluate the proposed model and the 5‐fold cross‐validation procedure was used to test the models. The resulting predictive model was shown to have a low root mean square error (MSE) with respect to the estimated clinker mineral composition compared to that using the industry standard “Bogue’ model”. The end goal of this work was to develop a single machine learning tool that allows the use of quality control data and process control variables to improve energy efficiency of the process in a continuous fashion. The proposed nonlinear model predictive control system (NMPC) can generate predicted kiln production characteristics based on manipulated variables in manner that accurately follows the target product quality values. Simulation results also show that the proposed model produced accurate predictions of kiln outputs that fell within the required constraints, while manipulating control variables within typical operational ranges.

Ali, Asem M.↗
DOE OSTI2020

Nitrate loading projection is sensitive to freeze-thaw cycle representation

Climate change can have substantial impacts on nitrogen runoff, which is a major cause of eutrophication, harmful algal blooms, and hypoxia in freshwaters and coastal regions. We examined responses of nitrate loading to climate change in the Upper Mississippi River Basin (UMRB) with an enhanced Soil and Water Assessment Tool with physically based Freeze-Thaw cycle representation (SWAT-FT), as compared with the original SWAT model that employs an empirical equation. Driven by future climate projections from five General Circulation Models (GCMs) from 1960 to 2099 under the Representative Concentrations Pathways (RCP) 8.5 scenario, we analyzed changes in riverine nitrate loadings, as well as terrestrial surface and subsurface contributions of the UMRB in the 21st century relative to the baseline period of 1960–1999. By the end of the 21st century, the original SWAT model predicted about a 50% increase in riverine nitrate loadings which is nearly twice as much as that estimated by SWAT-FT (ca. 25%). Such a large difference in projected nitrate changes can potentially mislead mitigation strategies that aim to reduce nitrogen runoff from the UMRB. Further analysis shows that the difference between the original SWAT model and SWAT-FT led to substantial discrepancies in the spatial distribution of surface and subsurface nitrate loadings in the UMRB. In general, SWAT-FT predicted more nitrate leaching for northwestern parts of the UMRB which are more sensitive to freeze-thaw cycle, mainly because SWAT-FT simulated less frequent frozen soils. This study highlights the importance of using physically based freeze-thaw cycle representation in water quality modeling. Design of future nitrogen runoff reduction strategies should include careful assessment of effects that land management has on the freeze-thaw cycles to provide reliable projection of water quality under climate change.

Wang, Qianfeng↗
DOE OSTI2022

CTF Theory Manual: Version 4.3

Coolant-Boiling in Rod Arrays—Two Fluids (COBRA-TF) is a thermal/hydraulic (T/H) simulation code designed for light water reactor (LWR) vessel analysis. It uses a two-fluid, three-field (i.e., fluid film, fluid drops, and vapor) modeling approach. Both subchannel and three-dimensional Cartesian forms of its governing equations are available for the solution. The code was originally developed by Pacific Northwest Laboratory in 1980, and had been used and modified by several institutions over the last few decades. COBRA-TF also found use at the Pennsylvania State University (PSU) by the Reactor Dynamics and Fuel Modeling Group (RDFMG) and has been improved, updated, and subsequently rebranded as CTF. CTF was later adopted in the early 2010s by Oak Ridge National Laboratory (ORNL) for use in the Consortium for Advanced Simulation of Light Water Reactors (CASL) program, which led to a significant advancement of the code software quality, modeling accuracy, testing systems, and capabilities for improved support of modeling of common LWR nominal and transient behavior. As part of the improvement process, it was necessary to generate sufficient documentation for the public domain code which had lacked such material upon being adopted by RDFMG. This document serves as a theory manual for CTF, detailing the many two-phase heat transfer, drag, and important accident scenario models contained in the code, as well as the numerical solution process utilized. Additional documents available in the CTF documentation suite include the user manual and verification and validation manual.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

A gated graph transformer for protein complex structure quality assessment and its performance in CASP15

Abstract Motivation Proteins interact to form complexes to carry out essential biological functions. Computational methods such as AlphaFold-multimer have been developed to predict the quaternary structures of protein complexes. An important yet largely unsolved challenge in protein complex structure prediction is to accurately estimate the quality of predicted protein complex structures without any knowledge of the corresponding native structures. Such estimations can then be used to select high-quality predicted complex structures to facilitate biomedical research such as protein function analysis and drug discovery. Results In this work, we introduce a new gated neighborhood-modulating graph transformer to predict the quality of 3D protein complex structures. It incorporates node and edge gates within a graph transformer framework to control information flow during graph message passing. We trained, evaluated and tested the method (called DProQA) on newly-curated protein complex datasets before the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) and then blindly tested it in the 2022 CASP15 experiment. The method was ranked 3rd among the single-model quality assessment methods in CASP15 in terms of the ranking loss of TM-score on 36 complex targets. The rigorous internal and external experiments demonstrate that DProQA is effective in ranking protein complex structures. Availability and implementation The source code, data, and pre-trained models are available at https://github.com/jianlin-cheng/DProQA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

CTF Theory Manual: Version 4.4

Coolant-Boiling in Rod Arrays – Two Fluids (COBRA-TF) is a thermal/hydraulic (T/H) simulation code designed for light-water reactor (LWR) vessel analysis. It uses a two-fluid, three-field (i.e., fluid film, fluid drops, and vapor) modeling approach. Both subchannel and 3D Cartesian forms of its governing equations are available for the solution. The code was originally developed by Pacific Northwest Laboratory in 1980 and has been used and modified by several institutions over the last few decades. COBRA-TF also found use at the Pennsylvania State University (PSU) by the Reactor Dynamics and Fuel Modeling Group (RDFMG) and has been improved, updated, and subsequently rebranded as CTF. CTF was later adopted in the early 2010s by Oak Ridge National Laboratory (ORNL) for use in the Consortium for Advanced Simulation of Light Water Reactors (CASL) program, which led to a significant advancement of the code’s software quality, modeling accuracy, testing systems, and capabilities for improved support in modeling common LWR nominal and transient behavior. As part of the improvement process, it was necessary to generate sufficient documentation for the public domain code which had lacked such material upon being adopted by RDFMG. This document serves as a theory manual for CTF, detailing the many two-phase heat transfer, drag, and important accident scenario models contained in the code, as well as the numerical solution process utilized. Additional documents available in the CTF documentation suite include the user manual and the verification and validation manual.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1992

Airborne lidar mapping of vertical ozone distributions in support of the 1990 Clean Air Act Amendments

The 1990 Clean Air Act Amendments mandated attainment of the ozone standard established by the U.S. Environmental Protection Agency. Improved photochemical models validated by experimental data are needed to develop strategies for reducing near surface ozone concentrations downwind of urban and industrial centers. For more than 10 years, lidar has been used on large aircraft to provide unique information on ozone distributions in the atmosphere. However, compact airborne lidar systems are needed for operation on small aircraft of the type typically used on regional air quality investigations to collect data with which to develop and validate air quality models. Data presented in this paper will consist of a comparison between airborne differential absorption lidar (DIAL) and airborne in-situ ozone measurements. Also discussed are future plans to improve the airborne ultraviolet-DIAL for ozone and other gas observations and addition of a Fourier Transform Infrared (FTIR) emission spectrometer to investigate the effects of other gas species on vertical ozone distribution.

Uthe, Edward E.↗
NASA NTRS2005

Aircraft Based Remotely Sensed Albedo and Surface Temperatures for Three US Cities

High spatial resolution thermal infrared and visible data obtained from aircraft were used to measure, map, and model the surface energy budget characteristics of surfaces typical of the urban landscape for three US cities. Aircraft data collected from the Advanced Thermal and Land Applications Sensor (ATLAS) scanner allowed a detail analysis of the city, determining its surface thermal and visible reflectance properties. These data are critical in providing data that can be used to evaluate the overall "fabric" of the cities in relation to the urban heat island and air quality modeling.

Luvall, Jeffrey C.↗
DOE OSTI2024

Transforming Agricultural Productivity with AI-Driven Forecasting: Innovations in Food Security and Supply Chain Optimization

Global food security is under significant threat from climate change, population growth, and resource scarcity. This review examines how advanced AI-driven forecasting models, including machine learning (ML), deep learning (DL), and time-series forecasting models like SARIMA/ARIMA, are transforming regional agricultural practices and food supply chains. Through the integration of Internet of Things (IoT), remote sensing, and blockchain technologies, these models facilitate the real-time monitoring of crop growth, resource allocation, and market dynamics, enhancing decision making and sustainability. The study adopts a mixed-methods approach, including systematic literature analysis and regional case studies. Highlights include AI-driven yield forecasting in European hydroponic systems and resource optimization in southeast Asian aquaponics, showcasing localized efficiency gains. Furthermore, AI applications in food processing, such as plasma, ozone and Pulsed Electric Field (PEF) treatments, are shown to improve food preservation and reduce spoilage. Key challenges—such as data quality, model scalability, and prediction accuracy—are discussed, particularly in the context of data-poor environments, limiting broader model applicability. The paper concludes by outlining future directions, emphasizing context-specific AI implementations, the need for public–private collaboration, and policy interventions to enhance scalability and adoption in food security contexts.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Annual PM2.5 and cardiovascular mortality rate data: Trends modified by county socioeconomic status in 2,132 US counties

This article contains data on county-level socioeconomic status for 2132 US counties and each county's average annual cardiovascular mortality rate (CMR) and fine particulate matter (PM2.5) concentration for 21 years (1990e2010). County CMR, PM2.5, and socioeconomic data were obtained from the US National Center for Health Statistics, US Environmental Protection Agency's Community Multiscale Air Quality modeling system, and the US Census, respectively. Annual socioeconomic indices were created using seven county-level measures from the 1990, 2000, and 2010 US Census using factor analysis. Quintiles of this index were used to generate categories of county socioeconomic status. This national data set contains data for annual PM2.5 and CMR changes over a time-period when there was a significant reduction in US air pollutants (following the enactment of the 1970 Clean Air Act). These data are associated with the article “The contribution of improved air quality to reduced cardiovascular mortality: Declines in socioeconomic differences over time” [1]. Data are stored in a comma separated value format and can be downloaded from the USEPA ScienceHub data repository (https://doi.org/10.23719/ 1506014).

Socioeconomic status↗
NASA NTRS2012

Bay Breeze Influence on Surface Ozone at Edgewood, MD During July 2011

Surface ozone (O3) was analyzed to investigate the role of the bay breeze on air quality at two locations in Edgewood, Maryland (lat: 39.4deg, lon: −76.3deg) for the month of July 2011. Measurements were taken as part of the first year of NASA's "Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality" (DISCOVER-AQ) Earth Venture campaign and as part of NASA's Geostationary for Coastal and Air Pollution Events Chesapeake Bay Oceanographic campaign with DISCOVER-AQ (Geo-CAPE CBODAQ). Geo-CAPE CBODAQ complements DISCOVER-AQ by providing ship-based observations over the Chesapeake Bay. A major goal of DISCOVER-AQ is determining the relative roles of sources, photochemistry and local meteorology during air quality events in the Mid-Atlantic region of the U.S. Surface characteristics, transport and vertical structures of O3 during bay breezes were identified using in-situ surface, balloon and aircraft data, along with remote sensing equipment. Localized late day peaks in O3 were observed during bay breeze days, maximizing an average of 3 h later compared to days without bay breezes. Of the 10 days of July 2011 that violated the U.S. Environmental Protection Agency (EPA) 8 h O3 standard of 75 parts per billion by volume (ppbv) at Edgewood, eight exhibited evidence of a bay breeze circulation. The results indicate that while bay breezes and the processes associated with them are not necessary to cause exceedances in this area, bay breezes exacerbate poor air quality that sustains into the late evening hours at Edgewood. The vertical and horizontal distributions of O3 from the coastal Edgewood area to the bay also show large gradients that are often determined by boundary layer stability. Thus, developing air quality models that can sufficiently resolve these dynamics and associated chemistry, along with more consistent monitoring of O3 and meteorology on and along the complex coastline of Chesapeake Bay must be a high priority.

bay breeze↗
DOE OSTI2023

Use-Inspired, Process-Oriented GCM Selection: Prioritizing Models for Regional Dynamical Downscaling

Dynamical downscaling is a crucial process for providing regional climate information for broad uses, using coarser-resolution global models to drive higher-resolution regional climate simulations. The pool of global climate models (GCMs) providing the fields needed for dynamical downscaling has increased from the previous generations of the Coupled Model Intercomparison Project (CMIP). However, with limited computational resources, the need for prioritizing the GCMs for subsequent downscaling studies remains. GCM selection for dynamical downscaling should focus on evaluating processes relevant for providing boundary conditions to the regional models and be inspired by regional uses such as the response of extremes to changes in the boundary conditions. This leads to the need for metrics representing processes of relevance to diverse stakeholders and subregions of a domain. Procedures to account for metric redundancy and the statistical distinguishability of GCM rankings are required. Further, procedures for selecting realizations from ensembles of top-performing GCM simulations can be used to span the range of climate change signals in multiple ways. As a result, distinct weighting of metrics and prioritization of particular realizations may depend on user needs. We provide high-level guidelines for such region-specific evaluations and address how CMIP7 might enable dynamical downscaling of a representative sample of high-quality models across representative shared socioeconomic pathways (SSPs).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Process-based and observation-constrained SOA simulations in China: the role of semivolatile and intermediate-volatility organic compounds and OH levels

Organic aerosol (OA) is a major component of tropospheric submicron aerosol that contributes to air pollution and causes adverse effects on human health. Chemical transport models have difficulties in reproducing the variability in OA concentrations in polluted areas, hindering understanding of the OA budget and sources. Herein, we apply both process-based and observation-constrained schemes to simulate OA in GEOS-Chem. Comprehensive data sets of surface OA, OA components, secondary organic aerosol (SOA) precursors, and oxidants were used for model–observation comparisons. The base models generally underestimate the SOA concentrations in China. In the revised schemes, updates were made on the emissions, volatility distributions, and SOA yields of semivolatile and intermediate-volatility organic compounds (SVOCs and IVOCs) and additional nitrous acid sources. With all the model improvements, both the process-based and observation-constrained SOA schemes can reproduce the observed mass concentrations of SOA and show spatial and seasonal consistency with each other. Our best model simulations suggest that anthropogenic SVOCs and IVOCs are the dominant source of SOA, with a contribution of over 50% in most of China, which should be considered for pollution mitigation in the future. The residential sector may be the predominant source of SVOCs and IVOCs in winter, despite large uncertainty remaining in the emissions of IVOCs from the residential sector in northern China. The industry sector is also an important source of IVOCs, especially in summer. More SVOC and IVOC measurements are needed to constrain their emissions. Besides, the results highlight the sensitivity of SOA to hydroxyl radical (OH) levels in winter in polluted environments. The addition of nitrous acid sources can lead to over 30% greater SOA mass concentrations in winter in northern China. It is important to have good OH simulations in air quality models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2014

Relationship Between Column-Density and Surface Mixing Ratio: Statistical Analysis of O3 and NO2 Data from the July 2011 Maryland DISCOVER-AQ Mission

To investigate the ability of column (or partial column) information to represent surface air quality, results of linear regression analyses between surface mixing ratio data and column abundances for O3 and NO2 are presented for the July 2011 Maryland deployment of the DISCOVER-AQ mission. Data collected by the P-3B aircraft, ground-based Pandora spectrometers, Aura/OMI satellite instrument, and simulations for July 2011 from the CMAQ air quality model during this deployment provide a large and varied data set, allowing this problem to be approached from multiple perspectives. O3 columns typically exhibited a statistically significant and high degree of correlation with surface data (R(sup 2) > 0.64) in the P- 3B data set, a moderate degree of correlation (0.16 < R(sup 2) < 0.64) in the CMAQ data set, and a low degree of correlation (R(sup 2) < 0.16) in the Pandora and OMI data sets. NO2 columns typically exhibited a low to moderate degree of correlation with surface data in each data set. The results of linear regression analyses for O3 exhibited smaller errors relative to the observations than NO2 regressions. These results suggest that O3 partial column observations from future satellite instruments with sufficient sensitivity to the lower troposphere can be meaningful for surface air quality analysis.

nitrogen dioxide↗
NASA NTRS2013

Lightning NOx Statistics Derived by NASA Lightning Nitrogen Oxides Model (LNOM) Data Analyses

What is the LNOM? The NASA Marshall Space Flight Center (MSFC) Lightning Nitrogen Oxides Model (LNOM) [Koshak et al., 2009, 2010, 2011; Koshak and Peterson 2011, 2013] analyzes VHF Lightning Mapping Array (LMA) and National Lightning Detection Network(TradeMark) (NLDN) data to estimate the lightning nitrogen oxides (LNOx) produced by individual flashes. Figure 1 provides an overview of LNOM functionality. Benefits of LNOM: (1) Does away with unrealistic "vertical stick" lightning channel models for estimating LNOx; (2) Uses ground-based VHF data that maps out the true channel in space and time to < 100 m accuracy; (3) Therefore, true channel segment height (ambient air density) is used to compute LNOx; (4) True channel length is used! (typically tens of kilometers since channel has many branches and "wiggles"); (5) Distinction between ground and cloud flashes are made; (6) For ground flashes, actual peak current from NLDN used to compute NOx from lightning return stroke; (7) NOx computed for several other lightning discharge processes (based on Cooray et al., 2009 theory): (a) Hot core of stepped leaders and dart leaders, (b) Corona sheath of stepped leader, (c) K-change, (d) Continuing Currents, and (e) M-components; and (8) LNOM statistics (see later) can be used to parameterize LNOx production for regional air quality models (like CMAQ), and for global chemical transport models (like GEOS-Chem).

Koshak, William↗