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John Sullivan

Publications and source records attributed to John Sullivan.

17 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Satellite Remote-Sensing Capability to Assess Tropospheric-Column Ratios of Formaldehyde and Nitrogen Dioxide: Case Study During the Long Island Sound Tropospheric Ozone Study 2018 (LISTOS 2018) Field Campaign

Satellite retrievals of tropospheric-column formaldehyde (HCHO) and nitrogen dioxide (NO 2 ) are frequently used to investigate the sensitivity of ozone (O 3 ) production to emissions of nitrogen oxides and volatile organic carbon compounds. This study inter-compared the systematic biases and uncertainties in retrievals of NO 2 and HCHO, as well as resulting HCHO–NO 2 ratios (FNRs), from two commonly applied satellite sensors to investigate O 3 production sensitivities (Ozone Monitoring Instrument, OMI, and TROPOspheric Monitoring Instrument, TROPOMI) using airborne remote-sensing data taken during the Long Island Sound Tropospheric Ozone Study 2018 between 25 June and 6 September 2018. Compared to aircraft-based HCHO and NO 2 observations, the accuracy of OMI and TROPOMI were magnitude-dependent with high biases in clean environments and a tendency towards more accurate comparisons to even low biases in moderately polluted to polluted regions. OMI and TROPOMI NO 2 systematic biases were similar in magnitude (normalized median bias, NMB = 5 %–6 %; linear regression slope ≈ 0.5–0.6), with OMI having a high median bias and TROPOMI resulting in small low biases. Campaign-averaged uncertainties in the three satellite retrievals (NASA OMI; Quality Assurance for Essential Climate Variables, QA4ECV OMI; and TROPOMI) of NO 2 were generally similar, with TROPOMI retrievals having slightly less spread in the data compared to OMI. The three satellite products differed more when evaluating HCHO retrievals. Campaign-averaged tropospheric HCHO retrievals all had linear regression slopes ∼0.5 and NMBs of 39 %, 17 %, 13 %, and 23 % for NASA OMI, QA4ECV OMI, and TROPOMI at finer (0.05° x 0.05°) and coarser (0.15° x 0.15°) spatial resolution, respectively. Campaign-averaged uncertainty values (root mean square error, RMSE) in NASA and QA4ECV OMI HCHO retrievals were ~9.0 x 10 15 molecules cm –2 (∼ 50 %–55 % of mean column abundance), and the higher-spatial-resolution retrievals from TROPOMI resulted in RMSE values ∼30 % lower. Spatially averaging TROPOMI tropospheric-column HCHO, along with NO 2 and FNRs, to resolutions similar to the OMI reduced the uncertainty in these retrievals. Systematic biases in OMI and TROPOMI NO 2 and HCHO retrievals tended to cancel out, resulting in all three satellite products comparing well to observed FNRs. However, while satellite-derived FNRs had minimal campaign-averaged median biases, unresolved errors in the indicator species did not cancel out in FNR calculations, resulting in large RMSE values compared to observations. Uncertainties in HCHO retrievals were determined to drive the unresolved biases in FNR retrievals.

Matthew S. Johnson↗
NASA NTRS2022

The Dynamical Role of the Chesapeake Bay on the Local Ozone Pollution Using Mesoscale Modeling—A Case Study

This study investigated the dynamic influence of the Chesapeake Bay (CB) on local ozone (O3) concentration and distribution using a weather forecasting model. The Weather Research and Forecasting model coupled with Chemistry (WRF–Chem) was employed to simulate O3 production and transportation near the CB. Baseline (water) as well as sensitivity (nowater) model experiments of bay circulation were carried out with and without bay water by changing the water surface from water to land (loam). First, the model performance simulating O3 was evaluated using the baseline experiment results and AirNow surface wind and O3 observations. The results showed that the model overestimates surface O3 by up to 20–30%. Further, the comparisons of the baseline and sensitivity experiments revealed higher O3 mixing ratios, primarily due to the resulting bay breeze circulation. These increases, after considering model overestimation, represent a mean bay dynamics circulation-induced contribution of up to 10% at night and 5% during the day. Furthermore, the boundary layer over northern CB, where it is at its narrowest width, was higher (by 1.2 km on average) during daytime due to higher surface temperatures observed. The boundary layer depth difference between the northern, central, and southern regions of the bay leads to a differential in the role of bay circulation dynamics in the observed O3 increase. The relatively wider swath of water surface over southern CB resulted in a lower boundary layer depth and stronger breeze circulation and this circulation contributed to O3 concentrations. Moreover, since the case selected has a minimal bay breeze circulation, the associated surface ozone enhancements represent what is expected at least at a minimum.

Chesapeak Bay↗
NASA NTRSDate not supplied

TPSAS-NF1676L-33873-DND

NOAA's National Air Quality Forecasting Capability (NAQFC) model provides 48-hour forecast of surface ozone and other atmospheric constituents serving as the nation's key tool actively used by regulatory professionals in assessing air quality events and the issuance of public air quality alerts. NAQFC incorporates input meteorology from North American Mesoscale Model (NAM) at 12 km horizontal resolution that is coupled to the Community Multi-scale Air Quality Model (CMAQ) to predict emissions, tropospheric gas and aerosol chemistry, and related deposition and removal processes. The NAQFC forecast simulations contain 35 steps in altitude, with about one third of these in a typical afternoon daytime boundary layer (0-1.5 km). While several studies have been conducted comparing NAQFC predictions to surface observations, investigations comparing the vertical distribution to observations remain limited. Vertical comparisons are needed in order to fully assess forecast model behavior and atmospheric mixing conditions leading to the formation of ozone at the surface. The Tropospheric Ozone Lidar Network (TOLNet) instruments provide a unique observational capability in this regard, as ozone vertical measurements can be directly compared to models, such as NAQFC, and provide additional insight into atmospheric processes and enhance forecast capabilities. In this study, we utilize multiple TOLNet lidar observations obtained during the Ozone Water Land Environmental Transition Studies (OWLETS-2017 & OWLETS-2018) and the Long Island Sound Tropospheric Ozone Study (LISTOS-2018) to compare with NAQFC vertical predictions of ozone. These campaigns took place at different coastal locations along the US East coast with each campaign having multiple TOLNet lidars present, providing a unique opportunity to examine vertical structure of ozone in regions with complex water-land boundaries. Preliminary comparison results will be presented on ozone temporal and vertical differences between NAQFC and the TOLNet lidars along with example case studies to illustrate insights into boundary layer and surface processes of trace gases.

Timothy Berkoff↗
NASA NTRSDate not supplied

Accelerating TEMPO Air Quality Science Through STAQS

Soon after the launch of Tropospheric Emissions: Monitoring of Pollution (TEMPO) mission, NASA is supporting the Synergistic TEMPO Air Quality Science field study (STAQS) in summer 2023. This study’s main objective is to accelerate science with geostationary air quality observations from TEMPO to better understand its use in air pollution research and applications. Science objectives include, but are not limited to, TEMPO L2 product evaluation, the interpretation of the spatiotemporal evolution of TEMPO data during air quality events, assessment of anthropogenic emissions, contributions to chemical transport modeling, and environmental justice applications. These objectives can be accomplished through the integration of satellite data with systematically repeated high-resolution aircraft- and ground-based measurements in multiple urban environments. The primary urban cities considered include Los Angeles, New York City, and Chicago in June-August 2023. Measurements consist of airborne remote sensing observations of air quality constituents of nitrogen dioxide (NO2), formaldehyde (HCHO), ozone, and aerosols using the GeoCape Airborne Simulator and High-Spectral Resolution Lidar 2/Differential Absorption Lidar (HSRL2/DIAL) on the NASA JSC G-V aircraft, greenhouse gas observations from Airborne Visible InfraRed Imaging Spectrometer - Next Generation (AVIRIS-NG) and High Altitude Lidar Observatory (HALO) on the NASA LaRC G-III aircraft, and ground-based remote sensing and in situ observations of ozone, NO2, and HCHO from the Tropospheric Ozone Lidar Network (TOLNet), Pandora spectrometers, and ground-based monitoring networks. Key partnership studies providing in situ airborne observations include the NOAA Atmospheric Emissions and Reactions Observed from Megacities to Marine Areas (AEROMMA) field study on the NASA DC-8 in the same primary target areas as STAQS and Greater New York Oxidant, Tropospheric Halogens, and Aerosol Measurements and Modeling (GOTHAMM) field study on the NSF C-130 near New York City. This presentation will include a current status update of the STAQS mission and an overview of its measurement strategies and science objectives with the goal of promoting continued discussions for building and strengthening collaborations prior to the mission.

Laura Judd↗
NASA NTRSDate not supplied

The TOLNet 2.0 Website: How an API Can Promote Open Science and FAIR Principles

The Tropospheric Ozone Lidar Network (TOLNet) has generated over a decade of ozone vertical profile data products over North America. The science value of the TOLNet data has been demonstrated in numerous peer-reviewed publications on air quality and other ozone relevant research. To support the broad spectrum of data use, the TOLNet team launched a major effort to upgrade the web-based data repository aiming to enhance the data discoverability and to enable machine-to-machine data upload and download processes. Specifically, the TOLNet website included an application programming interface (API), which supports machine-to-machine data search and data download. The API also extracts selected variables from the files, which can be retrieved as JSON objects and used to create data displays without having to download or open the underlying files. The TOLNet science team members can also use the API for automated data upload, including a data file scanning feature to ensure data product integrity. To be presented will include a summary of key features of data repositories, an actual use case of machine-to-machine data access/use, as well as our journey to make TOLNet data more FAIR, i.e., more findable, accessible, interoperable, and (re)usable.

Crystal Gummo↗
NASA NTRSDate not supplied

Observation of Pollution Events at Low Altitude During TRACER-AQ – The Advantage of Adaptive Resolution Lidar

The TRACER-AQ campaign happened in August-September 2021 in Houston, Texas. Its objective was to better understand the pollution events in the coastal environment and to evaluate models and satellite observations. As a supporting instrument, NASA’s Langley Mobile Ozone Lidar (LMOL) was installed on the Houston campus, adjacent to University of Houston’s air-quality site, and performed observations of ozone and aerosols. Co-located instrumentation included a Pandora spectrophotometer, in-situ trace-gas measurements, and ozonesonde launches. Thanks to an adaptive resolution retrieval scheme, LMOL was able to detect thin-layer O3 structures at low (~200m) altitude that are not captured by the ozonesondes, and would ordinarily be missed with traditional coarse TOLNet lidar vertical averaging. We show that some of these features are not present in the models and are likely coming from local, industry-generated, emissions.

Guillaume Gronoff↗
NASA NTRSDate not supplied

A NASA Airborne Lens Into Air Quality Field Studies of the Last Decade

Field studies add an enhanced perspective to our everyday observing system for air quality with the goals of better understanding the air we breathe and identifying solutions toward a healthier future. Over the last decade, over 10 air quality field studies were conducted around the US with other ranging internationally with support through large agency-led efforts down to the grass-roots collaborative style. This presentation will highlight how NASA airborne observations have fit as one piece of the integrated observing system for air quality during these field studies. Research topics to be discussed are centered around the idea of the transition to geostationary air quality satellite observations. Specific topics include how temporally and spatially resolved measurements help us learn about knowledge gaps in NOx emissions, satellite-proxies for surface air quality, as well as evaluating state-of-the-art chemical transport models and using the enhanced observations as tools for understand what models/satellites can and cannot resolve. Each research topic will aim to discuss how the field measurement strategies that made this work possible as well as specific challenges that still exist to take these results further. Lastly, this presentation will discuss strategies to be carried forward as well as new ones as a peek into future air quality airborne field work.

Laura Judd↗