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NASA NTRSDate not supplied

Analysis of CO Transport from Australia Fires and Its Impact to Ozone, Water Vapor and Clouds Using CrIS Single-Field-of-View Sounder Products and Other Remote Sensing and MERRA-2 Reanalysis Data

Australia’s unprecedented fire disasters at the end of 2019 to early 2020 emitted huge amounts of carbon monoxide (CO) and fire aerosol particles, and the long-range transport of the smoke plumes poses great impact to climate change, such as altering the Antarctic ozone (O3) hole. Spaceborne observations from satellites have been proven successful in monitoring CO from wildfires in the past decade and can be used to examine the associated CO transport. The primary remote sensing data used in this study is the Single Field of View (SFOV) Sounder Atmospheric Product (SiFSAP) derived from the Cross-track Infrared Sounder (CrIS) on SNPP, a new product that will be generated at NASA and released to the public in a few months. SiFSAP has a high spatial resolution of ~14 km and includes atmospheric profiles of temperature, water vapor, CO, O3, as well as cloud products, and as the SiFSAP algorithm performs both day- and night-time retrievals, it provides an additional observation for night-time in comparison to the day-time-only solar polar satellite instruments such as MOPPIT, OMPS, and TROPOMI. Most recent significant improvements in the quality of this product plus its good spatial resolution (better than most global weather and climate models) enable us to conduct process-oriented analysis of CO transport, and the variation of water vapor, O3 and clouds (optical depth, effective radius and cloud height) within and around the fire plumes. The wind fields from MERRA-2 reanalysis will be used to help characterize the transport. Other remote sensing data, such as CO from AIRS, MOPPIT and TROPOMI, O3 from OMPS and TROPOMI, as well as the UV aerosol index from OMI and OMPS, will be used and compared with SiFSAP sounder products. Cross-validation of CO and O3 from these different sensors with the corresponding MERRA-2 products will also be carried out. Along the plume transport path, some enhancement of O3 and correlation between fire aerosol, cloud optical depth and water vapor have been found but need further investigation. Using the transport of fire plumes over the Southern Pacific Ocean as a unique testbed, this study will demonstrate the trace species monitoring capability of current satellite observations, in particular, for CO and O3, and hopefully, it will provide some insight on the impacts on fire emissions on air quality and climate.

Xiaozhen (Shawn) Xiong↗
NASA NTRS2005

Lightning NOx Production and Its Consequences for Tropospheric Chemistry

Cloud-resolving case-study simulations of convective transport and lightning NO production have yielded results which are directly applicable to the design of lightning parameterizations for global chemical transport models. In this work we have used cloud-resolving models (the Goddard Cumulus Ensemble Model (GCE) and MMS) to drive an off-line cloud-scale chemical transport model (CSCTM). The CSCTM, in conjunction with aircraft measurements of NO x in thunderstorms and ground-l;>ased lightning observations, has been used to constrain the amount of NO produced per flash. Cloud and chemistry simulations for several case studies of storms in different environments will be presented. Observed lightning flash rates have been incorporated into the CSCTM, and several scenarios of NO production per intracloud (IC) and per cloud-to-ground (CG) flash have been tested for each storm. The resulting NOx mixing ratios are compared with aircraft measurements taken within the storm (typically the anvil region) to determine the most likely NO production scenario. The range of values of NO production per flash (or per meter of lightning channel length) that have been deduced from the model will be shown and compared with values of production in the literature that have been deduced from observed NO spikes and from anvil flux calculations. Results show that on a per flash basis, IC flashes are nearly as productive of NO as CG flashes. This result simplifies the lightning parameterization for global models (ie., an algorithm for estimating the IC/CG ratio is not necessary). Vertical profiles of lightning NOx mass at the end of the 3-D storm simulations have been summarized to yield suggested profiles for use in global models. Estimates of mean NO production per flash vary by a factor of three from one simulated storm to another. When combined with the global flash rate of 44 flashes per second from NASA's Optical Transient Detector (OTD) measurements, these estimates and the results from other techniques yield global NO production rates of2-9 TgN/year. Simulations of the photochemistry over the 24 hours following a storm has been performed to determine the additional ozone production which can be attributed to lightning NO. Convective transport of HOx precursors leads to the generation of a HOx plume which substantially aids the downstream ozone production.

Pickering, Kenneth E.↗
NASA NTRS2005

Lightning NO(x) Production and Its Consequences for Tropospheric Chemistry

Cloud-resolving case-study simulations of convective transport and lightning NO production have yielded results which are directly applicable to the design of lightning parameterizations for global chemical transport models. In this work we have used cloud-resolving models (the Goddard Cumulus Ensemble Model (GCE) and MMS) to drive an off-line cloud-scale chemical transport model (CSCTM). The CSCTM, in conjunction with aircraft measurements of NO(x) in thunderstorms and ground-based lightning observations, has been used to constrain the amount of NO produced per flash. Cloud and chemistry simulations for several case studies of storms in different environments will be presented. Observed lightning flash rates have been incorporated into the CSCTM, and several scenarios of NO production per intracloud (IC) and per cloud-to-ground (CG) flash have been tested for each storm. The resulting NO(x) mixing ratios are compared with aircraft measurements taken within the storm (typically the anvil region) to determine the most likely NO production scenario. The range of values of NO production per flash (or per meter of lightning channel length) that have been deduced from the model will be shown and compared with values of production in the literature that have been deduced from observed NO spikes and from anvil flux calculations. Results show that on a per flash basis, IC flashes are nearly as productive of NO as CG flashes. This result simplifies the lightning parameterization for global models (ie., an algorithm for estimating the ICKG ratio is not necessary). Vertical profiles of lightning NOx mass at the end of the 3-D storm simulations have been summarized to yield suggested profiles for use in global models. Estimates of mean NO production per flash vary by a factor of three from one simulated storm to another. When combined with the global flash rate of 44 flashes per second from NASA's Optical Transient Detector (OTD) measurements, these estimates and the results from other techniques yield global NO production rates of 2-9 TgN/year. Simulations of the photochemistry over the 24 hours following a storm has been performed to determine the additional ozone production which can be attributed to lightning NO. Convective transport of HO(x), precursors leads to the generation of a HO(x), plume which substantially aids the downstream ozone production.

Pickering, Kenneth E.↗
NASA NTRS1998

US Participation in the GOME and SCIAMACHY Projects

This report summarizes research done under NASA Grant NAGW-2541 through September 30, 1997. The research performed under this grant includes development and maintenance of scientific software for the GOME retrieval algorithms, consultation on operational software development for GOME, sensitivity and instrument studies to define GOME and SCIAMACHY instruments, consultation on optical and detector issues for both GOME and SCIAMACHY, consultation and development for SCIAMACHY near-real-time (NRT) and off-line (OL) data products, and development of infrared line-by-line atmospheric modeling and retrieval capability for SCIAMACHY. The European Space Agency selected the SAO to participate in GOME validation and science studies, part of the overall ERS AO. This provided access to all GOME data; The SAO activities that are carried out as a result of selection by ESA were funded by the present grant. The Global Ozone Monitoring Experiment was successfully launched on the ERS- 2 satellite on April 20, 1995, and remains working in normal fashion. SCIAMACHY is currently scheduled for launch in early 2000. The first two European ozone monitoring instruments (OMI), to fly on the q series of operational meteorological satellites being planned by Eumetsat, have been selected to be GOME-type instruments (the first, in fact, will be the refurbished GOME flight spare). K. Chance is the U.S. member of the OMI Users Advisory Group.

Chance, K. V.↗
DOE OSTI2021

Technical note: AQMEII4 Activity 1: evaluation of wet and dry deposition schemes as an integral part of regional-scale air quality models

We present in this technical note the research protocol for phase 4 of the Air Quality Model Evaluation International Initiative (AQMEII4). This research initiative is divided into two activities, collectively having three goals: (i) to define the current state of the science with respect to representations of wet and especially dry deposition in regional models, (ii) to quantify the extent to which different dry deposition parameterizations influence retrospective air pollutant concentration and flux predictions, and (iii) to identify, through the use of a common set of detailed diagnostics, sensitivity simulations, model evaluation, and reduction of input uncertainty, the specific causes for the current range of these predictions. Activity 1 is dedicated to the diagnostic evaluation of wet and dry deposition processes in regional air quality models (described in this paper), and Activity 2 to the evaluation of dry deposition point models against ozone flux measurements at multiple towers with multiyear observations (to be described in future submissions as part of the special issue on AQMEII4). The scope of this paper is to present the scientific protocols for Activity 1, as well as to summarize the technical information associated with the different dry deposition approaches used by the participating research groups of AQMEII4. In addition to describing all common aspects and data used for this multi-model evaluation activity, most importantly, we present the strategy devised to allow a common process-level comparison of dry deposition obtained from models using sometimes very different dry deposition schemes. The strategy is based on adding detailed diagnostics to the algorithms used in the dry deposition modules of existing regional air quality models, in particular archiving diagnostics specific to land use–land cover (LULC) and creating standardized LULC categories to facilitate cross-comparison of LULC-specific dry deposition parameters and processes, as well as archiving effective conductance and effective flux as means for comparing the relative influence of different pathways towards the net or total dry deposition. This new approach, along with an analysis of precipitation and wet deposition fields, will provide an unprecedented process-oriented comparison of deposition in regional air quality models. Examples of how specific dry deposition schemes used in participating models have been reduced to the common set of comparable diagnostics defined for AQMEII4 are also presented.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

Land and Atmosphere Near-Real-Time Capability for Earth Observing System

The past decade has seen a rapid increase in availability and usage of near-real-time data from satellite sensors. The EOSDIS (Earth Observing System Data and Information System) was not originally designed to provide data with sufficiently low latency to satisfy the requirements for near-real-time users. The EOS (Earth Observing System) instruments aboard the Terra, Aqua and Aura satellites make global measurements daily, which are processed into higher-level 'standard' products within 8-40 hours of observation and then made available to users, primarily earth science researchers. However, applications users, operational agencies, and even researchers desire EOS products in near-real-time to support research and applications, including numerical weather and climate prediction and forecasting, monitoring of natural hazards, ecological/invasive species, agriculture, air quality, disaster relief and homeland security. These users often need data much sooner than routine science processing allows, usually within 3 hours, and are willing to trade science product quality for timely access. While Direct Broadcast provides more timely access to data, it does not provide global coverage. In 2002, a joint initiative between NASA (National Aeronautics and Space Administration), NOAA (National Oceanic and Atmospheric Administration), and the DOD (Department of Defense) was undertaken to provide data from EOS instruments in near-real-time. The NRTPE (Near Real Time Processing Effort) provided products within 3 hours of observation on a best-effort basis. As the popularity of these near-real-time products and applications grew, multiple near-real-time systems began to spring up such as the Rapid Response System. In recognizing the dependence of customers on this data and the need for highly reliable and timely data access, NASA's Earth Science Division sponsored the Earth Science Data and Information System Project (ESDIS)-led development of a new near-real-time system called LANCE (Land, Atmosphere Near-Real-Time Capability for EOS) in 2009. LANCE consists of special processing elements, co-located with selected EOSDIS data centers and processing facilities. A primary goal of LANCE is to bring multiple near-real-time systems under one umbrella, offering commonality in data access, quality control, and latency. LANCE now processes and distributes data from the Moderate Resolution Imaging Spectroradiometer (MODIS), Atmospheric Infrared Sounder (AIRS), Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E), Microwave Limb Sounder (MLS) and Ozone Monitoring Instrument (OMI) instruments within 3 hours of satellite observation. The Rapid Response System and the Fire Information for Resource Management System (FIRMS) capabilities will be incorporated into LANCE in 2011. LANCE maintains a central website to facilitate easy access to data and user services. LANCE products are extensively tested and compared with science products before being made available to users. Each element also plans to implement redundant network, power and server infrastructure to ensure high availability of data and services. Through the user registration system, users are informed of any data outages and when new products or services will be available for access. Building on a significant investment by NASA in developing science algorithms and products, LANCE creates products that have a demonstrated utility for applications requiring near-real-time data. From lower level data products such as calibrated geolocated radiances to higher-level products such as sea ice extent, snow cover, and cloud cover, users have integrated LANCE data into forecast models and decision support systems. The table above shows the current near-real-time product categories by instrument. The ESDIS Project continues to improve the LANCE system and use the experience gained through practice to seek adjustments to improve the quality and performance of the system. For example, anGC-compliant Web Map Service (WMS) will be added shortly that will allow users to download geo-referenced MODIS images for arbitrary bounding boxes. Further, an OGC-compliant Web Coverage Service (WCS) will be added later this year that will expedite user access to arbitrary data subsets or re-formatted products. AIRS images are now served through WMS and available in multiple formats (PNG, GeoTIFF, KMZ). NASA has established a LANCE User Working Group to steer the development of the system and create a forum for sharing ideas and experiences that are expected to further improve the LANCE capabilities. The LANCE system has proved a success by satisfying the growing needs of the applications and operational communities for land and atmosphere data in near-real-time. NASA's Earth Sciences Division was able to leverage existing science research capabilities to provide the near-real-time community with products and imagery that support monitoring of disasters in a timely manner.

Murphy, Kevin J.↗
NASA NTRS2016

Effects of Polar Stratospheric Clouds in the Nimbus 7 LIMS Version 6 Data Set

The historic Limb Infrared Monitor of the Stratosphere (LIMS) measurements of 1978-1979 from the Nimbus 7 satellite were re-processed with Version 6 (V6) algorithms and archived in 2002. The V6 data set employs updated radiance registration methods, improved spectroscopic line parameters, and a common vertical resolution for all retrieved parameters. Retrieved profiles are spaced about every 1.6 of latitude along orbits and include the additional parameter of geopotential height. Profiles of O3 are sensitive to perturbations from emissions of polar stratospheric clouds (PSCs). This work presents results of implementing a first-order screening for effects of PSCs using simple algorithms based on vertical gradients of the O3 mixing ratio. Their occurrences are compared with the co-located, retrieved temperatures and related to the temperature thresholds needed for saturation of H2O and/or HNO3 vapor onto PSC particles. Observed daily locations where the major PSC screening criteria are satisfied are validated against PSCs observed with the Stratospheric Aerosol Monitor (SAM) II experiment also on Nimbus 7. Remnants of emissions from PSCs are characterized for O3 and HNO3 following the screening. PSCs may also impart a warm bias in the co-located LIMS temperatures, but by no more than 1-2K at the altitudes of where effects of PSCs are a maximum in the ozone; thus, no PSC screening was applied to the V6 temperatures. Minimum temperatures vary between 187 and 194K and often occur 1 to 2 km above where PSC effects are first identified in the ozone (most often between about 21 and 28 hPa). Those temperature-pressure values are consistent with conditions for the existence of nitric acid trihydrate (NAT) mixtures and to a lesser extent of super-cooled ternary solution (STS) droplets. A local, temporary uptake of HNO3 vapor of order 1-3 ppbv is indicated during mid-January for the 550K surface. Seven-month time series of the distributions of LIMS O3 and HNO3 are shown based on their gridded Level 3 data following the PSC screening. Zonal coefficients of both species are essentially free of effects from PSCs on the 550K surface, based on their average values along PV contours and in terms of equivalent latitude. Remnants of PSCs are still present in O3 on the 450K surface during mid- January. It is judged that the LIMS Level 3 data are of good quality for analyzing the larger-scale, stratospheric chemistry and transport processes during the Arctic winter of 1978- 1979.

Remsberg, Ellis↗
NASA NTRS2021

Technical Note: AQMEII4 Activity 1: Evaluation of Wet and Dry Deposition Schemes as an Integral Part of Regional-scale Air Quality Models

We present in this technical note the research protocol for phase 4 of the Air Quality Model Evaluation International Initiative (AQMEII4). This research initiative is divided into two activities, collectively having three goals: (i) to define the current state of the science with respect to representations of wet and especially dry deposition in regional models, (ii) to quantify the extent to which different dry deposition parameterizations influence retrospective air pollutant concentration and flux predictions, and (iii) to identify, through the use of a common set of detailed diagnostics, sensitivity simulations, model evaluation, and reduction of input uncertainty, the specific causes for the current range of these predictions. Activity 1 is dedicated to the diagnostic evaluation of wet and dry deposition processes in regional air quality models (described in this paper), and Activity 2 to the evaluation of dry deposition point models against ozone flux measurements at multiple towers with multiyear observations (to be described in future submissions as part of the special issue on AQMEII4). The scope of this paper is to present the scientific protocols for Activity 1, as well as to summarize the technical information associated with the different dry deposition approaches used by the participating research groups of AQMEII4. In addition to describing all common aspects and data used for this multi-model evaluation activity, most importantly, we present the strategy devised to allow a common process-level comparison of dry deposition obtained from models using sometimes very different dry deposition schemes. The strategy is based on adding detailed diagnostics to the algorithms used in the dry deposition modules of existing regional air quality models, in particular archiving diagnostics specific to land use–land cover (LULC) and creating standardized LULC categories to facilitate cross-comparison of LULC-specific dry deposition parameters and processes, as well as archiving effective conductance and effective flux as means for comparing the relative influence of different pathways towards the net or total dry deposition. This new approach, along with an analysis of precipitation and wet deposition fields, will provide an unprecedented process-oriented comparison of deposition in regional air quality models. Examples of how specific dry deposition schemes used in participating models have been reduced to the common set of comparable diagnostics defined for AQMEII4 are also presented.

Air Quality Model Evaluation International Initiat↗
NASA NTRSDate not supplied

Revisiting GloSSAC Using Space Based Measurements

We revisit Global Space-based stratospheric aerosol climatology (GloSSAC) and extend the dataset through 2021 in the latest version (version 2.2). Several space-based measurements have been used to construct GloSSAC. For version 2.2, important changes include implementation of a revised aerosol/cloud categorization for The Stratospheric Aerosol and Gas Experiment (SAGE III/ISS). SAGE III/ISS began its mission in June 2017. While SAGEIII/ISS makes reliable and robust solar occultation measurements in stratosphere—similar to its predecessors, interpreting aerosol extinction measurements in the vicinity of tropopause and in the troposphere have been a challenge for all SAGE measurements. Here, we study the challenges associated with the discrimination of aerosols and clouds from the extinction measurements. Here, we describe the methods implemented to categorize Clouds and aerosols using available SAGEIII/ISS aerosol measurements. We use version 5.2 of SAGE III/ISS extinction coefficients for the analysis. The current algorithm now classifies standard (background) and non-standard (enhanced) aerosols in the stratosphere and identify enhanced aerosols and aerosol/cloud mixture in the tropopause region. Extinction coefficient measurements from SAGE series of observations make an important contribution in the GloSSAC data base and therefore, the impact of cloud-filtered aerosol extinction coefficient measurements on the latest version of GloSSAC (version 2.2) is also discussed. Additionally, we discuss minor version changes occurred in other space-based measurements that include Optical Spectrograph and InfraRed Imaging System(OSIRIS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation(CALIPSO). We also plan on evaluating and using aerosol extinction profiles from other satellite data sets such as Ozone Mapping Profiler Suite (OMPS) in a future version.

stratospheric aerosol↗
NASA NTRSDate not supplied

Use of TEMPO as a Proxy for Hyperspectral Geostationary Ocean Color Measurements from the GeoXO OCX Instrument: Harnessing Machine Learning and Principal Component Techniques for Atmospheric and Glint Correction

Retrievals of ocean color from space are important for better understanding the ocean ecosystem. The launch of atmospheric geostationary hyperspectral sensors such as TEMPO, provides a unique opportunity to examine the diurnal variability in ocean ecology. While TEMPO does not have as high spatial resolution or full spectral coverage as planned coastal ocean sensors such as the Geosynchronous Littoral Imaging and Monitoring Radiometer (GLIMR) or GeoXO Ocean Color instrument (OCX), its hourly measurements provide coverage of regions such as Lake Erie and the Gulf of Mexico at spatial scales of approximately 5 km. These data can be useful for testing new algorithms. We will apply our newly developed machine learning based atmospheric correction approach for ocean color retrievals to TEMPO data. Our approach begins by decomposing measured radiances from hyperspectral sensors into spectral features that describe the scattering and absorption of the atmosphere as well as the underlying surface reflectance. The coefficients of the principal components are then used to train a neural network to predict ocean color properties derived from collocated MODIS/VIIRS physically-based retrievals. This machine learning approach does not rely on radiative transfer modeling, and the use of MODIS/VIIRS data for training accounts for possible calibration b in hyperspectral data. Previously, we applied our approach using blue and UV wavelengths with the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) to show that it can estimate ocean color properties in less-than-ideal conditions such as lightly to moderately clouded conditions as well as sun glint and thus improve the spatial coverage of ocean color measurements. TEMPO provides an opportunity to improve on this approach since it will provide collocated measurements at green and red wavelengths that were not available from OMI and TROPOMI and are important particularly for coastal waters. Additionally, our technique can be applied early in the mission and has potential to demonstrate the value of near real time ocean color products that are important for monitoring of harmful algae blooms and other oceanic phenomena.

Zachary Fasnacht↗
NASA NTRSDate not supplied

Enabling Analysis of Air Quality Data From Tropospheric Emissions: Monitoring of POllution (Tempo) Via Cloud-Based Tools

Launched in April 2023, the Tropospheric Emissions: Monitoring of POllution (TEMPO) instrument provides high-resolution measurements of key atmospheric pollutants, such as ozone, nitrogen dioxide, and formaldehyde. Maximizing the use and utility of this new source of air quality information requires streamlining data access for a wide variety of research, public health, and other interested users. These varied applications often require the data to be structured in different ways, e.g., specific formats, array shapes, or file sizes. To enable access to TEMPO data in different forms, the NASA Atmospheric Science Data Center (ASDC), as part of the NASA Earth Science Data and Information System (ESDIS), provides a variety of cloud-based data transformation and GIS visualization tools. This presentation demonstrates methods of accessing and working with TEMPO data through these services, while highlighting aspects of the software and algorithmic workflows that perform the necessary data transformations. Examples include data subsetting, concatenation, and visualizations accessible via Jupyter notebooks and GIS software.

Daniel Kaufman↗
NASA NTRS2007

Solutions Network Formulation Report. NASA's Potential Contributions for Using Solar Ultraviolet Radiation in Conjunction with Photocatalysis for Urban Air Pollution Mitigation and Increasing Air Quality

This Candidate Solution is based on using NASA Earth science research on atmospheric ozone and aerosols data as a means to predict and evaluate the effectiveness of photocatalytically created surfaces (building materials like glass, tile and cement) for air pollution mitigation purposes. When these surfaces are exposed to near UV light, organic molecules, like air pollutants and smog precursors, will degrade into environmentally friendly compounds. U.S. EPA (Environmental Protection Agency) is responsible for forecasting daily air quality by using the Air Quality Index (AQI) that is provided by AIRNow. EPA is partnered with AIRNow and is responsible for calculating the AQI for five major air pollutants that are regulated by the Clean Air Act. In this Solution, UV irradiance data acquired from the satellite mission Aura and the OMI Surface UV algorithm will be used to help understand both the efficacy and efficiency of the photocatalytic decomposition process these surfaces facilitate, and their ability to reduce air pollutants. Prediction models that estimate photocatalytic function do not exist. NASA UV irradiance data will enable this capability, so that air quality agencies that are run by state and local officials can develop and implement programs that utilize photocatalysis for urban air pollution control and, enable them to make effective decisions about air pollution protection programs.

Underwood, Lauren↗
NASA NTRS2017

Simulation of the Ozone Monitoring Instrument Aerosol Index Using the NASA Goddard Earth Observing System Aerosol Reanalysis Products

We provide an analysis of the commonly used Ozone Monitoring Instrument (OMI) aerosol index (AI) product for qualitative detection of the presence and loading of absorbing aerosols. In our analysis, simulated top-of-atmosphere (TOA) radiances are produced at the OMI footprints from a model atmosphere and aerosol profile provided by the NASA Goddard Earth Observing System (GEOS-5) Modern-Era Retrospective Analysis for Research and Applications aerosol reanalysis (MERRAero). Having established the credibility of the MERRAero simulation of the OMI AI in a previous paper we describe updates in the approach and aerosol optical property assumptions. The OMI TOA radiances are computed in cloud-free conditions from the MERRAero atmospheric state, and the AI is calculated. The simulated TOA radiances are fed to the OMI aerosol retrieval algorithms, and its retrieved AI (OMAERUV AI) is compared to the MERRAero calculated AI. Two main sources of discrepancy are discussed: one pertaining the OMI algorithm assumptions of the surface pressure, which are generally different from what the actual surface pressure of an observation is, and the other related to simplifying assumptions in the molecular atmosphere radiative transfer used in the OMI algorithms. Surface pressure assumptions lead to systematic biases in the OMAERUV AI, particularly over the oceans. Simplifications in the molecular radiative transfer lead to biases particularly in regions of topography intermediate to surface pressures of 600hPa and 1013.25hPa. Generally, the errors in the OMI AI due to these considerations are less than 0.2 in magnitude, though larger errors are possible, particularly over land. We recommend that future versions of the OMI algorithms use surface pressures from readily available atmospheric analyses combined with high-spatial resolution topographic maps and include more surface pressure nodal points in their radiative transfer lookup tables.

remote sensing↗
NASA NTRSDate not supplied

Modeling Atmospheric Science Knowledge from Research Publications

NASA Earth Science Data Centers contain enormous amounts of remote sensing digital data. It is often a significant challenge for users to find data suitable for their research topic in these vast archives. One of the approaches is the usage-driven dataset discovery, where users seek publications on projects similar to their intended study. For this approach to be effective, users need a clear connection between the underlying data in the publications and the study objectives; this is not often apparent to non-expert users. Tools and methodologies that can help facilitate and organize these connections are therefore valuable for creating improved knowledge mappings, which can be further used by search engines to suggest data or publications best tailored to a user’s specific research goal. As an illustration of these challenges, in this work we focus on the atmospheric chemistry processes related to Earth environmental impacts such as ozone depletion, aerosols, smog formation, acid rain, and radiative forcing. We further limit our study to publications that use data from the Microwave Limb Sounder (MLS) instrument flown on the Aura Earth Observing System. To create knowledge representations of science carried out in these publications, we use existing ontologies such as the Global Change Master Directory (GCMD) and Semantic Web for Earth and Environmental Terminology (SWEET). These ontologies together encompass term dictionaries that include measured variables, names of molecules or radicals, mission and instrument names, locations, action words, among many others. Based on these terms acknowledge graph database was populated with the terms retrieved from scientific publications that study atmospheric chemistry. These databases can be used to further enhance the automation of knowledge discovery and facilitate machine learning and artificial intelligence algorithms or applications. These tools and methods can also be extended to apply to content from other related Earth science domains.

Irina Gerasimov↗
NASA NTRS2014

Use Of EOS-AURA Observation In The MERRA-2 Reanalysis

Meteorological reanalyses provide multi-year gridded datasets that describe the evolution of the atmosphere. Such products use a data assimilation system, comprising of an atmospheric model, a broad suite of observations, and an analysis system that optimally combines the model forecast with the observations, using an algorithm that includes information about model and data accuracy. The mixture of observations is of central importance to the quality of the assimilated datasets. The Modern-era Retrospective Analysis for Research and Applications (MERRA) included constraints on the thermal structure of the middle atmosphere from nadir sounders on the NOAA polar-orbiting platforms (Stratospheric Sounding Units and Advanced Microwave Sounding Units). These instruments have peak sensitivities that occur well below the stratopause. As such, the radiance measurements do not provide strong constraints on stratopause temperature. The new MERRA-2 reanalysis is using EOS-MLS temperature retrievals after they are available: it will be demonstrated that these data lead to a more realistic stratopause structure in MERRA-2 than in MERRA. Similarly, the work demonstrates the improvements in lower stratospheric ozone in MERRA-2 than in MERRA, for the period when EOS-MLS ozone data are assimilated. This improvement occurs because of the ozone profile information offered by MLS in the low stratosphere, in contrast to the SBUV/2 data used for the rest of MERRA-2. The impacts of choosing to use the EOS-MLS datasets are discussed in context of the continuity of the data record in MERRA- 2.

Meteorological↗
NASA NTRSDate not supplied

TPSAS-NF1676L-29194-DND

The Stratospheric Aerosol and Gas Experiment (SAGE) III instrument was deployed on the International Space Station (ISS) in early March 2017. SAGE III/ISS is the latest in the NASA lineage of solar occultation satellite instruments designed to monitor the vertical distribution of aerosol, ozone and other trace gases in the upper atmosphere. Originally built in the early 2000s, the SAGE III/ISS instrument required extensive testing and refurbishment prior to deliver to ISS, including the replacement of the neutral density filter that has been associated with some instrument response issues during the METEOR/3M mission. In particular, METEOR/3M measurements of the oxygen A-band absorption feature near 762 nm that were to be utilized to retrieve profiles of temperature and pressure were adversely affected by this instrument issue. Preliminary examination of the SAGE III/ISS oxygen A-band spectra indicate that the measurements are of higher fidelity than from the METEOR/3M instrument, which may permit successful retrieval of science quality temperature and pressure products. In this paper, we will provide an overview of the temperature and pressure retrieval algorithm, present preliminary analyses of the SAGE III/ISS oxygen A-band measurements, and an initial assessment of the quality of the temperature and pressure retrievals.

Michael Pitts↗
NASA NTRS2012

SO2 over Central China: Measurements, Numerical Simulations and the Tropospheric Sulfur Budget

SO2 in central China was measured in situ from an aircraft and remotely using the Ozone Monitoring Instrument (OMI) from the Aura satellite; results were used to develop a numerical tool for evaluating the tropospheric sulfur budget - sources, sinks, transformation and transport. In April 2008, measured ambient SO2 concentrations decreased from approx.7 ppbv near the surface to approx. 1 ppbv at 1800 m altitude (an effective scale height of approx.800 m), but distinct SO2 plumes were observed between 1800 and 4500 m, the aircraft's ceiling. These free tropospheric plumes play a major role in the export of SO2 and in the accuracy of OMI retrievals. The mean SO2 column contents from aircraft measurements (0.73 DU, Dobson Units) and operational OMI SO2 products (0.63+/-0.26 DU) were close. The OMI retrievals were well correlated with in situ measurements (r = 0.84), but showed low bias (slope = 0.54). A new OMI retrieval algorithm was tested and showed improved agreement and bias (r = 0.87, slope = 0.86). The Community Multiscale Air Quality (CMAQ) model was used to simulate sulfur chemistry, exhibiting reasonable agreement (r = 0.62, slope = 1.33) with in situ SO2 columns. The mean CMAQ SO2 loading over central and eastern China was 54 kT, approx.30% more than the estimate from OMI SO2 products, 42 kT. These numerical simulations, constrained by observations, indicate that ",50% (35 to 61 %) of the anthropogenic sulfur emissions were transported downwind, and the overall lifetime of tropospheric SO2 was 38+/-7 h.

He, Hao↗
NASA NTRS2021

A Sulfur Dioxide Covariance-Based Retrieval Algorithm (COBRA): Application to TROPOMI Reveals New Emission Sources

Sensitive and accurate detection of sulfur dioxide (SO 2 ) from space is important for monitoring and estimating global sulfur emissions. Inspired by detection methods applied in the thermal infrared, we present here a new scheme to retrieve SO 2 columns from satellite observations of ultraviolet back-scattered radiances. The retrieval is based on a measurement error covariance matrix to fully represent the SO 2 -free radiance variability, so that the SO 2 slant column density is the only retrieved parameter of the algorithm. We demonstrate this approach, named COBRA, on measurements from the TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor (S-5P) satellite. We show that the method reduces significantly both the noise and biases present in the current TROPOMI operational DOAS SO 2 retrievals. The performance of this technique is also benchmarked against that of the Principal Component Algorithm (PCA) approach. We find that the quality of the data is similar and even slightly better with the proposed COBRA approach. The ability of the algorithm to retrieve SO 2 accurately is also further supported by comparison with ground-based observations. We illustrate the great sensitivity of the method with a high-resolution global SO 2 map, considering two and a half years of TROPOMI data. In addition to the known sources, we detect many new SO 2 emission hotspots worldwide. For the largest sources, we use the COBRA data to estimate SO 2 emission rates. Results are comparable to other recently published TROPOMI-based SO 2 emissions estimates, but the associated uncertainties are significantly lower than with the operational data. Next, for a limited number of weak sources, we demonstrate the potential of our data for quantifying SO 2 emissions with a detection limit of about 8 kt yr -1 , a factor of 4 better than the emissions derived from the Ozone Monitoring Instrument (OMI). We anticipate that the systematic use of our TROPOMI COBRA SO 2 column data set at a global scale will allow identifying and quantifying missing sources, and help improving SO 2 emission inventories.

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