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NASA NTRS2023

Characterization of Errors in Satellite-Based HCHO/NO 2 Tropospheric Column Ratios With Respect to Chemistry, Column-to-PBL Translation, Spatial Representation, and Retrieval Uncertainties

The availability of formaldehyde (HCHO) (a proxy for volatile organic compound reactivity) and nitrogen dioxide (NO 2 ) (a proxy for nitrogen oxides) tropospheric columns from ultraviolet–visible (UV–Vis) satellites has motivated many to use their ratios to gain some insights into the near-surface ozone sensitivity. Strong emphasis has been placed on the challenges that come with transforming what is being observed in the tropospheric column to what is actually in the planetary boundary layer (PBL) and near the surface; however, little attention has been paid to other sources of error such as chemistry, spatial representation, and retrieval uncertainties. Here we leverage a wide spectrum of tools and data to quantify those errors carefully. Concerning the chemistry error, a well-characterized box model constrained by more than 500 h of aircraft data from NASA's air quality campaigns is used to simulate the ratio of the chemical loss of HO 2 + RO 2 (LRO x ) to the chemical loss of NO x (LNO x ). Subsequently, we challenge the predictive power of HCHO/NO 2 ratios (FNRs), which are commonly applied in current research, in detecting the underlying ozone regimes by comparing them to LRO x /LNO x . FNRs show a strongly linear (R 2 =0.94) relationship with LRO x /LNO x , but only on the logarithmic scale. Following the baseline (i.e., ln(LRO x /LNO x ) = −1.0 ± 0.2) with the model and mechanism (CB06, r2) used for segregating NO x -sensitive from VOC-sensitive regimes, we observe a broad range of FNR thresholds ranging from 1 to 4. The transitioning ratios strictly follow a Gaussian distribution with a mean and standard deviation of 1.8 and 0.4, respectively. This implies that the FNR has an inherent 20 % standard error (1σ) resulting from not accurately describing the RO x –HO x cycle. We calculate high ozone production rates (PO 3 ) dominated by large HCHO × NO 2 concentration levels, a new proxy for the abundance of ozone precursors. The relationship between PO 3 and HCHO × NO 2 becomes more pronounced when moving towards NO x -sensitive regions due to nonlinear chemistry; our results indicate that there is fruitful information in the HCHO × NO 2 metric that has not been utilized in ozone studies. The vast amount of vertical information on HCHO and NO 2 concentrations from the air quality campaigns enables us to parameterize the vertical shapes of FNRs using a second-order rational function permitting an analytical solution for an altitude adjustment factor to partition the tropospheric columns into the PBL region. We propose a mathematical solution to the spatial representation error based on modeling isotropic semivariograms. Based on summertime-averaged data, the Ozone Monitoring Instrument (OMI) loses 12 % of its spatial information at its native resolution with respect to a high-resolution sensor like the TROPOspheric Monitoring Instrument (TROPOMI) (> 5.5 × 3.5 km 2 ). A pixel with a grid size of 216 km 2 fails at capturing ∼ 65 % of the spatial information in FNRs at a 50 km length scale comparable to the size of a large urban center (e.g., Los Angeles). We ultimately leverage a large suite of in situ and ground-based remote sensing measurements to draw the error distributions of daily TROPOMI and OMI tropospheric NO 2 and HCHO columns. At a 68 % confidence interval (1σ), errors pertaining to daily TROPOMI observations, either HCHO or tropospheric NO 2 columns, should be above 1.2–1.5 × 10 16 molec. cm −2 to attain a 20 %–30 % standard error in the ratio. This level of error is almost non-achievable with the OMI given its large error in HCHO. The satellite column retrieval error is the largest contributor to the total error (40 %–90 %) in the FNRs. Due to a stronger signal in cities, the total relative error (< 50 %) tends to be mild, whereas areas with low vegetation and anthropogenic sources (e.g., the Rocky Mountains) are markedly uncertain (> 100 %). Our study suggests that continuing development in the retrieval algorithm and sensor design and calibration is essential to be able to advance the application of FNRs beyond a qualitative metric.

Amir H. Souri↗
NASA NTRS2023

Highly Resolved Mapping of NO 2 Vertical Column Densities From GeoTASO Measurements Over a Megacity and Industrial Area During the KORUS-AQ Campaign

The Korea–United States Air Quality (KORUS-AQ) campaign is a joint study between the United States National Aeronautics and Space Administration (NASA) and the South Korea National Institute of Environmental Research (NIER) to monitor megacity and transboundary air pollution around the Korean Peninsula using airborne and ground-based measurements. Here, tropospheric nitrogen dioxide (NO 2 ) slant column density (SCD) measurements were retrieved from Geostationary Trace and Aerosol Sensor Optimization (GeoTASO) L1B data during the KORUS-AQ campaign (2 May to 10 June 2016). The retrieved SCDs were converted to tropospheric vertical column densities using the air mass factor (AMF) obtained from a radiative transfer calculation with trace gas profiles and aerosol property inputs simulated with the Community Multiscale Air Quality (CMAQ) model and surface reflectance data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). For the first time, we examine highly resolved (250 m × 250 m resolution) tropospheric NO 2 over the Seoul and Busan metropolitan regions and the industrial region of Anmyeon. We reveal that the maximum NO 2 vertical column densities (VCDs) were 4.94×10 16 and 1.46×10 17 molec. cm −2 at 09:00 and 15:00 LT over Seoul, respectively, 6.86×10 16 and 4.89×10 16 molec. cm −2 in the morning and afternoon over Busan, respectively, and 1.64×10 16 molec. cm −2 over Anmyeon. The VCDs retrieved from the GeoTASO airborne instrument were correlated with those obtained from the Ozone Monitoring Instrument (OMI) (r=0.48), NASA's Pandora Spectrometer System (r=0.91), and NO 2 mixing ratios obtained from in situ measurements (r=0.07 in the morning, r=0.26 in the afternoon over the Seoul, and r>0.56 over Busan). Based on our results, GeoTASO is useful for identifying NO 2 hotspots and their spatial distribution in highly populated cities and industrial areas.

Gyo-Hwang Choo↗
NASA NTRSDate not supplied

Atmospheric Formaldehyde Trend and Its Source Attributions in the Recent Decades

Formaldehyde (CH 2 O) is one of the most important reactive trace gases in the atmosphere with an important role in tropospheric chemistry and in the control of surface air quality. Sources of CH 2 O includes direct emissions and in situ production from the oxidation of volatile organic compounds (VOCs). In this study, we investigate spatial-temporal variations in atmospheric CH 2 O and how changes in CH 2 O couple with tropospheric ozone variability during the past two decades using recently available retrievals from the Ozone Monitoring Instrument (OMI) aboard NASA’s Aura satellite and the RefD1 simulation from the NASA Goddard Earth Observing System Chemistry Climate Model (GEOSCCM). Our initial analysis provides evidence of significant increases in atmospheric CH 2 O concentrations over several of the world’s major anthropogenic regions, including China, India, and the Middle East, and several biomass burning regions, including South America, southern Africa, boreal regions and Indonesia. We furthermore will identify regional source contributions to CH 2 O trends, including anthropogenic VOCs emissions, biogenic isoprene emissions and open fires. In particular, we will focus on anthropogenic emission regions to examine the role of changing anthropogenic VOC emissions on CH 2 O abundance.

Atmospheric Formaldehyde↗
NASA NTRS2023

Use of Machine Learning and Principal Component Analysis to Retrieve Nitrogen Dioxide (NO 2 ) With Hyperspectral Imagers and Reduce Noise in Spectral Fitting

Nitrogen dioxide (NO 2 ) is an important trace-gas pollutant and climate agent whose presence also leads to spectral interference in ocean color retrievals. NO 2 column densities have been retrieved with satellite UV–Vis spectrometers such as the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI) that typically have spectral resolutions of the order of 0.5 nm or better and spatial footprints as small as 3.6 km × 5.6 km. These NO 2 observations are used to estimate emissions, monitor pollution trends, and study effects on human health. Here, we investigate whether it is possible to retrieve NO 2 amounts with lower-spectral-resolution hyperspectral imagers such as the Ocean Color Instrument (OCI) that will fly on the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite set for launch in early 2024. OCI will have a spectral resolution of 5 nm and a spatial resolution of ∼ 1 km with global coverage in 1–2 d. At this spectral resolution, small-scale spectral structure from NO 2 absorption is still present. We use real spectra from the OMI to simulate OCI spectra that are in turn used to estimate NO 2 slant column densities (SCDs) with an artificial neural network (NN) trained on target OMI retrievals. While we obtain good results with no noise added to the OCI simulated spectra, we find that the expected instrumental noise substantially degrades the OCI NO 2 retrievals. Nevertheless, the NO 2 information from OCI may be of value for ocean color retrievals. OCI retrievals can also be temporally averaged over timescales of the order of months to reduce noise and provide higher-spatial-resolution maps that may be useful for downscaling lower-spatial-resolution data provided by instruments such as OMI and TROPOMI; this downscaling could potentially enable higher-resolution emissions estimates and be useful for other applications. In addition, we show that NNs that use coefficients of leading modes of a principal component analysis of radiance spectra as inputs appear to enable noise reduction in NO 2 retrievals. Once trained, NNs can also substantially speed up NO 2 spectral fitting algorithms as applied to OMI, TROPOMI, and similar instruments that are flying or will soon fly in geostationary orbit.

NO2↗
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

Principal Component and Machine Learning Approach to Gap Fill Hyperspectral Ocean Color Satellite Retrievals

Retrievals of ocean color properties from space are important for monitoring the health of the ocean ecosystem but such retrievals can be limited spatially due to conditions such as clouds, aerosols, and sun glint. Gap filling of ocean color retrievals is typically performed by combining retrievals from multiple satellites or temporally averaging multiple days of retrievals. Despite these techniques large gaps still exist posing challenges for near real time monitoring of events like harmful algae blooms. To address these limitations, we developed a spatial gap filling approach applying machine learning approach to hyperspectral instruments to learn how to perform an atmospheric correction under challenging retrieval conditions. In this approach a principal component analysis is used to decompose the hyperspectral measurements into spectral components that describe the scattering and absorption of the atmosphere mixed with the surface spectral signatures. The coefficients of the principal components are used to train a neural network to predict ocean color properties derived from a standard MODIS ocean color algorithm. We apply the approach to two hyperspectral UV/VIS sensors, the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) to show that it can be used to estimate ocean color properties such as chlorophyll, remote sensing reflectance, and fluorescence line height. This method could be used as a gap-filling technique for the future Ocean Color Instrument (OCI) onboard upcoming NASA's Plankton, Aerosol Cloud, ocean Ecosystem (PACE) satellite to provide additional information for monitoring the health of our global oceans. Additionally, it could be applied to the first NASA and Smithsonian geostationary Tropospheric Emissions: Monitoring of Pollution (TEMPO) spectrometer to better understand diurnal variability in inland and coastal ocean ecology.

Zachary Fasnacht↗
NASA NTRSDate not supplied

Using Machine Learning to Estimate Surface-Level SO2 Concentrations from Satellite-Based Measurements

Sulfur dioxide (SO2) is a criteria air pollutant due to its contributions to aerosol formation, rainfall acidification, and harm to human health. The placement of air quality monitoring sites is typically biased towards urban areas, leaving large areas with very limited monitoring data. The Ozone Monitoring Instrument (OMI) has been used to provide estimates of SO2 vertical column densities (VCDs) globally at spatial resolution of 10s of kms once per day. OMI SO2 VCDs have been previously used to estimate surface SO2 concentrations using chemical transport model (CTM) simulations. The CTMs use estimated emissions and assimilated meteorological data, and simulate the chemical and physical processes that determine the vertical profile of SO2, which can be used to derive a ratio between the surface concentrations and VCDs. These models are complex, computationally expensive, and have large uncertainties in the simulated surface-to-VCD ratio due to biases in emissions and relatively coarse resolution. Machine learning techniques are comparatively easier to use, much less computationally expensive to use after training, and can produce more accurate estimations of surface concentrations than the CTM-based method. The interpretation of machine learning models often poses challenges, and in some cases, non-physical variables unrelated to SO2 are used as predictors. In this work, we create an artificial neural network (ANN) to relate OMI retrievals and archived GEOS-FP boundary layer heights to surface SO2 concentrations from the ChinaHighAirPollutants ChinaHighSO2 dataset (CHAP; Wei et al., 2023) on a seasonal average timescale from 2013-2018. Our model only utilizes five variables that are directly relevant to the satellite retrieval, lifetime, and spatial distribution of SO2. The model was trained on 16 seasons (four of each) with independent validation (one of each season) and testing datasets (one of each season) to avoid overfitting. Our ANN generates surface SO2 concentrations that are sensitive (slope = 0.51) and consistent (r = 0.74) with the CHAP data, but are underpredicted by an average of 1.2 ppbv with a mean absolute error of 2.2 ppbv. These results are better than recent studies utilizing the CTM method. To our knowledge, this is the best performing machine learning model that only uses physical variables to predict surface SO2. Our work demonstrates that a carefully constructed, simple ML model can accurately estimate surface-based SO2 concentrations from satellite VCD measurements, and this technique has future promise to expend to newer, higher resolution satellites and other air pollutants.

SO2, air quality, OMI, machine learning↗
NASA NTRS2024

Sun-as-a-Star Spectral Line Variability in the 300–2390 nm Wavelength Range

Combining the near-daily Ozone Monitoring Instrument (OMI) and Tropospheric Monitoring Instrument (TROPOMI) measurements of solar spectra, we construct line indices (line-core to line-flanks ratios) for various transitions (mainly Fe I) in the 300–2390 nm spectral domain. The indices are supplemented by the wavelength-binned fluxes from OMI and Total and Spectral Solar Irradiance Sensor (TSIS-1). To study the short-term (solar-rotational) patterns, we normalize the indices and fluxes to the minimum-activity epoch, then de-trend them with 81 day running means. Comparisons of the de-trended TSIS-1 and OMI fluxes with the NASA-NOAA-LASP SSI (NNLSSI1) model show excellent agreement, to (0.5–2.2) × 10 -4 in the normalized and de-trended data. The data are subjected to a multiregression analysis against quantities representing the facular brightening and the sunspot darkening. The de-trended line indices and average fluxes show different sensitivities to these two solar magnetic-activity manifestations, with the fluxes being far more susceptible to the sunspot component. The de-trended line indices experience a rapid drop of activity levels towards longer wavelengths, albeit with a large rebound in the short-wave infrared (SWIR) domain that is caused by the ubiquitous, temperature-sensitive CO transitions. The wavelength-dependent activity also falls, however much slower, in the de-trended average fluxes. Qualitatively similar behavior is captured by semiempirical models.

Solar spectral Irradiance↗
NASA NTRS2004

Common Calibration Source for Monitoring Long-term Ozone Trends

Accurate long-term satellite measurements are crucial for monitoring the recovery of the ozone layer. The slow pace of the recovery and limited lifetimes of satellite monitoring instruments demands that datasets from multiple observation systems be combined to provide the long-term accuracy needed. A fundamental component of accurately monitoring long-term trends is the calibration of these various instruments. NASA s Radiometric Calibration and Development Facility at the Goddard Space Flight Center has provided resources to minimize calibration biases between multiple instruments through the use of a common calibration source and standardized procedures traceable to national standards. The Facility s 50 cm barium sulfate integrating sphere has been used as a common calibration source for both US and international satellite instruments, including the Total Ozone Mapping Spectrometer (TOMS), Solar Backscatter Ultraviolet 2 (SBUV/2) instruments, Shuttle SBUV (SSBUV), Ozone Mapping Instrument (OMI), Global Ozone Monitoring Experiment (GOME) (ESA), Scanning Imaging SpectroMeter for Atmospheric ChartographY (SCIAMACHY) (ESA), and others. We will discuss the advantages of using a common calibration source and its effects on long-term ozone data sets. In addition, sphere calibration results from various instruments will be presented to demonstrate the accuracy of the long-term characterization of the source itself.

Kowalewski, Matthew↗
NASA NTRS2022

Combined UV and IR Ozone Profile Retrieval from TROPOMI and CrIS Measurements

Vertical ozone profiles from combined spectral measurements in the ultraviolet and infrared spectral range were retrieved by using data from the TROPOspheric Monitoring Instrument on the Sentinel-5 Precursor (TROPOMI/S5P) and the Cross-track Infrared Sounder on the Suomi National Polar-orbiting Partnership (CrIS/Suomi-NPP), which are flying in loose formation 3 min apart in the same orbit. A previous study of ozone profiles retrieved exclusively from TROPOMI UV spectra showed that the vertical resolution in the troposphere is clearly limited (Mettig et al., 2021). The vertical resolution and the vertical extent of the ozone profiles is improved by combining both wavelength ranges compared to retrievals limited to UV or IR spectral data only. The combined retrieval particularly improves the accuracy of the retrieved tropospheric ozone and to a lesser degree stratospheric ozone up to 30 km. An increase in the degrees of freedom (DOF) by 1 DOF was found in the UV + IR retrieval compared to the UV-only retrieval. Compared to previous publications, which investigated combinations of UV and IR observations from the Ozone Monitoring Instrument and Tropospheric Emission Spectrometer (OMI and TES) and Global Ozone Monitoring Experiment version 2 and Infrared Atmospheric Sounding Interferometer (GOME-2 and IASI) pairs, the degree of freedom is lower, which is attributed to the reduced spectral resolution of CrIS compared to TES or IASI. Tropospheric lidar and ozonesondes were used to validate the ozone profiles and tropospheric ozone content (TOC). In their comparison with tropospheric lidars, both ozone profiles and TOCs show smaller biases for the retrieved data from the combined UV + IR observation than from the UV observations alone. For the ozone profiles below 10 km, the mean differences are around ±10 % and the mean TOC varies around ±3 DU. We show that TOCs from the combined retrieval agree better with ozonesonde results at northern latitudes than the UV-only and IR-only retrievals and also have lower scatter. In the tropics, the IR-only retrieval shows the best agrement with TOCs derived from ozonesondes. While in general the TOCs show good agreement with ozonesonde data, the profiles have a positive bias of around 30 % between 10 and 15 km. The reason is probably a positive stratospheric bias from the IR retrieval. The comparison of the UV + IR and UV ozone profiles up to 30 km with the Microwave Limb Sounder (MLS) demonstrates the improvement of the UV + IR profile in the stratosphere above 18 km. In comparison to the UV-only approach the retrieval shows improvements of up to 10 % depending on latitude but can also show worse results in some regions and latitudes.

Ozone↗
NASA NTRS2014

Comparison of Spectral Radiance Calibration Techniques Used for Backscatter Ultraviolet Satellite Instruments

Methods for determining the absolute radiometric calibration sensitivities of backscatter ultraviolet (BUV) satellite instruments are compared as part of an effort to minimize pre-launch calibration errors. An internally illuminated integrating sphere source has been used for the Shuttle Solar BUV (SSBUV), Total Ozone Mapping Spectrometer (TOMS), Ozone Mapping Instrument (OMI), and Global Ozone Monitoring Experiment 2 (GOME-2) using standardized procedures traceable to national standards. These sphere-based sensitivities agree to within three percent [k equals 2] relative to calibrations performed using an external diffuser illuminated by standard irradiance sources, the customary radiance calibration method for BUV instruments. The uncertainty for these calibration techniques as implemented at the NASA Goddard Space Flight Centers Radiometric Calibration and Development Laboratory is shown to be 4 percent at 250nm [k equals 2] when using a single traceable calibration standard. Significant reduction in the uncertainty of nearly 1 percent is demonstrated when multiple calibration standards are used.

residual errors↗
NASA NTRS2003

Facets and Challenges of Managing the Development of a $1 Billion Satellite

Contents include the folloving: Presidents budget. Office of Administrator chart. Aura project organization chart. Agreements. Aura observatory configuration. Aura science questions. EOS Aura atmospheric profile measurements. Science objectives: tracking ozone layer recovery; global measurements of air quality; impact of atmospheric constituente on climate. MLS instruments, OH measurement example. Osone monitoring instrument (OMI). OMI technology. OMI will track global ozone changes.

Loiacono, John↗
NASA NTRS2001

A New Method to Cross Calibrate and Validate TOMS, SBUV/2, and SCIAMACHY Measurements

A unique method to validate back scattered ultraviolet (buv) type satellite data that complements the measurements from existing ground networks is proposed. The method involves comparing the zenith sky radiance measurements from the ground to the nadir radiance measurements taken from space. Since the measurements are compared directly, the proposed method is superior to any other method that involves comparing derived products (for example, ozone), because comparison of derived products involve inversion algorithms which are susceptible to several type of errors. Forward radiative transfer (RT) calculations show that for an aerosol free atmosphere, the ground-based zenith sky radiance measurement and the satellite nadir radiance measurements can be predicted with an accuracy of better than 1 percent. The RT computations also show that for certain values of the solar zenith angles, the radiance comparisons could be better than half a percent. This accuracy is practically independent of ozone amount and aerosols in the atmosphere. Experiences with the Shuttle Solar Backscatter Ultraviolet (SSBUV) program show that the accuracy of the ground-based zenith sky radiance measuring instrument can be maintained at a level of a few tenth of a percent. This implies that the zenith sky radiance measurements can be used to validate Total Ozone Mapping Spectrometer (TOMS), Solar Backscatter Ultraviolet (SBUV/2), and The SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) radiance data. Also, this method will help improve the long term precision of the measurements for better trend detection and the accuracy of other BUV products such as tropospheric ozone and aerosols. Finally, in the long term, this method is a good candidate to inter-calibrate and validate long term observations of upcoming operational instruments such as Global Ozone Monitoring Experiment (GOME-2), Ozone Mapping Instrument (OMI), Ozone Dynamics Ultraviolet Spectrometer (ODUS), and Ozone Mapping and Profiler Suite (OMPS).

Ahmad, Ziauddin↗
NASA NTRS2006

Recovery of the Antarctic Ozone Hole

The Antarctic ozone hole develops each year and culminates by early Spring. Antarctic ozone values have been monitored since 1979 using satellite observations from the TOMS and OMI instruments. The severity of the hole has been assessed using the minimum total ozone value from the October monthly mean (depth of the hole), the average size during the September-October period, and the ozone mass deficit. Ozone is mainly destroyed by halogen catalytic cycles, and these losses are modulated by temperature variations in the collar of the polar lower stratospheric vortex. In this presentation, we show the relationships of halogens and temperature to both the size and depth of the hole. Because atmospheric halogen levels are responding to international agreements that limit or phase out production, the amount of halogens in the stratosphere should decrease over the next few decades. We use two methods to estimate ozone hole recovery. First, we use projections of halogen levels combined with age-of-air estimates in a parametric model. Second, we use a coupled chemistry climate model to assess recovery. We find that the ozone hole is recovering at an extremely slow rate and that large ozone holes will regularly recur over the next 2 decades. Furthermore, full recovery to 1980 levels will not occur until approximately 2068. We will also show some error estimates of these dates and the impact of climate change on the recovery.

Newman, Paul A.↗
NASA NTRS2000

Critical Evaluation of 0-30 km Profile Information in Ground-Based Zenith-Sky and Satellite-Measured Backscattered UV Radiation

We now have several decades of experience in deriving vertical ozone profiles from the measurements of diffuse ultraviolet radiation by both ground and satellite-based instruments using Umkehr and BUV techniques. Continuing technological advances are pushing the state-of-the-art of these measurements to high spectral resolution and broader wavelength coverage. These modern instruments include the ground-based Brewer and satellite-based Global Ozone Monitoring Experiment (GOME) instruments, as well as advanced instruments being developed by ESA(SCIAMACHY), Netherlands(OMI) and Japan(ODUS). However, one of the issues that remains unresolved is the 0-30 km ozone profile information retrievable from these measurements. Though it is commonly believed that both the Umkehr and the satellite-based BUV techniques have very limited profile information below 30 km, there are those who argue that the data from these instruments should continue to be reported in this altitude range for they compare well with ozonesondes and hence there is useful scientific information. Others claim that the limitations of the Umkehr and BUV techniques are largely due to their low spectral resolution, and that the profile information below 30 km can be greatly improved by going to high spectral resolution instruments, such as Brewer and GOME. The purpose of this paper is to provide a critical evaluation of the 0-30 km ozone profile information in the various UV remote sensing techniques. We use a database of individual ozone profiles created using ozonesondes and SAGE and 4D ozone fields generated by data assimilation techniques to simulate radiances measured by the various techniques. We then apply a common inversion approach to all the methods to systematically examine how much profile information is available simply from the knowledge of total ozone, how much additional profile information is added by the traditional Dobson Umkehr and satellite buv techniques, and how much better one can do by adding additional wavelengths and by going to higher spectral resolution. An open and honest discussion of these issues is critical to provide the ozone science community a realistic assessment of the quality of data in the lower stratosphere and troposphere expected from both past and future instruments.

Bhartia, Pawan↗
NASA NTRS2014

Highlights from a Decade of OMI-TOMS Total Ozone Observations on EOS Aura

Total ozone measurements from OMI have been instrumental in meeting Aura science objectives. In the last decade, OMI has extended the length of the TOMS total ozone record to over 35 years to monitor stratospheric ozone recovery. OMI-TOMS total ozone measurements have also been combined synergistically with measurements from other Aura instruments and MLS in particular, which provides vertically resolved information that complements the total O3 mapping capability of OMI. With this combined approach, the EOS Aura platform has produced more accurate and detailed measurements of tropospheric ozone. This has led in turn to greater understanding of the sources and transport of tropospheric ozone as well as its radiative forcing effect. The combined use of OMI and MLS data was also vital to the analysis of the severe Arctic ozone depletion event of 2011. The quality of OMI-TOMS total O3 data used in these studies is the result of several factors: a mature and well-validated algorithm, the striking stability of the OMI instrument, and OMI's hyperspectral capabilities used to derive cloud pressures. The latter has changed how we think about the effects of clouds on total ozone retrievals. We will discuss the evolution of the operational V8.5 algorithm and provide an overview and motivation for V9. After reviewing results and developments of the past decade, we finally highlight how ozone observations from EOS Aura are playing an important role in new ozone mapping missions.

Ozone↗
NASA NTRS2004

US Team Member Participation in the Ozone Monitoring Experiment on EOS CHEM

Progress on OMI operational algorithms for BrO, OClO, and HCHO includes: A parameterization scheme for OMI instrument function lab measurements was developed for both UV and Visible instrument channels. The scheme was implemented for all three PGEs and extensively tested. PGEs now have the option of either using the parameterized lab measurements or fitting a symmetric or asymmetric Gaussian function on-line.

Chance, Kelly↗
NASA NTRS2014

Real Time Volcanic Cloud Products and Predictions for Aviation Alerts

Volcanic eruptions can inject significant amounts of sulfur dioxide (SO2) and volcanic ash into the atmosphere, posing a substantial risk to aviation safety. Ingesting near-real time and Direct Readout satellite volcanic cloud data is vital for improving reliability of volcanic ash forecasts and mitigating the effects of volcanic eruptions on aviation and the economy. NASA volcanic products from the Ozone Monitoring Insrument (OMI) aboard the Aura satellite have been incorporated into Decision Support Systems of many operational agencies. With the Aura mission approaching its 10th anniversary, there is an urgent need to replace OMI data with those from the next generation operational NASA/NOAA Suomi National Polar Partnership (SNPP) satellite. The data provided from these instruments are being incorporated into forecasting models to provide quantitative ash forecasts for air traffic management. This study demonstrates the feasibility of the volcanic near-real time and Direct Readout data products from the new Ozone Monitoring and Profiling Suite (OMPS) ultraviolet sensor onboard SNPP for monitoring and forecasting volcanic clouds. The transition of NASA data production to our operational partners is outlined. Satellite observations are used to constrain volcanic cloud simulations and improve estimates of eruption parameters, resulting in more accurate forecasts. This is demonstrated for the 2012 eruption of Copahue. Volcanic eruptions are modeled using the Goddard Earth Observing System, Version 5 (GEOS-5) and the Goddard Chemistry Aerosol and Radiation Transport (GOCART) model. A hindcast of the disruptive eruption from Iceland's Eyjafjallajokull is used to estimate aviation re-routing costs using Metron Aviation's ATM Tools.

Volcanic ash↗