Ground-based zenith sky abundances and in situ gas cross sections for ozone and nitrogen dioxide with the Earth Observing System Aura Ozone Monitoring Instrument

Abstract. 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 near-UV aerosol retrieval algorithms (known as OMAERUV) is compared to the MERRAero calculated AI. Two main sources of discrepancy are discussed: one pertaining to 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 600 and 1013.25 hPa. 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.

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Review of “Simulation of the Ozone Monitoring Instrument Aerosol Index using the NASA Goddard Earth Observing System Aerosol Reanalysis Products”

10.5194/amt-2017-87-rc1 ◽

2017 ◽

Author(s):

Anonymous

Keyword(s):

Ozone Monitoring Instrument ◽

Monitoring Instrument ◽

Earth Observing System ◽

Ozone Monitoring ◽

Aerosol Index

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Ozone Monitoring Instrument (OMI) Aura nitrogen dioxide standard product version 4.0 with improved surface and cloud treatments

Atmospheric Measurement Techniques ◽

10.5194/amt-14-455-2021 ◽

2021 ◽

Vol 14 (1) ◽

pp. 455-479

Author(s):

Lok N. Lamsal ◽

Nickolay A. Krotkov ◽

Alexander Vasilkov ◽

Sergey Marchenko ◽

Wenhan Qin ◽

...

Keyword(s):

Nitrogen Dioxide ◽

Retrieval Algorithm ◽

Data Sets ◽

Ozone Monitoring Instrument ◽

Monitoring Instrument ◽

Standard Product ◽

Independent Observations ◽

Moderate Resolution ◽

Ozone Monitoring ◽

Moderate Resolution Imaging Spectroradiometer

Abstract. We present a new and improved version (V4.0) of the NASA standard nitrogen dioxide (NO2) product from the Ozone Monitoring Instrument (OMI) on the Aura satellite. This version incorporates the most salient improvements for OMI NO2 products suggested by expert users and enhances the NO2 data quality in several ways through improvements to the air mass factors (AMFs) used in the retrieval algorithm. The algorithm is based on the geometry-dependent surface Lambertian equivalent reflectivity (GLER) operational product that is available on an OMI pixel basis. GLER is calculated using the vector linearized discrete ordinate radiative transfer (VLIDORT) model, which uses as input high-resolution bidirectional reflectance distribution function (BRDF) information from NASA's Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) instruments over land and the wind-dependent Cox–Munk wave-facet slope distribution over water, the latter with a contribution from the water-leaving radiance. The GLER combined with consistently retrieved oxygen dimer (O2–O2) absorption-based effective cloud fraction (ECF) and optical centroid pressure (OCP) provide improved information to the new NO2 AMF calculations. The new AMFs increase the retrieved tropospheric NO2 by up to 50 % in highly polluted areas; these differences arise from both cloud and surface BRDF effects as well as biases between the new MODIS-based and previously used OMI-based climatological surface reflectance data sets. We quantitatively evaluate the new NO2 product using independent observations from ground-based and airborne instruments. The new V4.0 data and relevant explanatory documentation are publicly available from the NASA Goddard Earth Sciences Data and Information Services Center (https://disc.gsfc.nasa.gov/datasets/OMNO2_V003/summary/, last access: 8 November 2020), and we encourage their use over previous versions of OMI NO2 products.

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Linearization of the effect of slit function changes for improving Ozone Monitoring Instrument ozone profile retrievals

Atmospheric Measurement Techniques ◽

10.5194/amt-12-3777-2019 ◽

2019 ◽

Vol 12 (7) ◽

pp. 3777-3788 ◽

Cited By ~ 2

Author(s):

Juseon Bak ◽

Xiong Liu ◽

Kang Sun ◽

Kelly Chance ◽

Jae-Hwan Kim

Keyword(s):

Cross Sections ◽

Shape Factor ◽

Optimal Estimation ◽

Ozone Monitoring Instrument ◽

Thermally Induced ◽

Monitoring Instrument ◽

Spectral Fitting ◽

Ozone Profile ◽

Ozone Monitoring ◽

Hartley Band

Abstract. We introduce a method that accounts for errors caused by the slit function in an optimal-estimation-based spectral fitting process to improve ozone profile retrievals from the Ozone Monitoring Instrument (OMI) ultraviolet measurements (270–330 nm). Previously, a slit function was parameterized as a standard Gaussian by fitting the full width at half maximum (FWHM) of the slit function from climatological OMI solar irradiances. This cannot account for the temporal variation in slit function in irradiance, the intra-orbit changes due to thermally induced change and scene inhomogeneity, and potential differences in the slit functions of irradiance and radiance measurements. As a result, radiance simulation errors may be induced due to convolving reference spectra with incorrect slit functions. To better represent the shape of the slit functions, we implement a more generic super Gaussian slit function with two free parameters (slit width and shape factor); it becomes standard Gaussian when the shape factor is fixed to be 2. The effects of errors in slit function parameters on radiance spectra, referred to as pseudo absorbers (PAs), are linearized by convolving high-resolution cross sections or simulated radiances with the partial derivatives of the slit function with respect to the slit parameters. The PAs are included in the spectral fitting scaled by fitting coefficients that are iteratively adjusted as elements of the state vector along with ozone and other fitting parameters. The fitting coefficients vary with cross-track and along-track pixels and show sensitivity to heterogeneous scenes. The PA spectrum is quite similar in the Hartley band below 310 nm for both standard and super Gaussians, but is more distinctly structured in the Huggins band above 310 nm with the use of super Gaussian slit functions. Finally, we demonstrate that some spikes of fitting residuals are slightly smoothed by accounting for the slit function errors. Comparisons with ozonesondes demonstrate noticeable improvements when using PAs for both standard and super Gaussians, especially for reducing the systematic biases in the tropics and midlatitudes (mean biases of tropospheric column ozone reduced from -1.4∼0.7 to 0.0∼0.4 DU) and reducing the standard deviations of tropospheric ozone column differences at high latitudes (by 1 DU for the super Gaussian). Including PAs also makes the retrievals consistent between standard and super Gaussians. This study corroborates the slit function differences between radiance and irradiance, demonstrating that it is important to account for such differences in the ozone profile retrievals.

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Validation of Ozone Monitoring Instrument nitrogen dioxide columns

Journal of Geophysical Research Atmospheres ◽

10.1029/2007jd008908 ◽

2008 ◽

Vol 113 (D15) ◽

Cited By ~ 156

Author(s):

E. A. Celarier ◽

E. J. Brinksma ◽

J. F. Gleason ◽

J. P. Veefkind ◽

A. Cede ◽

...

Keyword(s):

Nitrogen Dioxide ◽

Ozone Monitoring Instrument ◽

Monitoring Instrument ◽

Ozone Monitoring

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

10.5194/amt-2017-87 ◽

2017 ◽

Author(s):

Peter R. Colarco ◽

Santiago Gasso' ◽

Changwoo Ahn ◽

Virginie Buchard ◽

Arlindo M. da Silva ◽

...

Keyword(s):

Radiative Transfer ◽

Surface Pressure ◽

Model Atmosphere ◽

Ozone Monitoring Instrument ◽

Aerosol Optical Property ◽

Actual Surface ◽

Monitoring Instrument ◽

Earth Observing System ◽

Ozone Monitoring ◽

Aerosol Index

Abstract. 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 600 hPa and 1013.25 hPa. 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.

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Evaluating Tropospheric Nitrogen Dioxide over South and East Asia in UKCA using OMI Satellite data

10.5194/egusphere-egu2020-19775 ◽

2020 ◽

Author(s):

Alok Pandey ◽

David Stevenson ◽

Alcide Zhao ◽

Richard Pope ◽

Krishan Kumar

Keyword(s):

Nitrogen Dioxide ◽

East Asia ◽

System Model ◽

Atmospheric Composition ◽

Ozone Monitoring Instrument ◽

Satellite Measurements ◽

Earth Observing System ◽

The United Kingdom ◽

Ozone Monitoring ◽

The Uk

We compare tropospheric nitrogen dioxide (NO2) in the United Kingdom Chemistry and Aerosol (UKCA) model v11.0 with satellite measurements from NASA Earth Observing System (EOS) Aura satellite Ozone Monitoring Instrument (OMI) troposphere NO2 data over South and East Asia (S/A). UKCA is the atmospheric composition component of the UK Earth System Model (UKESM). UKCA has been run on ARCHER (UK National Supercomputing Service) with (a) nudged hourly outputs over S/A as well as monthly outputs globally and (b) free run monthly globally output for 2005-2015. OMI satellite Averaging Kernels (AK) has been applied on the model hourly outputs for accurate model satellite comparison for 2005 - 2015. OMI and UKCA data has been analysed spatially and temporally. Background UKCA and OMI tropospheric column NO2 typically ranges between 0-3 x 1015 molecules/cm2. Model is overestimating tropospheric NO2 over the S/A predominantly during winter by a factor of ~2.5.&#160;

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