The use of radiative transfer modeling to compare normalized radiances from different instruments

2020 ◽  
Author(s):  
Colin Seftor ◽  
Glen Jaross ◽  
Leslie Moy ◽  
Natalya Kramarova ◽  
Eun-su Yang
Keyword(s):  
Radiative Transfer ◽  
Ozone Monitoring Instrument ◽  
Monitoring Instrument ◽  
Ozone Profile ◽  
Ozone Monitoring ◽  
Radiative Transfer Modeling ◽  
Comparison Technique

<p>Measured sun-normalized radiances (S-NRs) from both the Ozone Mapping and Profiler Suite (OMPS) Nadir Mapper (NM) and Nadir Profiler (NP) on the Suomi National Polar-orbiting Partnership (SNPP) satellite have been validated to the 2% level through, in part, comparisons with radiative transfer code calculations using co-located ozone profile retrievals inputs from the Microwave Limb Sounder (MLS) on the Aura satellite. To minimize the effects of clouds and aerosols, only low reflectivity and low aerosol scenes were used. We will describe the details of the comparison technique, including how low reflectivity / low aerosol scenes were determined.  We will also show results where we extend our study to compare measured S-NRs from the OMPS nadir sensors with those from both the Ozone Monitoring Instrument (OMI) on Aura sensor and, if available, the Version 2 dataset from the TROPOMI sensor on the Sentinel 5 Precursor (S5P) satellite.</p>

scholarly journals Simulation of the Ozone Monitoring Instrument aerosol index using the NASA Goddard Earth Observing System aerosol reanalysis products

2017 ◽  
Vol 10 (11) ◽  
pp. 4121-4134 ◽  
Author(s):  
Peter R. Colarco ◽  
Santiago Gassó ◽  
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 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.

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

2019 ◽  
Vol 12 (7) ◽  
pp. 3777-3788 ◽  
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.

scholarly journals Ozone profile retrievals from the Ozone Monitoring Instrument

2010 ◽  
Vol 10 (5) ◽  
pp. 2521-2537 ◽  
Author(s):  
X. Liu ◽  
P. K. Bhartia ◽  
K. Chance ◽  
R. J. D. Spurr ◽  
T. P. Kurosu
Keyword(s):  
Degrees Of Freedom ◽  
Optimal Estimation ◽  
Anthropogenic Pollution ◽  
Horizontal Resolution ◽  
Ozone Monitoring Instrument ◽  
Monitoring Instrument ◽  
Ozone Profile ◽  
Ozone Monitoring ◽  
The Tropics ◽  
Middle Stratosphere

Abstract. Ozone profiles from the surface to about 60 km are retrieved from Ozone Monitoring Instrument (OMI) ultraviolet radiances using the optimal estimation technique. OMI provides daily ozone profiles for the entire sunlit portion of the earth at a horizontal resolution of 13 km×48 km for the nadir position. The retrieved profiles have sufficient accuracy in the troposphere to see ozone perturbations caused by convection, biomass burning and anthropogenic pollution, and to track their spatiotemporal transport. However, to achieve such accuracy it has been necessary to calibrate OMI radiances carefully (using two days of Aura/Microwave Limb Sounder data taken in the tropics). The retrieved profiles contain ~6–7 degrees of freedom for signal, with 5–7 in the stratosphere and 0–1.5 in the troposphere. Vertical resolution varies from 7–11 km in the stratosphere to 10–14 km in the troposphere. Retrieval precisions range from 1% in the middle stratosphere to 10% in the lower stratosphere and troposphere. Solution errors (i.e., root sum square of precisions and smoothing errors) vary from 1–6% in the middle stratosphere to 6–35% in the troposphere, and are dominated by smoothing errors. Total, stratospheric, and tropospheric ozone columns can be retrieved with solution errors typically in the few Dobson unit range at solar zenith angles less than 80°.

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

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.

scholarly journals Development of an Ozone Monitoring Instrument (OMI) aerosol index (AI) data assimilation scheme for aerosol modeling over bright surfaces – a step toward direct radiance assimilation in the UV spectrum

2021 ◽  
Vol 14 (1) ◽  
pp. 27-42
Author(s):  
Jianglong Zhang ◽  
Robert J. D. Spurr ◽  
Jeffrey S. Reid ◽  
Peng Xian ◽  
Peter R. Colarco ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Data Assimilation ◽  
Ozone Monitoring Instrument ◽  
Uv Spectrum ◽  
Model Simulations ◽  
Monitoring Instrument ◽  
Radiance Assimilation ◽  
Ozone Monitoring ◽  
Aerosol Index ◽  
Assimilation Scheme

Abstract. Using the Vector LInearized Discrete Ordinate Radiative Transfer (VLIDORT) code as the main driver for forward model simulations, a first-of-its-kind data assimilation scheme has been developed for assimilating Ozone Monitoring Instrument (OMI) aerosol index (AI) measurements into the Naval Aerosol Analysis and Predictive System (NAAPS). This study suggests that both root mean square error (RMSE) and absolute errors can be significantly reduced in NAAPS analyses with the use of OMI AI data assimilation when compared to values from NAAPS natural runs. Improvements in model simulations demonstrate the utility of OMI AI data assimilation for aerosol model analysis over cloudy regions and bright surfaces. However, the OMI AI data assimilation alone does not outperform aerosol data assimilation that uses passive-based aerosol optical depth (AOD) products over cloud-free skies and dark surfaces. Further, as AI assimilation requires the deployment of a fully multiple-scatter-aware radiative transfer model in the forward simulations, computational burden is an issue. Nevertheless, the newly developed modeling system contains the necessary ingredients for assimilation of radiances in the ultraviolet (UV) spectrum, and our study shows the potential of direct radiance assimilation at both UV and visible spectrums, possibly coupled with AOD assimilation, for aerosol applications in the future. Additional data streams can be added, including data from the TROPOspheric Monitoring Instrument (TROPOMI), the Ozone Mapping and Profiler Suite (OMPS), and eventually the Plankton, Aerosol, Cloud and ocean Ecosystem (PACE) mission.

scholarly journals Ozone profile retrievals from the Ozone Monitoring Instrument

2009 ◽  
Vol 9 (5) ◽  
pp. 22693-22738 ◽  
Author(s):  
X. Liu ◽  
P. K. Bhartia ◽  
K. Chance ◽  
R. J. D. Spurr ◽  
T. P. Kurosu
Keyword(s):  
Optimal Estimation ◽  
Anthropogenic Pollution ◽  
Horizontal Resolution ◽  
Ozone Monitoring Instrument ◽  
Monitoring Instrument ◽  
The Earth ◽  
Ozone Profile ◽  
Ozone Monitoring ◽  
The Tropics ◽  
Middle Stratosphere

Abstract. Ozone profiles from the surface to about 60 km are retrieved from Ozone Monitoring Instrument (OMI) ultraviolet radiances using the optimal estimation technique. OMI provides daily ozone profiles for the entire sunlit portion of the earth at a horizontal resolution of 13 km×48 km for the nadir position. The retrieved profiles have sufficient accuracy in the troposphere to see ozone perturbations caused by convection, biomass burning and anthropogenic pollution, and to track their spatiotemporal transport. However, to achieve such accuracy it has been necessary to calibrate OMI radiances carefully (using two days of Aura/Microwave Limb Sounder data taken in the tropics). The retrieved profiles contain ~6–7° of freedom for signal, with 5–7 in the stratosphere and 0–1.5 in the troposphere. Vertical resolution varies from 7–11 km in the stratosphere to 10–14 km in the troposphere. Retrieval precisions range from 1% in the middle stratosphere to 10% in the lower stratosphere and troposphere. Solution errors (i.e., root sum square of precisions and smoothing errors) vary from 1–6% in the middle stratosphere to 6–35% in the troposphere, and are dominated by smoothing errors. Total, stratospheric, and tropospheric ozone columns can be retrieved with solution errors typically in the few Dobson unit range at solar zenith angles less than 80°.

scholarly journals Validation of 10-year SAO OMI Ozone Profile (PROFOZ) Product Using Ozonesonde Observations

2017 ◽  
Author(s):  
Guanyu Huang ◽  
Xiong Liu ◽  
Kelly Chance ◽  
Kai Yang ◽  
Pawan K. Bhartia ◽  
...  
Keyword(s):  
Ozone Monitoring Instrument ◽  
Data Set ◽  
Monitoring Instrument ◽  
Ozone Profile ◽  
Before And After ◽  
Ozone Monitoring ◽  
The Tropics ◽  
Good Agreement

Abstract. We validate the Ozone Monitoring Instrument (OMI) ozone-profile (PROFOZ) product from October 2004 through December 2014 retrieved by the Smithsonian Astrophysical Observatory (SAO) algorithm against ozonesonde observations. We also evaluate the effects of OMI Row anomaly (RA) on the retrieval by dividing the data set into before and after the occurrence of serious OMI RA, i.e., pre-RA (2004–2008) and post-RA (2009–2014). The retrieval shows good agreement with ozonesondes in the tropics and mid-latitudes and for pressure

Science objectives of the ozone monitoring instrument

2006 ◽  
Vol 44 (5) ◽  
pp. 1199-1208 ◽  
Author(s):  
P.F. Levelt ◽  
E. Hilsenrath ◽  
G.W. Leppelmeier ◽  
G.H.J. van den Oord ◽  
P.K. Bhartia ◽  
...  
Keyword(s):  
Ozone Monitoring Instrument ◽  
Monitoring Instrument ◽  
Ozone Monitoring

scholarly journals Deriving the slit functions from OMI solar observations and its implications for ozone-profile retrieval

2017 ◽  
Vol 10 (10) ◽  
pp. 3677-3695 ◽  
Author(s):  
Kang Sun ◽  
Xiong Liu ◽  
Guanyu Huang ◽  
Gonzalo González Abad ◽  
Zhaonan Cai ◽  
...  
Keyword(s):  
Atmospheric Composition ◽  
Half Maximum ◽  
Ozone Monitoring Instrument ◽  
Gaussian Functions ◽  
Solar Observations ◽  
Ozone Profile ◽  
Retrieval Algorithms ◽  
Ozone Monitoring ◽  
Cross Track ◽  
Over Time

Abstract. The Ozone Monitoring Instrument (OMI) has been successfully measuring the Earth's atmospheric composition since 2004, but the on-orbit behavior of its slit functions has not been thoroughly characterized. Preflight measurements of slit functions have been used as a static input in many OMI retrieval algorithms. This study derives on-orbit slit functions from the OMI irradiance spectra assuming various function forms, including standard and super-Gaussian functions and a stretch to the preflight slit functions. The on-orbit slit functions in the UV bands show U-shaped cross-track dependences that cannot be fully represented by the preflight ones. The full widths at half maximum (FWHM) of the stretched preflight slit functions for detector pixels at large viewing angles are up to 30 % larger than the nadir pixels for the UV1 band, 5 % larger for the UV2 band, and practically flat in the VIS band. Nonetheless, the on-orbit changes of OMI slit functions are found to be insignificant over time after accounting for the solar activity, despite of the decaying of detectors and the occurrence of OMI row anomaly. Applying the derived on-orbit slit functions to ozone-profile retrieval shows substantial improvements over the preflight slit functions based on comparisons with ozonesonde validations.

Sign in / Sign up

Export Citation Format

Share Document