Cloud pressure retrieval using the O<sub>2</sub>‐O<sub>2</sub> absorption band at 477 nm

Acarreta, J. R.; Haan, J. F. de; Stammes, P.

doi:10.1029/2003jd003915

Cited by 262 publications

(368 citation statements)

References 47 publications

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“…We use the MODIS gap-filled BRDF Collection 5 product MCD43GF (Schaaf et al, 2002(Schaaf et al, , 2011 (Acarreta et al, 2004;Sneep et al, 2008), but differs in a few respects described below. Both the RRS and O 2 -O 2 algorithms utilize the MLER concept.…”

Section: Modis Brdf Data Setmentioning

confidence: 99%

“…Many satellite UV-vis algorithms are based on the socalled mixed Lambert equivalent reflectivity (MLER) model, first introduced by Seftor et al (1994). For example, the MLER concept is currently used in most trace-gas (Boersma et al, 2011;Bucsela et al, 2013) and cloud (Acarreta et al, 2004; retrieval algorithms for the Ozone Monitoring Instrument (OMI), a Dutch-Finnish UVvis sensor (Levelt et al, 2006) onboard the NASA Aura satellite. The MLER model treats cloud and ground as horizontally homogeneous Lambertian surfaces and mixes them using the independent pixel approximation (IPA).…”

Section: Introductionmentioning

confidence: 99%

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Accounting for the effects of surface BRDF on satellite cloud and trace-gas retrievals: a new approach based on geometry-dependent Lambertian equivalent reflectivity applied to OMI algorithms

et al. 2017

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Abstract. Most satellite nadir ultraviolet and visible cloud, aerosol, and trace-gas algorithms make use of climatological surface reflectivity databases. For example, cloud and NO 2 retrievals for the Ozone Monitoring Instrument (OMI) use monthly gridded surface reflectivity climatologies that do not depend upon the observation geometry. In reality, reflection of incoming direct and diffuse solar light from land or ocean surfaces is sensitive to the sun-sensor geometry. This dependence is described by the bidirectional reflectance distribution function (BRDF). To account for the BRDF, we propose to use a new concept of geometry-dependent Lambertian equivalent reflectivity (LER). Implementation within the existing OMI cloud and NO 2 retrieval infrastructure requires changes only to the input surface reflectivity database. The geometry-dependent LER is calculated using a vector radiative transfer model with high spatial resolution BRDF information from the Moderate Resolution Imaging Spectroradiometer (MODIS) over land and the Cox-Munk slope distribution over ocean with a contribution from water-leaving radiance. We compare the geometry-dependent and climatological LERs for two wavelengths, 354 and 466 nm, that are used in OMI cloud algorithms to derive cloud fractions. A detailed comparison of the cloud fractions and pressures derived with climatological and geometry-dependent LERs is carried out. Geometry-dependent LER and corresponding retrieved cloud products are then used as inputs to our OMI NO 2 algorithm. We find that replacing the climatological OMI-based LERs with geometry-dependent LERs can increase NO 2 vertical columns by up to 50 % in highly polluted areas; the differences include both BRDF effects and biases between the MODIS and OMI-based surface reflectance data sets. Only minor changes to NO 2 columns (within 5 %) are found over unpolluted and overcast areas.

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Section: Modis Brdf Data Setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Accounting for the effects of surface BRDF on satellite cloud and trace-gas retrievals: a new approach based on geometry-dependent Lambertian equivalent reflectivity applied to OMI algorithms

et al. 2017

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“…Parameters include viewing geometry, NO 2 profile shape, and the pressure and reflectivity of clouds and terrain. The cloud information is obtained from the OMI cloud O 2 -O 2 algorithm [Acarreta et al, 2004].…”

Section: Omi Tropospheric No 2 Columnsmentioning

confidence: 99%

Ground‐level nitrogen dioxide concentrations inferred from the satellite‐borne Ozone Monitoring Instrument

et al. 2008

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[1] We present an approach to infer ground-level nitrogen dioxide (NO 2 ) concentrations by applying local scaling factors from a global three-dimensional model (GEOS-Chem) to tropospheric NO 2 columns retrieved from the Ozone Monitoring Instrument (OMI) onboard the Aura satellite. Seasonal mean OMI surface NO 2 derived from the standard tropospheric NO 2 data product (Version 1.0.5, Collection 3) varies by more than two orders of magnitude (<0.1->10 ppbv) over North America. Two ground-based data sets are used to validate the surface NO 2 estimate and indirectly validate the OMI tropospheric NO 2 retrieval: photochemical steady-state (PSS) calculations of NO 2 based on in situ NO and O 3 measurements, and measurements from a commercial chemiluminescent NO 2 analyzer equipped with a molybdenum converter. An interference correction algorithm for the latter is developed using laboratory and field measurements and applied using modeled concentrations of the interfering species. The OMI-derived surface NO 2 mixing ratios are compared with an in situ surface NO 2 data obtained from the U.S. Environmental Protection Agency's Air Quality System (AQS) and Environment Canada's National Air Pollution Surveillance (NAPS) network for 2005 after correcting for the interference in the in situ data. The overall agreement of the OMI-derived surface NO 2 with the corrected in situ measurements and PSS-NO 2 is À11-36%. A larger difference in winter/spring than in summer/fall implies a seasonal bias in the OMI NO 2 retrieval. The correlation between the OMI-derived surface NO 2 and the ground-based measurements is significant (correlation coefficient up to 0.86) with a tendency for higher correlations in polluted areas. The satellite-derived data base of ground level NO 2 concentrations could be valuable for assessing exposures of humans and vegetation to NO 2 , supplementing the capabilities of the ground-based networks, and evaluating air quality models and the effectiveness of air quality control strategies.Citation: Lamsal, L. N., R.

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“…We compute tropospheric AMFs following the formulation of Palmer et al [2001] and Eskes and Boersma [2003] with the DAK radiative transfer model , accounting for solar and viewing zenith angle (at the surface), and relative azimuth angle, and furthermore take into account the effects of clouds (for SCIAMACHY from the FRESCO [Koelemeijer et al, 2001] and for OMI from the O 2 -O 2 [Acarreta et al, 2004] cloud algorithm), surface albedo (from the Herman and Celarier [1997] and Koelemeijer et al [2002] data sets as described Boersma et al [2004]), and the a priori vertical profile shape from TM4. No independent aerosol correction is applied as it is effectively accounted for in the cloud retrievals as shown in .…”

Section: Introductionmentioning

confidence: 99%

Intercomparison of SCIAMACHY and OMI tropospheric NO₂ columns: Observing the diurnal evolution of chemistry and emissions from space

et al. 2008

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[1] Concurrent (August 2006) measurements of tropospheric NO 2 columns from OMI aboard Aura (1330 local overpass time) and SCIAMACHY aboard Envisat (1000 local overpass time) offer an opportunity to examine the consistency between the two instruments under tropospheric background conditions and the effect of different observing times. For scenes with tropospheric NO 2 columns <5.0 Â 10 15 molecules cm À2 , SCIAMACHY and OMI agree within 1.0-2.0 Â 10 15 molecules cm À2 , consistent with the detection limits of both instruments. We find evidence for a low bias of 0.2 Â 10 15 molecules cm À2 in OMI observations over remote oceans. Over the fossil fuel source regions at northern midlatitudes, we find that SCIAMACHY observes up to 40% higher NO 2 at 1000 local time (LT) than OMI at 1330 LT. Over biomass burning regions in the tropics, SCIAMACHY observes up to 40% lower NO 2 columns than OMI. These differences are present in the spectral fitting of the data (slant column) and are augmented in the fossil fuel regions and dampened in the tropical biomass burning regions by the expected increase in air mass factor as the mixing depth rises from 1000 to 1330 LT. Using a global 3-D chemical transport model (GEOS-Chem), we show that the 1000-1330 LT decrease in tropospheric NO 2 column over fossil fuel source regions can be explained by photochemical loss, dampened by the diurnal cycle of anthropogenic emissions that has a broad daytime maximum. The observed 1000-1330 LT NO 2 column increase over tropical biomass burning regions points to a sharp midday peak in emissions and is consistent with a diurnal cycle of emissions derived from geostationary satellite fire counts.

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Cloud pressure retrieval using the O₂‐O₂ absorption band at 477 nm

Cited by 262 publications

References 47 publications

Accounting for the effects of surface BRDF on satellite cloud and trace-gas retrievals: a new approach based on geometry-dependent Lambertian equivalent reflectivity applied to OMI algorithms

Accounting for the effects of surface BRDF on satellite cloud and trace-gas retrievals: a new approach based on geometry-dependent Lambertian equivalent reflectivity applied to OMI algorithms

Ground‐level nitrogen dioxide concentrations inferred from the satellite‐borne Ozone Monitoring Instrument

Intercomparison of SCIAMACHY and OMI tropospheric NO₂ columns: Observing the diurnal evolution of chemistry and emissions from space

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Cloud pressure retrieval using the O2‐O2 absorption band at 477 nm

Cited by 262 publications

References 47 publications

Accounting for the effects of surface BRDF on satellite cloud and trace-gas retrievals: a new approach based on geometry-dependent Lambertian equivalent reflectivity applied to OMI algorithms

Accounting for the effects of surface BRDF on satellite cloud and trace-gas retrievals: a new approach based on geometry-dependent Lambertian equivalent reflectivity applied to OMI algorithms

Ground‐level nitrogen dioxide concentrations inferred from the satellite‐borne Ozone Monitoring Instrument

Intercomparison of SCIAMACHY and OMI tropospheric NO2 columns: Observing the diurnal evolution of chemistry and emissions from space

Contact Info

Product

Resources

About

Cloud pressure retrieval using the O₂‐O₂ absorption band at 477 nm

Intercomparison of SCIAMACHY and OMI tropospheric NO₂ columns: Observing the diurnal evolution of chemistry and emissions from space