Simulation study of the aerosol information content in OMI spectral reflectance measurements

The Ozone Monitoring Instrument (OMI) is an imaging UV-VIS solar backscatter spectrometer and is designed and used primarily to retrieve trace gases like O<sub>3</sub> and NO<sub>2</sub> from the measured Earth reflectance spectrum in the UV-visible (270–500 nm). However, also aerosols are an important science target of OMI. The multi-wavelength algorithm is used to retrieve aerosol parameters from OMI spectral reflectance measurements in up to 20 wavelength bands. A Principal Component Analysis (PCA) is performed to quantify the information content of OMI reflectance measurements on aerosols and to assess the capability of the multi-wavelength algorithm to discern various aerosol types. This analysis is applied to synthetic reflectance measurements for desert dust, biomass burning aerosols, and weakly absorbing anthropogenic aerosol with a variety of aerosol optical thicknesses, aerosol layer altitudes, refractive indices and size distributions. The range of aerosol parameters considered covers the natural variability of tropospheric aerosols. This theoretical analysis is performed for a large number of scenarios with various geometries and surface albedo spectra for ocean, soil and vegetation. When the surface albedo spectrum is accurately known and clouds are absent, OMI reflectance measurements have 2 to 4 degrees of freedom that can be attributed to aerosol parameters. This information content depends on the observation geometry and the surface albedo spectrum. An additional wavelength band is evaluated, that comprises the O<sub>2</sub>-O<sub>2</sub> absorption band at a wavelength of 477 nm. It is found that this wavelength band adds significantly more information than any other individual band

Quantification of uncertainty in aerosol optical thickness retrieval arising from aerosol microphysical model and other sources, applied to Ozone Monitoring Instrument (OMI) measurements

Satellite instruments are nowadays successfully utilised for measuring atmospheric aerosol in many applications as well as in research. Therefore, there is a growing need for rigorous error characterisation of the measurements. Here, we introduce a methodology for quantifying the uncertainty in the retrieval of aerosol optical thickness (AOT). In particular, we concentrate on two aspects: uncertainty due to aerosol microphysical model selection and uncertainty due to imperfect forward modelling. We apply the introduced methodology for aerosol optical thickness retrieval of the Ozone Monitoring Instrument (OMI) on board NASA's Earth Observing System (EOS) Aura satellite, launched in 2004. We apply statistical methodologies that improve the uncertainty estimates of the aerosol optical thickness retrieval by propagating aerosol microphysical model selection and forward model error more realistically. For the microphysical model selection problem, we utilise Bayesian model selection and model averaging methods. Gaussian processes are utilised to characterise the smooth systematic discrepancies between the measured and modelled reflectances (i.e. residuals). The spectral correlation is composed empirically by exploring a set of residuals. The operational OMI multi-wavelength aerosol retrieval algorithm OMAERO is used for cloud-free, over-land pixels of the OMI instrument with the additional Bayesian model selection and model discrepancy techniques introduced here. The method and improved uncertainty characterisation is demonstrated by several examples with different aerosol properties: weakly absorbing aerosols, forest fires over Greece and Russia, and Sahara desert dust. The statistical methodology presented is general; it is not restricted to this particular satellite retrieval application

Quantifying the single-scattering albedo for the January 2017 Chile wildfires from simulations of the OMI absorbing aerosol index

The absorbing aerosol index (AAI) is a qualitative parameter directly calculated from satellite-measured reflectance. Its sensitivity to absorbing aerosols in combination with a long-term data record since 1978 makes it an important parameter for climate research. In this study, we attempt to quantify aerosol absorption by retrieving the single-scattering albedo (ω0) at 550&thinsp;nm from the satellite-measured AAI. In the first part of this study, AAI sensitivity studies are presented exclusively for biomass-burning aerosols. Later on, we employ a radiative transfer model (DISAMAR) to simulate the AAI measured by the Ozone Monitoring Instrument (OMI) in order to derive ω0 at 550&thinsp;nm. Inputs for the radiative transfer calculations include satellite measurement geometry and surface conditions from OMI, aerosol optical thickness (τ) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and aerosol microphysical parameters from the AErosol RObotic NETwork (AERONET), respectively. This approach is applied to the Chile wildfires for the period from 26 to 30 January 2017, when the OMI-observed AAI of this event reached its peak. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) overpasses missed the evolution of the smoke plume over the research region; therefore the aerosol profile is parameterized. The simulated plume is at an altitude of 4.5–4.9&thinsp;km, which is in good agreement with available CALIOP backscatter coefficient measurements. The data may contain pixels outside the plume, so an outlier detection criterion is applied. The results show that the AAI simulated by DISAMAR is consistent with satellite observations. The correlation coefficients fall into the range between 0.85 and 0.95. The retrieved mean ω0 at 550&thinsp;nm for the entire plume over the research period from 26 to 30 January 2017 varies from 0.81 to 0.87, whereas the nearest AERONET station reported ω0 between 0.89 and 0.92. The difference in geolocation between the AERONET site and the plume, the assumption of homogeneous plume properties, the lack of the aerosol profile information and the uncertainties in the inputs for radiative transfer calculation are primarily responsible for this discrepancy in ω0.</p

Global satellite analysis of the relation between aerosols and short-lived trace gases

The spatial and temporal correlations between concurrent satellite observations of aerosol optical thickness (AOT) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and tropospheric columns of nitrogen dioxide (NO&lt;sub&gt;2&lt;/sub&gt;), sulfur dioxide (SO&lt;sub&gt;2&lt;/sub&gt;), and formaldehyde (HCHO) from the Ozone Monitoring Instrument (OMI) are used to infer information on the global composition of aerosol particles. When averaging the satellite data over large regions and longer time periods, we find significant correlation between MODIS AOT and OMI trace gas columns for various regions in the world. This shows that these enhanced aerosol and trace gas concentrations originate from common sources, such as fossil fuel combustion, biomass burning, and organic compounds released from the biosphere. This leads us to propose that satellite-inferred AOT to NO&lt;sub&gt;2&lt;/sub&gt; ratios for regions with comparable photochemical regimes can be used as indicators for the relative regional pollution control of combustion processes. Indeed, satellites observe low AOT to NO&lt;sub&gt;2&lt;/sub&gt; ratios over the eastern United States and western Europe, and high AOT to NO&lt;sub&gt;2&lt;/sub&gt; ratios over comparably industrialized regions in eastern Europe and China. Emission databases and OMI SO&lt;sub&gt;2&lt;/sub&gt; observations over these regions suggest a much stronger sulfur contribution to aerosol formation than over the well-regulated areas of the eastern United States and western Europe. Furthermore, satellite observations show AOT to NO&lt;sub&gt;2&lt;/sub&gt; ratios are a factor 100 higher over biomass burning regions than over industrialized areas, reflecting the unregulated burning practices with strong primary particle emissions in the tropics compared to the heavily controlled combustion processes in the industrialized Northern Hemisphere. Simulations with a global chemistry transport model (GEOS-Chem) capture most of these variations, although on regional scales significant differences are found. Wintertime aerosol concentrations show strongest correlations with NO&lt;sub&gt;2&lt;/sub&gt; throughout most of the Northern Hemisphere. During summertime, AOT is often (also) correlated with enhanced HCHO concentrations, reflecting the importance of secondary organic aerosol formation in that season. We also find significant correlations between AOT and HCHO over biomass burning regions, the tropics in general, and over industrialized regions in southeastern Asia. The distinct summertime maximum in AOT (0.4 at 550 nm) and HCHO over the southeastern United States strengthens existing hypotheses that local emissions of volatile organic compounds lead to the formation of secondary organic aerosols there. GEOS-Chem underestimates the AOT over the southeastern United States by a factor of 2, most likely due to too strong precipitation and too low SOA yield in the model

Fast Simulators for Satellite Cloud Optical Centroid Pressure Retrievals, 1. Evaluation of OMI Cloud Retrievals

The cloud Optical Centroid Pressure (OCP), also known as the effective cloud pressure, is a satellite-derived parameter that is commonly used in trace-gas retrievals to account for the effects of clouds on near-infrared through ultraviolet radiance measurements. Fast simulators are desirable to further expand the use of cloud OCP retrievals into the operational and climate communities for applications such as data assimilation and evaluation of cloud vertical structure in general circulation models. In this paper, we develop and validate fast simulators that provide estimates of the cloud OCP given a vertical profile of optical extinction. We use a pressure-weighting scheme where the weights depend upon optical parameters of clouds and/or aerosol. A cloud weighting function is easily extracted using this formulation. We then use fast simulators to compare two different satellite cloud OCP retrievals from the Ozone Monitoring Instrument (OMI) with estimates based on collocated cloud extinction profiles from a combination of CloudS at radar and MODIS visible radiance data. These comparisons are made over a wide range of conditions to provide a comprehensive validation of the OMI cloud OCP retrievals. We find generally good agreement between OMI cloud OCPs and those predicted by CloudSat. However, the OMI cloud OCPs from the two independent algorithms agree better with each other than either does with the estimates from CloudSat/MODIS. Differences between OMI cloud OCPs and those based on CloudSat/MODIS may result from undetected snow/ice at the surface, cloud 3-D effects, low altitude clouds missed by CloudSat, and the fact that CloudSat only observes a relatively small fraction of an OMI field-of-view

Retrieval and validation of ozone columns derived from measurements of SCIAMACHY on Envisat

International audienceThis paper describes a new ozone column retrieval algorithm and its application to SCIAMACHY measurements. The TOSOMI algorithm is based on the Differential Optical Absorption Spectroscopy (DOAS) technique and implements several improvements over older algorithms. These improvements include aspects like (i) the explicit treatment of rotational Raman scattering, (ii) an improved air-mass factor formulation which is based on a simulation of the reflectivity spectrum and a subsequent DOAS fit of this simulated spectrum, (iii) the use of an improved ozone climatology and a column dependent air-mass factor, (iv) the use of daily varying ECMWF temperature profile analyses. The results of three validation exercises are reported. The TOSOMI columns are compared with an extensive set of ground-based observations (Brewer, Dobson) for the years 2003 and 2004. Secondly, a direct comparison for January?June 2003 with two new GOME retrievals, GDP Version 4 and TOGOMI, is presented. Third, data assimilation is used to study the dependence of the TOSOMI columns with retrieval parameters such as the viewing angle, cloud fraction and geographical location. These comparisons show a good consistency on the percent level between the GOME and SCIAMACHY algorithms. The present TOSOMI implementation (v0.32) shows an offset of about ?1.5% with respect to ground-based observations and the GOME retrievals

Total ozone column derived from GOME and SCIAMACHY using KNMI retrieval algorithms: Validation against Brewer measurements at the Iberian Peninsula

This article focuses on the validation of the total ozone column (TOC) data set acquired by the Global Ozone Monitoring Experiment (GOME) and the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) satellite remote sensing instruments using the Total Ozone Retrieval Scheme for the GOME Instrument Based on the Ozone Monitoring Instrument (TOGOMI) and Total Ozone Retrieval Scheme for the SCIAMACHY Instrument Based on the Ozone Monitoring Instrument (TOSOMI) retrieval algorithms developed by the Royal Netherlands Meteorological Institute. In this analysis, spatially colocated, daily averaged ground-based observations performed by five well-calibrated Brewer spectrophotometers at the Iberian Peninsula are used. The period of study runs from January 2004 to December 2009. The agreement between satellite and ground-based TOC data is excellent (R2 higher than 0.94). Nevertheless, the TOC data derived from both satellite instruments underestimate the ground-based data. On average, this underestimation is 1.1% for GOME and 1.3% for SCIAMACHY. The SCIAMACHY-Brewer TOC differences show a significant solar zenith angle (SZA) dependence which causes a systematic seasonal dependence. By contrast, GOME-Brewer TOC differences show no significant SZA dependence and hence no seasonality although processed with exactly the same algorithm. The satellite-Brewer TOC differences for the two satellite instruments show a clear and similar dependence on the viewing zenith angle under cloudy conditions. In addition, both the GOME-Brewer and SCIAMACHY-Brewer TOC differences reveal a very similar behavior with respect to the satellite cloud properties, being cloud fraction and cloud top pressure, which originate from the same cloud algorithm (Fast Retrieval Scheme for Clouds from the Oxygen A-Band (FRESCO+)) in both the TOSOMI and TOGOMI retrieval algorithms.This work was partially supported by the Andalusian Regional Government through projects P08‐RNM ‐3568 andP10‐RNM‐6299, the Spanish Ministry of Science and Technology throughprojects CGL2010–18782 and CSD2007–00067, and the European Unionthrough ACTRIS project (EU INFRA‐2010‐1.1.16‐262254)

Comparison of aerosol optical depths from the Ozone Monitoring Instrument (OMI) on Aura with results from airborne sunphotometry, other space and ground measurements during MILAGRO/INTEX-B

Airborne sunphotometer measurements are used to evaluate retrievals of extinction aerosol optical depth (AOD) from spatially coincident and temporally near-coincident measurements by the Ozone Monitoring Instrument (OMI) aboard the Aura satellite during the March 2006 Megacity Initiative-Local And Global Research Observations/Phase B of the Intercontinental Chemical Transport Experiment (MILAGRO/INTEX-B). The 14-channel NASA Ames Airborne Tracking Sunphotometer (AATS) flew on nine missions over the Gulf of Mexico and four in or near the Mexico City area. Retrievals of AOD from near-coincident AATS and OMI measurements are compared for three flights over the Gulf of Mexico for flight segments when the aircraft flew at altitudes 60–70 m above sea level, and for one flight over the Mexico City area where the aircraft was restricted to altitudes ~320–800 m above ground level over the rural area and ~550–750 m over the city. OMI-measured top of atmosphere (TOA) reflectances are routinely inverted to yield aerosol products such as AOD and aerosol absorption optical depth (AAOD) using two different retrieval algorithms: a near-UV (OMAERUV) and a multiwavelength (OMAERO) technique. This study uses the archived Collection 3 data products from both algorithms. In particular, AATS and OMI AOD comparisons are presented for AATS data acquired in 20 OMAERUV retrieval pixels (15 over water) and 19 OMAERO pixels (also 15 over water). At least four pixels for one of the over-water coincidences and all pixels for the over-land case were cloud-free. Coincident AOD retrievals from 17 pixels of the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Aqua are available for two of the over-water flights and are shown to agree with AATS AODs to within root mean square (RMS) differences of 0.00–0.06, depending on wavelength. Near-coincident ground-based AOD measurements from ground-based sun/sky radiometers operated as part of the Aerosol Robotic Network (AERONET) at three sites in and near Mexico City are also shown and are generally consistent with the AATS AODs (which exclude any AOD below the aircraft) both in magnitude and spectral dependence. The OMAERUV algorithm retrieves AODs corresponding to a non-absorbing aerosol model for all three over-water comparisons whereas the OMAERO algorithm retrieves best-fit AODs corresponding to an absorbing biomass-burning aerosol model for two of the three over-water cases. For the four cloud-free pixels in one over-water coincidence (10 March), the OMAERUV retrievals underestimate the AATS AODs by ~0.20, which exceeds the expected retrieval uncertainty, but retrieved AODs agree with AATS values within uncertainties for the other two over-water events. When OMAERO retrieves AODs corresponding to a biomass-burning aerosol over water, the values significantly overestimate the AATS AODs (by up to 0.55). For the Mexico City coincidence, comparisons are presented for a non-urban region ~50–70 km northeast of the city and for a site near the center of the city. OMAERUV retrievals are consistent with AERONET AOD magnitudes for the non-urban site, but are nearly double the AATS and AERONET AODs (with differences of up to 0.29) in the center of the city. Corresponding OMAERO retrievals exceed the AATS and/or AERONET AODs by factors of 3 to 10