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The TROPOspheric Monitoring Instrument (TROPOMI) level-2 aerosol layer height (ALH) product has now been released to the general public. This product is retrieved using TROPOMI's measurements of the oxygen A-band, radiative transfer model (RTM) calculations augmented by neural networks and an iterative optimal estimation technique. The TROPOMI ALH product will deliver ALH estimates over cloud-free scenes over the ocean and land that contain aerosols above a certain threshold of the measured UV aerosol index (UVAI) in the ultraviolet region. This paper provides background for the ALH product and explores its quality by comparing ALH estimates to similar quantities derived from spaceborne lidars observing the same scene. The spaceborne lidar chosen for this study is the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission, which flies in formation with NASA's A-train constellation since 2006 and is a proven source of data for studying ALHs. The influence of the surface and clouds is discussed, and the aspects of the TROPOMI ALH algorithm that will require future development efforts are highlighted. A case-by-case analysis of the data from the four selected cases (mostly around the Saharan region with approximately 800 co-located TROPOMI pixels and CALIOP profiles in June and December 2018) shows that ALHs retrieved from TROPOMI using the operational Sentinel-5 Precursor Level-2 ALH algorithm is lower than CALIOP aerosol extinction heights by approximately 0.5 km. Looking at data beyond these cases, it is clear that there is a significant difference when it comes to retrievals over land, where these differences can easily go over 1 km on average.

This overview paper highlights the successes of the Ozone Monitoring Instrument (OMI) on board the Aura satellite spanning a period of nearly 14 years. Data from OMI has been used in a wide range of applications and research resulting in many new findings. Due to its unprecedented spatial resolution, in combination with daily global coverage, OMI plays a unique role in measuring trace gases important for the ozone layer, air quality, and climate change. With the operational very fast delivery (VFD; direct readout) and near real-time (NRT) availability of the data, OMI also plays an important role in the development of operational services in the atmospheric chemistry domain.

An algorithm setup for the operational Aerosol Layer Height product for TROPOMI on the Sentinel-5 Precursor mission is described and discussed, applied to GOME-2A data, and evaluated with lidar measurements. The algorithm makes a spectral fit of reflectance at the O2 A band in the near-infrared and the fit window runs from 758 to 770 nm. The aerosol profile is parameterised by a scattering layer with constant aerosol volume extinction coefficient and aerosol single scattering albedo and with a fixed pressure thickness. The algorithm's target parameter is the height of this layer. In this paper, we apply the algorithm to observations from GOME-2A in a number of systematic and extensive case studies, and we compare retrieved aerosol layer heights with lidar measurements. Aerosol scenes cover various aerosol types, both elevated and boundary layer aerosols, and land and sea surfaces. The aerosol optical thicknesses for these scenes are relatively moderate. Retrieval experiments with GOME-2A spectra are used to investigate various sensitivities, in which particular attention is given to the role of the surface albedo. From retrieval simulations with the single-layer model, we learn that the surface albedo should be a fit parameter when retrieving aerosol layer height from the O2 A band. Current uncertainties in surface albedo climatologies cause biases and non-convergences when the surface albedo is fixed in the retrieval. Biases disappear and convergence improves when the surface albedo is fitted, while precision of retrieved aerosol layer pressure is still largely within requirement levels. Moreover, we show that fitting the surface albedo helps to ameliorate biases in retrieved aerosol layer height when the assumed aerosol model is inaccurate. Subsequent retrievals with GOME-2A spectra confirm that convergence is better when the surface albedo is retrieved simultaneously with aerosol parameters. However, retrieved aerosol layer pressures are systematically low (i.e., layer high in the atmosphere) to the extent that retrieved values no longer realistically represent actual extinction profiles. When the surface albedo is fixed in retrievals with GOME-2A spectra, convergence deteriorates as expected, but retrieved aerosol layer pressures become much higher (i.e., layer lower in atmosphere). The comparison with lidar measurements indicates that retrieved aerosol layer heights are indeed representative of the underlying profile in that case. Finally, subsequent retrieval simulations with two-layer aerosol profiles show that a model error in the assumed profile (two layers in the simulation but only one in the retrieval) is partly absorbed by the surface albedo when this parameter is fitted. This is expected in view of the correlations between errors in fit parameters and the effect is relatively small for elevated layers (less than 100 hPa). If one of the scattering layers is near the surface (boundary layer aerosols), the effect becomes surprisingly large, in such a way that the retrieved height of the single layer is above the two-layer profile. Furthermore, we find that the retrieval solution, once retrieval converges, hardly depends on the starting values for the fit. Sensitivity experiments with GOME-2A spectra also show that aerosol layer height is indeed relatively robust against inaccuracies in the assumed aerosol model, even when the surface albedo is not fitted. We show spectral fit residuals, which can be used for further investigations. Fit residuals may be partly explained by spectroscopic uncertainties, which is suggested by an experiment showing the improvement of convergence when the absorption cross section is scaled in agreement with Butz et al. (2013) and Crisp et al. (2012), and a temperature offset to the a priori ECMWF temperature profile is fitted. Retrieved temperature offsets are always negative and quite large (ranging between −4 and −8 K), which is not expected if temperature offsets absorb remaining inaccuracies in meteorological data. Other sensitivity experiments investigate fitting of stray light and fluorescence emissions. We find negative radiance offsets and negative fluorescence emissions, also for non-vegetated areas, but from the results it is not clear whether fitting these parameters improves the retrieval. Based on the present results, the operational baseline for the Aerosol Layer Height product currently will not fit the surface albedo. The product will be particularly suited for elevated, optically thick aerosol layers. In addition to its scientific value in climate research, anticipated applications of the product for TROPOMI are providing aerosol height information for aviation safety and improving interpretation of the Absorbing Aerosol Index.

An improved nitrogen dioxide (NO2) slant column density retrieval for the Ozone Monitoring Instrument (OMI) in the 405–465 nm spectral region is presented. Since the launch of OMI on board NASA's EOS-Aura satellite in 2004, differential optical absorption spectroscopy (DOAS) retrievals of NO2 slant column densities have been the starting point for the KNMI DOMINO and NASA SP NO2 vertical column data as well as the OMI NO2 data of some other institutes. However, recent intercomparisons between NO2 retrievals from OMI and other UV/Vis and limb spectrometers, as well as ground-based measurements, suggest that OMI stratospheric NO2 is biased high. This study revises and, for the first time, fully documents the OMI NO2 retrieval in detail. The representation of the OMI slit function to convolve high-resolution reference spectra onto the relevant spectral grid is improved. The window used for the wavelength calibration is optimised, leading to much-reduced fitting errors. Ozone and water vapour spectra used in the fit are updated, reflecting the recently improved knowledge of their absorption cross section in the literature. The improved spectral fit also accounts for absorption by the O2–O2 collision complex and by liquid water over clear-water areas. The main changes in the improved spectral fitting result from the updates related to the wavelength calibration: the RMS error of the fit is reduced by 23% and the NO2 slant column by 0.85 × 1015 molec cm−2, independent of latitude, solar zenith angle and NO2 value. Including O2–O2 and liquid water absorption and updating the O3 and water vapour cross-section spectra further reduces NO2 slant columns on average by 0.35 × 1015 molec cm−2, accompanied by a further 9% reduction in the RMS error of the fit. The improved OMI NO2 slant columns are consistent with independent NO2 retrievals from other instruments to within a range that can be explained by photochemically driven diurnal increases in stratospheric NO2 and by small differences in fitting window and approach. The revisions indicate that current OMI NO2 slant columns suffered mostly from an additive positive offset, which is removed by the improved wavelength calibration and representation of the OMI slit function. It is therefore anticipated that the improved NO2 slant columns are most important to retrievals of spatially homogeneous stratospheric NO2 rather than to heterogeneous tropospheric NO2.

In recent years TNO has investigated and developed different innovative opto-mechanical designs to realize advanced spectrometers for space applications in a more compact and cost-effective manner. This offers multiple advantages: a compact instrument can be flown on a much smaller platform or as add-on on a larger platform; a low-cost instrument opens up the possibility to fly multiple instruments in a satellite constellation, improving both global coverage and temporal sampling (e.g. multiple overpasses per day to study diurnal processes); in this way a constellation of low-cost instruments may provide added value to the larger scientific and operational satellite missions (e.g. the Copernicus Sentinel missions); a small, lightweight spectrometer can easily be mounted on a small aircraft or high-altitude UAV (offering high spatial resolution). Moreover, a low-cost instrument may allow us to break through the ‘cost spiral’: lower cost will allow us to take more technical risk and thus be more innovative. This will lead to a much faster development cycle than customary for current Earth-observation instruments. Finally, the TNO designs offer flexibility to tune the performance (spectral range, spectral resolution) of the spectrometer to a specific application (like air quality, climate, water quality, etc ). Thus, based on the same basic system design, these instruments offers a wide range of applications to a variety of clients, both inside and outside the scientific community using a quasi-recurrent instrument (reducing the development cost for each instrument). In this paper the most mature design of a hyperspectral imaging spectrometer (named ‘Spectrolite’) is used to illustrated this innovative approach. For this purpose chapter II will introduce the Spectrolite concept, design philosophy and breadboard (BB). Chapter III will discuss how the Breadboard was transformed into an airborne instrument (for a test flight over Berlin as part of AROMAPEX [7]) in the time frame of only a month. The (reduced) Level 0-1B data processor and corresponding characterization measurements (performed in only 5 days) are presented in Chapter IV, followed by achieved NO2 retrieval results from the airborne test flight over Berlin in chapter V. Finally chapter VI will provide a conclusion and present the next steps for Spectrolite.

Satellite remote sensing is increasingly being used to monitor air quality over localized sources such as the Canadian oil sands. Following an initial study, significantly low biases have been identified in current NO2 and SO2 retrieval products from the Ozone Monitoring Instrument (OMI) satellite sensor over this location resulting from a combination of its rapid development and small spatial scale. Air mass factors (AMFs) used to convert line-of-sight "slant" columns to vertical columns were re-calculated for this region based on updated and higher resolution input information including absorber profiles from a regional-scale (15 km × 15 km resolution) air quality model, higher spatial and temporal resolution surface reflectivity, and an improved treatment of snow. The overall impact of these new Environment Canada (EC) AMFs led to substantial increases in the peak NO2 and SO2 average vertical column density (VCD), occurring over an area of intensive surface mining, by factors of 2 and 1.4, respectively, relative to estimates made with previous AMFs. Comparisons are made with long-term averages of NO2 and SO2 (2005–2011) from in situ surface monitors by using the air quality model to map the OMI VCDs to surface concentrations. This new OMI-EC product is able to capture the spatial distribution of the in situ instruments (slopes of 0.65 to 1.0, correlation coefficients of >0.9). The concentration absolute values from surface network observations were in reasonable agreement, with OMI-EC NO2 and SO2 biased low by roughly 30%. Several complications were addressed including correction for the interference effect in the surface NO2 instruments and smoothing and clear-sky biases in the OMI measurements. Overall these results highlight the importance of using input information that accounts for the spatial and temporal variability of the location of interest when performing retrievals.

We describe a new algorithm for the retrieval of nitrogen dioxide (NO2) vertical columns from nadir-viewing satellite instruments. This algorithm (SP2) is the basis for the Version 2.1 OMI This algorithm (SP2) is the basis for the Version 2.1 Ozone Monitoring Instrument (OMI) NO2 Standard Product and features a novel method for separating the stratospheric and tropospheric columns. NO2 Standard Product and features a novel method for separating the stratospheric and tropospheric columns. The approach estimates the stratospheric NO2 directly from satellite data without using stratospheric chemical transport models or assuming any global zonal wave pattern. Tropospheric NO2 columns are retrieved using air mass factors derived from high-resolution radiative transfer calculations and a monthly climatology of NO2 profile shapes. We also present details of how uncertainties in the retrieved columns are estimated. The sensitivity of the retrieval to assumptions made in the stratosphere–troposphere separation is discussed and shown to be small, in an absolute sense, for most regions. We compare daily and monthly mean global OMI NO2 retrievals using the SP2 algorithm with those of the original Version 1 Standard Product (SP1) and the Dutch DOMINO product. The SP2 retrievals yield significantly smaller summertime tropospheric columns than SP1, particularly in polluted regions, and are more consistent with validation studies. SP2 retrievals are also relatively free of modeling artifacts and negative tropospheric NO2 values. In a reanalysis of an INTEX-B validation study, we show that SP2 largely eliminates an ~20% discrepancy that existed between OMI and independent in situ springtime NO2 SP1 measurements.

We retrieve slant column NO2 from the super-zoom mode of the Ozone Monitoring Instrument (OMI) to explore its utility for understanding NOx emissions and variability. Slant column NO2 is operationally retrieved from OMI (Boersma et al., 2007; Bucsela et al., 2006) with a nadir footprint of 13 × 24 km2, the result of averaging eight detector elements on board the instrument. For 85 orbits in late 2004, OMI reported observations from individual "super-zoom" detector elements (spaced at 13 × 3 km2 at nadir). We assess the spatial response of these individual detector elements in-flight and determine an upper-bound on spatial resolution of 9 km, in good agreement with on-ground calibration (7 km FWHM). We determine the precision of the super-zoom mode to be 2.1 × 1015 molecules cm−2, approximately a factor of √8 lower than an identical retrieval at operational scale as expected if random noise dominates the uncertainty. We retrieve slant column NO2 over the Satpura power plant in India; Seoul, South Korea; Dubai, United Arab Emirates; and a set of large point sources on the Rihand Reservoir in India using differential optical absorption spectroscopy (DOAS). Over these sources, the super-zoom mode of OMI observes variation in slant column NO2 of up to 30 × the instrumental precision within one operational footprint.

We present an improved tropospheric nitrogen dioxide column retrieval algorithm (DOMINO v2.0) for OMI based on better air mass factors (AMFs) and a correction for across-track stripes resulting from calibration errors in the OMI backscattered reflectances. Since October 2004, NO2 retrievals from the Ozone Monitoring Instrument (OMI), a UV/Vis nadir spectrometer onboard NASA's EOS-Aura satellite, have been used with success in several scientific studies focusing on air quality monitoring, detection of trends, and NOx emission estimates. Dedicated evaluations of previous DOMINO tropospheric NO2 retrievals indicated their good quality, but also suggested that the tropospheric columns were susceptible to high biases (by 0–40%), probably because of errors in the air mass factor calculations. Here we update the DOMINO air mass factor approach. We calculate a new look-up table (LUT) for altitude-dependent AMFs based on more realistic atmospheric profile parameters, and include more surface albedo and surface pressure reference points than before. We improve the sampling of the TM4 model, resulting in a priori NO2 profiles that are better mixed throughout the boundary layer. We evaluate the NO2 profiles simulated with the improved TM4 sampling as used in the AMF calculations and show that they are highly consistent with in situ NO2 measurements from aircraft during the INTEX-A and INTEX-B campaigns in 2004 and 2006. Our air mass factor calculations are further updated by the implementation of a high-resolution terrain height and a high-resolution surface albedo climatology based on OMI measurements. Together with a correction for across-track stripes, the overall impact of the improved terrain height and albedo descriptions is modest (

[1] The Dutch-Finnish Ozone Monitoring Instrument (OMI) on board NASA's EOS-Aura satellite is measuring ozone, NO2, and other trace gases with daily global coverage. To correct these trace gas retrievals for the presence of clouds, there are two OMI cloud products, based on different physical processes, namely, absorption by O2–O2 at 477 nm (OMCLDO2) and rotational Raman scattering (RRS) in the UV (OMCLDRR). Both cloud products use a Lambertian cloud model with albedo 0.8 and contain the effective (i.e., radiometric) cloud fraction and the cloud pressure. First, the theoretical framework for the Lambertian cloud model is given and the concept of effective cloud fraction is discussed. Next, an intercomparison of the effective cloud fractions from both products is presented, as well as a comparison with MODIS cloud data. It is shown that the O2–O2 and RRS effective cloud fractions correlate very well (95%) but that there is an offset of about 0.10. From MODIS geometric cloud fraction and cloud optical thickness data a MODIS effective cloud fraction was calculated. The effective cloud fractions from OMCLDO2 and MODIS show a high correlation of 92% with a very small offset (0.01). In order to guide users, a summary of the validation status of effective cloud fraction and cloud pressure from the OMCLDO2 and OMCLDRR cloud products is presented.

The Ozone Monitoring Instrument (OMI) is an imaging UV-VIS solar backscatter spectrometer and is designed and used primarily to retrieve trace gases like O3 and NO2 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 O2-O2 absorption band at a wavelength of 477 nm. It is found that this wavelength band adds significantly more information than any other individual band.

We present a new algorithm for the near-real time retrieval ? within 3 h of the actual satellite measurement ? of tropospheric NO2 columns from the Ozone Monitoring Instrument (OMI). The retrieval system is based on the combined retrieval-assimilation-modelling approach developed at KNMI for off-line tropospheric NO2 from the GOME and SCIAMACHY satellite instruments. We have adapted the off-line system such that the required a priori information ndash; profile shapes and stratospheric background NO2 ndash; is now immediately available upon arrival of the OMI NO2 slant columns and cloud data at KNMI. Slant column NO2 and cloud information arrives at KNMI typically within 80 min of actual OMI observations. Slant columns for NO2 are retrieved using differential optical absorption spectroscopy (DOAS) in the 405?465 nm range. Cloud fraction and cloud pressure are provided by a new cloud retrieval algorithm that uses the absorption of the O2?O2 collision complex near 477 nm. On-line availability of stratospheric slant columns and NO2 profiles is achieved by running the TM4 chemistry transport model (CTM) forward in time based on forecast ECMWF meteo and assimilated NO2 information from all previously observed orbits. OMI NO2 slant columns, after correction for spurious across-track variability, show a random error for individual pixels of approximately 0.7×1015molec.cm?2. As NO2 retrievals are very sensitive to clouds, we evaluated the consistency of cloud fraction and cloud pressure from the new O2?O2 (OMI) algorithm and from the Fast Retrieval Scheme for Cloud Observables (FRESCO). Cloud parameters from the O2?O2 (OMI) algorithm have similar frequency distributions as cloud parameters retrieved from FRESCO (SCIAMACHY) for August 2006. On average, OMI cloud fractions are higher by 0.011, and OMI cloud pressures exceed FRESCO cloud pressures by 60 hPa. As a consistency check, we intercompared OMI near-real time NO2 columns measured at 13:45 h local time to SCIAMACHY off-line NO2 columns measured at 10:00 h local time. In August 2006, both instruments observe very similar spatial patterns of tropospheric NO2 columns, and small differences for most locations on Earth where tropospheric NO2 columns are small. For regions that are strongly polluted, SCIAMACHY observes higher tropospheric NO2 columns than OMI.

This paper describes the algorithm for deriving the total column ozone from spectral radiances and irradiances measured by the Ozone Monitoring Instrument (OMI) on the Earth Observing System Aura satellite. The algorithm is based on the differential optical absorption spectroscopy technique. The main characteristics of the algorithm as well as an error analysis are described. The algorithm has been successfully applied to the first available OMI data. First comparisons with ground-based instruments are very encouraging and clearly show the potential of the method.

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.

The space borne measurements provide global view of atmospheric aerosol distribution. The Ozone Monitoring Instrument (OMI) on board NASAs Earth Observing System (EOS) Aura satellite is a Dutch-Finnish nadir-viewing solar backscatter spectrometer measuring in the ultraviolet and visible wavelengths. OMI measures several trace gases and aerosols that are important in many air quality and climate studies. The OMI aerosol measurements are used, for example, for detecting volcanic ash plumes, wild fires and transportation of desert dust. We present a methodology for improving the uncertainty quantification in the aerosols retrieval algorithm. We have used the OMI measurements in this feasibility study. Our focus is on the uncertainties originating from the pre-calculated aerosol models. These models are never complete descriptions of the reality. This aerosol model uncertainty is estimated using Gaussian processes with computational tools from spatial statistics. Our approach is based on smooth systematic differences between the observed and modelled reflectances. When acknowledging this model inadequacy in the estimation of aerosol optical thickness (AOT), the uncertainty estimates are more realistic. We present here a real world example of applying the methodology.

[1] 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.

[1] In this paper we present the validation results of the operational vertical ozone profiles retrieved from the nadir observations by the Ozone Monitoring Instrument (OMI) aboard the NASA Earth Observing System (EOS) Aura platform. The operational ozone profile retrieval algorithm was developed at the Royal Netherlands Meteorological Institute and the OMI mission data has been processed and made publicly available. Advantages of these nadir sounded ozone profiles are the excellent spatial resolution at nadir and daily global coverage while the vertical resolution is limited to 6–7 km. Comparisons with well-validated ozone profile recordings by the Microwave Limb Sounder (MLS) and the Tropospheric Emission Spectrometer (TES), both aboard the NASA EOS-Aura platform, provide an excellent opportunity for validation because of the large amount of collocations with OMI due to the instruments significant geographical overlap. In addition, comparisons with collocated ozone profiles from the Stratospheric Aerosol and Gas Experiment (SAGE-II), the Halogen Occultation Experiment (HALOE), the Global Ozone Monitoring by the Occultation of Stars (GOMOS) and the Optical Spectrograph and Infrared Imager System (OSIRIS) satellite instruments and balloon-borne electrochemical concentration cell (ECC) ozonesondes are presented. OMI stratospheric ozone profiles are found to agree within 20% with global correlative data except for both the polar regions during local spring. For ozone in the troposphere OMI shows a systematic positive bias versus the correlative data sets of order 60% in the tropics and 30% at midlatitude regions. The largest source of error in the tropospheric ozone profile is the fit to spectral stray light in the operational algorithm.

[1] Multi-sensor cloud height observations are investigated and compared in terms of vertical and latitudinal distributions of monthly mean cloud occurrence frequency (COF). Although this study emphasizes the standard Multiangle Imaging SpectroRadiometer (MISR) cloud top height (CTH) retrieval, the strengths and weakness among different passive and active remote sensing techniques with respect to cloud detection and height assessment are also discussed. The standard MISR CTH retrieval is less sensitive to high thin cirrus than the Atmospheric Infrared Sounder (AIRS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), but MISR provides more accurate CTH retrievals in the middle and lower troposphere compared with other passive sensors, especially for clouds in the planetary boundary layer.

Dutch Aerosol and Nitrogen Dioxide Experiments for Validation of OMI and SCIAMACHY (DANDELIONS) is a project that encompasses validation of spaceborne measurements of NO2 by the Ozone Monitoring Instrument (OMI) and Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY), and of aerosol by OMI and Advanced Along-Track Scanning Radiometer (AATSR), using an extensive set of ground-based and balloon measurements over the polluted area of the Netherlands. We present an extensive data set of ground-based, balloon, and satellite data on NO2, aerosols, and ozone obtained from two campaigns within the project, held during May-June 2005 and September 2006. We have used these data for first validation of OMI NO2, and the data are available through the Aura Validation Data Center website for use in other validation efforts. In this paper we describe the available data, and the methods and instruments used, including the National Institute of Public Health and the Environment (RIVM) NO2 lidar. We show that NO2 from Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) compares well with in situ measurements. We show that different MAX-DOAS instruments, operating simultaneously during the campaign, give very similar results. We also provide unique information on the spatial homogeneity and the vertical and temporal variability of NO2, showing that during a number of days, the NO2 columns derived from measurements in different directions varied significantly, which implies that, under polluted conditions, measurements in one single azimuth direction are not always representative for the averaged field that the satellite observes. In addition, we show that there is good agreement between tropospheric NO2 from OMI and MAX-DOAS, and also between total NO2 from OMI and direct-sun observations. Observations of the aerosol optical thickness (AOT) show that values derived with three ground-based instruments correspond well with each other, and with aerosol optical thicknesses observed by OMI. Copyright 2008 by the American Geophysical Union. U7 - Export Date: 2 August 2010 U7 - Source: Scopus U7 - Art. No.: D16S46

[1] This paper presents a comparison of NO 2 data measured with the Ozone Monitoring Instrument (OMI) on board the EOS-AURA satellite with ground-based direct-Sun Brewer measurement data. Since its deployment in July 2004, OMI has provided more than 2 years of daily high-resolution (∼13 x 24 km 2 at nadir) NO 2 vertical column density maps. We describe the retrieval, which includes an estimation of the stratospheric and tropospheric fraction of total NO 2 columns, the air mass factor (AMF) correction based on detected tropospheric NO 2 enhancements, and the generation of the gridded data product. We present a validation study of the gridded NO 2 data set using data from a Brewer MK3 double monochromator in direct-Sun mode located at NASA Goddard Space Flight Center in Greenbelt, Maryland, USA. Monthly averages of coinciding measurements correlate well (r = 0.9) but OMI data are about 25% lower than the Brewer measurement data (slope 0.75, intercept -0.38 x 10 15 molecules/cm 2 ). We present a detailed uncertainty analysis for both ground and satellite data and discuss the possible reasons for the observed differences.

[1] We present an analysis of in situ NO2 measurements from aircraft experiments between summer 2004 and spring 2006. The data are from the INTEX-A, PAVE, and INTEX-B campaigns and constitute the most comprehensive set of tropospheric NO2 profiles to date. Profile shapes from INTEX-A and PAVE are found to be qualitatively similar to annual mean profiles from the GEOS-Chem model. Using profiles from the INTEX-B campaign, we perform error-weighted linear regressions to compare the Ozone Monitoring Instrument (OMI) tropospheric NO2 columns from the near-real-time product (NRT) and standard product (SP) with the integrated in situ columns. Results indicate that the OMI SP algorithm yields NO2 amounts lower than the in situ columns by a factor of 0.86 (±0.2) and that NO2 amounts from the NRT algorithm are higher than the in situ data by a factor of 1.68 (±0.6). The correlation between the satellite and in situ data is good (r = 0.83) for both algorithms. Using averaging kernels, the influence of the algorithm's a priori profiles on the satellite retrieval is explored. Results imply that air mass factors from the a priori profiles are on average slightly larger (∼10%) than those from the measured profiles, but the differences are not significant.

[1] In this paper we present validation results of the total ozone column data products of the Ozone Monitoring Instrument (OMI) by using airborne observations by the CCD Actinic Flux Spectrometer (CAFS) instrument. CAFS was flown during Aura Validation Experiment (AVE) campaigns organized by NASA in support of the validation of EOS-Aura satellite data products. The accuracy of individual CAFS total ozone column estimates of 2.0% on average is sufficient to meet the OMI validation requirements of 3.0%. A climatology was used to estimate the ozone column below the aircraft altitude. AVE validation results show that the OMI-TOMS total ozone column data product is of overall high quality as CAFS and OMI-TOMS agree to within less than 1% with a standard deviation of 8 DU (2–3%) with no significant dependence on total ozone column, latitude, cloud fraction, or solar zenith angle. The primary shortcoming in OMI-DOAS collection 2 total ozone column data is the air mass factor reflected in the dependence on solar zenith angle of the CAFS and OMI-DOAS total ozone column difference. Fortunately, the CAFS aircraft data collected during AVE provided useful insights that could not be obtained in any other way into where OMI satellite data retrieval improvements were needed. For OMI-DOAS collection 3 the air mass factor issue has been solved by calibration optimization and retrieval algorithm improvements, bringing airborne CAFS and OMI-DOAS satellite data to agreement within less than 1.5% with a standard deviation of 9 DU (2–3%), in compliance with the OMI validation requirements.

[1] Verification of the geolocation assigned to individual ground pixels as measured by the Ozone Monitoring Instrument (OMI) aboard the NASA EOS-Aura satellite was performed by comparing geophysical Earth surface details as observed in OMI false color images with the high-resolution continental outline vector map as provided by the Interactive Data Language (IDL) software tool from ITT Visual Information Solutions. The OMI false color images are generated from the OMI visible channel by integration over 20-nm-wide spectral bands of the Earth radiance intensity around 484 nm, 420 nm, and 360 nm wavelength per ground pixel. Proportional to the integrated intensity, we assign color values composed of CRT standard red, green, and blue to the OMI ground pixels. Earth surface details studied are mostly high-contrast coast lines where arid land or desert meets deep blue ocean. The IDL high-resolution vector map is based on the 1993 CIA World Database II Map with a 1-km accuracy. Our results indicate that the average OMI geolocation offset over the years 2005–2006 is 0.79 km in latitude and 0.29 km in longitude, with a standard deviation of 1.64 km in latitude and 2.04 km in longitude, respectively. Relative to the OMI nadir pixel size, one obtains mean displacements of � 6.1% in latitude and � 1.2% in longitude, with standard deviations of 12.6% and 7.9%, respectively. We conclude that the geolocation assigned to individual OMI ground pixels is sufficiently accurate to support scientific studies of atmospheric features as observed in OMI level 2 satellite data products, such as air quality issues on urban scales or volcanic eruptions and its plumes, that occur on spatial scales comparable to or smaller than OMI nadir pixels.

The Ozone Monitoring Instrument (OMI) project team uses two total ozone retrieval algorithms in order to maintain the long-term record established with Total Ozone Mapping Spectrometer (TOMS) data as well as to improve the ozone column estimate using the hyperspectral capability of OMI. The purpose of this study is to assess where the algorithms produce comparable results and where the differences are significant. Starting with the same set of Earth reflectance data, the total ozone data used in this study have been derived using OMI-TOMS and OMI-Differential Optical Absorption Spectroscopy (DOAS) algorithms. OMI-TOMS is based on the TOMS version 8 algorithm that has been used to process TOMS data taken since November 1978. The OMI-DOAS retrieval algorithm was developed specifically for OMI. It takes advantage of the hyperspectral feature of the OMI instrument to reduce errors due to aerosols, clouds, surface, and sulfur dioxide from volcanic eruptions. The OMI-DOAS algorithm also has improved correction for cloud height. The mean differences in the ozone column derived from the two algorithms vary from 0 to 9 DU (0-3%), and their correlation coefficients vary between 0.89 and 0.99 with latitude and season. The largest differences occur in the polar regions and over clouds. Some of the differences are due to stray light, dark current, and other instrumental errors that have been corrected in the new version of the OMI radiance/irradiance data set (collection 3). Other differences are algorithmic. OMI-DOAS algorithmic errors identified through this analysis are also being corrected in collection 3 reprocessing. However, for consistency with the long-term TOMS record, OMI-TOMS collection 3 data will still be based on the TOMS V8 algorithm. Preliminary analysis shows much better agreement in the two total ozone data sets after reprocessing. Reprocessed collection 3 data from both algorithms will be available before the end of 2007. Continuing the TOMS total ozone column data record that dates back to November 1978 is the primary OMI mission goal that is achievable with either OMI total ozone column data product. Copyright 2008 by the American Geophysical Union. U7 - Export Date: 2 August 2010 U7 - Source: Scopus U7 - Art. No.: D16S28

The Ozone Monitoring Instrument (OMI) on NASA's AURA satellite is one of the first instruments measuring an extensive set of daily air quality parameters from space. This anwers to a growing interest in obtaining space data for the air we breath next to the higher altitude air masses. OMI combines UV-Visible nadir viewing spectroscopy, small ground pixels and daily global coverage. As SCIAMACHY on ENVISAT, it uses the high sensitivity of UV-Visible spectroscopy to the lowest kilometers of the atmosphere and the boundary layer. It has fairly small ground pixels (13 × 24 km2) to allow for a high fraction of cloudfree observations and to help to observing detailed spatial features. Daily measurements form a prerequisite for air quality data. This paper shows examples of OMI air quality measurements and it explains in what sense these measurements can be improved. This makes use of the results of a study in the Netherlands on air quality user requirements and instrument design for an OMI/SCIAMACHY follow-on instrument. The improvements are implemented in a new instrument, TROPOMI: an O2A channel is added to improve on surface and cloud albedo, cloud fraction and cloud top height and a new separate module is added for the SWIR wavelengths for measuring CO and CH4. The ground pixel is reduced to 10 × 10 km2 and signal-tonoises are improved to allow measurements in low albedo areas. The design makes use of newly developed detectors and new optics to reduce the overall instrument size. Lastly, the paper discusses the problem of data continuity for the coming 10-15 years, i.e. after AURA and ENVISAT end of life.

This 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.

Data from the Along Tract Scanning Radiometer 2 (ATSSR-2) on board the European ERS-2 satellite have been used to derive the spatial distribution of the aerosol optical depth (AOD) over Europe for August 1997. The AOD was retrieved in cloud free areas using the dual view algorithm. The results agree with co-located ground based sun-photometer data within 0.1. The AOD from ATSR-2 with a resolution of 1 x 1 km2 were averaged on a grid of 0.1° x 0.1°, to produce daily maps of the spatial aerosol distribution over Europe. A composite map of AOD over Europe was constructed by averaging all daily maps for August 1997. This composite map shows a large spatial AOD gradients with variations of a factor of 3 over a few hundreds of kilometers. The AOD at 0.555 μm for relatively clean areas is around 0.1 while in strong industrialised areas the AOD can be 0.5 or higher.

We examine aerosol extinction and optical thickness from the Lidar Atmospheric Sensing Experiment (LASE), the in situ nephelometer and absorption photometer on the University of Washington C-131A aircraft, and the NASA Ames Airborne Tracking Sun Photometer (AATS-6) on the C-131A measured during the Tropospheric Aerosol Radiative Forcing Observational Experiment (TARFOX) over the east coast of the United States in July 1996. On July 17 and 24 the LASE profiles of aerosol extinction and aerosol optical thickness (AOT) had a bias difference of 0.0055 km-1 (10%) and a root-mean-square difference of 0.026 km-1 (42%) when compared to corresponding profiles derived from the airborne in situ data when the nephelometer measurements are adjusted to ambient relative humidities. Larger differences for two other days were associated with much smaller aerosol optical thicknesses (July 20) and differences in the locations sampled by the two aircraft (July 26). LASE profiles of AOT are about 10% higher than those derived from the airborne Sun photometer, which in turn are about 10-15% higher than those derived from the airborne in situ measurements. These differences are generally within the error estimates of the various measurements. The LASE measurements of AOT generally agree with AOT derived from both the Along-Track and Scanning Radiometer 2 (ATSR 2) sensor flown on the European Remote Sensing Satellite 2 (ERS-2) and from the Moderate-Resolution Imaging Spectroradiometer (MODIS) airborne simulator (MAS) which flew with LASE on the NASA ER-2 aircraft. Effective particle sizes derived from the MAS data indicate that the LASE retrievals of AOT are valid for effective particle radii less than 0.4 μm. Variations in the relative humidity derived from the LASE water vapor measurements on July 26 are found to be highly correlated with variations in the effective particle size derived from the MAS. Copyright 2000 by the American Geophysical Union.

Aerosol optical depth (AOD) and Angstrom coefficients over Europe retrieved from satellite data for August 1997 provide information on the spatial variations of these aerosol properties. The AOD results are compared with initial results from model calculations, showing the relative influences of sulphate and nitrate aerosol.

With the development of new satellite sensors with more spectral information and improved calibration, it is now also possible to retrieve over land surfaces. Results from two retrieval algorithms for use over land, different satellite instruments and different methods, are presented.

We review the standard nitrogen dioxide (NO2) data product (Version 1.0.), which is based on measurements made in the spectral region 415–465 nm by the Ozone Monitoring Instrument (OMI) on the NASA Earth Observing System-Aura satellite. A number of ground- and aircraft-based measurements have been used to validate the data product's three principal quantities: stratospheric, tropospheric, and total NO2 column densities under nearly or completely cloud-free conditions. The validation of OMI NO2 is complicated by a number of factors, the greatest of which is that the OMI observations effectively average the NO2 over its field of view (minimum 340 km2), while a ground-based instrument samples at a single point. The tropospheric NO2 field is often very inhomogeneous, varying significantly over tens to hundreds of meters, and ranges from 1016 cm−2 over urban and industrial areas. Because of OMI's areal averaging, when validation measurements are made near NO2 sources the OMI measurements are expected to underestimate the ground-based, and this is indeed seen. Further, we use several different instruments, both new and mature, which might give inconsistent NO2 amounts; the correlations between nearby instruments is 0.8–0.9. Finally, many of the validation data sets are quite small and span a very short length of time; this limits the statistical conclusions that can be drawn from them. Despite these factors, good agreement is generally seen between the OMI and ground-based measurements, with OMI stratospheric NO2 underestimated by about 14% and total and tropospheric columns underestimated by 15–30%. Typical correlations between OMI NO2 and ground-based measurements are generally >0.6.