Ozone Monitoring Instrument Instrument Research Articles

Use of machine learning and principal component analysis to retrieve nitrogen dioxide (NO2) with hyperspectral imagers and reduce noise in spectral fitting

Abstract. Nitrogen dioxide (NO2) is an important trace-gas pollutant and climate agent whose presence also leads to spectral interference in ocean color retrievals. NO2 column densities have been retrieved with satellite UV–Vis spectrometers such as the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI) that typically have spectral resolutions of the order of 0.5 nm or better and spatial footprints as small as 3.6 km × 5.6 km. These NO2 observations are used to estimate emissions, monitor pollution trends, and study effects on human health. Here, we investigate whether it is possible to retrieve NO2 amounts with lower-spectral-resolution hyperspectral imagers such as the Ocean Color Instrument (OCI) that will fly on the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite set for launch in early 2024. OCI will have a spectral resolution of 5 nm and a spatial resolution of ∼ 1 km with global coverage in 1–2 d. At this spectral resolution, small-scale spectral structure from NO2 absorption is still present. We use real spectra from the OMI to simulate OCI spectra that are in turn used to estimate NO2 slant column densities (SCDs) with an artificial neural network (NN) trained on target OMI retrievals. While we obtain good results with no noise added to the OCI simulated spectra, we find that the expected instrumental noise substantially degrades the OCI NO2 retrievals. Nevertheless, the NO2 information from OCI may be of value for ocean color retrievals. OCI retrievals can also be temporally averaged over timescales of the order of months to reduce noise and provide higher-spatial-resolution maps that may be useful for downscaling lower-spatial-resolution data provided by instruments such as OMI and TROPOMI; this downscaling could potentially enable higher-resolution emissions estimates and be useful for other applications. In addition, we show that NNs that use coefficients of leading modes of a principal component analysis of radiance spectra as inputs appear to enable noise reduction in NO2 retrievals. Once trained, NNs can also substantially speed up NO2 spectral fitting algorithms as applied to OMI, TROPOMI, and similar instruments that are flying or will soon fly in geostationary orbit.

Long term (2005–2016) study of formaldehyde (HCHO) columns from satellite data in two regions in the south of Mexico. Evidence of the impact of agricultural activity

In order to determine the driver factors and the principal months of enhancement of HCHO over the Oaxaca and the Chiapas regions, important agricultural areas located in the south of Mexico, HCHO columns from the Ozone Monitoring Instrument (OMI) were monthly averaged (HCHOOMI) and used in conjunction with monthly averaged isoprene emissions (isoprene), monthly NOx emissions from biomass burning (NOx_bb), and monthly averaged NOx emissions from soils with their different sources (biomes: NOx_bio, fertilization and manure: NOx_fer, deposition of N: NOx_dep, pulses: NOx_pul, and total: NOx_tot) from January 2005 to December 2016. Based on scatterplots grouped by month due to the seasonality of the variables, Spearman rank correlations, and multiple linear regression models, we determined that isoprene was an important driver of HCHOOMI in the Oaxaca region, contributing both to its seasonality and peak presence during April and May; whereas NOx_bb was a relevant source in the enhancement of HCHOOMI during April and May, months in which the fire season occurs in the south of Mexico. In the Chiapas region, we determined that both isoprene and NOx_fer were important drivers of the seasonality of HCHOOMI and its peak months during April and May. NOx_bb was predominantly involved in the HCHOOMI enhancement during April and May. The overall multiple linear regression model for each study region was significant (F-statistic = 21.35, p<0.0001 for the Oaxaca region; F-statistic = 101.59, p<0.0001 for the Chiapas region) and explained 72% of the variation of HCHOOMI in the Oaxaca region, 85% in the Chiapas region. Based on the multiple linear regression model for each study region, monthly averaged modeled HCHO columns (HCHOMOD) were obtained. With the estimated regression coefficients, the contribution (in %) of the relevant emission fluxes for each region were calculated in order to know which source was the predominant one in the exacerbation of HCHOMOD. During the peak months of HCHOMOD (April and May) in the Oaxaca region, isoprene predominates over NOx_bb. In the Chiapas region both NOx_bb and NOx_fer caused the exacerbation of HCHOMOD during April and May, whereas NOx_fer was the major variable that caused the increase of HCHOMOD during June, July, and August, especially from 2014 to 2016. Overall, in this region the emissions of NOx were more important in the increase of HCHOMOD than isoprene. NOx_bb and NOx_fer were enhanced principally due to the agricultural activity taking place in the Oaxaca and Chiapas regions. Thus, this work demonstrates the impact of agricultural activity on HCHO columns observed by the OMI instrument. In addition, this work shows that HCHO columns can be modeled using HCHO columns obtained from satellite data and emission inventories. The analysis performed here can be applied to other similar regions not only in Mexico but elsewhere around the world.

Synergy of Using Nadir and Limb Instruments for Tropospheric Ozone Monitoring (SUNLIT)

Abstract. Satellite measurements in nadir and limb viewing geometry provide a complementary view of the atmosphere. An effective combination of the limb and nadir measurements can give new information about atmospheric composition. In this work, we present tropospheric ozone column datasets that have been created using a combination of total ozone columns from OMI (Ozone Monitoring Instrument) and TROPOMI (TROPOspheric Monitoring Instrument) with stratospheric ozone column datasets from several available limb-viewing instruments: MLS (Microwave Limb Sounder), OSIRIS (Optical Spectrograph and InfraRed Imaging System), MIPAS (Michelson Interferometer for Passive Atmospheric Sounding), SCIAMACHY (SCanning Imaging Spectrometer for Atmospheric CHartographY), OMPS-LP (Ozone Mapping and Profiles Suite – Limb Profiler), and GOMOS (Global Ozone Monitoring by Occultation of Stars). We have developed further the methodological aspects of the assessment of tropospheric ozone using the residual method supported by simulations with the chemistry transport model SILAM (System for Integrated modeLling of Atmospheric coMposition). It has been shown that the accurate assessment of ozone in the upper troposphere and the lower stratosphere (UTLS) is of high importance for detecting the ground-level ozone patterns. The stratospheric ozone column is derived from a combination of ozone profiles from several satellite instruments in limb-viewing geometry. We developed a method for the data homogenization, which includes the removal of biases and a posteriori estimation of random uncertainties, thus making the data from different instruments compatible with each other. The high-horizontal- and vertical-resolution dataset of ozone profiles is created via interpolation of the limb profiles from each day to a 1∘×1∘ horizonal grid. A new kriging-type interpolation method, which takes into account data uncertainties and the information about natural ozone variations from the SILAM-adjusted ozone field, has been developed. To mitigate the limited accuracy and coverage of the limb profile data in the UTLS, a smooth transition to the model data is applied below the tropopause. This allows for the estimation of the stratospheric ozone column with full coverage of the UTLS. The derived ozone profiles are in very good agreement with collocated ozonesonde measurements. The residual method was successfully applied to OMI and TROPOMI clear-sky total ozone data in combination with the stratospheric ozone column from the developed high-resolution limb profile dataset. The resulting tropospheric ozone column is in very good agreement with other satellite data. The global distributions of tropospheric ozone exhibit enhancements associated with the regions of high tropospheric ozone production. The main datasets created are (i) a monthly 1∘×1∘ global tropospheric ozone column dataset (from ground to 3 km below the tropopause) using OMI and limb instruments, (ii) a monthly 1∘×1∘ global tropospheric ozone column dataset using TROPOMI and limb instruments, and (iii) a daily 1∘×1∘ interpolated stratospheric ozone column from limb instruments. Other datasets, which are created as an intermediate step of creating the tropospheric ozone column data, are (i) a daily 1∘×1∘ clear-sky and total ozone column from OMI and TROPOMI, (ii) a daily 1∘×1∘ homogenized and interpolated dataset of ozone profiles from limb instruments, and (iii) a daily 1∘×1∘ dataset of ozone profiles from SILAM simulations with adjustment to satellite data. These datasets can be used in various studies related to variability and trends in ozone distributions in both the troposphere and the stratosphere. The datasets are processed from the beginning of OMI and TROPOMI measurements until December 2020 and are planned to be regularly extended in the future.

Ozone trends on equatorial and tropical regions of South America using Dobson spectrophotometer, TOMS and OMI satellites instruments

The ozone layer filters solar ultraviolet radiation and is affected by several photochemical and dynamical processes in the atmosphere. Stratospheric ozone formation takes place primarily in tropical regions and it is the consequence of reactions between atmospheric oxygen and the ultraviolet solar radiation. For the purpose of this paper, the atmospheric TOC (Total Ozone Content) measurements are calculated using Dobson spectrophotometers, in conjunction of satellite data of TOMS (Total Ozone Mapping Spectrometer), and OMI (Ozone Monitoring Instrument), at Cachoeira Paulista (22.7oS, 45.0oW) (CP) and Natal (5.8oS, 35.2oW) (NT), Brazil. The time series considered extend from 1974 to 2013 for CP and from 1978 to 2013 for NT. The mean differences in ozone values between Dobson and TOMS, and between Dobson and OMI, are about 3% and 2%, respectively. The correlations of the Dobson spectrophotometers with TOMS are inferior to the correlations of Dobson to OMI data, due to the improved retrieval and algorithm of the more recent OMI instrument. The observed long-term ozone trends obtained for CP and NT with Dobsons, are: −2.76%, for the period of 1974–2013, and -0.78%, for the period of 1978–2013, respectively. The trend in TOC per decade is of −0.71 ± 0.4% at CP, and of −0.22 ± 0.2%, at NT. The estimated seasonal trends for CP in June-July-August are −3.3%, compared to NT, which yields −1% in the same period. NT seasonal trend for December-January-February is −1.3%. This shows that there is a more significant decrease at CP than at NT, which agrees with the fact that the most active ozone production occurs near the equator. The average ozone amounts were 268.3 ± 13.1 DU for CP and 265.9 ± 10.9 DU for NT, and a seasonal cycle with a minimum of 256.0 DU at CP and NT in autumn and maximum of 283 DU at CP and 279 DU at NT in spring, of southern hemisphere.

Analysis of tropospheric NO2 over Iraq using OMI satellite measurements

Tropospheric nitrogen dioxide (NO2) is a trace gas with important impact on atmospheric chemistry, human health and a key pollutant in particular cities, measured from space since the mid-1990s by the GOME, SCIAMACHY, OMI, and GOME-2 instruments. This study present ten years (monthly and yearly averaged) dataset from Ozone Monitoring Instrument (OMI) used to investigate tropospheric NO2 characteristics and variations over Iraq during 2005–2014. Annual NO2 shows an elevation from the northern to the southern and highest values was at central parts of Iraq. Monthly distributions revels higher values NO2 in winter and summer than spring and autumn seasons, and rising NO2 throughout study period over industrial and crowded urban zones. The trend analysis over Baghdad shows a linear growth rate 9.8% per year with an annual average (5.6·1015 molecules per 1 cm2). The air mass trajectory analysis as hotspot regions shows seasonal fluctuations between winter and summer seasons depend on weather conditions and topography. The increased NO2 values in winter are due to anthropogenic emissions and subsequent plumes from Europe. In addition, in summer because of hot weather and large paddy fields emissions. The lowest NO2 value was at monsoon period mostly linked to the rains. The OMI data and satellite information are able to observe the troposphere NO2 elevation at different regions.

Analysis of tropospheric NO2 over Iraq using OMI satellite measurements

Tropospheric nitrogen dioxide (NO2) is a trace gas with important impact on atmospheric chemistry, human health and a key pollutant in particular cities, measured from space since the mid-1990s by the GOME, SCIAMACHY, OMI, and GOME-2 instruments. This study present ten years (monthly and yearly averaged) dataset from Ozone Monitoring Instrument (OMI) used to investigate tropospheric NO2 characteristics and variations over Iraq during 2005–2014. Annual NO2 shows an elevation from the northern to the southern and highest values was at central parts of Iraq. Monthly distributions revels higher values NO2 in winter and summer than spring and autumn seasons, and rising NO2 throughout study period over industrial and crowded urban zones. The trend analysis over Baghdad shows a linear growth rate 9.8% per year with an annual average (5.6·1015 molecules per 1 cm2). The air mass trajectory analysis as hotspot regions shows seasonal fluctuations between winter and summer seasons depend on weather conditions and topography. The increased NO2 values in winter are due to anthropogenic emissions and subsequent plumes from Europe. In addition, in summer because of hot weather and large paddy fields emissions. The lowest NO2 value was at monsoon period mostly linked to the rains. The OMI data and satellite information are able to observe the troposphere NO2 elevation at different regions.

Spatially continuous mapping of daily global ozone distribution (2004–2014) with the Aura OMI sensor

AbstractTotal ozone data from the Aura Ozone Monitoring Instrument (OMI) play an important role in the monitoring of the spatial distribution and temporal change of total ozone. However, since September 2005, and especially after mid‐2006, due to row anomalies in the OMI instrument, one third to one half of the OMI total ozone data has been missing. In this study, we generate a spatially continuous and daily global total ozone product (2004–2014) by quantitatively reconstructing the level 3 (gridded) total ozone data via a new two‐step method, which takes full advantage of the temporal and spatial correlation characteristics. First, a preliminary prediction is made based on an adaptive weighted temporal fitting method. Residual correction based on an anisotropic kriging method is then proposed to further improve the prediction accuracy. To assess the efficacy of the proposed method, a comparison of different gap filling algorithms through a series of simulated experiments was performed. On this basis, we further evaluated the proposed product with Brewer spectrophotometers' total ozone columns. The evaluation results suggest that the proposed method outperforms the other algorithms, and its product is better able to capture total ozone variation than the MERRA‐2 assimilated ozone product. The total ozone product produced in this study can be freely downloaded from http://sendimage.whu.edu.cn/send‐resource‐download/.

Multi-satellite sensor study on precipitation-induced emission pulses of NO&amp;lt;sub&amp;gt;&amp;lt;i&amp;gt;x&amp;lt;/i&amp;gt;&amp;lt;/sub&amp;gt; from soils in semi-arid ecosystems

Abstract. We present a top-down approach to infer and quantify rain-induced emission pulses of NOx ( ≡ NO + NO2), stemming from biotic emissions of NO from soils, from satellite-borne measurements of NO2. This is achieved by synchronizing time series at single grid pixels according to the first day of rain after a dry spell of prescribed duration. The full track of the temporal evolution several weeks before and after a rain pulse is retained with daily resolution. These are needed for a sophisticated background correction, which accounts for seasonal variations in the time series and allows for improved quantification of rain-induced soil emissions. The method is applied globally and provides constraints on pulsed soil emissions of NOx in regions where the NOx budget is seasonally dominated by soil emissions. We find strong peaks of enhanced NO2 vertical column densities (VCDs) induced by the first intense precipitation after prolonged droughts in many semi-arid regions of the world, in particular in the Sahel. Detailed investigations show that the rain-induced NO2 pulse detected by the OMI (Ozone Monitoring Instrument), GOME-2 and SCIAMACHY satellite instruments could not be explained by other sources, such as biomass burning or lightning, or by retrieval artefacts (e.g. due to clouds). For the Sahel region, absolute enhancements of the NO2 VCDs on the first day of rain based on OMI measurements 2007–2010 are on average 4 × 1014 molec cm−2 and exceed 1 × 1015 molec cm−2 for individual grid cells. Assuming a NOx lifetime of 4 h, this corresponds to soil NOx emissions in the range of 6 up to 65 ng N m−2 s−1, which is in good agreement with literature values. Apart from the clear first-day peak, NO2 VCDs are moderately enhanced (2 × 1014 molec cm−2) compared to the background over the following 2 weeks, suggesting potential further emissions during that period of about 3.3 ng N m−2 s−1. The pulsed emissions contribute about 21–44 % to total soil NOx emissions over the Sahel.

Comparison of operational satellite SO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; products with ground-based observations in northern Finland during the Icelandic Holuhraun fissure eruption

Abstract. This paper shows the results of the comparison of satellite SO2 observations from OMI (Ozone Monitoring Instrument) and OMPS (Ozone Mapping Profiler Suite) with ground-based measurements during the Icelandic Holuhraun fissure eruption in September 2014. The volcanic plume reached Finland on several days during the month of September. The SO2 total columns from the Brewer direct sun (DS) measurements in Sodankylä (67.42° N, 26.59° E), northern Finland, are compared to the satellite data. The operational satellite SO2 products are evaluated for high latitude conditions (e.g. large solar zenith angle, SZA). The results show that the best agreement can be found for lowest SZAs, close-to-nadir satellite pixels, cloud fraction below 0.3 and small distance between the station and the centre of the pixel. Under good retrieval conditions, the difference between satellite data and Brewer measurements remains mostly below the uncertainty on the satellite SO2 retrievals (up to about 2 DU at high latitudes). The satellite products assuming a priori profile with SO2 predominantly in the planetary boundary layer give total column values with the best agreement with the ground-based data. The analysis of the SO2 surface concentrations at four air quality stations in northern Finland shows that the volcanic plume coming from Iceland was located very close to the surface. This is connected to the fact that this was a fissure eruption and most of the SO2 was emitted into the troposphere. This is an exceptional case because the SO2 volcanic emissions directly affect the air quality levels at surface in an otherwise pristine environment like northern Finland. The time evolution of the SO2 concentrations peaks during the same days when large SO2 total column values are measured by the Brewer in Sodankylä and enhanced SO2 signal is visible over northern Finland from the satellite maps. Thus, the satellite retrievals were able to detect the spatiotemporal evolution of the volcanic plume as compared to the surface observations. Furthermore, direct-broadcast SO2 satellite data (from both OMI and OMPS instruments) are compared for the first time against ground-based observations.

Intercomparison of MODIS, MISR, OMI, and CALIPSO aerosol optical depth retrievals for four locations on the Indo-Gangetic plains and validation against AERONET data

This study provides an intercomparison of aerosol optical depth (AOD) retrievals from satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS), Multiangle Imaging Spectroradiometer (MISR), Ozone Monitoring Instrument (OMI), and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) instrumentation over Karachi, Lahore, Jaipur, and Kanpur between 2007 and 2013, with validation against AOD observations from the ground-based Aerosol Robotic Network (AERONET). Both MODIS Deep Blue (MODISDB) and MODIS Standard (MODISSTD) products were compared with the AERONET products.The MODISSTD–AERONET comparisons revealed a high degree of correlation for the four investigated sites at Karachi, Lahore, Jaipur, and Kanpur, the MODISDB–AERONET comparisons revealed even better correlations, and the MISR–AERONET comparisons also indicated strong correlations, as did the OMI–AERONET comparisons, while the CALIPSO–AERONET comparisons revealed only poor correlations due to the limited number of data points available.We also computed figures for root mean square error (RMSE), mean absolute error (MAE) and root mean bias (RMB). Using AERONET data to validate MODISSTD, MODISDB, MISR, OMI, and CALIPSO data revealed that MODISSTD data was more accurate over vegetated locations than over un-vegetated locations, while MISR data was more accurate over areas close to the ocean than over other areas. The MISR instrument performed better than the other instruments over Karachi and Kanpur, while the MODISSTD AOD retrievals were better than those from the other instruments over Lahore and Jaipur. We also computed the expected error bounds (EEBs) for both MODIS retrievals and found that MODISSTD consistently outperformed MODISDB in all of the investigated areas. High AOD values were observed by the MODISSTD, MODISDB, MISR, and OMI instruments during the summer months (April–August); these ranged from 0.32 to 0.78, possibly due to human activity and biomass burning. In contrast, high AOD values were observed by the CALIPSO instrument between September and December, due to high concentrations of smoke and soot aerosols. The variable monthly AOD figures obtained with different sensors indicate overestimation by MODISSTD, MODISDB, OMI, and CALIPSO instruments over Karachi, Lahore, Jaipur and Kanpur, relative to the AERONET data, but underestimation by the MISR instrument.

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

Abstract. 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.

Intercomparison of satellite and ground-based ozone total column measurements

Ozone total column (OTC) measurements made in 2009–2012 near St. Petersburg by a Fourier Transform Infrared (FTIR) spectrometer (Peterhof, St. Petersburg State University (SPbSU)), an M-124 filter ozonometer, and a Dobson spectrophotometer (Voeikovo, MGO), as well as measurements made by a spectrometer ozone monitoring instrument (OMI) (onboard the AURA satellite) have been analyzed and compared. Comparisons have been performed both between ensembles of ground-based measurement data, as well as between ground-based and satellite data. It has been shown that the standard deviation for all devices is 2.5–4.5%; here, the FTIR and Dobson instruments measuring the direct sun are in better agreement with OMI than the M-124 ozonometer measuring the zenith-scattered solar radiation as well. A seasonal cycle in discrepancy with amplitude of 1.5% has been detected between two series of OTC measurements made by M-124 and OMI instruments for a total of 850 days. In fall and winter, the ground-based measurements underestimate the OTC values in comparison with satellite data; in spring and summer, the situation is reversed: ground-based data overestimate the OTC values. Also, it has been revealed that FTIR measurements systematically overestimate the OTC values in comparison with other instruments: from 1.4% (for Dobson) to 3.4% (for OMI). Taking into account the spatial and temporal discrepancy of independent ensembles of measurements and an analysis of standard deviations between ground-based and satellite measurement data, the FTIR spectrometer (SPbSU) can be recommended for OTC satellite data validation.

A comparison of AIRS, MODIS and OMI sulphur dioxide retrievals in volcanic clouds

Volcanic degassing is a major contributor to the global sulphur dioxide (SO2) budget, characterized by quiescent emissions in the lower troposphere with sporadic, spatially variable explosive eruptions into the upper troposphere and lower stratosphere (UTLS). The volcanic input of SO2 to the atmosphere can be quantified using a suite of satellite-based instruments with a range of orbits and resolutions, resulting in differing estimates of SO2 extent and concentration from eruptions. We compare near-coincident retrievals of SO2 from the Moderate Resolution Imaging Spectroradiometer (MODIS), Atmospheric Infrared Radiation Sounder (AIRS) and Ozone Monitoring Instrument (OMI) at four eruptive settings. The OMI instrument is the most sensitive, with the ability to detect both low and high altitude clouds, but as an ultraviolet sensor, retrievals are limited to daytime, unlike the infrared sensors. AIRS retrievals are up to an order of magnitude less sensitive than OMI, restricted to water-free clouds in the upper troposphere. MODIS has the lowest sensitivity and is therefore constrained to the largest eruptions. Total tonnages from each sensor reflect these varying sensitivities along with potential calibration discrepancies. Results suggest that by using a number of instruments in synergy a more complete method of eruption detection is achieved.

Comparison of ground-based FTIR and Brewer O&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt; total column with data from two different IASI algorithms and from OMI and GOME-2 satellite instruments

Abstract. An intercomparison of ozone total column measurements derived from various platforms is presented in this work. Satellite data from Infrared Atmospheric Sounding Interferometer (IASI), Ozone Monitoring Instrument (OMI) and Global Ozone Monitoring Experiment (GOME-2) are compared with data from two ground-based spectrometers (Fourier Transform Infrared spectrometer FTIR and Brewer), located at the Network for Detection of Atmospheric Composition Change (NDACC) super-site of Izaña (Tenerife), measured during a campaign from March to June 2009. These ground-based observing systems have already been demonstrated to perform consistent, precise and accurate ozone total column measurements. An excellent agreement between ground-based and OMI/GOME-2 data is observed. Results from two different algorithms for deriving IASI ozone total column are also compared: the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT/ESA) operational algorithm and the LISA (Laboratoire Inter-universitaire des Systèmes Atmosphériques) algorithm. A better agreement was found with LISA's analytical approach based on an altitude-dependent Tikhonov-Philips regularization: correlations are 0.94 and 0.89 compared to FTIR and Brewer, respectively; while the operational IASI ozone columns (based on neural network analysis) show correlations of 0.90 and 0.85, respectively, compared to the O3 columns obtained from FTIR and Brewer.

A multi-decadal history of biomass burning plume heights identified using aerosol index measurements

Abstract. We have quantified the relationship between Aerosol Index (AI) measurements and plume height for young biomass burning plumes using coincident Ozone Monitoring Instrument (OMI) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) measurements. This linear relationship allows the determination of high-altitude plumes wherever AI data are available, and it provides a data set for validating global fire plume heights in chemistry transport models. We find that all plumes detected from June 2006 to February 2009 with an AI value ≥9 are located at altitudes higher than 5 km. Older high-altitude plumes have lower AI values than young plumes at similar altitudes. We have examined available AI data from the OMI and TOMS instruments (1978–2009) and find that large AI plumes occur more frequently over North America than over Australia or Russia/Northeast Asia. According to the derived relationship, during this time interval, 181 plumes, in various stages of their evolution, reached altitudes above 8 km.

Influence of turbidity and clouds on satellite total ozone data over Madrid (Spain)

Abstract. This article focuses on the comparison of the total ozone column data from three satellite instruments; Total Ozone Mapping Spectrometers (TOMS) on board the Earth Probe (EP), Ozone Monitoring Instrument (OMI) on board AURA and Global Ozone Monitoring Experiment (GOME) on board ERS/2, with ground-based measurement recorded by a well calibrated Brewer spectrophotometer located in Madrid during the period 1996–2008. A cluster classification based on solar radiation (global, direct and diffuse), cloudiness and aerosol index allow selecting hazy, cloudy, very cloudy and clear days. Thus, the differences between Brewer and satellite total ozone data for each cluster have been analyzed. The accuracy of EP-TOMS total ozone data is affected by moderate cloudiness, showing a mean absolute bias error (MABE) of 2.0%. In addition, the turbidity also has a significant influence on EP-TOMS total ozone data with a MABE ~1.6%. Those data are in contrast with clear days with MABE ~1.2%. The total ozone data derived from the OMI instrument show clear bias at clear and hazy days with small uncertainties (~0.8%). Finally, the total ozone observations obtained with the GOME instrument show a very smooth dependence with respect to clouds and turbidity, showing a robust retrieval algorithm over these conditions.

Validating NO2 measurements in the vertical atmospheric column with the OMI instrument aboard the EOS Aura satellite against ground-based measurements at the Zvenigorod Scientific Station

Data on the NO2 content in the vertical column of the atmosphere obtained with the Ozone Monitoring Instrument (OMI) aboard the EOS Aura satellite (United States) in the period from October 2004 to October 2007 are compared with the results of ground-based measurements at the Zvenigorod Scientific Station (55.7° N, 36.8° E). The “unpolluted”; part of the total NO2 content in the atmospheric column, which mostly represents the stratosphere, and the NO2 contents in the vertical column of the troposphere, including the lower layer, which is subject to pollution, are included in the comparison. The correlation coefficient between the results of ground-based and satellite measurements of the “unpolluted” total NO2 content is ∼0.9. The content values measured with the OMI instrument are smaller than the results of ground-based measurements (on average, by (0.30 ± 0.03) × 1015 cm−2 or by (11 ± 1)%). Therms discrepancy between the satellite and ground-based data is 0.6 × 1015 cm−2. The NO2 content in the vertical column of the troposphere from the results of satellite measurements is, on average, (1.4 ± 0.5) × 1015 cm−2, (or about 35%) smaller than from the results of ground-based measurements, and the rms discrepancy between them is about 200%. The correlation coefficient between these data is ∼0.4. This considerable discrepancy is evidently caused by the strong spatial (horizontal) inhomogeneity and the temporal variability of the NO2 field during episodes of pollution, which leads to different (and often uncorrelated) estimates of the NO2 content in the lower troposphere due to different spatial resolutions of ground-based and satellite measurements.

Comparing OMI‐TOMS and OMI‐DOAS total ozone column data

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.

The High-Resolution Solar Reference Spectrum between 250 and 550 nm and its Application to Measurements with the Ozone Monitoring Instrument

We have constructed a new high resolution solar reference spectrum in the spec- tral range between 250 and 550 nm. The primary use of this spectrum is for the calibration of the Dutch - Finnish Ozone Monitoring Instrument (OMI), but other applications are men- tioned. The incentive for deriving a new high resolution solar reference spectrum is that available spectra do not meet our requirements on radiometric accuracy or spectral resolu- tion. In this paper we explain the steps involved in constructing the new spectrum, based on available low and high resolution spectra and discuss the main sources of uncertainty. We compare the result with solar measurements obtained with the OMI as well as with other UV-VIS space-borne spectrometers with a similar spectral resolution. We obtain excellent agreement with the OMI measurements, which indicates that both the newly derived solar reference spectrum and our characterization of the OMI instrument are well understood. We also find good agreement with previously published low resolution spectra. The absolute intensity scale, wavelength calibration and representation of the strength of the Fraunhofer lines have been investigated and optimized to obtain the resulting high resolution solar ref- erence spectrum.

Prelaunch characterization of the Ozone Monitoring Instrument transfer function in the spectral domain

A method and an experimental measurement setup to accurately characterize the instrument transfer function in the spectral domain for hyperspectral spectrometers in the ultraviolet-visible wavelength range are described. The application to the on-ground calibration of the Ozone Monitoring Instrument (OMI) on board the Earth Observing System Aura satellite is presented and discussed. With this method and setup, based on an echelle grating, a sampling of the instrument transfer function in the spectral domain can be selected and is not limited by the spectral resolution and sampling of the spectrometer that is being characterized. The importance of accurately knowing the OMI instrument transfer functions in the spectral domain for in-flight differential optical absorption spectroscopy retrievals and wavelength calibration is discussed. The analysis of the OMI measurement data is presented and shows that the instrument transfer functions in the spectral domain as a function of wavelength and viewing angle can be determined with high accuracy.