Ozone Monitoring Instrument Measurements Research Articles

Estimates of Hyperspectral Surface and Underwater UV Planar and Scalar Irradiances from OMI Measurements and Radiative Transfer Computations

Quantitative assessment of the UV effects on aquatic ecosystems requires an estimate of the in-water hyperspectral radiation field. Solar UV radiation in ocean waters is estimated on a global scale by combining extraterrestrial solar irradiance from the Total and Spectral Solar Irradiance Sensor (TSIS-1), satellite estimates of cloud/surface reflectivity, ozone from the Ozone Monitoring Instrument (OMI) and in-water chlorophyll concentration from the Moderate Resolution Imaging Spectroradiometer (MODIS) with radiative transfer computations in the ocean-atmosphere system. A comparison of the estimates of collocated OMI-derived surface irradiance with Marine Optical Buoy (MOBY) measurements shows a good agreement within 5% for different seasons. To estimate scalar irradiance at the ocean surface and in water, we propose scaling the planar irradiance, calculated from satellite observation, on the basis of Hydrolight computations. Hydrolight calculations show that the diffuse attenuation coefficients of scalar and planar irradiance with depth are quite close to each other. That is why the differences between the planar penetration and scalar penetration depths are small and do not exceed a couple of meters. A dominant factor defining the UV penetration depths is chlorophyll concentration. There are other constituents in water that absorb in addition to chlorophyll; the absorption from these constituents can be related to that of chlorophyll in Case I waters using an inherent optical properties (IOP) model. Other input parameters are less significant. The DNA damage penetration depths vary from a few meters in areas of productive waters to about 30–35 m in the clearest waters. A machine learning approach (an artificial neural network, NN) was developed based on the full physical algorithm for computational efficiency. The NN shows a very good performance in predicting the penetration depths (within 2%).

Radiative transfer acceleration based on the principal component analysis and lookup table of corrections: optimization and application to UV ozone profile retrievals

Abstract. In this work, we apply a principal component analysis (PCA)-based approach combined with lookup tables (LUTs) of corrections to accelerate the Vector Linearized Discrete Ordinate Radiative Transfer (VLIDORT) model used in the retrieval of ozone profiles from backscattered ultraviolet (UV) measurements by the Ozone Monitoring Instrument (OMI). The spectral binning scheme, which determines the accuracy and efficiency of the PCA-RT performance, is thoroughly optimized over the spectral range 265 to 360 nm with the assumption of a Rayleigh-scattering atmosphere above a Lambertian surface. The high level of accuracy (∼ 0.03 %) is achieved from fast-PCA calculations of full radiances. In this approach, computationally expensive full multiple scattering (MS) calculations are limited to a small set of PCA-derived optical states, while fast single scattering and two-stream MS calculations are performed, for every spectral point. The number of calls to the full MS model is only 51 in the application to OMI ozone profile retrievals with the fitting window of 270–330 nm where the RT model should be called at fine intervals (∼ 0.03 nm with ∼ 2000 wavelengths) to simulate OMI measurements (spectral resolution: 0.4–0.6 nm). LUT corrections are implemented to accelerate the online RT model due to the reduction of the number of streams (discrete ordinates) from 8 to 4, while improving the accuracy at the level attainable from simulations using a vector model with 12 streams and 72 layers. Overall, we speed up our OMI retrieval by a factor of 3.3 over the previous version, which has already been significantly sped up over line-by-line calculations due to various RT approximations. Improved treatments for RT approximation errors using LUT corrections improve spectral fitting (2 %–5 %) and hence retrieval errors, especially for tropospheric ozone by up to ∼ 10 %; the remaining errors due to the forward model errors are within 5 % in the troposphere and 3 % in the stratosphere.

Spatially and temporally coherent reconstruction of tropospheric NO2 over China combining OMI and GOME-2B measurements

Tropospheric NO2 columns retrieved from ozone monitoring instrument (OMI) are widely used, even though there is a significant loss of spatial coverage due to multiple factors. This work introduces a framework for reconstructing gaps in the OMI NO2 data over China by using machine learning and an adaptive weighted temporal fitting method with NO2 measurements from Global Ozone Monitoring Experiment–2B, and surface measurements. The reconstructed NO2 has four important characteristics. First, there is improved spatial and temporal coherence on a day-to-day basis, allowing new scientific findings to be made. Second, the amount of data doubled, with 40% more data available. Third, the results are reliable overall, with a good agreement with Multi-AXis Differential Optical Absorption Spectroscopy measurements (R: 0.75–0.85). Finally, the mean of reconstructed NO2 vertical columns during 2015 and 2018 is consistent with the original data in the spatial distribution, while the standard deviation decreases in most places over Mainland China. This novel finding is expected to contribute to both air quality and climate studies.

Validation of OMI-DOAS total ozone column amounts against ground-based measurements at an African equatorial belt site.

The total ozone column amount (TOCA) values from the Ozone Monitoring Instrument (OMI) derived from OMI/Aura ozone (O3) differential optical absorption spectroscopy (DOAS) V003 (OMDOAO3) have been validated against the ground-based TOCA values derived from Dobson and the Norwegian Institute for Air Research UV measurements in Kampala (0.31º N, 32.58º E, 1200 m), Uganda, for the period between 2005 and 2018. Under all-sky conditions, the OMI retrieval algorithm was found to underestimate the TOCA values with mean bias (MnB), root mean square error (RMSE), and correlation coefficient (r) values ranging from about -3.4% to -1.7%, 2.4% to 4.9%, and 0.73 to 0.90, respectively. When only days with a radiation modification factor greater than or equal to 65% were considered, the MnB, RMSE, and r values between TOCA values derived from ground-based and OMI measurements improved, and they ranged from -2.5% to -1.3%, 1.4% to 3.8%, and 0.8 to 0.91, respectively. A good agreement was found between TOCA values derived from Dobson measurements and those derived from OMI satellite measurements with MnB, RMSE, and r values of about -1.8%, 1.4%, and 0.91, respectively. This was due to the fact that Dobson measurements were taken only when the sky was perceived clear. The underestimation of TOCA values by the OMI retrieval algorithm was found to be due mainly to clouds and aerosols.

Ceramic industry at Morbi as a large source of SO2 emissions in India

Observations from the Ozone Monitoring Instrument (OMI), onboard the NASA's Earth Observing System (EOS) Aura satellite, reveal a large SO2 “hotspot” over Morbi, Gujarat, India, while the available emissions inventories do not report any major sources in this region. There are no industries that are typically associated with the elevated SO2 such as large power plants, smelters, or oil refineries in the Morbi region. Our analysis shows that the elevated SO2 source is attributed to the ceramic industries in an area of ~7 × 7 km2 near the city. OMI-estimated SO2 emissions from the Morbi ceramic industries have been near or above 100 kt y−1 since 2009, which are similar to emission levels from larger Indian oil refineries (such as Essar and Jamnagar) and power stations (such as Mundra Thermal Power Station and Ultra Mega Power Plant) located in Gujarat, India. According to OMI measurements, the SO2 emissions from the Morbi ceramic industries are presently five times higher than they were in 2005. This study demonstrates that in the absence of any other information about SO2 emissions from the ceramic industry, these satellite-based estimates can fill the gap in emission inventories in a timely fashion.

A geometry-dependent surface Lambertian-equivalent reflectivity product for UV–Vis retrievals – Part 2: Evaluation over open ocean

Abstract. Satellite-based cloud, aerosol, and trace-gas retrievals from ultraviolet (UV) and visible (Vis) wavelengths depend on the accurate representation of surface reflectivity. Current UV and Vis retrieval algorithms typically use surface reflectivity climatologies that do not account for variation in satellite viewing geometry or surface roughness. The concept of geometry-dependent surface Lambertian-equivalent reflectivity (GLER) is implemented for water surfaces to account for surface anisotropy using a Case 1 water optical model and the Cox–Munk slope distribution for ocean surface roughness. GLER is compared with Lambertian-Equivalent reflectivity (LER) derived from the Ozone Monitoring Instrument (OMI) for clear scenes at 354, 388, 440, and 466 nm. We show that GLER compares well with the measured LER data over the open ocean and captures the directionality effects not accounted for in climatological LER databases. Small biases are seen when GLER and the OMI-derived LER are compared. GLER is biased low by up to 0.01–0.02 at Vis wavelengths and biased high by around 0.01 in the UV, particularly at 354 nm. Our evaluation shows that GLER is an improvement upon climatological LER databases as it compares well with OMI measurements and captures the directionality effects of surface reflectance.

Surface erythemal UV irradiance in the continental United States derived from ground-based and OMI observations: quality assessment, trend analysis and sampling issues

Abstract. Surface full-sky erythemal dose rate (EDR) from the Ozone Monitoring Instrument (OMI) at both satellite overpass time and local noon time is evaluated against ground measurements at 31 sites from the US Department of Agriculture's (USDA) UV-B Monitoring and Research Program (UVMRP) over the period of 2005–2017. We find that both OMI overpass and solar noon time EDR are highly correlated with the measured counterparts (with a linear correlation coefficient of 0.90 and 0.88, respectively). Although the comparison statistics are improved with a longer time window (0.5–1.0 h) for pairing surface and OMI measurements, both OMI overpass and local noon time EDRs have 7 % overestimation that is larger than 6 % uncertainty in the ground measurements and show different levels of dependence on solar zenith angle (SZA) and to lesser extent on cloud optical depth. The ratio of EDR between local noon and OMI overpass time is often (95 % in frequency) larger than 1 with a mean of 1.18 in the OMI product; in contrast, the same ratio from surface observation is normally distributed with 22 % of the times less than 1 and a mean of 1.38. This contrast in part reflects the deficiency in the OMI surface UV algorithm that assumes constant atmospheric conditions between overpass and noon time. The probability density functions (PDFs) for both OMI and ground measurements of noontime EDR are in statistically significant agreement, showing dual peaks at ∼20 and ∼200 mW m−2, respectively; the latter is lower than 220 mW m−2, the value at which the PDF of daily EDR from ground measurements peaks, and this difference indicates that the largest EDR value for a given day may not often occur at local noon. Lastly, statistically significant positive trends of EDR are found in the northeastern US in OMI data, but opposite trends are found within ground-based data (regardless of sampling for either noontime or daily averages). While positive trends are consistently found between OMI and surface data for EDR over the southern Great Plains (Texas and Oklahoma), their values are within the uncertainty of ground measurements. Overall, no scientifically sound trends can be found among OMI data for aerosol total and absorbing optical depth, cloud optical depth and total ozone to explain coherently the surface UV trends revealed either by OMI or ground-based estimates; these data also cannot reconcile trend differences between the two estimates (of EDR from OMI and surface observations). Future geostationary satellites with better spatiotemporal resolution data should help overcome spatiotemporal sampling issues inherent in OMI data products and therefore improve the estimates of surface UV flux and EDR from space.

Accuracy of satellite total column ozone measurements in polar vortex conditions: Comparison with ground-based observations in 1979–2013

This study presents the first detailed long-term analysis of satellite and ground-based Dobson, Brewer, SAOZ (Système D'Analyse par Observations Zénithales) and DOAS (Differential Optical Absorption Spectrometer) measurements inside the Antarctic vortex for the 1979–2013 period. In general, the satellite measurements show a good agreement with ground-based measurements at all stations (correlation coefficient > 0.95). The average relative difference between ground-based and satellite measurements is about ±1–3% and the mean bias error (MBE) is within ±4 DU, depending on the satellite instrument and ground-based station. The satellite measurements show good stability over their operational period, for which the estimated drifts are about ±0.1–5%/decade and are statistically insignificant for most instruments. The Nimbus TOMS (Total Ozone Monitoring Spectrometer) and OMI (Ozone Monitoring Instrument) measurements are stable over the period, but the Earth Probe-TOMS measurements show deterioration after 2000. Also, most satellite instruments show no dependency on satellite solar zenith angle (SZA), but reveal significant dependency on stratospheric temperature. In addition, the mean relative difference shows larger variation with lower total column ozone (TCO). This long-term and multi-instrument comparison exercise would help both ground-based and satellite measurement community to better analyse accuracy of the TCO measurements from the instruments and examine the stability of their long-term measurements. In addition, the SZA and temperature dependency assessment would also help the ozone observation community to use better absorption cross-sections for TCO retrievals.

A Cloud-Ozone Data Product from Aura OMI and MLS Satellite Measurements.

Ozone within deep convective clouds is controlled by several factors involving photochemical reactions and transport. Gas-phase photochemical reactions and heterogeneous surface chemical reactions involving ice, water particles, and aerosols inside the clouds all contribute to the distribution and net production and loss of ozone. Ozone in clouds is also dependent on convective transport that carries low troposphere/boundary layer ozone and ozone precursors upward into the clouds. Characterizing ozone in thick clouds is an important step for quantifying relationships of ozone with tropospheric H2O, OH production, and cloud microphysics/transport properties. Although measuring ozone in deep convective clouds from either aircraft or balloon ozonesondes is largely impossible due to extreme meteorological conditions associated with these clouds, it is possible to estimate ozone in thick clouds using backscattered solar UV radiation measured by satellite instruments. Our study combines Aura Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS) satellite measurements to generate a new research product of monthly-mean ozone concentrations in deep convective clouds between 30°S to 30°N for October 2004 - April 2016. These measurements represent mean ozone concentration primarily in the upper levels of thick clouds and reveal key features of cloud ozone including: persistent low ozone concentrations in the tropical Pacific of ~10 ppbv or less; concentrations of up to 60 pphv or greater over landmass regions of South America, southern Africa, Australia, and India/east Asia; connections with tropical ENSO events; and intra-seasonal/Madden-Julian Oscillation variability. Analysis of OMI aerosol measurements suggests a cause and effect relation between boundary layer pollution and elevated ozone inside thick clouds over land-mass regions including southern Africa and India/east Asia.

Intercomparison of total column ozone data from the Pandora spectrophotometer with Dobson, Brewer, and OMI measurements over Seoul, Korea

Abstract. Daily total column ozone (TCO) measured using the Pandora spectrophotometer (no. 19) was compared with data from the Dobson (no. 124) and Brewer (no. 148) spectrophotometers, as well as from the Ozone Monitoring Instrument (OMI) (with two different algorithms, Total Ozone Mapping Spectrometer (TOMS) TOMS and differential optical absorption spectroscopy (DOAS) methods), over the 2-year period between March 2012 and March 2014 at Yonsei University, Seoul, Korea. Based on the linear-regression method, the TCO from Pandora is closely correlated with those from other instruments with regression coefficients (slopes) of 0.95 (Dobson), 1.00 (Brewer), 0.98 (OMI-TOMS), and 0.97 (OMI-DOAS), and determination coefficients (R2) of 0.95 (Dobson), 0.97 (Brewer), 0.96 (OMI-TOMS), and 0.95 (OMI-DOAS). The daily averaged TCO from Pandora has within 3 % differences compared to TCO values from other instruments. For the Dobson measurements in particular, the difference caused by the inconsistency in observation times when compared with the Pandora measurements was up to 12.5 % because of diurnal variations in the TCO values. However, the comparison with Brewer after matching the observation time shows agreement with large R2 and small biases. The TCO ratio between Brewer and Pandora shows the 0.98 ± 0.03, and the distributions for relative differences between two instruments are 89.2 and 57.1 % of the total data within the error ranges of 3 and 5 %, respectively. The TCO ratio between Brewer and Pandora also is partially dependent on solar zenith angle. The error dependence by the observation geometry is essential to the further analysis focusing on the sensitivity of aerosol and the stray-light effect in the instruments.

A decade of global volcanic SO2 emissions measured from space

The global flux of sulfur dioxide (SO2) emitted by passive volcanic degassing is a key parameter that constrains the fluxes of other volcanic gases (including carbon dioxide, CO2) and toxic trace metals (e.g., mercury). It is also a required input for atmospheric chemistry and climate models, since it impacts the tropospheric burden of sulfate aerosol, a major climate-forcing species. Despite its significance, an inventory of passive volcanic degassing is very difficult to produce, due largely to the patchy spatial and temporal coverage of ground-based SO2 measurements. We report here the first volcanic SO2 emissions inventory derived from global, coincident satellite measurements, made by the Ozone Monitoring Instrument (OMI) on NASA’s Aura satellite in 2005–2015. The OMI measurements permit estimation of SO2 emissions from over 90 volcanoes, including new constraints on fluxes from Indonesia, Papua New Guinea, the Aleutian Islands, the Kuril Islands and Kamchatka. On average over the past decade, the volcanic SO2 sources consistently detected from space have discharged a total of ~63 kt/day SO2 during passive degassing, or ~23 ± 2 Tg/yr. We find that ~30% of the sources show significant decadal trends in SO2 emissions, with positive trends observed at multiple volcanoes in some regions including Vanuatu, southern Japan, Peru and Chile.

Recent reduction in NOx emissions over China: synthesis of satellite observations and emission inventories

Tropospheric nitrogen dioxide (NO2) column densities detected from space are widely used to infer trends in terrestrial nitrogen oxide (NOx) emissions. We study changes in NO2 column densities using the Ozone Monitoring Instrument (OMI) over China from 2005 to 2015 and compare them with the bottom-up inventory to examine NOx emission trends and their driving forces. From OMI measurements we detect the peak of NO2 column densities at a national level in the year 2011, with average NO2 column densities deceasing by 32% from 2011 to 2015 and corresponding to a simultaneous decline of 21% in bottom-up emission estimates. A significant variation in the peak year of NO2 column densities over regions is observed. Because of the reasonable agreement between the peak year of NO2 columns and the start of deployment of denitration devices, we conclude that power plants are the primary contributor to the NO2 decline, which is further supported by the emission reduction of 56% from the power sector in the bottom-up emission inventory associated with the penetration of selective catalytic reduction (SCR) increasing from 18% to 86% during 2011–2015. Meanwhile, regulations for vehicles also make a significant contribution to NOx emission reductions, in particular for a few urbanized regions (e.g., Beijing and Shanghai), where they implemented strict regulations for vehicle emissions years before the national schedule for SCR installations and thus reached their NO2 peak 2–3 years ahead of the deployment of denitration devices for power plants.

A global catalogue of large SO<sub>2</sub> sources and emissions derived from the Ozone Monitoring Instrument

Abstract. Sulfur dioxide (SO2) measurements from the Ozone Monitoring Instrument (OMI) satellite sensor processed with the new principal component analysis (PCA) algorithm were used to detect large point emission sources or clusters of sources. The total of 491 continuously emitting point sources releasing from about 30 kt yr−1 to more than 4000 kt yr−1 of SO2 per year have been identified and grouped by country and by primary source origin: volcanoes (76 sources); power plants (297); smelters (53); and sources related to the oil and gas industry (65). The sources were identified using different methods, including through OMI measurements themselves applied to a new emission detection algorithm, and their evolution during the 2005–2014 period was traced by estimating annual emissions from each source. For volcanic sources, the study focused on continuous degassing, and emissions from explosive eruptions were excluded. Emissions from degassing volcanic sources were measured, many for the first time, and collectively they account for about 30 % of total SO2 emissions estimated from OMI measurements, but that fraction has increased in recent years given that cumulative global emissions from power plants and smelters are declining while emissions from oil and gas industry remained nearly constant. Anthropogenic emissions from the USA declined by 80 % over the 2005–2014 period as did emissions from western and central Europe, whereas emissions from India nearly doubled, and emissions from other large SO2-emitting regions (South Africa, Russia, Mexico, and the Middle East) remained fairly constant. In total, OMI-based estimates account for about a half of total reported anthropogenic SO2 emissions; the remaining half is likely related to sources emitting less than 30 kt yr−1 and not detected by OMI.

How big is an OMI pixel?

Abstract. The Ozone Monitoring Instrument (OMI) is a push-broom imaging spectrometer, observing solar radiation backscattered by the Earth's atmosphere and surface. The incoming radiation is detected using a static imaging CCD (charge-coupled device) detector array with no moving parts, as opposed to most of the previous satellite spectrometers, which used a moving mirror to scan the Earth in the across-track direction. The field of view (FoV) of detector pixels is the solid angle from which radiation is observed, averaged over the integration time of a measurement. The OMI FoV is not quadrangular, which is common for scanning instruments, but rather super-Gaussian shaped and overlapping with the FoV of neighbouring pixels. This has consequences for pixel-area-dependent applications, like cloud fraction products, and visualisation.The shapes and sizes of OMI FoVs were determined pre-flight by theoretical and experimental tests but never verified after launch. In this paper the OMI FoV is characterised using collocated MODerate resolution Imaging Spectroradiometer (MODIS) reflectance measurements. MODIS measurements have a much higher spatial resolution than OMI measurements and spectrally overlap at 469 nm. The OMI FoV was verified by finding the highest correlation between MODIS and OMI reflectances in cloud-free scenes, assuming a 2-D super-Gaussian function with varying size and shape to represent the OMI FoV. Our results show that the OMPIXCOR product 75FoV corner coordinates are accurate as the full width at half maximum (FWHM) of a super-Gaussian FoV model when this function is assumed. The softness of the function edges, modelled by the super-Gaussian exponents, is different in both directions and is view angle dependent.The optimal overlap function between OMI and MODIS reflectances is scene dependent and highly dependent on time differences between overpasses, especially with clouds in the scene. For partially clouded scenes, the optimal overlap function was represented by super-Gaussian exponents around 1 or smaller, which indicates that this function is unsuitable to represent the overlap sensitivity function in these cases. This was especially true for scenes before 2008, when the time differences between Aqua and Aura overpasses was about 15 min, instead of 8 min after 2008. During the time between overpasses, clouds change the scene reflectance, reducing the correlation and influencing the shape of the optimal overlap function.

Multi-satellite sensor study on precipitation-induced emission pulses of NO<sub><i>x</i></sub> 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.

The role of cloud contamination, aerosol layer height and aerosol model in the assessment of the OMI near-UV retrievals over the ocean

Abstract. Retrievals of aerosol optical depth (AOD) at 388 nm over the ocean from the Ozone Monitoring Instrument (OMI) two-channel near-UV algorithm (OMAERUV) have been compared with independent AOD measurements. The analysis was carried out over the open ocean (OMI and MODerate-resolution Imaging Spectrometer (MODIS) AOD comparisons) and over coastal and island sites (OMI and AERONET, the AErosol RObotic NETwork). Additionally, a research version of the retrieval algorithm (using MODIS and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) information as constraints) was utilized to evaluate the sensitivity of the retrieval to different assumed aerosol properties. Overall, the comparison resulted in differences (OMI minus independent measurements) within the expected levels of uncertainty for the OMI AOD retrievals (0.1 for AOD < 0.3, 30 % for AOD > 0.3). Using examples from case studies with outliers, the reasons that led to the observed differences were examined with specific purpose to determine whether they are related to instrument limitations (i.e., pixel size, calibration) or algorithm assumptions (such as aerosol shape, aerosol height). The analysis confirms that OMAERUV does an adequate job at rejecting cloudy scenes within the instrument's capabilities. There is a residual cloud contamination in OMI pixels with quality flag 0 (the best conditions for aerosol retrieval according to the algorithm), resulting in a bias towards high AODs in OMAERUV. This bias is more pronounced at low concentrations of absorbing aerosols (AOD 388 nm ∼ < 0.5). For higher aerosol loadings, the bias remains within OMI's AOD uncertainties. In pixels where OMAERUV assigned a dust aerosol model, a fraction of them (< 20 %) had retrieved AODs significantly lower than AERONET and MODIS AODs. In a case study, a detailed examination of the aerosol height from CALIOP and the AODs from MODIS, along with sensitivity tests, was carried out by varying the different assumed parameters in the retrieval (imaginary index of refraction, size distribution, aerosol height, particle shape). It was found that the spherical shape assumption for dust in the current retrieval is the main cause of the underestimate. In addition, it is demonstrated in an example how an incorrect assumption of the aerosol height can lead to an underestimate. Nevertheless, this is not as significant as the effect of particle shape. These findings will be incorporated in a future version of the retrieval algorithm.

Investigations of the Diurnal Variation of Vertical HCHO Profiles Based on MAX-DOAS Measurements in Beijing: Comparisons with OMI Vertical Column Data

An investigation into the diurnal characteristics of vertical formaldehyde (HCHO) profiles was conducted based on multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements in Beijing during the CAREBEIJING campaign, covering a month-long period through August and September 2006. Vertical HCHO profiles were retrieved based on a combined differential optical absorption spectroscopy (DOAS) technique and an optimal estimation method (OEM). The HCHO volume-mixing ratio (VMR) was found to be highest in the layer from the surface up to an altitude of 1 km and to decrease with altitude above this layer. In all retrieved profiles, HCHO was not detected in the layer from 3–4 km. Over the diurnal cycle, the HCHO VMR values were generally highest at 15:00 local time (LT) and were lower in the morning and late afternoon. The mean HCHO VMRs were 6.17, 1.82, and 0.80 ppbv for the 0–1, 1–2, and 2–3-km layers, respectively, at 15:00 LT, whereas they were 3.54 (4.79), 1.06 (1.43), and 0.46 (0.63) ppbv for the 0–1, 1–2, and 2–3-km layers, respectively, at 09:00 (17:00) LT. The HCHO VMRs reached their highest values at 15:00 LT on August 19, which were 17.71, 5.20, and 2.31 ppbv for the 0–1, 1–2, and 2–3-km layers, respectively. This diurnal pattern implies that the photo-oxidation of volatile organic compounds (VOCs) was most active at 15:00 LT for several days during the campaign period. In a comparison of the derived HCHO VCDs with those obtained from the Ozone Monitoring Instrument (OMI) measurements, the HCHO vertical column density (VCD) values obtained from the OMI measurements tend to be smaller than those from the MAX-DOAS.

Observation of ozone enhancement in the lower troposphere over East Asia from a space-borne ultraviolet spectrometer

Abstract. We report observations from space using ultraviolet (UV) radiance for significant enhancement of ozone in the lower troposphere over central and eastern China (CEC). The recent retrieval products of the Ozone Monitoring Instrument (OMI) onboard the Earth Observing System (EOS) Aura satellite revealed the spatial and temporal variation of ozone distributions in multiple layers in the troposphere. We compared the OMI-derived ozone over Beijing with airborne measurements by the Measurement of Ozone and Water Vapor by Airbus In-Service Aircraft (MOZAIC) program. The correlation between OMI and MOZAIC ozone in the lower troposphere was reasonable, which assured the reliability of OMI ozone retrievals in the lower troposphere under enhanced ozone conditions. The ozone enhancement was clearly observed over CEC, with Shandong Province as its center, and was most notable in June in any given year. Similar seasonal variations were observed throughout the 9-year OMI measurement period of 2005 to 2013. A considerable part of this ozone enhancement could be attributed to the emissions of ozone precursors from industrial activities and automobiles, and possibly from open crop residue burning (OCRB) after the winter wheat harvest. The ozone distribution presented in this study is also consistent with some model studies. The lower tropospheric ozone distribution is first shown from OMI retrieval in this study, and the results will be useful in clarifying any unknown factors that influence ozone distribution by comparison with model simulations.

Observations of tropospheric NO2 using ground based MAX-DOAS and OMI measurements during the Shanghai World Expo 2010

During the Shanghai World Expo 2010 ground based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements of tropospheric nitrogen dioxide (NO2) were performed to investigate the effects of emission control measures during that time. In this study we measured NO2 using four identical MAX-DOAS instruments in Shanghai from April 2009 to November 2010. We combined our MAX-DOAS data, the Ozone Monitoring Instrument (OMI) satellite observations and meteorological information from the National Centers for Environmental Prediction final reanalysis data (NCEP FNL) in order to investigate the spatial distribution of NO2 over Shanghai and the effects of emission control measures during the Expo. In general, the comparison of cloud screened MAX-DOAS data and OMI observations are in good correlation (Pearson correlation coefficient between 0.67 and 0.93 for the four measurement stations). In addition, we compared the MAX-DOAS and OMI NO2 data from the Shanghai Expo in 2010 to the same time of the year in 2009. The results show that the NO2 columns were reduced up to ∼ 30% in the area of central Shanghai during the Expo but no significant reduction of NO2 levels was found in the nearby industrial area. The overall NO2 reduction from May, July and September 2010 ranged from 7.5% to 14.5%, which is comparable to observations in previous studies. Our results revealed that the NO2 reduction was mainly achieved by emission control policies on transportation sources in the city rather than the controls from nearby provinces.

U.S. NO2 trends (2005–2013): EPA Air Quality System (AQS) data versus improved observations from the Ozone Monitoring Instrument (OMI)

Emissions of nitrogen oxides (NOx) and, subsequently, atmospheric levels of nitrogen dioxide (NO2) have decreased over the U.S. due to a combination of environmental policies and technological change. Consequently, NO2 levels have decreased by 30–40% in the last decade. We quantify NO2 trends (2005–2013) over the U.S. using surface measurements from the U.S. Environmental Protection Agency (EPA) Air Quality System (AQS) and an improved tropospheric NO2 vertical column density (VCD) data product from the Ozone Monitoring Instrument (OMI) on the Aura satellite. We demonstrate that the current OMI NO2 algorithm is of sufficient maturity to allow a favorable correspondence of trends and variations in OMI and AQS data. Our trend model accounts for the non-linear dependence of NO2 concentration on emissions associated with the seasonal variation of the chemical lifetime, including the change in the amplitude of the seasonal cycle associated with the significant change in NOx emissions that occurred over the last decade. The direct relationship between observations and emissions becomes more robust when one accounts for these non-linear dependencies. We improve the OMI NO2 standard retrieval algorithm and, subsequently, the data product by using monthly vertical concentration profiles, a required algorithm input, from a high-resolution chemistry and transport model (CTM) simulation with varying emissions (2005–2013). The impact of neglecting the time-dependence of the profiles leads to errors in trend estimation, particularly in regions where emissions have changed substantially. For example, trends calculated from retrievals based on time-dependent profiles offer 18% more instances of significant trends and up to 15% larger total NO2 reduction versus the results based on profiles for 2005. Using a CTM, we explore the theoretical relation of the trends estimated from NO2 VCDs to those estimated from ground-level concentrations. The model-simulated trends in VCDs strongly correlate with those estimated from surface concentrations (r = 0.83, N = 355). We then explore the observed correspondence of trends estimated from OMI and AQS data. We find a significant, but slightly weaker, correspondence (i.e., r = 0.68, N = 208) than predicted by the model and discuss some of the important factors affecting the relationship, including known problems (e.g., NOz interferents) associated with the AQS data. This significant correspondence gives confidence in trend and surface concentration estimates from OMI VCDs for locations, such as the majority of the U.S. and globe, that are not covered by surface monitoring networks. Using our improved trend model and our enhanced OMI data product, we find that both OMI and AQS data show substantial downward trends from 2005 to 2013, with an average reduction of 38% for each over the U.S. The annual reduction rates inferred from OMI and AQS measurements are larger (−4.8 ± 1.9%/yr, −3.7 ± 1.5%/yr) from 2005 to 2008 than 2010 to 2013 (−1.2 ± 1.2%/yr, −2.1 ± 1.4%/yr). We quantify NO2 trends for major U.S. cities and power plants; the latter suggest larger negative trend (−4.0 ± 1.5%/yr) between 2005 and 2008 and smaller or insignificant changes (−0.5 ± 1.2%/yr) during 2010–2013.