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The global fire emission inventories depend on ground and airborne measurements of species-specific emission factors (EFs), which translate dry matter losses due to fires to actual trace gas and aerosol emissions. The EFs of nitrogen oxides (NOx) and carbon monoxide (CO) can function as a proxy for combustion efficiency to distinguish flaming from smoldering combustion. The uncertainties in these EFs remain large as they are limited by the spatial and temporal representativeness of the measurements. The global coverage of satellite observations has the advantage of filling this gap, making these measurements highly complementary to ground-based or airborne data. We present a new analysis of biomass burning pollutants using space-borne data to investigate the spatiotemporal efficiency of fire combustion. Column measurements of nitrogen dioxide and carbon monoxide (XNO2 and XCO) from the TROPOspheric Monitoring Instrument (TROPOMI) are used to quantify the relative atmospheric enhancements of these species over different fire-prone regions around the world. We find spatial and temporal patterns in the ΔXNO2 ∕ ΔXCO ratio that point to distinct differences in biomass burning behavior. Such differences are induced by the burning phase of the fire (e.g., high-temperature flaming vs. low-temperature smoldering combustion) and burning practice (e.g., the combustion of logs, coarse woody debris and soil organic matter vs. the combustion of fine fuels such as savanna grasses). The sampling techniques and the signal-to-noise ratio of the retrieved ΔXNO2 ∕ ΔXCO signals were quantified with WRF-Chem experiments and showed similar distinct differences in combustion types. The TROPOMI measurements show that the fraction of surface smoldering combustion is much larger for the boreal forest fires in the upper Northern Hemisphere and peatland fires in Indonesia. These types of fires cause a much larger increase (3 to 6 times) in ΔXCO relative to ΔXNO2 than elsewhere in the world. The high spatial and temporal resolution of TROPOMI also enables the detection of spatial gradients in combustion efficiency at smaller regional scales. For instance, in the Amazon, we found higher combustion efficiency (up to 3-fold) for savanna fires than for the nearby tropical deforestation fires. Out of two investigated fire emission products, the TROPOMI measurements support the broad spatial pattern of combustion efficiency rooted in GFED4s. Meanwhile, TROPOMI data also add new insights into regional variability in combustion characteristics that are not well represented in the different emission inventories, which can help the fire modeling community to improve their representation of the spatiotemporal variability in EFs.

This paper reports on consolidated ground-based validation results of the atmospheric NO2 data produced operationally since April 2018 by the TROPOspheric Monitoring Instrument (TROPOMI) on board of the ESA/EU Copernicus Sentinel-5 Precursor (S5P) satellite. Tropospheric, stratospheric, and total NO2 column data from S5P are compared to correlative measurements collected from, respectively, 19 Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS), 26 Network for the Detection of Atmospheric Composition Change (NDACC) Zenith-Scattered-Light DOAS (ZSL-DOAS), and 25 Pandonia Global Network (PGN)/Pandora instruments distributed globally. The validation methodology gives special care to minimizing mismatch errors due to imperfect spatio-temporal co-location of the satellite and correlative data, e.g. by using tailored observation operators to account for differences in smoothing and in sampling of atmospheric structures and variability and photochemical modelling to reduce diurnal cycle effects. Compared to the ground-based measurements, S5P data show, on average, (i) a negative bias for the tropospheric column data, of typically −23 % to −37 % in clean to slightly polluted conditions but reaching values as high as −51 % over highly polluted areas; (ii) a slight negative median difference for the stratospheric column data, of about −0.2 Pmolec cm−2, i.e. approx. −2 % in summer to −15 % in winter; and (iii) a bias ranging from zero to −50 % for the total column data, found to depend on the amplitude of the total NO2 column, with small to slightly positive bias values for columns below 6 Pmolec cm−2 and negative values above. The dispersion between S5P and correlative measurements contains mostly random components, which remain within mission requirements for the stratospheric column data (0.5 Pmolec cm−2) but exceed those for the tropospheric column data (0.7 Pmolec cm−2). While a part of the biases and dispersion may be due to representativeness differences such as different area averaging and measurement times, it is known that errors in the S5P tropospheric columns exist due to shortcomings in the (horizontally coarse) a priori profile representation in the TM5-MP chemical transport model used in the S5P retrieval and, to a lesser extent, to the treatment of cloud effects and aerosols. Although considerable differences (up to 2 Pmolec cm−2 and more) are observed at single ground-pixel level, the near-real-time (NRTI) and offline (OFFL) versions of the S5P NO2 operational data processor provide similar NO2 column values and validation results when globally averaged, with the NRTI values being on average 0.79 % larger than the OFFL values.

Airborne and ground-based Pandora spectrometer NO2 column measurements were collected during the 2018 Long Island Sound Tropospheric Ozone Study (LISTOS) in the New York City/Long Island Sound region, which coincided with early observations from the Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) instrument. Both airborne- and ground-based measurements are used to evaluate the TROPOMI NO2 Tropospheric Vertical Column (TrVC) product v1.2 in this region, which has high spatial and temporal heterogeneity in NO2. First, airborne and Pandora TrVCs are compared to evaluate the uncertainty of the airborne TrVC and establish the spatial representativeness of the Pandora observations. The 171 coincidences between Pandora and airborne TrVCs are found to be highly correlated (r2= 0.92 and slope of 1.03), with the largest individual differences being associated with high temporal and/or spatial variability. These reference measurements (Pandora and airborne) are complementary with respect to temporal coverage and spatial representativity. Pandora spectrometers can provide continuous long-term measurements but may lack areal representativity when operated in direct-sun mode. Airborne spectrometers are typically only deployed for short periods of time, but their observations are more spatially representative of the satellite measurements with the added capability of retrieving at subpixel resolutions of 250 m × 250 m over the entire TROPOMI pixels they overfly. Thus, airborne data are more correlated with TROPOMI measurements (r2=0.96) than Pandora measurements are with TROPOMI (r2=0.84). The largest outliers between TROPOMI and the reference measurements appear to stem from too spatially coarse a priori surface reflectivity (0.5∘) over bright urban scenes. In this work, this results during cloud-free scenes that, at times, are affected by errors in the TROPOMI cloud pressure retrieval impacting the calculation of tropospheric air mass factors. This factor causes a high bias in TROPOMI TrVCs of 4 %–11 %. Excluding these cloud-impacted points, TROPOMI has an overall low bias of 19 %–33 % during the LISTOS timeframe of June–September 2018. Part of this low bias is caused by coarse a priori profile input from the TM5-MP model; replacing these profiles with those from a 12 km North American Model–Community Multiscale Air Quality (NAMCMAQ) analysis results in a 12 %–14 % increase in the TrVCs. Even with this improvement, the TROPOMI-NAMCMAQ TrVCs have a 7 %–19 % low bias, indicating needed improvement in a priori assumptions in the air mass factor calculation. Future work should explore additional impacts of a priori inputs to further assess the remaining low biases in TROPOMI using these datasets.

In this work, a ∼21-year global dataset from four different satellite sensors with a mid-morning overpass (GOME/ERS-2, SCIAMACHY/ENVISAT, GOME-2/Metop-A, and GOME-2/Metop-B) is compiled to study the long-term tropospheric NO2 patterns and trends. The Global Ozone Monitoring Experiment (GOME) and GOME-2 data are “corrected” relative to the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) data to produce a self-consistent dataset that covers the period April 1996–September 2017. The highest tropospheric NO2 concentrations are seen over urban, industrialized, and highly populated areas and over ship tracks in the oceans. Tropospheric NO2 has generally decreased during the last 2 decades over the industrialized and highly populated regions of the western world (a total decrease of the order of ∼49 % over the US, the Netherlands, and the UK; ∼36 % over Italy and Japan; and ∼32 % over Germany and France) and increased over developing regions (a total increase of ∼160 % over China and ∼33 % over India). It is suggested here that linear trends cannot be used efficiently worldwide for such long periods. Tropospheric NO2 is very sensitive to socioeconomic changes (e.g., environmental protection policies, economic recession, warfare, etc.) which may cause either short-term changes or even a reversal of the trends. The application of a method capable of detecting the year when a reversal of trends happened shows that tropospheric NO2 concentrations switched from positive to negative trends and vice versa over several regions around the globe. A country-level analysis revealed clusters of countries that exhibit similar positive-to-negative or negative-to-positive trend reversals, while 29 out of a total of 64 examined megacities and large urban agglomerations experienced a trend reversal at some point within the last 2 decades.

New high quality satellite measurements of nitrogen dioxide (NO2) of the TROPOspheric Monitoring Instrument (TROPOMI) over snow-covered regions of Siberia reveal previously undocumented but significant NO2 emissions associated with the natural gas industry in Western Siberia. Besides gas drilling and natural gas power plants, also gas compressor stations for the transport of natural gas are sources of high amounts of nitrogen oxides (NOx = NO + NO2), which are emitted in otherwise pristine regions. The emissions per station from these remote gas compressor stations are at least an order of magnitude larger than those reported for North American gas compressor stations, possibly related to less stringent environmental regulations in Siberia compared to the United States. This discovery was made possible thanks to a newly developed technique for discriminating snow covered surfaces from clouds, which allows for satellite measurements of tropospheric NO2 columns over large boreal snow-covered areas. While retrievals over snow-covered areas were traditionally filtered out, we used the retrieved column of air in these observations to distinguish clouds higher in the atmosphere from the snow at the surface. This results in 23% more TROPOMI observations on an annual basis. Furthermore, these observations have a precision four times better than nearly any TROPOMI observation over other areas and surfaces around the world. These new results highlight the potential of TROPOMI on Sentinel 5P as well as future satellite missions for monitoring small-scale emissions and emissions at high latitudes.

Global observations of tropospheric nitrogen dioxide (NO2) columns have been shown to be feasible from space, but consistent multi-sensor records do not yet exist, nor are they covered by planned activities at the international level. Harmonised, multi-decadal records of NO2 columns and their associated uncertainties can provide crucial information on how the emissions and concentrations of nitrogen oxides evolve over time. Here we describe the development of a new, community best-practice NO2 retrieval algorithm based on a synthesis of existing approaches. Detailed comparisons of these approaches led us to implement an enhanced spectral fitting method for NO2, a 1°  ×  1° TM5-MP data assimilation scheme to estimate the stratospheric background and improve air mass factor calculations. Guided by the needs expressed by data users, producers, and WMO GCOS guidelines, we incorporated detailed per-pixel uncertainty information in the data product, along with easily traceable information on the relevant quality aspects of the retrieval. We applied the improved QA4ECV NO2 algorithm to the most current level-1 data sets to produce a complete 22-year data record that includes GOME (1995–2003), SCIAMACHY (2002–2012), GOME-2(A) (2007 onwards) and OMI (2004 onwards). The QA4ECV NO2 spectral fitting recommendations and TM5-MP stratospheric column and air mass factor approach are currently also applied to S5P-TROPOMI. The uncertainties in the QA4ECV tropospheric NO2 columns amount to typically 40 % over polluted scenes. The first validation results of the QA4ECV OMI NO2 columns and their uncertainties over Tai'an, China, in June 2006 suggest a small bias (−2 %) and better precision than suggested by uncertainty propagation. We conclude that our improved QA4ECV NO2 long-term data record is providing valuable information to quantitatively constrain emissions, deposition, and trends in nitrogen oxides on a global scale.

The global source of lightning-produced NOx (LNOx) is estimated by assimilating observations of NO2, O3, HNO3, and CO measured by multiple satellite measurements into a chemical transport model. Included are observations from the Ozone Monitoring Instrument (OMI), Microwave Limb Sounder (MLS), Tropospheric Emission Spectrometer (TES), and Measurements of Pollution in the Troposphere (MOPITT) instruments. The assimilation of multiple chemical data sets with different vertical sensitivity profiles provides comprehensive constraints on the global LNOx source while improving the representations of the entire chemical system affecting atmospheric NOx, including surface emissions and inflows from the stratosphere. The annual global LNOx source amount and NO production efficiency are estimated at 6.3 Tg N yr−1 and 310 mol NO flash−1, respectively. Sensitivity studies with perturbed satellite data sets, model and data assimilation settings lead to an error estimate of about 1.4 Tg N yr−1 on this global LNOx source. These estimates are significantly different from those estimated from a parameter inversion that optimizes only the LNOx source from NO2 observations alone, which may lead to an overestimate of the source adjustment. The total LNOx source is predominantly corrected by the assimilation of OMI NO2 observations, while TES and MLS observations add important constraints on the vertical source profile. The results indicate that the widely used lightning parameterization based on the C-shape assumption underestimates the source in the upper troposphere and overestimates the peak source height by up to about 1 km over land and the tropical western Pacific. Adjustments are larger over ocean than over land, suggesting that the cloud height dependence is too weak over the ocean in the Price and Rind (1992) approach. The significantly improved agreement between the analyzed ozone fields and independent observations gives confidence in the performance of the LNOx source estimation.

A data assimilation system has been developed to estimate global nitrogen oxides (NOx) emissions using OMI tropospheric NO2 columns (DOMINO product) and a global chemical transport model (CTM), the Chemical Atmospheric GCM for Study of Atmospheric Environment and Radiative Forcing (CHASER). The data assimilation system, based on an ensemble Kalman filter approach, was applied to optimize daily NOx emissions with a horizontal resolution of 2.8° during the years 2005 and 2006. The background error covariance estimated from the ensemble CTM forecasts explicitly represents non-direct relationships between the emissions and tropospheric columns caused by atmospheric transport and chemical processes. In comparison to the a priori emissions based on bottom-up inventories, the optimized emissions were higher over eastern China, the eastern United States, southern Africa, and central-western Europe, suggesting that the anthropogenic emissions are mostly underestimated in the inventories. In addition, the seasonality of the estimated emissions differed from that of the a priori emission over several biomass burning regions, with a large increase over Southeast Asia in April and over South America in October. The data assimilation results were validated against independent data: SCIAMACHY tropospheric NO2 columns and vertical NO2 profiles obtained from aircraft and lidar measurements. The emission correction greatly improved the agreement between the simulated and observed NO2 fields; this implies that the data assimilation system efficiently derives NOx emissions from concentration observations. We also demonstrated that biases in the satellite retrieval and model settings used in the data assimilation largely affect the magnitude of estimated emissions. These dependences should be carefully considered for better understanding NOx sources from top-down approaches.

A single coherent total ozone dataset, called the Multi Sensor Reanalysis (MSR), has been created from all available ozone column data measured by polar orbiting satellites in the near-ultraviolet Huggins band in the last thirty years. Fourteen total ozone satellite retrieval datasets from the instruments TOMS (on the satellites Nimbus-7 and Earth Probe), SBUV (Nimbus-7, NOAA-9, NOAA-11 and NOAA-16), GOME (ERS-2), SCIAMACHY (Envisat), OMI (EOS-Aura), and GOME-2 (Metop-A) have been used in the MSR. As first step a bias correction scheme is applied to all satellite observations, based on independent ground-based total ozone data from the World Ozone and Ultraviolet Data Center. The correction is a function of solar zenith angle, viewing angle, time (trend), and stratospheric temperature. As second step data assimilation was applied to create a global dataset of total ozone analyses. The data assimilation method is a sub-optimal implementation of the Kalman filter technique, and is based on a chemical transport model driven by ECMWF meteorological fields. The chemical transport model provides a detailed description of (stratospheric) transport and uses parameterisations for gas-phase and ozone hole chemistry. The MSR dataset results from a 30-year data assimilation run with the 14 corrected satellite datasets as input, and is available on a grid of 1×1½ degrees with a sample frequency of 6 h for the complete time period (1978–2008). The Observation-minus-Analysis (OmA) statistics show that the bias of the MSR analyses is less than 1 percent with an RMS standard deviation of about 2 percent as compared to the corrected satellite observations used.

We compare a full-year (2006) record of surface air NO2 concentrations measured in Israeli cities to coinciding retrievals of tropospheric NO2 columns from satellite sensors (SCIAMACHY aboard ENVISAT and OMI aboard Aura). This provides a large statistical data set for validation of NO2 satellite measurements in urban air, where validation is difficult yet crucial for using these measurements to infer NOx emissions by inverse modeling. Assuming that NO2 is well-mixed throughout the boundary layer (BL), and using observed average seasonal boundary layer heights, near-surface NO2 concentrations are converted into BL NO2 columns. The agreement between OMI and (13:45) BL NO2 columns (slope=0.93, n=542), and the comparable results at 10:00 h for SCIAMACHY, allow a validation of the seasonal, weekly, and diurnal cycles in satellite-derived NO2. OMI and BL NO2 columns show consistent seasonal cycles (winter NO2 1.6–2.7× higher than summer). BL and coinciding OMI columns both show a strong weekly cycle with 45–50% smaller NO2 columns on Saturday relative to the weekday mean, reflecting the reduced weekend activity, and validating the weekly cycle observed from space. The diurnal difference between SCIAMACHY (10:00) and OMI (13:45) NO2 is maximum in summer when SCIAMACHY is up to 40% higher than OMI, and minimum in winter when OMI slightly exceeds SCIAMACHY. A similar seasonal variation in the diurnal difference is found in the source region of Cairo. The surface measurements in Israel cities confirm this seasonal variation in the diurnal cycle. Using simulations from a global 3-D chemical transport model (GEOS-Chem), we show that this seasonal cycle can be explained by a much stronger photochemical loss of NO2 in summer than in winter.

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 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 – profile shapes and stratospheric background NO2 – is now immediately available upon arrival (within 80 min of observation) of the OMI NO2 slant columns and cloud data at KNMI. 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 0.7×1015 molec cm−2. Cloud parameters from OMI's O2-O2 algorithm have similar frequency distributions as retrieved from SCIAMACHY's Fast Retrieval Scheme for Cloud Observables (FRESCO) for August 2006. On average, OMI cloud fractions are higher by 0.011, and OMI cloud pressures exceed FRESCO cloud pressures by 60 hPa. A sequence of OMI observations over Europe in October 2005 shows OMI's capability to track changeable NOx air pollution from day to day in cloud-free situations.

This paper describes the pre-operational analysis and forecasting system developed during MACC (Monitoring Atmospheric Composition and Climate) and continued in MACC-II (Monitoring Atmospheric Composition and Climate: Interim Implementation) European projects to provide air quality services for the European continent. The paper gives an overall picture of its status at the end of MACC-II (summer 2014). This system is based on seven state-of-the art models developed and run in Europe (CHIMERE, EMEP, EURAD-IM, LOTOS-EUROS, MATCH, MOCAGE and SILAM). These models are used to calculate multi-model ensemble products. The MACC-II system provides daily 96 h forecasts with hourly outputs of 10 chemical species/aerosols (O3, NO2, SO2, CO, PM10, PM2.5, NO, NH3, total NMVOCs and PAN + PAN precursors) over 8 vertical levels from the surface to 5 km height. The hourly analysis at the surface is done a posteriori for the past day using a selection of representative air quality data from European monitoring stations. The performances of the system are assessed daily, weekly and 3 monthly (seasonally) through statistical indicators calculated using the available representative air quality data from European monitoring stations. Results for a case study show the ability of the median ensemble to forecast regional ozone pollution events. The time period of this case study is also used to illustrate that the median ensemble generally outperforms each of the individual models and that it is still robust even if two of the seven models are missing. The seasonal performances of the individual models and of the multi-model ensemble have been monitored since September 2009 for ozone, NO2 and PM10 and show an overall improvement over time. The change of the skills of the ensemble over the past two summers for ozone and the past two winters for PM10 are discussed in the paper. While the evolution of the ozone scores is not significant, there are improvements of PM10 over the past two winters that can be at least partly attributed to new developments on aerosols in the seven individual models. Nevertheless, the year to year changes in the models and ensemble skills are also linked to the variability of the meteorological conditions and of the set of observations used to calculate the statistical indicators. In parallel, a scientific analysis of the results of the seven models and of the ensemble is also done over the Mediterranean area because of the specificity of its meteorology and emissions. The system is robust in terms of the production availability. Major efforts have been done in MACC-II towards the operationalisation of all its components. Foreseen developments and research for improving its performances are discussed in the conclusion.

We present a systematic comparison of tropospheric NO2 from 17 global atmospheric chemistry models with three state-of-the-art retrievals from the Global Ozone Monitoring Experiment (GOME) for the year 2000. The models used constant anthropogenic emissions from IIASA/EDGAR3.2 and monthly emissions from biomass burning based on the 1997–2002 average carbon emissions from the Global Fire Emissions Database (GFED). Model output is analyzed at 10:30 local time, close to the overpass time of the ERS-2 satellite, and collocated with the measurements to account for sampling biases due to incomplete spatiotemporal coverage of the instrument. We assessed the importance of different contributions to the sampling bias: correlations on seasonal time scale give rise to a positive bias of 30–50% in the retrieved annual means over regions dominated by emissions from biomass burning. Over the industrial regions of the eastern United States, Europe and eastern China the retrieved annual means have a negative bias with significant contributions (between –25% and +10% of the NO2 column) resulting from correlations on time scales from a day to a month. We present global maps of modeled and retrieved annual mean NO2 column densities, together with the corresponding ensemble means and standard deviations for models and retrievals. The spatial correlation between the individual models and retrievals are high, typically in the range 0.81–0.93 after smoothing the data to a common resolution. On average the models underestimate the retrievals in industrial regions, especially over eastern China and over the Highveld region of South Africa, and overestimate the retrievals in regions dominated by biomass burning during the dry season. The discrepancy over South America south of the Amazon disappears when we use the GFED emissions specific to the year 2000. The seasonal cycle is analyzed in detail for eight different continental regions. Over regions dominated by biomass burning, the timing of the seasonal cycle is generally well reproduced by the models. However, over Central Africa south of the Equator the models peak one to two months earlier than the retrievals. We further evaluate a recent proposal to reduce the NOx emission factors for savanna fires by 40% and find that this leads to an improvement of the amplitude of the seasonal cycle over the biomass burning regions of Northern and Central Africa. In these regions the models tend to underestimate the retrievals during the wet season, suggesting that the soil emissions are higher than assumed in the models. In general, the discrepancies between models and retrievals cannot be explained by a priori profile assumptions made in the retrievals, neither by diurnal variations in anthropogenic emissions, which lead to a marginal reduction of the NO2 abundance at 10:30 local time (by 2.5–4.1% over Europe). Overall, there are significant differences among the various models and, in particular, among the three retrievals. The discrepancies among the retrievals (10–50% in the annual mean over polluted regions) indicate that the previously estimated retrieval uncertainties have a large systematic component. Our findings imply that top-down estimations of NOx emissions from satellite retrievals of tropospheric NO2 are strongly dependent on the choice of model and retrieval.

Satellite observations of trace gases in the atmosphere offer a promising method for global verification of emissions and improvement of global emission inventories. Here, an inverse modelling approach based on four-dimensional variational (4D-var) data assimilation is presented and applied to synthetic measurements of atmospheric methane. In this approach, emissions and initial concentrations are optimised simultaneously, thus allowing inversions to be carried out on time scales of weeks to months, short compared with the lifetime of methane. Observing System Simulation Experiments (OSSEs) have been performed to demonstrate the feasibility of the method and to investigate the utility of SCIAMACHY observations for methane source estimation. The impact of a number of parameters on the error in the methane emission field retrieved has been analysed. These parameters include the measurement error, the error introduced by the presence of clouds, and the spatial resolution of the emission field. It is shown that 4D-var is an efficient method to deal with large amounts of satellite data and to retrieve emissions at high resolution. Some important conclusions regarding the SCIAMACHY measurements can be drawn. (i) The observations at their estimated precision of 1.5 to 2% will contribute considerably to uncertainty reduction in monthly, subcontinental (~500 km) methane source strengths. (ii) Systematic measurement errors well below 1% have a dramatic impact on the quality of the derived emission fields. Hence, every effort should be made to identify and remove such systematic errors. (iii) It is essential to take partly cloudy pixels into account in order to achieve sufficient spatial coverage. (iv) The uncertainty in measured cloud parameters may at some point become the limiting factor for methane emission retrieval, rather than the uncertainty in measured methane itself.

SUMMARY Nadir ozone profiles retrieved from the GOME (Global Ozone Monitoring Experiment) instrument are assimilated with a global three-dimensional atmospheric ozone model. The assimilation procedure is based on the Kalman filter equations, and is an extension of an existing assimilation procedure for total ozone columns. As a novelty, a three-dimensional covariance model is developed using a single parameterisation for correlations in all directions, instead of the usually applied separation in horizontal and vertical directions. The parameterisation is anisotropic in all directions, accounting for the different correlation lengths of ozone with respect to altitude, latitude, and longitude. The assimilation procedure includes full use of the averaging kernel information provided with the GOME retrieval product. The averaging kernels account for the smaller sensitivity of the GOME instrument below the ozone maximum and the limited vertical resolution. A singular value decomposition of the kernels is used to reduce the large data volume. A one year data set of GOME ozone profiles is assimilated for the year 2000. Independent data from ozone sondes is used to validate the results. A case study shows that the assimilation of GOME profiles is able to improve the simulation of the vertical ozone distribution even in case of strong vertical gradients.

The global source of lightning-produced NOx (LNOx) is estimated by assimilating observations of NO2, O3, HNO3, and CO measured by multiple satellite measurements. Included are observations from the Ozone Monitoring Instrument (OMI), Microwave Limb Sounder (MLS), Tropospheric Emission Spectrometer (TES), and Measurements of Pollution in the Troposphere (MOPITT) instruments. The assimilation of multiple chemical datasets with different vertical sensitivity profiles provides comprehensive constraints on the global LNOx source while improving the representations of the entire chemical system affecting atmospheric NOx, including surface emissions and inflows from the stratosphere. The annual global LNOx source amount and NO production efficiency are estimated at 6.3 Tg N yr−1 and 350 mol NO flash−1, respectively. Sensitivity studies with perturbed satellite datasets, model and data assimilation settings leads to an error estimate of about 1.4 Tg N yr−1 on this global LNOx source. These estimates are significantly different from those derived from NO2 observations alone, which may lead to an overestimate of the source adjustment. The total LNOx source is predominantly corrected by the assimilation of OMI NO2 observations, while TES and MLS observations add important constraints on the vertical source profile. The results indicate that the widely used lightning parameterization based on the C-shape assumption underestimates the source in the upper troposphere and overestimates the peak source height by up to about 1 km over land and the tropical western Pacific. Adjustments are larger over ocean than over land, suggesting that the cloud height dependence is too weak over the ocean in the Price and Rind (1992) approach. The significantly improved agreement between the analysed ozone fields and independent observations gives confidence in the performance of the LNOx source estimation.

We have developed an advanced chemical data assimilation system to combine observations of chemical compounds from multiple satellites. NO2, O3, CO, and HNO3 measurements from the OMI, TES, MOPITT, and MLS satellite instruments are assimilated into the global chemical transport model CHASER for the years 2006–2007. The CHASER data assimilation system (CHASER-DAS), based on the local ensemble transform Kalman filter technique, simultaneously optimizes the chemical species, as well as the emissions of O3 precursors, while taking their chemical feedbacks into account. With the available datasets, an improved description of the chemical feedbacks can be obtained, especially related to the NOx-CO-OH-O3 set of chemical reactions. Comparisons against independent satellite, aircraft, and ozonesonde data show that the data assimilation results in substantial improvements for various chemical compounds. These improvements include a reduced negative tropospheric NO2 column bias (by 40–85%), a reduced negative CO bias in the Northern Hemisphere (by 40–90%), and a reduced positive O3 bias in the middle and upper troposphere (from 30–40% to within 10%). These changes are related to increased tropospheric OH concentrations by 5–15% in the tropics and the Southern Hemisphere in July. Observing System Experiments (OSEs) have been conducted to quantify the relative importance of each data set on constraining the emissions and concentrations. The OSEs confirm that the assimilation of individual data sets results in a strong influence on both assimilated and non-assimilated species through the inter-species error correlation and the chemical coupling described by the model.

We have developed an advanced chemical data assimilation system to combine observations of chemical compounds from multiple satellites. NO2, O3, CO, and HNO3 measurements from the Ozone Monitoring Instrument (OMI), Tropospheric Emission Spectrometer (TES), Measurement of Pollution in the Troposphere (MOPITT), and Microwave Limb Sounder (MLS) satellite instruments are assimilated into the global chemical transport model CHASER for the years 2006–2007. The CHASER data assimilation system (CHASER-DAS), based on the local ensemble transform Kalman filter technique, simultaneously optimizes the chemical species, as well as the emissions of O3 precursors, while taking their chemical feedbacks into account. With the available datasets, an improved description of the chemical feedbacks can be obtained, especially related to the NOx-CO-OH-O3 set of chemical reactions. Comparisons against independent satellite, aircraft, and ozonesonde data show that the data assimilation results in substantial improvements for various chemical compounds. These improvements include a reduced negative tropospheric NO2 column bias (by 40–85%), a reduced negative CO bias in the Northern Hemisphere (by 40–90%), and a reduced positive O3 bias in the middle and upper troposphere (from 30–40% to within 10%). These changes are related to increased tropospheric OH concentrations by 5–15% in the tropics and the Southern Hemisphere in July. Observing System Experiments (OSEs) have been conducted to quantify the relative importance of each data set on constraining the emissions and concentrations. The OSEs confirm that the assimilation of individual data sets results in a strong influence on both assimilated and non-assimilated species through the inter-species error correlation and the chemical coupling described by the model. The simultaneous adjustment of the emissions and concentrations is a powerful approach to correcting the tropospheric ozone budget and profile analyses.

A data assimilation system has been developed to estimate global nitrogen oxides (NOx) emissions using OMI tropospheric NO2 columns (DOMINO product) and a global chemical transport model (CTM), CHASER. The data assimilation system, based on an ensemble Kalman filter approach, was applied to optimize daily NOx emissions with a horizontal resolution of 2.8° during the years 2005 and 2006. The background error covariance estimated from the ensemble CTM forecasts explicitly represents non-direct relationships between the emissions and tropospheric columns caused by atmospheric transport and chemical processes. In comparison to the a priori emissions based on bottom-up inventories, the optimized emissions were higher over Eastern China, the Eastern United States, Southern Africa, and Central-Western Europe, suggesting that the anthropogenic emissions are mostly underestimated in the inventories. In addition, the seasonality of the estimated emissions differed from that of the a priori emission over several biomass burning regions, with a large increase over Southeast Asia in April and over South America in October. The data assimilation results were validated against independent data: SCIAMACHY tropospheric NO2 columns and vertical NO2 profiles obtained from aircraft and lidar measurements. The emission correction greatly improved the agreement between the simulated and observed NO2 fields; this implies that the data assimilation system efficiently derives NOx emissions from concentration observations. We also demonstrated that biases in the satellite retrieval and model settings used in the data assimilation largely affect the magnitude of estimated emissions. These dependences should be carefully considered for better understanding NOx sources from top-down approaches.

A data set of ground based tropospheric NO2 column observations from De Bilt, the Netherlands, has been compared with the regional air quality model Lotos-Euros. The size of the data set (355 days spread over 14 months, 2106 hourly averages) enables statistically significant conclusions, despite a strong variability in both data sets, and allows to study the seasonal, weekly and diurnal variability and dependence on meteorological variables. The model was run on a 7×7 km grid, and based on an emission data base with the same resolution. With this resolution the model is able to resolve the major sources in the neighborhood of the measurement location. Since for the largest part the observations were performed under cloudy conditions, a retrieval study was done to assess the effect of clouds on the retrieval accuracy. It was found that the sensitivity to NO2 in the boundary layer is almost unchanged by clouds, provided that the cloud bottom height is above the NO2 and that a viewing elevation angle is used of 30° above the horizon. Partially cloudy conditions, even when above the NO2, may have a significant positive or negative impact on individual measurements, but when averaged over time do not cause a significant bias. In general a good agreement was found between modeled and measured tropospheric NO2 columns, with an average difference of less than 1% of the average tropospheric column (14.5 · 10 15 molec cm−2). This holds for both the cloud covered and cloud free observations, and the comparisons show very little cloud cover dependence after the cloud corrections. Hourly differences between observations and model show a Gaussian behavior with a standard deviation σ = 5.5 · 1015 molec cm−2. For daily averages of tropospheric NO2 columns, a correlation 0.72 was found for all observations, and 0.79 for cloud free conditions. The measured and modeled tropospheric NO2 columns have an almost identical distribution over the wind directions, when averaged over 12 sectors of 30°. A significant difference between model and measurements was found for the average weekly cycle, which shows a much stronger decrease in the weekend for the observations, and for the diurnal cycle, for which the observed range is about twice as large as the modeled range. In addition the observations show a decrease with increasing temperature, whereas the model shows no dependency on the temperature for this data set which did not include summer months. The results of the comparison demonstrate that averaged over a long time period, the tropospheric NO2 column observations are representative for a large spatial area despite the fact that they were obtained in an urban region. This makes the MAX-DOAS technique, more than in situ techniques, especially suitable for validation of satellite observations and air quality models in urban regions.

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 (2 simulated aloft, where sensitivity to NO2 is higher, and amount to reductions in tropospheric NO2 columns of up to 20% in winter, and 10% in summer over extended polluted areas. We investigate the impact of aerosols on the NO2 retrieval, and based on a comparison of concurrent retrievals of clouds from OMI and aerosols from MODIS Aqua, we find empirical evidence that OMI cloud retrievals are sensitive to the presence of scattering aerosols. It follows that an implicit correction for the effects of aerosols occurs through the aerosol-induced cloud parameters in DOMINO, and we show that such an empirical correction amounts to a 20 %AMF reduction in summer and ±10% changes in winter over the eastern United States.

[1] The Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) satellite spectrometer provides detailed information on the nitrogen dioxide (NO 2) content in the planetary boundary layer. NO 2 tropospheric column retrievals of SCIAMACHY and its predecessor Global Ozone Monitoring Experiment are characterized by errors of the order of 40%. We present here a new SCIAMACHY tropospheric retrieval data set for the year 2003. The cloud free satellite observations are compared to surface measurements and simulations over western Europe performed with the regional air-quality model CHIMERE. The model has a resolution of 50 km similar to the satellite observations. For these comparisons, averaging kernels are applied to the collocated model profiles to remove the dependency of the comparison on a priori NO 2 profile information used in the retrieval. The consistency of both SCIAMACHY and CHIMERE outputs over sites where surface measurements are available allows us to be confident in evaluation of the model over large areas not covered by surface observations. CHIMERE underestimates surface NO 2 concentrations for urban and suburban stations which we mainly attribute to the low representativeness of point observations. No such bias is found for rural locations. The yearly average SCIAMACHY and CHIMERE spatial NO 2 distributions show a high degree of quantitative agreement over rural and urban sites: a bias of 5% (relative to the retrievals) and a correlation coefficient of 0.87 (n = 2003). On a seasonal basis, biases are smaller than 20% and correlation coefficients are larger than 0.75. Spatial correlations between both the model and satellite columns and the European Monitoring and Evaluation Program (EMEP) emission inventory are high in summer (r = 0.74, n = 1779) and low in winter (r = 0.48, n = 1078), related to seasonal changes in lifetime and transport. On the other hand, CHIMERE and SCIAMACHY columns are mutually consistent in summer (r = 0.82) and in winter (r = 0.79). This shows that CHIMERE simulates the transport and chemical processes with a reasonable accuracy. The NO 2 columns show a high daily variability. The daily NO 2 pollution plumes observed by SCIAMACHY are often well described by CHIMERE both in extent and in location. This result demonstrates the capabilities of a satellite instrument such as SCIAMACHY to monitor the NO 2 concentrations over large areas on a daily basis. It provides evidence that present and future satellite missions, in combination with CTM and surface data, will contribute to improve quantitative air quality analyses at a continental scale. Citation: Blond, N., K. F. Boersma, H. J. Eskes, R. J. van der A, M. Van Roozendael, I. De Smedt, G. Bergametti, and R. Vautard (2007), Intercomparison of SCIAMACHY nitrogen dioxide observations, in situ measurements and air quality modeling results over Western Europe

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.

We present a systematic comparison of tropospheric NO2 from 17 global atmospheric chemistry models with three state-of-the-art retrievals from the Global Ozone Monitoring Experiment (GOME) for the year 2000. The models used constant anthropogenic emissions from IIASA/EDGAR3.2 and monthly emissions from biomass burning based on the 1997–2002 average carbon emissions from the Global Fire Emissions Database (GFED). Model output is analyzed at 10:30 local time, close to the overpass time of the ERS-2 satellite, and collocated with the measurements to account for sampling biases due to incomplete spatiotemporal coverage of the instrument. We assessed the importance of different contributions to the sampling bias: correlations on seasonal time scale give rise to a positive bias of 30–50% in the retrieved annual means over regions dominated by emissions from biomass burning. Over the industrial regions of the eastern United States, Europe and eastern China the retrieved annual means have a negative bias with significant contributions (between −25% and +10% of the NO2 column) resulting from correlations on time scales from a day to a month. We present global maps of modeled and retrieved annual mean NO2 column densities, together with the corresponding ensemble means and standard deviations for models and retrievals. The spatial correlation between the individual models and retrievals are high, typically in the range 0.81–0.93 after smoothing the data to a common resolution. On average the models underestimate the retrievals in industrial regions, especially over eastern China and over the Highveld region of South Africa, and overestimate the retrievals in regions dominated by biomass burning during the dry season. The discrepancy over South America south of the Amazon disappears when we use the GFED emissions specific to the year 2000. The seasonal cycle is analyzed in detail for eight different continental regions. Over regions dominated by biomass burning, the timing of the seasonal cycle is generally well reproduced by the models. However, over Central Africa south of the Equator the models peak one to two months earlier than the retrievals. We further evaluate a recent proposal to reduce the NOx emission factors for savanna fires by 40% and find that this leads to an improvement of the amplitude of the seasonal cycle over the biomass burning regions of Northern and Central Africa. In these regions the models tend to underestimate the retrievals during the wet season, suggesting that the soil emissions are higher than assumed in the models. In general, the discrepancies between models and retrievals cannot be explained by a priori profile assumptions made in the retrievals, neither by diurnal variations in anthropogenic emissions, which lead to a marginal reduction of the NO2 abundance at 10:30 local time (by 2.5–4.1% over Europe). Overall, there are significant differences among the various models and, in particular, among the three retrievals. The discrepancies among the retrievals (10–50% in the annual mean over polluted regions) indicate that the previously estimated retrieval uncertainties have a large systematic component. Our findings imply that top-down estimations of NOx emissions from satellite retrievals of tropospheric NO2 are strongly dependent on the choice of model and retrieval.

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.

Tropospheric NO2 column retrievals from the Global Ozone Monitoring Experiment (GOME) satellite spectrometer are used to quantify the source strength and 3D distribution of lightning produced nitrogen oxides (NOx=NO2+NO2). A sharp increase of NO2 is observed at convective cloud tops with increasing cloud top height, consistent with a power-law behaviour with power 5±2. Convective production of clouds with the same cloud height are found to produce NO2 with a ratio 1.6/1 for continents compared to oceans. This relation between cloud properties and NO2 is used to construct a 10:30 local time global lightning NO2 production map for 1997. An extensive statistical comparison is conducted to investigate the capability of the TM3 chemistry transport model to reproduce observed patterns of lightning NO2 in time and space. This comparison uses the averaging kernel to relate modelled profiles of NO2 to observed NO2 columns. It exploits a masking scheme to minimise the interference of other NOx sources on the observed total columns. Simulations are performed with two lightning parametrisations, one relating convective preciptation (CP scheme) to lightning flash distributions, and the other relating the fifth power of the cloud top height (H5 scheme) to lightning distributions. The satellite-retrieved NO2 fields show significant correlations with the simulated lightning contribution to the NO2 concentrations for both parametrisations. Over tropical continents modelled lightning NO2 shows remarkable quantitative agreement with observations. Over the oceans however, the two model lightning parametrisations overestimate the retrieved NO2 attributed to lightning. Possible explanations for these overestimations are discussed. The ratio between satellite-retrieved NO2 and modelled lightning NO2 is used to rescale the original modelled lightning NOx production. Eight estimates of the lightning NOx production in 1997 are obtained from spatial and temporal correlation methods, from cloud-free and cloud-covered observations, and from two different lightning parametrisations. Accounting for a wide variety of random and possible systematic errors, we estimate the global NOx production from lightning to be in the range 1.1–6.4 TgN in 1997.