OMI tropospheric NO2 profiles from cloud slicing: Constraints on surface emissions, convective transport and lightning NOx

We derive annual and seasonal global climatologies of tropospheric NO2 profiles from OMI cloudy observations for the year 2006 using the cloud-slicing method on six pressure levels centered at about 280, 380, 500, 620, 720 and 820 hPa. A comparison between OMI and the TM4 model tropospheric NO2 profiles reveals striking overall similarities, which confer great confidence to the cloud-slicing approach to provide details that pertain to annual as well as seasonal means, along with localized discrepancies that seem to probe into particular model processes. Anomalies detected at the lowest levels can be traced to deficiencies in the model surface emission inventory, at mid-tropospheric levels to convective transport and horizontal advective diffusion, and at the upper tropospheric levels to model lightning NOx production and the placement of deeply transported NO2 plumes such as from the Asian summer monsoon. The vertical information contained in the OMI cloud-sliced NO2 profiles provides a global observational constraint that can be used to evaluate chemistry transport models (CTMs) and guide the development of key parameterization schemes.Geoscience & Remote SensingCivil Engineering and Geoscience

Evaluation of broadband surface solar irradiance derived from the Ozone Monitoring Instrument

Surface solar irradiance (SSI) data are important for planning and estimating the production of solar power plants. Long-term high quality surface solar radiation data are needed for monitoring climate change. This paper presents a new surface solar irradiance dataset, the broadband (0.2–4 ?m) surface solar irradiance product derived from the Ozone Monitoring Instrument (OMI). The OMI SSI algorithm is based on the Heliosat method and uses the OMI O2–O2 cloud product as main input. The OMI SSI data are validated against the globally distributed Baseline Surface Radiation Network (BSRN) measurements at 19 stations for the year 2008. Furthermore, the monthly mean OMI SSI data are compared to independent surface solar irradiance products from International Satellite Cloud Climatology Project Flux Data (ISCCP-FD) and Clouds and the Earth's Radiant Energy System (CERES) data for the year 2005. The mean difference between OMI SSI and BSRN global (direct + diffuse) irradiances is ? 1.2 W m? 2 (? 0.2%), the root mean square error is 100.1 W m? 2 (18.1%), and the mean absolute error is 67.8 W m? 2 (12.2%). The differences between OMI SSI and BSRN global irradiances are smaller over continental and coastal sites and larger over deserts and islands. OMI SSI has a good agreement with the CERES shortwave (SW) model B surface downward flux (SDF) product. The correlation coefficient and index of agreement between monthly mean 1-degree gridded OMI SSI and CERES SW SDF are > 0.99. OMI SSI is lower than CERES SW SDF which is partly due to the solar zenith angle. On average, OMI SSI is 13.5 W m? 2 (2.5%) lower than the ISCCP-FD SW surface downward flux and the correlation coefficient and index of agreement are > 0.98 for every month.Geoscience & Remote SensingCivil Engineering and Geoscience

Intercomparison of daytime stratospheric NO2 satellite retrievals and model simulations

This paper evaluates the agreement between stratospheric NO2 retrievals from infrared limb sounders (Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) and High Resolution Dynamics Limb Sounder (HIRDLS)) and solar UV/VIS backscatter sensors (Ozone Monitoring Instrument (OMI), Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) limb and nadir) over the 2005–2007 period and across the seasons. The observational agreement is contrasted with the representation of NO2 profiles in 3-D chemical transport models such as the Whole Atmosphere Community Climate Model (WACCM) and TM4. A conclusion central to this work is that the definition of a reference for stratospheric NO2 columns formed by consistent agreement among SCIAMACHY, MIPAS and HIRDLS limb records (all of which agree to within 0.25 × 1015 molecules cm?2 or better than 10%) allows us to draw attention to relative errors in other data sets, e.g., (1) WACCM overestimates NO2 densities in the extratropical lower stratosphere, particularly in the springtime and over northern latitudes by up to 35% relative to limb observations, and (2) there are remarkable discrepancies between stratospheric NO2 column estimates from limb and nadir techniques, with a characteristic seasonally and latitudinally dependent pattern. We find that SCIAMACHY nadir and OMI stratospheric columns show overall biases of ?0.5 × 1015 molecules cm?2 (?20%) and +0.6 × 1015 molecules cm?2 (+20%) relative to limb observations, respectively. It is argued that additive biases in nadir stratospheric columns are not expected to affect tropospheric retrievals significantly, and that they can be attributed to errors in the total slant column density, related either to algorithmic or instrumental effects. In order to obtain accurate and long-term time series of stratospheric NO2, an effort towards the harmonization of currently used differential optical absorption spectroscopy (DOAS) approaches to nadir retrievals becomes essential, as well as their agreement to limb and ground-based observations, particularly now that limb techniques are giving way to nadir observations as the next generation of climate and air quality monitoring instruments pushes forth.Geoscience & Remote SensingCivil Engineering and Geoscience

Maritime NO_x Emissions Over Chinese Seas Derived From Satellite Observations

By applying an inversion algorithm to NOx satellite observations from Ozone Monitoring Instrument, monthly NOx emissions for a 10 year period (2007 to 2016) over Chinese seas are presented for the first time. No effective regulations on NOx emissions have been implemented for ships in China, which is reflected in the trend analysis of maritime emissions. The maritime emissions display a continuous increase rate of about 20% per year until 2012 and slow down to 3% after that. The seasonal cycle of shipping emissions has regional variations, but all regions show lower emissions during winter. Simulations by an atmospheric chemistry transport model show a notable influence of maritime emissions on air pollution over coastal areas, especially in summer. The satellite-derived spatial distribution and the magnitude of maritime emissions over Chinese seas are in good agreement with bottom-up studies based on the Automatic Identification System of ships.Atmospheric Remote Sensin

MAX-DOAS observations of aerosols, formaldehyde and nitrogen dioxide in the Beijing area: Comparison of two profile retrieval approaches

A four year data set of MAX-DOAS observations in the Beijing area (2008–2012) is analysed with a focus on NO2, HCHO, and aerosols. Two very different retrieval methods are applied. Method A describes the tropospheric profile with 13 layers and makes use of the optimal estimation method. Method B uses 2–4 parameters to describe the tropospheric profile and an inversion based on a least-squares fit. For each constituent (NO2, HCHO and aerosols) the retrieval outcomes are compared in terms of tropospheric columns, surface concentrations, and "characteristic profile heights" (i.e. the height below which 75% of the vertically integrated tropospheric column resides). We find best agreement between the two methods for tropospheric NO2 columns, with a standard deviation of relative differences below 10%, a correlation of 0.99 and a linear regression with a slope of 1.03. For tropospheric HCHO columns we find a similar slope, but also a systematic bias of almost 10% which is likely related to differences in profile height. Aerosol optical depths (AODs) retrieved with method B are 20% high compared to method A. They are more in agreement with AERONET measurements, which are on average only 5% lower, however with considerable relative differences (standard deviation ~25%). With respect to near surface volume mixing ratios and aerosol extinction we find considerably larger relative differences: 10 ± 30%, ?23 ± 28% and ?8 ± 33% for aerosols, HCHO and NO2 respectively. The frequency distributions of these near-surface concentrations show however a quite good agreement, and this indicates that near-surface concentrations derived from MAX-DOAS are certainly useful in a climatological sense. A major difference between the two methods is the dynamic range of retrieved characteristic profile heights which is larger for method B than for method A. This effect is most pronounced for HCHO, where retrieved profile shapes with method A are very close to the a priori, and moderate for NO2 and aerosols which on average show quite good agreement for characteristic profile heights below 1.5 km. One of the main advantages of method A is the stability, even under suboptimal conditions (e.g., in the presence of clouds). Method B is generally more unstable and this explains probably a substantial part of the quite large relative differences between the two methods. However, despite a relatively low precision for individual profile retrievals it appears as if seasonally averaged profile heights retrieved with method B are less biased towards a priori assumptions than those retrieved with method A. This gives confidence in the result obtained with method B, namely that aerosol profiles tend on average to be higher than NO2 profiles in spring and summer, whereas they seem on average to be of the same height in winter, a result which is especially relevant in relation to the validation of satellite retrievals.Geoscience & Remote SensingCivil Engineering and Geoscience

MAX-DOAS observations of aerosols, formaldehyde and nitrogen dioxide in the Beijing area: Comparison of two profile retrieval approaches

A 4-year data set of MAX-DOAS observations in the Beijing area (2008–2012) is analysed with a focus on NO2, HCHO and aerosols. Two very different retrieval methods are applied. Method A describes the tropospheric profile with 13 layers and makes use of the optimal estimation method. Method B uses 2–4 parameters to describe the tropospheric profile and an inversion based on a least-squares fit. For each constituent (NO2, HCHO and aerosols) the retrieval outcomes are compared in terms of tropospheric column densities, surface concentrations and "characteristic profile heights" (i.e. the height below which 75% of the vertically integrated tropospheric column density resides). We find best agreement between the two methods for tropospheric NO2 column densities, with a standard deviation of relative differences below 10%, a correlation of 0.99 and a linear regression with a slope of 1.03. For tropospheric HCHO column densities we find a similar slope, but also a systematic bias of almost 10% which is likely related to differences in profile height. Aerosol optical depths (AODs) retrieved with method B are 20% high compared to method A. They are more in agreement with AERONET measurements, which are on average only 5% lower, however with considerable relative differences (standard deviation ~ 25%). With respect to near-surface volume mixing ratios and aerosol extinction we find considerably larger relative differences: 10 ± 30, ?23 ± 28 and ?8 ± 33% for aerosols, HCHO and NO2 respectively. The frequency distributions of these near-surface concentrations show however a quite good agreement, and this indicates that near-surface concentrations derived from MAX-DOAS are certainly useful in a climatological sense. A major difference between the two methods is the dynamic range of retrieved characteristic profile heights which is larger for method B than for method A. This effect is most pronounced for HCHO, where retrieved profile shapes with method A are very close to the a priori, and moderate for NO2 and aerosol extinction which on average show quite good agreement for characteristic profile heights below 1.5 km. One of the main advantages of method A is the stability, even under suboptimal conditions (e.g. in the presence of clouds). Method B is generally more unstable and this explains probably a substantial part of the quite large relative differences between the two methods. However, despite a relatively low precision for individual profile retrievals it appears as if seasonally averaged profile heights retrieved with method B are less biased towards a priori assumptions than those retrieved with method A. This gives confidence in the result obtained with method B, namely that aerosol extinction profiles tend on average to be higher than NO2 profiles in spring and summer, whereas they seem on average to be of the same height in winter, a result which is especially relevant in relation to the validation of satellite retrievals.Geoscience & Remote SensingCivil Engineering and Geoscience