Oog voor klimaat en luchtkwaliteit

Geoscience and Remote SensingCivil Engineering and Geoscience

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

We derive a global climatology of tropospheric NO2 profiles from OMI cloudy observations for the year 2006 using the cloud slicing method on six pressure levels centered 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, 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

Impact of aerosols on the OMI tropospheric NO2 retrievals over industrialized regions: How accurate is the aerosol correction of cloud-free scenes via a simple cloud model? (discussion paper)

The Ozone Monitoring Instrument (OMI) instrument has provided daily global measurements of tropospheric NO2 for more than a decade. Numerous studies have drawn attention to the complexities related to measurements of tropospheric NO2 in the presence of aerosols. Fine particles affect the OMI spectral measurements and the length of the average light path followed by the photons. However, they are not explicitly taken into account in the current OMI tropospheric NO2 retrieval chain. Instead, the operational OMI O2-O2 cloud retrieval algorithm is applied both to cloudy scenes and to cloud free scenes with aerosols present. This paper describes in detail the complex interplay between the spectral effects of aerosols, the OMI O2-O2 cloud retrieval algorithm and the impact on the accuracy of the tropospheric NO2 retrievals through the computed Air Mass Factor (AMF) over cloud-free scenes. Collocated OMI NO2 and MODIS Aqua aerosol products are analysed over East China, in industrialized area. In addition, aerosol effects on the tropospheric NO2 AMF and the retrieval of OMI cloud parameters are simulated. Both the observation-based and the simulation-based approach demonstrate that the retrieved cloud fraction linearly increases with increasing Aerosol Optical Thickness (AOT), but the magnitude of this increase depends on the aerosol properties and surface albedo. This increase is induced by the additional scattering effects of aerosols which enhance the scene brightness. The decreasing effective cloud pressure with increasing AOT represents primarily the absorbing effects of aerosols. The study cases show that the actual aerosol correction based on the implemented OMI cloud model results in biases between ?20 and ?40 % for the DOMINO tropospheric NO2 product in cases of high aerosol pollution (AOT ? 0.6) and elevated particles. On the contrary, when aerosols are relatively close to the surface or mixed with NO2, aerosol correction based on the cloud model results in overestimation of the DOMINO tropospheric NO2 product, between 10 and 20 %. These numbers are in line with comparison studies between ground-based and OMI tropospheric NO2 measurements under conditions with high aerosol pollution and elevated particles. This highlights the need to implement an improved aerosol correction in the computation of tropospheric NO2 AMFs.Geoscience & Remote SensingCivil Engineering and Geoscience

NOx emission estimates during the 2014 Youth Olympic Games in Nanjing (discussion paper)

The Nanjing Government has taken temporary environmental regulations to guarantee good air quality during the Youth Olympic Games (YOG) in 2014. We study the effect of those regulations by applying the emission estimate algorithm DECSO (Daily Emission estimates Constrained by Satellite Observations) to measurements of the Ozone Monitoring Instrument (OMI). We improved DECSO by updating the chemical transport model CHIMERE from v2006 to v2013 and by adding an Observation minus Forecast (OmF) criterion to filter outlying satellite retrievals due to high aerosol concentrations. The comparison of model results with both ground and satellite observations indicates that CHIMERE v2013 is better performing than CHIMERE v2006. After filtering the satellite observations with high aerosol loads that were leading to large OmF values, unrealistic jumps in the emission estimates are removed. Despite the cloudy conditions during the YOG we could still see a decrease of tropospheric NO2 column concentrations of about 32% in the OMI observations as compared to the average NO2 concentrations from 2005 to 2012. The results of the improved DECSO algorithm for NOx emissions show a reduction of at least 25% during the YOG period. This indicates that air quality regulations taken by the local government were successful. The algorithm is also able to detect an emission reduction of 10% during the Chinese Spring Festival. This study demonstrates the capacity of the DECSO algorithm to capture the change of NOx emissions on a monthly scale. We also show that the observed concentrations and the derived emissions show different patterns that provide complimentary information. For example, the Nanjing smog episode in December 2013 led to a strong increase in NO2 concentrations without an increase in NOx emissions. Furthermore, DECSO gives us important information of the non-trivial seasonal relation between NOx emissions and NO2 concentrations on a local scale.Geoscience & Remote SensingCivil Engineering and Geoscience

NOx emission estimates during the 2014 Youth Olympic Games in Nanjing

The Nanjing Government applied temporary environmental regulations to guarantee good air quality during the Youth Olympic Games (YOG) in 2014. We study the effect of those regulations by applying the emission estimate algorithm DECSO (Daily Emission estimates Constrained by Satellite Observations) to measurements of the Ozone Monitoring Instrument (OMI). We improved DECSO by updating the chemical transport model CHIMERE from v2006 to v2013 and by adding an Observation minus Forecast (OmF) criterion to filter outlying satellite retrievals due to high aerosol concentrations. The comparison of model results with both ground and satellite observations indicates that CHIMERE v2013 is better performing than CHIMERE v2006. After filtering the satellite observations with high aerosol loads that were leading to large OmF values, unrealistic jumps in the emission estimates are removed. Despite the cloudy conditions during the YOG we could still see a decrease of tropospheric NO2 column concentrations of about 32 % in the OMI observations when compared to the average NO2 columns from 2005 to 2012. The results of the improved DECSO algorithm for NOx emissions show a reduction of at least 25 % during the YOG period and afterwards. This indicates that air quality regulations taken by the local government have an effect in reducing NOx emissions. The algorithm is also able to detect an emission reduction of 10 % during the Chinese Spring Festival. This study demonstrates the capacity of the DECSO algorithm to capture the change of NOx emissions on a monthly scale. We also show that the observed NO2 columns and the derived emissions show different patterns that provide complimentary information. For example, the Nanjing smog episode in December 2013 led to a strong increase in NO2 concentrations without an increase in NOx emissions. Furthermore, DECSO gives us important information on the non-trivial seasonal relation between NOx emissions and NO2 concentrations on a local scale.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 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