During the 2008 Olympic and Paralympic Games in Beijing (from 8 August to 17 September), local authorities enforced strong measures to reduce air pollution during the events. Inside and outside Beijing traffic was restricted, polluting industry was shut down temporarily and construction activities were put on hold.
To evaluate the direct effect of these measures,
Mijling et al. 2009 used the tropospheric nitrogen dioxide tropospheric column observations from the satellite instruments GOME-2 and OMI. By interpreting these data against simulations from the regional chemistry transport model CHIMERE a reduction of nitrigen dioxide concentrations of approximately 60% is found above Beijing during the Olympic period. The air quality measures were especially effective in the Beijing area, but also noticeable in surrounding cities of Tianjin (30% reduction) and Shijiazhuang (20% reduction). After the Olympic Games the concentrations do not return immediately to their pre-Olympic values, suggesting that air quality in Beijing has improved systematically or that economic activity is only slowly recovering.
Figure: Mean tropospheric NO2 concentrations as observed by GOME-2 during the Olympic events in Beijing in 2008, compared with observations in the same period in 2007. Due to intensive air quality measures during the Olympic Games, the high NO2 concentration in Beijing strongly reduce, while high concentrations in the cities of Tianjin (at 110 km) and Shijiazhuang (at 250 km) persist.
The evolution and three dimensional structure of the Antarctic ozone hole can now be studied
on a day-to-day basis using the latest data from the GOME-2 satellite instrument, as shown in a new study that just has been accepted for publication in Geophysical Research Letters.
van Peet et al. 2009 show that GOME-2 offers accurate vertical profiles of ozone with a high enough horizontal and vertical reolution to capture the compex dynamics of the yearly occuring phenomenon in the South Pole stratosphere.
Comparing liquid water path data from the SEVIRI sensor and from ground-based microwave radiometers,
Greuell and Roebeling [2009] optimized the validation of satellite-derived cloud properties so that differences due to a number of validation issues were minimized. Their method incorporates a parallax correction, Gaussian averaging of the satellite and the ground data and a time scale for averaging the ground data that is much longer than the time that the clouds need to move across the considered satellite pixels.
OMI retrievals of trace gases, clouds and aerosols depend critically on the quality of the observed radiance and irradiance data. The Ozone Monitoring Instrument is equipped with a CCD-camera that allows simultaneous Earth-viewing under 60 individual angles. In the first years of operation, this novel technique appeared susceptible to calibration offsets that changed with angle. In a recent paper,
Dobber et al. [2008] have significantly improved the calibration of the OMI level-1b data. Collection 3 data are publicly available since 12 October 2007, and can be obtained through
www.knmi.nl/omi.
More than 85% of the ground pixels of GOME(-2) and SCIAMACHY are influenced by clouds.
Wang et al. [2008] have improved the FRESCO cloud detection algorithm by including Rayleigh scattering. Validation of the improved algorithm, FRESCO+, with groundbased radar
observations shows that the FRESCO+ cloud pressure is the mid-level of clouds. It appears that FRESCO+ gives more reliable
cloud pressures over partly cloudy pixels than FRESCO. Globally averaged, the FRESCO+ cloud pressure is about 50 hPa higher
than FRESCO. Application of FRESCO+ to cloud correction of tropospheric NO
2 measurements from SCIAMACHY shows
that the algorithm works better than the previous version.
The reflectance of the Earth surface is a critical parameter for satellite retrievals of the atmospheric
trace gases, clouds and aerosols.
Kleipool et al. [2008] used three years of data from the
Ozone Monitoring Instrument (OMI) to derive the surface reflectance of the globe on a 0.5 by 0.5 degree grid
for every month of the year. The reflectance is given for 23 wavelengths between 328 and 500 nm.
The data compares well with existing albedo climatologies derived from other satellite instruments (TOMS, GOME, MODIS),
and significantly improves on these data sets by better spectral and/or spatial resolution.
The thermodynamic phase of clouds plays an important role in the Earth's energy balance.
Geostationary satellite instruments such as SEVIRI are capable of providing cloud phase
information at high resolution.
Wolters et al. [2008]
evaluated three methods for cloud phase determination from SEVIRI data against cloud phase observations from lidar and
cloud radar at Cabauw, The Netherlands. It was found that all methods are well suitable
for detection of both the annual and diurnal cycle of cloud phase.
The 2005 and 2006 DANDELIONS campaigns brought together an unprecedented variety of measurement techniques to measure
NO
2 at Cabauw, The Netherlands. Using these instruments viewing in different directions,
Brinksma et al. [2008] show significant spatial variability in NO
2 fields. This means that the modest agreement (
r=0.6)
found between the ground-based and OMI NO
2 retrievals is as good as it gets.
In the framework of the AMFIC project, Bas Mijling and others developed a system for monitoring and forecasting tropospheric
pollutants over China. The
web site
of the forecast service supported Olympic teams in Beijing during the Games.
Mark Kroon et al. [2008] have analyzed
OMI total ozone data retrieved with two different algorithms. The results provide information necessary
for long-term ozone trend studies.
Different cloud retrievals using OMI and PARASOL have been analyzed shedding light on the
interpretation of these clouds products. More details in a recently published paper by
Maarten Sneep et al. [2008].
Trends in a ten year data record of tropospheric nitrogen dioxide (NO
2) from
GOME and SCIAMACHY have been analyzed showing reductions in Western countries and
strong increases in China over the last ten years. Read more in the paper by
Ronald van der A et al. [2008].
Last updated on 25 March 2009 (Folkert Boersma)
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