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AIUS (Atmospheric Infrared Ultraspectral Sounder) is an infrared occultation spectrometer onboard the Chinese GaoFen-5 satellite, which covers a spectral range of 2.4–13.3 μm (750–4100 cm−1) with a spectral resolution of about 0.02 cm−1. AIUS is designed to measure and study chemical processes of ozone (O3) and other trace gases in the upper troposphere and stratosphere around Antarctic. In this study, the corresponding retrieval methodology is described. The retrieval simulations based on the simulated spectra of AIUS have been carried out, with a focus on O3. The relative difference between the retrieved and the true O3 profiles is within 5% from the 15 km to 70 km and about 10% below 15 km. The corresponding averaging kernels illustrate that the overall retrieval information mainly come from the spectra, not the a priori. The retrieval experiments also demonstrate that the shape of the retrieved profiles resembles the shape of the true profile even if the shape of the a priori profile is different from that of the true profile. Further, we perform the O3 retrieval from the real ACE-FTS (Atmospheric Chemistry Experiment-Fourier Transform Spectrometer) measurements and compare the results with the official ACE-FTS Level-2 products. Overall, both profiles agree well in the stratosphere where the retrieval sensitivity is high. The relative difference between both profiles is about 15% below 70 km, which may due to the measurement errors and different forward model parameters.

We propose a new method to estimate surface-level particulate matter (PM) concentrations by using satellite-retrieved Aerosol Optical Thickness (AOT). This method considers the distribution and variation of Planetary Boundary Layer (PBL) height and relative humidity (RH) at the regional scale. The method estimates surface-level particulate matter concentrations using the data simulated by an atmospheric boundary layer model RAMS and satellite-retrieved AOT. By incorporation MODIS AOT, PBL height and RH simulated by RAMS, this method is applied to estimate the surface-level PM2.5 concentrations in North China region. The result is evaluated by using 16 ground-based observations deployed in the research region, and the result shows a good agreement between estimated PM2.5 concentrations and observations, and the coefficient of determination R 2 is 0.61 between the estimated PM2.5 concentrations and the observations. In addition, surface-level PM2.5 concentrations are also estimated by using MODIS AOT, ground-based LIDAR observations and RH measurements. A comparison between the two estimated PM2.5 concentrations shows that the new method proposed in this paper is better than the traditional method. The coefficient of determination R 2 is improved from 0.32 to 0.62.

PM2.5 has become one of the primary pollutants in China, and heavy smogs usually occurred over the eastern China in recent years. Some scientific issues such as the sources of the fine particles, the formation mechanism of the severe pollutions and the interactions between air quality and regional climate change, are hot points in the field of air quality monitoring and modeling studies. Satellite remote sensing has been recognized as an important and effective approach to analyze these scientific issues. We have developed a series of retrieval algorithms to assess the spatial and temporal variations of haze pollutions, aerosols, gaseous pollutants, and greenhouse gases (GHGs). This paper puts forward three directions of future research in atmospheric environment remote sensing in China. The first one is to improve the satellite retrieving methods of atmospheric components in order to better quantify their amounts over China. The second one is to enhance the performance of atmospheric models by refining emission inventory and by assisting the source apportionment of pollutants based on satellite data. So far, there are still many unclear problems concerning atmospheric emissions and formation processes of the haze clouds, which may introduce large uncertainties in model simulations. The last one is to advance our understanding of the complex interactions between haze clouds and regional climate change in the eastern China.

Carbon dioxide retrieval in thermal infrared band have to face the challenge that the signature of CO2 variations is small compared to spectral effects of temperature and water variability. Chahine et al. (2005) presented the method of vanishing partial derivatives (VPD) to retrieve CO2 and this method has been successful applied in the AIRS CO2 product. The VPD method has stricter criteria than Optimal Estimation (OE) method, and very small part of AIRS observation could be used for retrieval. We retrieved CO2 profile over East Asia by general OE method. The apriori profile is derived from EOF method, and the apriori covariance is derived from RAMS-CMAQ (Regional Atmospheric Modeling System and Models-3 Community Multi-scale Air Quality) incorporated VRPM (vegetation photosynthesis and respiration module). The validation was carried out by compared to CONTRAIL aircraft data and 9 surface stations in East Asia. A standard deviation of 2ppm compared to CONTRAIL aircraft was reached and a mean bias of 4 ppm was shown when compare retrieved XCO2 to surface CO2 from surface stations.

Inelastic vibrational Raman scattering by liquid water is one significant limitation to the accuracy of the retrieval of trace gas constituents in atmosphere over waters, particularly over clear ocean waters, while using satellite data with differential optical absorption spectroscopy technique (DOAS). The effect which is similar to the Ring effect in atmosphere results in the filling-in of Fraunhofer lines, which is known as solar absorption lines. The inelastic component of the liquid water scattering causes a net increase of radiance in the line because more radiations shift to the wavelength of an absorption line than from this wavelength to other wavelengths. The solar spectrum transmitting atmosphere is convolved with vibrational Raman scattering coefficient of liquid water, divided by the original computed spectrum, with a cubic polynomial subtracted off, to create differential water Ring spectrum. This method is suggested in order to obtain an effective differential water Ring coeffient for the DOAS fitting process, which could be used to improve the accuracy of the retrieval of the trace gases concentration. The method does not rely on radiative transfer model of water, which would be time-consuming and depending on lot of parameters. Therefore, it is very fast and convenient.