Accurate cloud detection is very important for infrared (IR) radiance assimilation; improved cloud detection could reduce cloud contamination and hence improve the assimilation. Although operational numerical weather prediction (NWP) centers are using IR sounder radiance data for cloud detection, collocated high spatial resolution imager data could help sounder subpixel cloud detection and characterization. IR sounder radiances with improved cloud detection using Atmospheric Infrared Sounder (
Mid-troposphere CO 2 data retrieved by the AIRS (atmospheric infrared sounder) were validated with five ground-based stations and aircraft measurements in the Northern Hemisphere. AIRS CO 2 products show good agreement with ground and aircraft observations. The data had a monthly average accuracy better than 3 ppmv. In this study, the spatial and temporal distribution of mid-troposphere CO 2 from January 2003 to December 2008 was analyzed based on this satellite product. The average concentration of atmospheric CO 2 was higher in the Northern Hemisphere than in the Southern Hemisphere. The yearly average results show a gradual increase from 2003 to 2008. In China, the annual growth rate was about 2 ppmv/a, similar to the United States, Europe, Australia and India, but was slightly lower than Canada and Russia. Mid-troposphere CO 2 concentrations were higher over northern China than over southern areas, due to differences in natural conditions and industrial layout. There were four centers of high CO 2 concentration between 35° and 45°N over China, with low concentrations over Yunnan Province. There was a significant seasonal CO 2 variation with peak concentration in spring and the lowest concentration in autumn. mid-troposphere CO 2 , satellite remote sensing, ground based validation, temporal and spatial distribution Citation:Bai W G, Zhang X Y, Zhang P. Temporal and spatial distribution of tropospheric CO 2 over China based on satellite observations.
Spaceborne measurements by the Atmospheric Infrared Sounder (AIRS) on the EOS/Aqua satellite provide a global view of methane (CH 4 ) distribution in the mid-upper troposphere (MUT-CH 4 ). The focus of this study is to analyze the spatiotemporal variations in MUT-CH 4 over China from 2003 to 2008. Validation of AIRS CH 4 products versus Fourier transform infrared profiles demonstrates that its RMS error is mostly less than 1.5%. A typical atmospheric methane profile is found that shows how concentrations decrease as height increases because of surface emissions. We found that an important feature in the seasonal variation in CH 4 is the two peaks that exist in summer and winter in most parts of China, which is also observed in in-situ measurements at Mt. Waliguan, Qinghai Province, China (36.2879°N 100.8964°E, 3810 m). Also, in the summer, only one peak existed in western and southern China since there are no more significant anthropogenic sources in winter than at any other time of the year.
With the successful launch of FengYun‐4A (FY‐4A), the first satellite in a new Chinese geostationary weather satellite series (FY‐4 series), which carries a high spectral resolution infrared (IR) sounder called GIIRS (Geosynchronous Interferometric Infrared Sounder), and vertical atmospheric profiles can be obtained frequently at the regional scale. A fast radiative transfer model is a key component for quantitative applications of GIIRS radiance measurements, including deriving soundings in near real time for situation awareness and radiance assimilation in numerical weather prediction models. The weighted least squares method on enhancing the accuracy of RTTOV (Radiative Transfer for TOVS) for GIIRS is developed. Besides, currently, fast radiative transfer models for IR sensors are based on global training profiles, since GIIRS is targeted for regional observations; it is beneficial for local weather related applications using local training profiles, which better represent the characteristics of that weather regime. A local training profile data set has been developed for GIIRS using the RTTOV approach, comparisons with line‐by‐line radiative transfer model indicate that weighted least squares method provides better accuracy (smaller root‐mean‐square error) in the brightness temperature simulation for the middlewave band of GIIRS than the ordinary least squares method, and the local training profiles have further remarkable improvements on brightness temperature simulation over the global training profiles, especially for GIIRS longwave band. The methods can be applied to RTTOV development for other IR sensors onboard the geostationary satellites.
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