Advances in Earth Science ›› 2017, Vol. 32 ›› Issue (2): 139-150. doi: 10.11867/j.issn.1001-8166.2017.02.0139

• Orginal Article • Previous Articles     Next Articles

Research Progress of Quality Control for AIRS Data

Gen Wang 1, 2( ), Hua Zhang 3, *( ), Yin Yang 3   

  1. 1.The Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110016, China
    2.Anhui Meteorological Information Centre, Hefei 230031, China
    3.National Meteorological Center of China, Beijing 100081, China
  • Received:2016-10-03 Revised:2016-12-18 Online:2017-02-20 Published:2017-02-20
  • Contact: Hua Zhang;
  • About author:

    First author:Wang Gen(1983-),male,Taizhou City, Jiangsu Province, Engineer. Research areas include satellite data assimilation, numerical simulation of GRAPES and multi-source data

  • Supported by:
    Project supported by the Shenyang Institute of Atmospheric Environment of China Meteorological Administration “Application of non-gaussian model on AIRS water channel brightness temperature assimilation”(No.2016SYIAE14);The China’s Research and Development Special Fund for Public Welfare Industry “Technology research and application demonstration of monitoring and warning of the combination satellite-ground on crops wet damages”(No.GYHY201406028)

Gen Wang, Hua Zhang, Yin Yang. Research Progress of Quality Control for AIRS Data[J]. Advances in Earth Science, 2017, 32(2): 139-150.

Satellite data assimilation can provide accurate initial field for Numerical Weather Prediction (NWP) models. So far, data variational assimilation is based on the theory where error obeys Gaussian distribution, so as to apply the least square method. During classical variational assimilation, if the data contain outliers, the results of optimal parameter estimation is meaningless. Therefore, quality control is quite necessary for Atmospheric Infrared Sounder (AIRS) data before data assimilation. This paper made a comment of the advances in the quality control using AIRS data, which analyzed and discussed the research status from five aspects: channel selection, outliers elimination, bias correction, cloud detection and data sparseness. Three methods for channel selection were summarized, which are stepwise iterative method based on information entropy, the cumulative effect coefficient of principal component and principal components—Stepwise regression, respectively. Comparatively, stepwise iterative method based on information entropy is more widely used, but the selected channels are weak related; Channel combination with large amount of information can be obtained through the method of principal components—stepwise regression, but the implementation process is time-consuming due to the algorithm. Both the lane of law and the double weight method were used in outliers elimination, and the result shows that the latter one is better. Two kinds of bias correction method including off-line and on-line, were introduced, which contain static, adaptive, regression method, variational, method based on the radiative transfer model, bias correction with Kalman filter and dynamic update of bias correction technique. It is found that the timeliness of static method is better; while variational method could solve the problems of data drift and so on. The result is better when using bias correction based on the model and Kalman methods, but it is more time-consuming and not suitable for business application. Generally, the effect and timeliness of dynamic update one is the best among them. In this paper four kinds of cloud detection method are discussed here, including the sky field-of-view, sky channel, cloud radiation correction and different instrument cloud products matching. The first two methods are more feasible from the perspective of timeliness for numerical prediction, but the data quantity using could detection method of sky field-of-view is less than sky channel, leading to discarding of lots of channel data in climate sensitive area such as upper channel, and thus affecting the quality of analysis field. Further on, the methods of hops jumper, box and principal component applied to AIRS data sparseness were analyzed. From assimilation timeliness and operability, box method is feasible; although there is high complexity with algorithm of principal component analysis, which has a certain application prospect. After reviewing the quality control section, some further research directions in these fields were given respectively.

No related articles found!
Full text