Journal of Meteorological Research

, Volume 28, Issue 4, pp 645–655 | Cite as

Applications of an AMSR-E RFI detection and correction algorithm in 1-DVAR over land

  • Ying Wu (吴 莹)Email author
  • Fuzhong Weng (翁富忠)


Land retrievals using passive microwave radiometers are sensitive to small fluctuations in land brightness temperatures. As such, the radio-frequency interference (RFI) signals emanating from man-made microwave radiation transmitters can result in large errors in land retrievals. RFI in C- and X-band channels can contaminate remotely sensed measurements, as experienced with the Advanced Microwave Scanning Radiometer (AMSR-E) and the WindSat sensor. In this work, applications of an RFI detection and correction algorithm in retrieving a comprehensive suite of geophysical parameters from AMSR-E measurements using the one-dimensional variational retrieval (1-DVAR) method are described. The results indicate that the values of retrieved parameters, such as land skin temperature (LST), over these areas contaminated by RFI are much higher than those from the global data assimilation system (GDAS) products. The results also indicate that the differences between new retrievals and GDAS products are decreased evidently through taking into account the RFI correction algorithm. In addition, the convergence metric (χ2) of 1-DVAR is found to be a new method for identifying regions where land retrievals are affected by RFI. For example, in those regions with much stronger RFI, such as Europe and Japan, χ 2 of 1-DVAR is so large that convergence cannot be reached and retrieval results may not be reliable or cannot be obtained. Furthermore, χ 2 also decreases with the RFI-corrected algorithm for those regions with moderate or weak RFI. The results of RFI detected by χ 2 are almost consistent with those identified by the spectral difference method.

Key words

microwave remote sensing radio-frequency interference (RFI) AMSR-E 1-DVAR 


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Copyright information

© The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological AdministrationNanjing University of Information Science & TechnologyNanjingChina
  2. 2.School of Atmospheric PhysicsNanjing University of Information Science & TechnologyNanjingChina
  3. 3.NOAA/NESDIS/Center for Satellite Application and ResearchCollege ParkUSA

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