Applications of an AMSR-E RFI detection and correction algorithm in 1-DVAR over land
- 388 Downloads
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 wordsmicrowave remote sensing radio-frequency interference (RFI) AMSR-E 1-DVAR
Unable to display preview. Download preview PDF.
- Hallikainen, M., J. Kainulainen, J. Seppanen, et al., 2010: Studies of radio frequency interference at L-band using an airborne 2-D interferometric radiometer. 2010 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Honolulu, HI, IEEE, 2490–2491, doi: 10.1109/IGARSS.2010.5651866.CrossRefGoogle Scholar
- Han, Y., van P. Delst, Q. Liu, et al., 2006: Community Radiative Transfer Model (CRTM): Version 1, NOAA Technical Report NESDIS 122. NOAA, Washington, DC, 33 pp.Google Scholar
- Japan Aerospace Exploration Agency (JAXA) Press, 2012: Launch Result of the Global Changing Observation Mission 1st-Water “SHIZUKU” (GCOM-W1) and the Korean Multi-Purpose Satellite 3 (KOMPSAT-3) by H-IIA Launch Vehicle No. 21. Available online at: http://www.jaxa.jp/press/2012/05/20120518_h2af21_e.html, 2012.Google Scholar
- Zheng, W., J. Meng, H. Wei, et al., 2009: Improvement of satellite data utilization in NCEP operational NWP modeling and data assimilation systems. Proc. 2nd Workshop Remote Sens. Model. Surf. Properties, Toulouse, France, June 9–11, 14 pp.Google Scholar