Open AccessA New Dynamic Emissivity-retrieval Scheme Based on Wavelet Atlas Constraints and Its Application into Land Surface Satellite Data Assimilation
Author(s) -
Qunbo Huang,
Yan Yan,
Yan Luo,
Weifeng Wang,
Bainian Liu,
Weimin Zhang,
De Xing
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/310/2/022047
Subject(s) - emissivity , remote sensing , atlas (anatomy) , wavelet , satellite , pixel , computer science , depth sounding , multispectral image , environmental science , geology , geography , computer vision , physics , optics , cartography , paleontology , astronomy
Accurate description of surface information, including land surface emissivity, is a prerequisite for assimilating satellite observations over land. In this study, we used an emissivity atlas, essentially an image matrix containing noise from unknown sources, to improve the assimilation of observational data. First, we proposed a wavelet method for advanced image processing to denoise the emissivity atlas and developed a wavelet emissivity atlas suitable for the temperature sounder onboard the Chinese FY-3C satellite. Second, based on a real-time pixel dynamic emissivity retrieval scheme and using the wavelet atlas as the constraint set, we constrained dynamic retrieval emissivity values to eliminate retrieval errors. Finally, we designed and optimized a new assimilation system that can successfully utilize low-level microwave sounding channels of the FY-3C satellite. The results showed that the first-guess departures of the middle- and low-level channels were significantly improved as a result of the improved surface emissivity values. Compared with the emissivity atlas scheme, over-land observational data of the middle- and low-level channels could be increased by approximately 4–8%. Moreover, the addition of a large amount of new observational data did not negatively affect the analysis fields but rather improved short-term prediction results.