Complement analysis for the wavelet transform method for separating temperature and emissivity

This paper presents a complement analysis for the wavelet transform method for separating temperature and emissivity (WTTES) with different wavelets, wavelet levels and biased atmospheric downwelling radiance. According to the results, the WTTES algorithm is quite insensitive to the choice of the wavelet. By comparing the retrievals with different wavelet levels, a wavelet level of n=3 or n=4 is more recommended in most cases. In addition, compared with the white noise, the WTTES algorithm is more sensitive to the atmospheric downwelling radiance with bias errors. For the profile with a bias error of 10%, the RMSE of the emissivity retrievals can be increased approximately 0.17%-2.33%, which depends on the specified water vapor content of the profile. However, different from the obvious errors on emissivity, the overall accuracies of the temperature retrievals under different atmospheric profiles are all less than 0.7K, which means the WTTES algorithm is still feasible to retrieve the temperature under the condition of biased moisture profiles.