Cloud detection using infrared atmospheric sounding interferometer observations by logistic regression

ABSTRACT

Hyper-spectral infrared radiance data play an important role in cloud detection. To improve the cloud detection accuracy, this study proposes a novel cloud detection method based on the logistic regression model that uses the Infrared Atmospheric Sounding Interferometer (IASI) radiance data of four characteristic channels as the training features. Due to significant differences in the terrain between the land and the sea, the data from the oceans and continents are trained separately. Thereafter, the proposed scheme is verified and compared with existing methods. The results show that the accuracy of the proposed method (97% at sea and 88% on land) outperforms that of the existing Advanced Very High Resolution Radiometer (AVHRR)/IASI scheme (75% at sea and 55% on land). In addition, the proposed method uses only IASI observations as input and thus does not require the use of other auxiliary data.

Additional information

Funding

The authors acknowledge the support of the National Natural Science Foundation of China (41675097) and the National Natural Science Foundation of China (41375113).