EGU2020-3397
https://doi.org/10.5194/egusphere-egu2020-3397
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

A CrIS Cloud Detection Method Based on CrIS and ATMS Measurements

Li Guan and Qiumeng Xue
Li Guan and Qiumeng Xue
  • Nanjing University of Information Science and Technology, Nanjing, China (liguanlily@189.cn)

The Suomi National Polar-orbiting Partnership (SNPP) satellite carrying the Cross-track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS) instruments can provide high quality hyperspectral infrared (IR) data and microwave (MW) measurements. It is very important to ensure the accuracy of cloud detection in the infrared hyperspectral measurements before they are used for geophysical retrievals or data assimilation. Therefore, a cloud detection method using the CrIS hyperspectral radiances at longwave (709.5-746.0 cm-1) and shortwave (2190-2250 cm-1) bands and the ATMS measurements is introduced in this paper. Four steps are included in this algorithm: identifying clear FOV, estimating the number of cloud formations, thermal contrast, and cloud mask classification. Specifically, each CrIS field-of-view (FOV) is preliminarily assigned as clear or cloudy by comparing the measured IR radiances and simulated IR clear radiances which are generated from the MW-retrieved geophysical state vector based on a physical inversion method. Secondly, the number of cloud formations within one CrIS field-of-regard (FOR) is estimated using the principal component analysis (PCA). Next, CrIS radiances at longwave channels and shortwave bands are used to evaluate the thermal contrast within the FOR. Based on the above informations each FOR will finally be assigned a cloud mask classification. The cloud mask results derived from this technique are also analyzed.

How to cite: Guan, L. and Xue, Q.: A CrIS Cloud Detection Method Based on CrIS and ATMS Measurements, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3397, https://doi.org/10.5194/egusphere-egu2020-3397, 2020