Articles | Volume 17, issue 2
https://doi.org/10.5194/amt-17-453-2024
https://doi.org/10.5194/amt-17-453-2024
Research article
 | 
24 Jan 2024
Research article |  | 24 Jan 2024

First results of cloud retrieval from the Geostationary Environmental Monitoring Spectrometer

Bo-Ram Kim, Gyuyeon Kim, Minjeong Cho, Yong-Sang Choi, and Jhoon Kim

Data sets

OMI/Aura Level 1B Averaged Solar Irradiances V00 Quintus Kleipool https://doi.org/10.5067/Aura/OMI/DATA1401

OMI/Aura Level 1B VIS Global Geolocated Earth Shine Radiances 1-orbit L2 Swath 13x24 km V003 Marcel Dobber https://doi.org/10.5067/Aura/OMI/DATA1004

OMI/Aura Cloud Pressure and Fraction (O2-O2 Absorption) 1-Orbit L2 Swath 13x24km V003 Pepijn Veefkind https://doi.org/10.5067/Aura/OMI/DATA2007

Sentinel-5P TROPOMI L1B Radiance product band 4 (UVIS detector), Copernicus Sentinel data https://doi.org/10.5067/SENTINEL5P/S5P_L1B_RA_BD4.1

Sentinel-5P TROPOMI Cloud 1-Orbit L2 5.5km x 3.5km Copernicus Sentinel data https://doi.org/10.5270/S5P-w1qgt16

CALIPSO Lidar Level 2 Vertical Feature Mask (VFM), V4-21 NASA/LARC/SD/ASDC https://doi.org/10.5067/CALIOP/CALIPSO/CAL_LID_L2_VFM-Standard-V4-21

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Short summary
This study introduces the GEMS cloud algorithm and validates its results using data from GEMS and other environmental satellites. The GEMS algorithm is able to detect the lowest cloud heights among the four satellites, and its effective cloud fraction and cloud centroid pressure are well reflected in the retrieval results. The study highlights the algorithm's usefulness in correcting errors in trace gases caused by clouds in the East Asian region.