Preprints
https://doi.org/10.5194/egusphere-2024-604
https://doi.org/10.5194/egusphere-2024-604
15 Mar 2024
 | 15 Mar 2024
Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

Improved Mean Field Estimates of GEMS AOD L3 Product: Using Spatio-temporal Variability

Sooyon Kim, Yeseul Cho, Hanjeong Ki, Seyoung Park, Dagun Oh, Seungjun Lee, Yeonghye Cho, Jhoon Kim, Wonjin Lee, Jaewoo Park, Ick Hoon Jin, and Sangwook Kang

Abstract. This study presents advancements in the processing of satellite remote sensing data, focusing mainly on Aerosol Optical Depth (AOD) retrievals from the Geostationary Environment Monitoring Spectrometer (GEMS). The transformation of Level 2 (L2) data, which includes atmospheric state retrievals, into higher-quality Level 3 (L3) data is crucial in remote sensing. Our contributions lie in two novel improvements to the processing algorithm. First, we improve the inverse distance weighting algorithm by incorporating quality flag information into the weight calculation. By assigning weights inversely proportional to the number of unreliable grids, the method can provide more accurate L3 products. We validate this approach through simulation studies and apply it to GEMS AOD data across various regions and wavelengths. The use of the quality flags in the algorithm can provide a more accurate analysis in remote sensing. Second, we employ a spatio-temporal merging method to address both spatial and temporal variability in AOD data, a departure from previous approaches that solely focused on spatial variability. Our method considers temporal variations spanning previous time intervals. Furthermore, the computed mean fields show similar spatio-temporal patterns to the previous studies, confirming that they can capture real-world phenomena. Lastly, utilizing this procedure, we compute the mean field estimates for GEMS AOD data, which can provide a deeper understanding of the impact of aerosols on climate change and public health.

Sooyon Kim, Yeseul Cho, Hanjeong Ki, Seyoung Park, Dagun Oh, Seungjun Lee, Yeonghye Cho, Jhoon Kim, Wonjin Lee, Jaewoo Park, Ick Hoon Jin, and Sangwook Kang

Status: open (until 03 May 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-604', Won Chang, 06 Apr 2024 reply
Sooyon Kim, Yeseul Cho, Hanjeong Ki, Seyoung Park, Dagun Oh, Seungjun Lee, Yeonghye Cho, Jhoon Kim, Wonjin Lee, Jaewoo Park, Ick Hoon Jin, and Sangwook Kang
Sooyon Kim, Yeseul Cho, Hanjeong Ki, Seyoung Park, Dagun Oh, Seungjun Lee, Yeonghye Cho, Jhoon Kim, Wonjin Lee, Jaewoo Park, Ick Hoon Jin, and Sangwook Kang

Viewed

Total article views: 160 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
116 34 10 160 6 8
  • HTML: 116
  • PDF: 34
  • XML: 10
  • Total: 160
  • BibTeX: 6
  • EndNote: 8
Views and downloads (calculated since 15 Mar 2024)
Cumulative views and downloads (calculated since 15 Mar 2024)

Viewed (geographical distribution)

Total article views: 175 (including HTML, PDF, and XML) Thereof 175 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 12 Apr 2024
Download
Short summary
The manuscript describes new work that improves the processing of GEMS AOD data by adding a better weighting algorithm with quality flags that was tested in simulations and a new spatio-temporal merging method that helps us learn more about how aerosols affect health and the climate.