Preprints
https://doi.org/10.5194/amt-2022-80
https://doi.org/10.5194/amt-2022-80
 
11 Mar 2022
11 Mar 2022
Status: this preprint is currently under review for the journal AMT.

Impact of the revisit frequency on cloud climatology for CALIPSO, EarthCARE, Aeolus, and ICESat-2 satellite lidar missions

Andrzej Z. Kotarba Andrzej Z. Kotarba
  • Space Research Centre, Polish Academy of Sciences, 00-716 Warsaw, Poland

Abstract. Space profiling lidars offer a unique insight into cloud properties in Earth’s atmosphere, and are considered the most reliable source of total (column-integrated) cloud amount (CA), and true (geometrical) cloud top height (CTH). However, lidar-based cloud climatologies suffer from infrequent sampling: every n-days, and only along the ground track. This study therefore evaluated four lidar missions, namely CALIPSO (revisit every n = 16 days), EarthCARE (n = 25), Aeolus (n = 7), and ICESat-2 (n = 91), to test the hypothesis that each mission provides accurate data on CA and CTH. CA/CTH values for a hypothetical daily revisit mission were used as reference (data simulated with Meteosat 15-minute cloud observations, assumed to be a proxy for ground truth). Our results demonstrated that this hypothesis is invalid, unless individual lidar transects are averaged over an area 10×10° in longitude and latitude (or larger). If this is not the case, the required accuracy of 1 % (for CA) or 150 m (for CTH) cannot be met, either for a single-year annual or monthly mean, or for a >10-year climatology. A CALIPSO-focused test demonstrated that the annual mean CA estimate is very sensitive to infrequent sampling, and that this factor alone can result in 14 % or 7 % average uncertainty with 1° or 2.5° resolution data, respectively. Consequently, applications that use gridded lidar data should consider calculating confidence intervals, or a similar measure of uncertainty. Our results suggest that CALIPSO, and its follow-on mission EarthCARE, are very likely to produce consistent cloud records despite the difference in sampling frequency.

Andrzej Z. Kotarba

Status: open (until 29 May 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-80', David Winker, 26 Apr 2022 reply

Andrzej Z. Kotarba

Andrzej Z. Kotarba

Viewed

Total article views: 273 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
220 45 8 273 4 5
  • HTML: 220
  • PDF: 45
  • XML: 8
  • Total: 273
  • BibTeX: 4
  • EndNote: 5
Views and downloads (calculated since 11 Mar 2022)
Cumulative views and downloads (calculated since 11 Mar 2022)

Viewed (geographical distribution)

Total article views: 263 (including HTML, PDF, and XML) Thereof 263 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 24 May 2022
Download
Short summary
Space profiling lidars offer a unique insight into cloud properties in Earth’s atmosphere, and are considered the most reliable source of cloud information. However, lidar-based cloud climatologies suffer from infrequent sampling: every 7 to 91 days, and only along the ground track. This study evaluated how accurate are the cloud data from existing (CALIPSO, ICESat-2, Aeolus) and planned (EarthCARE) space lidars, when compared to a cloud climatology obtained with observations taken every day.