Preprints
https://doi.org/10.5194/amt-2022-80
https://doi.org/10.5194/amt-2022-80
 
11 Mar 2022
11 Mar 2022
Status: a revised version of this preprint was accepted for the journal AMT and is expected to appear here in due course.

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: closed

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
    • AC1: 'Reply on RC1', Andrzej Kotarba, 20 Jun 2022
  • RC2: 'Comment on amt-2022-80', J. Yorks, 08 Jun 2022
    • AC2: 'Reply on RC2', Andrzej Kotarba, 20 Jun 2022

Status: closed

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
    • AC1: 'Reply on RC1', Andrzej Kotarba, 20 Jun 2022
  • RC2: 'Comment on amt-2022-80', J. Yorks, 08 Jun 2022
    • AC2: 'Reply on RC2', Andrzej Kotarba, 20 Jun 2022

Andrzej Z. Kotarba

Andrzej Z. Kotarba

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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.