Preprints
https://doi.org/10.5194/amt-2022-61
https://doi.org/10.5194/amt-2022-61
 
21 Mar 2022
21 Mar 2022
Status: this preprint is currently under review for the journal AMT.

3D cloud envelope and cloud development velocity from simulated CLOUD/C3IEL stereo images

Paolo Dandini1, Céline Cornet1, Renaud Binet2, Laetitia Fenouil2, Vadim Holodovsky3, Yoav Schechner3, Didier Ricard4, and Daniel Rosenfeld5 Paolo Dandini et al.
  • 1Laboratoire d’Optique Atmosphérique, CNRS/Université de Lille, Villeneuve d’Ascq, FR
  • 2Centre National d’études spatiales, Toulouse, FR
  • 3Viterbi Faculty of Electrical and Computer Engineering, Technion - Israel Institute of Technology, Haifa 32000, Israel
  • 4CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
  • 5Institute of Earth Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel

Abstract. A method to derive the 3D cloud envelope and the cloud development velocity from high spatial and temporal resolution satellite imagery is presented. The CLOUD instrument of the recently proposed C3IEL mission lends itself well to observing at high spatial and temporal resolutions the development of convective cells. Space-borne visible cameras simultaneously image, under multiple view angles, the same surface domain, every 20 s over a time interval of 200 s. In this paper, we present a method for retrieving cloud development velocity from simulated multi-angular-high-resolution TOA radiance cloud fields. The latter are obtained via the radiative transfer model 3DMCPOL, for a deep convective cloud case generated via the atmospheric research model Meso-NH, and via the image renderer Mitsuba for a cumulus case generated via the atmospheric research model SAM. Matching cloud features are found between simulations via block matching. Image coordinates of tie points are mapped to spatial coordinates via 3D stereo reconstruction of the external cloud envelope for each acquisition. The accuracy of the retrieval of cloud topography is quantified in terms of RMSE and bias that are respectively, less than 25 m and 15 m for the horizontal components and less than 40 m and 25 m for the vertical component. The inter-acquisition 3D velocity is then derived for each pair of tie points separated by 20 s. An independent method based on optimizing the superposition of the cloud top, issued from the atmospheric research model, allows to obtain a ground estimate for the velocity from two consecutive acquisitions. The distribution of retrieved velocity and ground estimate exhibits small biases but significant discrepancy in terms of distribution width. Furthermore, the average velocities derived from the mean altitude from ground for a cluster of localized cloud features identified over several acquisitions, both in the simulated images and in the physical point cloud, are in good agreement.

Paolo Dandini et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-61', Anonymous Referee #1, 23 May 2022
  • RC2: 'Comment on amt-2022-61', Anonymous Referee #2, 09 Jun 2022
  • AC1: 'Comment on amt-2022-61', Paolo Dandini, 10 Jun 2022

Paolo Dandini et al.

Paolo Dandini et al.

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Short summary
3D cloud envelope and cloud development velocity are derived from realistic simulations of multi-view C3IEL/CLOUD images. Cloud development velocity is derived by finding matching features for acquisitions separated by 20 s and by mapping points from image to space via 3D reconstruction of the cloud envelope. The retrieved cloud topography as well as the velocities derived from features tracked over several acquisitions are in good agreement with the estimates obtained from the physical models.