the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Mar 2023
Cloud optical and physical properties retrieval from EarthCARE multi-spectral imager: the M-COP products
Abstract. ESA’s Cloud, Aerosol and Radiation Explorer EarthCARE is the first mission which will provide measurements from active profiling, passive imaging and a broad-band radiometer from a single satellite platform. The passive multi-spectral imager (MSI) features four solar and three thermal infrared channels, and has a swath of 150 km and a spatial pixel resolution of 500 m. The MSI observations will provide across-track information on clouds and aerosol to extend the active profiling information into the swath. In this paper, we present the algorithm used for retrieving the cloud optical and physical products (M-COP), specifically cloud optical thickness, effective radius and top height. The algorithm is based on the solar and terrestrial MSI channels within an optimal estimation framework. The advantage of optimal estimation is that it enables full error propagation given by the uncertainties in measurements and a-priori information. The MSI cloud algorithm has been successfully exercised on different imagers and on synthetically generated MSI observations.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
(17396 KB)
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(17396 KB) - Metadata XML
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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-305', Anonymous Referee #1, 08 Apr 2023
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AC1: 'Reply on RC1', Anja Hünerbein, 03 Jul 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-305/egusphere-2023-305-AC1-supplement.pdf
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AC1: 'Reply on RC1', Anja Hünerbein, 03 Jul 2023
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RC2: 'Comment on egusphere-2023-305', Anonymous Referee #2, 07 May 2023
The manuscript describes an algorithm to derive cloud optical thickness, particles effective radius, and cloud top effective temperature using EarthCARE’s multi-spectral imager (MSI). Cloud water path is also derived using optical thickness and effective radius. The retrieval uses 7 channels of MSI. The retrieval is done though a forward model in an iterative way to minimize a const function. The const function is the sum of the difference of modeled radiances and observed radiances and retrieval cloud properties and their a priori estimates. The const function takes into account of measurement uncertainty based on signal-to-noise ratio and forward model errors. The forward model errors are assumed to be independent. The algorithm is tested with EarthCARE simulator HALIFAX scene and MODIS data. Also, M-CLD properties are validated within the framework of the CGMS internatonal cloud working group.
The manuscript is well written and results are easy to understand. I only have minor comments or clarifications.
Equation 1. Is this for all 0.67, 0.865, 1.65, and 2.21 channels?
Line 141. What is theta_c?
Line 242. S changes weights to sum up the elements to compute the cost function. How do you estimate diagonal term of Sa? Does this covariance matrix depend on region or cloud type? If variances are fixed, could you present the size of variances in a table? This is nice, in principle, if we know the covariance matrix. But in practice, we do not really know Sa. How sensitive the final solution is to the covariance matrix?
Table 1. Change “cloud particle size” to “cloud particle radius”.
Figures. Generally, axis labels and legends are too small. For example, Figure 2 (legend), Figures 3, 4, 5 and 6 (labels for the color bar).
Figure 7 and 8 do not mean very much to readers who are not a member of the cloud working group unless all title or legends are explained.
Figure 9. Could you provide mean difference and RMS difference between MODIS M-COP values in a table? It is even better If the authors have compared more scenes and provide robust statistics.
Citation: https://doi.org/10.5194/egusphere-2023-305-RC2 -
AC2: 'Reply on RC2', Anja Hünerbein, 03 Jul 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-305/egusphere-2023-305-AC2-supplement.pdf
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AC2: 'Reply on RC2', Anja Hünerbein, 03 Jul 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-305', Anonymous Referee #1, 08 Apr 2023
-
AC1: 'Reply on RC1', Anja Hünerbein, 03 Jul 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-305/egusphere-2023-305-AC1-supplement.pdf
-
AC1: 'Reply on RC1', Anja Hünerbein, 03 Jul 2023
-
RC2: 'Comment on egusphere-2023-305', Anonymous Referee #2, 07 May 2023
The manuscript describes an algorithm to derive cloud optical thickness, particles effective radius, and cloud top effective temperature using EarthCARE’s multi-spectral imager (MSI). Cloud water path is also derived using optical thickness and effective radius. The retrieval uses 7 channels of MSI. The retrieval is done though a forward model in an iterative way to minimize a const function. The const function is the sum of the difference of modeled radiances and observed radiances and retrieval cloud properties and their a priori estimates. The const function takes into account of measurement uncertainty based on signal-to-noise ratio and forward model errors. The forward model errors are assumed to be independent. The algorithm is tested with EarthCARE simulator HALIFAX scene and MODIS data. Also, M-CLD properties are validated within the framework of the CGMS internatonal cloud working group.
The manuscript is well written and results are easy to understand. I only have minor comments or clarifications.
Equation 1. Is this for all 0.67, 0.865, 1.65, and 2.21 channels?
Line 141. What is theta_c?
Line 242. S changes weights to sum up the elements to compute the cost function. How do you estimate diagonal term of Sa? Does this covariance matrix depend on region or cloud type? If variances are fixed, could you present the size of variances in a table? This is nice, in principle, if we know the covariance matrix. But in practice, we do not really know Sa. How sensitive the final solution is to the covariance matrix?
Table 1. Change “cloud particle size” to “cloud particle radius”.
Figures. Generally, axis labels and legends are too small. For example, Figure 2 (legend), Figures 3, 4, 5 and 6 (labels for the color bar).
Figure 7 and 8 do not mean very much to readers who are not a member of the cloud working group unless all title or legends are explained.
Figure 9. Could you provide mean difference and RMS difference between MODIS M-COP values in a table? It is even better If the authors have compared more scenes and provide robust statistics.
Citation: https://doi.org/10.5194/egusphere-2023-305-RC2 -
AC2: 'Reply on RC2', Anja Hünerbein, 03 Jul 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-305/egusphere-2023-305-AC2-supplement.pdf
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AC2: 'Reply on RC2', Anja Hünerbein, 03 Jul 2023
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Cited
7 citations as recorded by crossref.
- Cloud top heights and aerosol columnar properties from combined EarthCARE lidar and imager observations: the AM-CTH and AM-ACD products M. Haarig et al. 10.5194/amt-16-5953-2023
- The EarthCARE mission – science and system overview T. Wehr et al. 10.5194/amt-16-3581-2023
- The EarthCARE mission: science data processing chain overview M. Eisinger et al. 10.5194/amt-17-839-2024
- Cloud top heights and aerosol layer properties from EarthCARE lidar observations: the A-CTH and A-ALD products U. Wandinger et al. 10.5194/amt-16-4031-2023
- Broadband radiative quantities for the EarthCARE mission: the ACM-COM and ACM-RT products J. Cole et al. 10.5194/amt-16-4271-2023
- An intercomparison of EarthCARE cloud, aerosol, and precipitation retrieval products S. Mason et al. 10.5194/amt-17-875-2024
- Cloud mask algorithm from the EarthCARE Multi-Spectral Imager: the M-CM products A. Hünerbein et al. 10.5194/amt-16-2821-2023
Sebastian Bley
Hartwig Deneke
Jan Fokke Meirink
Gerd-Jan van Zadelhoff
Andi Walther
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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