the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 Aug 2024
Retrieving cloud base height and geometric thickness using the oxygen A-band channel of GCOM-C/SGLI
Abstract. Measurements with a 763 nm channel, located within the oxygen A-band and equipped on the Second-generation Global Imager (SGLI) onboard the JAXA’s Global Change Observation Mission – Climate (GCOM-C) satellite, have the potential to retrieve cloud base height (CBH) and cloud geometric thickness (CGT) through passive remote sensing. This study implemented an algorithm to retrieve the CBH using the SGLI 763 nm channel in combination with several other SGLI channels in the visible, shortwave infrared, and thermal infrared regions. In addition to CBH, the algorithm can simultaneously retrieve other key cloud properties, including cloud optical thickness (COT), cloud effective radius, ice COT fraction as the cloud thermodynamic phase, cloud top height (CTH), and CGT. Moreover, the algorithm can be seamlessly applied to global clouds comprised of liquid, ice, and mixed phases. The SGLI-retrieved CBH exhibited quantitative consistency with CBH data obtained from the ground-based ceilometer network, ship-borne ceilometer, satellite-borne radar and lidar observations, as evidenced by sufficiently high correlations and small biases. These results provide practical evidence that the retrieval of CBH is indeed possible using the SGLI 763 nm channel. Moreover, the results lend credence to the future use of SGLI CBH data, including the estimation of the surface downward longwave radiative flux from clouds. Nevertheless, issues remain that must be addressed to enhance the value of SGLI-derived cloud retrieval products. These include the systematic bias of SGLI CTH related to cirrus clouds and the bias of SGLI CBH caused by multi-layer clouds.
- Preprint
(6890 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 01 Oct 2024)
-
CC1: 'Comment on amt-2024-141', Luca Lelli, 12 Sep 2024
reply
I read with interest this good article demonstrating the possibility of inferring cloud base height from a single channel in the oxygen absorption band as measured by SGLI but also with the support of multispectral measurements across the e/m spectrum.
It is not my intention with this commentary to provide a full review of the article or to judge the maturity of the work for possible publication. Since I myself am active in remote sensing of cloud properties, I would like to bring the following points to the authors' attention.
- In the introductory paragraph, at lines 53-65, there are two inaccuracies. This paragraph cites past work that "derive CBH and CGT using satellite-based passive instruments instead of active instruments" (line 53-54).
The Desmons et al (2019) citation at line 59 is incorrect. In that paper, an algorithm is presented that analyzes the sensitivity of the oxygen B-band centered around 688 nm to changes in cloud fraction and cloud pressure. By "cloud pressure", however, is meant a generic pressure (or height, once this value is converted with the help of an atmospheric profile) located at about the midpoint of the cloud body. The physical reasons are well known, namely that in the forward model of the algorithm the clouds are modeled not as real scattering bodies, but as Lambertian diffusers, for which light is not allowed to penetrate the clouds. But if the process of the photon penetration within a cloud is neglected, then any increase of the oxygen absoprtion line is interpreted as an existence of a cloud at a level that is lower that the actual altitude. This is a feature of the algorithm presented in Desmons et al (2019) and appropriate referenceses therein. In summary, the consequence of this assumption is that it is not possible for the algorithm to approximate multiple scattering inside the clouds, consequently it is not possible to derive any information about the height of the base of the clouds themselves. The authors in Desmons et al (2019), moreover, make no mention of any attempt to find information about CBH or CGT.
The Desmons et al, 2019, reference cannot be cited in the context of the retrieval of CBH nor CGT. - The second clarification I would like to bring to the authors' attention concerns the quote from Rozanov and Kokhanovsky, 2004 at line 65.
In that article, a set of Global Imager (GLI) and MERIS measurements is indeed analyzed, but the algorithm is concerned with the feasibility of deriving CTH and CBH (hence CGT) at the spectral resolution characteristic of the GOME, GOME-2 and SCIAMACHY family of instruments. Application of the algorithm, based this time on a realistic model of clouds composed of Mie droplets and a Gamma distribution, can be found in Rozanov and Kokhanovsky (2006) for GOME on ERS-2 and in Lelli and Vountas (2018) for SCIAMACHY on Envisat. In the second paper (Figure 3 and Table 1), the authors will find climatological values of CBH derived from SCIAMACHY directly comparable to their Figure 10 (page 23).
V. V. Rozanov and A. A. Kokhanovsky, "Determination of cloud geometrical thickness using backscattered solar light in a gaseous absorption band," in IEEE Geoscience and Remote Sensing Letters, vol. 3, no. 2, pp. 250-253, April 2006, doi: 10.1109/LGRS.2005.863388
Lelli, L. and Vountas, M., 2018. Aerosol and cloud bottom altitude covariations from multisensor spaceborne measurements. In Remote Sensing of Aerosols, Clouds, and Precipitation (pp. 109-127). Elsevier. http://dx.doi.org/10.1016/B978-0-12-810437-8.00005-0 - At line 136 the authors cite Rozanov & Kokhanovsky (2004) again in the context of "using an oxygen A-band channel paired with a TIR channel" (line 135). The Rozanov & Kokhanovksy paper makes no mention of TIR channles for the retrieval of cloud properties, because it focuses on the reflectance at Vis/NIR wavelenghts.
This comment naturally leads me to ask the following question, also in light of the concepts presented by the authors in section 4.1 (Potential uncertainty in CBH retrieval).
Clearly, the accuracy of CBH depends on the accuracy of TIR-derived CTH and COT. This is even more important because in reflection, the signal arriving at the satellite will be generated through a different radiation-matter interaction process than in the Vis-NIR, so there will be a difference in the depth of light penetration (i.e. water has asingle scatterign albedo tending to 1 in the oxygen spectral bands while it fluctuates between 0.6 and 0.4 in the thermal infrared).
It would be extremely interesting if the authors could provide a more quantitative assessment of the errors in coincident COT,CTH(TIR) and CBH(NIR) as preliminary provided in Figure 15 (page 5689) of our paper in ACP (Lelli et al. 2014). There, one can see that errors in CBH are roughly proportional to CTH(NIR) by a factor in range 1.5 - 2.5. This is systematic and well behaved when COT/CTH and CBH are both retrieved in Vis/NIR. I am currently working on this issue and It is not known to me any error assessment in the case of a simultaneous and concurrent retrieval of COT/CTH from the TIR and the CBH from the NIR.
Lelli, L., Kokhanovsky, A. A., Rozanov, V. V., Vountas, M., and Burrows, J. P.: Linear trends in cloud top height from passive observations in the oxygen A-band, Atmos. Chem. Phys., 14, 5679–5692, https://doi.org/10.5194/acp-14-5679-2014, 2014
Citation: https://doi.org/10.5194/amt-2024-141-CC1 - In the introductory paragraph, at lines 53-65, there are two inaccuracies. This paragraph cites past work that "derive CBH and CGT using satellite-based passive instruments instead of active instruments" (line 53-54).
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 116 | 21 | 8 | 145 | 6 | 4 |
- HTML: 116
- PDF: 21
- XML: 8
- Total: 145
- BibTeX: 6
- EndNote: 4
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1