Across-track Extension of Retrieved Cloud and Aerosol Properties for the EarthCARE Mission: The ACM-3D Product
Abstract. The narrow cross-section of cloud and aerosol properties retrieved by L2-algorithms that operate on data from EarthCARE’s nadir-pointing sensors gets “broadened” across-track by an algorithm that is described and demonstrated here. This Scene Construction Algorithm (SCA) consists of four sub-algorithms. At its core is a radiance-matching procedure that works with measurements made by EarthCARE’s Multi-Spectral Imager (MSI). In essence, an off-nadir pixel gets filled with retrieved profiles that are associated with a (nearby) nadir pixel whose MSI radiances best match those of the off-nadir pixel. The SCA constructs a 3D array of cloud and aerosol (and surface) properties for entire frames that measure ~6,000 km along-track by 150 km across-track (i.e., the MSI’s full swath). Constructed domains out to ~15 km on both sides of nadir are used explicitly downstream as input for 3D radiative transfer models that predict top-of-atmosphere (TOA) broadband solar and thermal fluxes and radiances. These quantities are compared to com-mensurate measurements made by EarthCARE’s BroadBand Radiometer (BBR), thus facilitating a continuous closure assessment of the retrievals. Three 6,000 km x 200 km frames of synthetic EarthCARE observations were used to demonstrate the SCA. The main conclusion is that errors in modelled TOA fluxes that stem from use of 3D domains produced by the SCA are expected to be less than ±5 W m-2 and rarely larger than ±10 W m-2. As such, the SCA, as purveyor of in-formation needed to run 3D radiative transfer models, should help more than hinder the radiative closure assessment of EarthCARE’s L2 retrievals.
Zhipeng Qu et al.
Zhipeng Qu et al.
Zhipeng Qu et al.
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The authors describe the scene construction algorithm (SCA) for EarthCARE Level-2 data products. The algorithm is based on earlier work by Barker et al. (2011, 2012) that uses spectral radiances to transfer cloud and aerosol vertical profiles derived over ground-track of active sensors to cross-track pixels. The manuscript includes descriptions of three stages of screening process and how to determine buffer zones. The authors define the error due to SCA as the domain averaged radiance difference between observed radiances and transplanted radiances, scaled by the mean flux over the ground-track portion of the domain. The authors show that the error due to SCA is well below 10 Wm-2, which is the error budget of EarthCARE including all algorithms.
The manuscript is well written and easy to follow. I only have minor comments.
The paper does not discuss what channels/wavelengths are used for the scene construction algorithm and the total number of ks described on line 95. Channels used in the SCA are probably different for day and night. But do they also vary depending on scenes/locations? Some details are discussed in Barker et al. (2011). But it is not obvious from their paper which combination of cannels is used in the actual SCA. Could you include descriptions of channels/wavelengths used in the SCA?
One of conclusions is that the error by the SCA is less than 5 Wm-2 or 3 Wm-2. However, this is based on results using the Hawaii frame (line 276 to 280). Is this also true for other two frames used for testing?
It is not described anywhere in the manuscript what four sub-algorithms are.
The last sentence of the abstract and Section 3.3.
The direct way to show that the SCA helps more than hinder toward achieving the radiative closure goal of EarthCARE is to show that TOA flux error with and without the SCA. But this study did not address that. The SCA can help reducing the TOA flux error in two says. One way is by identifying clear-sky for the entire BBR footprint. If I look Figure 2 of Ham et al. (2015), nearly 30% of along-track clear-sky scenes contains up to 10% clouds. The SCA should reduce the TOA flux error identifying clouds present off-nadir. Second, the SCA can provide better off-nadir cloud information than no information. Top two plots of Figure 4 of Ham et al. (2015) show improvements of TOA fluxes (smaller differences between CERES-derived and computed fluxes) with the SCA. Also, the left plot of Figure 6 shows the improvement for almost all cloud types. If the authors prefer to estimate in their way, both effects together can be estimated by limiting the area of averaging radiance just over along-track in Eq. (5).
The authors describe three stages of screen processes. If a domain contains corrupted data are rejected (line 154), I am wondering what is the fraction of domains that pass this screen process. Do you have an estimate of how often domains are rejected? Could you include the number (yield) based on scenes the authors worked on so far? If active sensor retrievals systematically fail for certain type of clouds, such as deep convective clouds, then these clouds have never been included in the radiative closure assessment. Could you include authors thoughts/concerns that the closure is performed preferably toward certain cloud types?
One of variables used for screening is the solar zenith angle. Currently, the threshold of the solar zenith angle is 75 degrees. In addition, homogeneous land surface and standard deviation of surface elevations are used to screen scenes to be used for radiative closure. Can the authors estimate the faction of Ds that pass these screening process?
Line 213. Could you explain what 2020 mean?
Ham, S.H., S. Kato, H. W. Barker, F. G. Rose, and S. Sun-Mack, Improving the modeling of short-wave radiation through the use of a 3D scene construction algorithm, Q. J. R. Meteorol. Soc., (2015), DOI: 10.1002/qj.2491.