Comparisons of bispectral and polarimetric retrievals of marine boundary layer cloud microphysics: case studies using a LES–satellite retrieval simulator

Abstract

Abstract. Many passive remote-sensing techniques have been developed to retrieve cloud microphysical properties from satellite-based sensors, with the most common approaches being the bispectral and polarimetric techniques. These two vastly different retrieval techniques have been implemented for a variety of polar-orbiting and geostationary satellite platforms, providing global climatological data sets. Prior instrument comparison studies have shown that there are systematic differences between the droplet size retrieval products (effective radius) of bispectral (e.g., MODIS, Moderate Resolution Imaging Spectroradiometer) and polarimetric (e.g., POLDER, Polarization and Directionality of Earth's Reflectances) instruments. However, intercomparisons of airborne bispectral and polarimetric instruments have yielded results that do not appear to be systematically biased relative to one another. Diagnosing this discrepancy is complicated, because it is often difficult for instrument intercomparison studies to isolate differences between retrieval technique sensitivities and specific instrumental differences such as calibration and atmospheric correction. In addition to these technical differences the polarimetric retrieval is also sensitive to the dispersion of the droplet size distribution (effective variance), which could influence the interpretation of the droplet size retrieval. To avoid these instrument-dependent complications, this study makes use of a cloud remote-sensing retrieval simulator. Created by coupling a large-eddy simulation (LES) cloud model with a 1-D radiative transfer model, the simulator serves as a test bed for understanding differences between bispectral and polarimetric retrievals. With the help of this simulator we can not only compare the two techniques to one another (retrieval intercomparison) but also validate retrievals directly against the LES cloud properties. Using the satellite retrieval simulator, we are able to verify that at high spatial resolution (50 m) the bispectral and polarimetric retrievals are highly correlated with one another within expected observational uncertainties. The relatively small systematic biases at high spatial resolution can be attributed to different sensitivity limitations of the two retrievals. In contrast, a systematic difference between the two retrievals emerges at coarser resolution. This bias largely stems from differences related to sensitivity of the two retrievals to unresolved inhomogeneities in effective variance and optical thickness. The influence of coarse angular resolution is found to increase uncertainty in the polarimetric retrieval but generally maintains a constant mean value.

Observations of Aerosol‐Cloud Interactions During the North Atlantic Aerosol and Marine Ecosystem Study

“…Multiple views of the same location allow the RSP to observe the sharply defined cloud bow feature originating from single-scattered light near cloud top. This allows microphysical properties to be retrieved from a mean penetration depth of 0.5 τ from cloud top (Miller et al, 2018). The cloud droplet size distribution is retrieved from information in the relative shape of the cloudbow structure (Alexandrov, Cairns, Emde, et al, 2012).…”

Spatial distribution of cloud droplet size properties from Airborne Hyper-Angular Rainbow Polarimeter (AirHARP) measurements

“…We will look at this phenomenon in more detail in the AirHARP data in the sections below. Here we point out that large-eddy simulations (LESs) of similar heterogenous clouds show similar spatial distributions of intensity, CDR, and CDV (Miller et al, 2018), with one representative case shown in Fig. 8c.…”

“…COT can also be retrieved with assistance from an external radiative transfer simulation (Alexandrov et al, 2012a). The 3D multiple scattering effects of shadowing and illumination (Marshak et al, 2006;Varnai and Marshak, 2002) bias the radiometric method, whereas the polarimetric retrieval is sensitive to scattered photons from a COT up to ∼ 3, lessening the impact of this effect (Miller et al, 2018). Sub-pixel clouds and spatial heterogeneities can affect both methods, as discussed in later sections (Zhang and Platnick, 2011;Breon and Doutriaux-Boucher, 2005;Shang et al, 2015).…”

“…Figure 1 shows theoretical Mie simulations that mimic the polarized cloudbow for particular values of CDR, CDV, and wavelength. To resolve the cloudbow patterns from space and retrieve the CDR and CDV of the cloud, the multi-angle polarimetric instruments must satisfy a minimum viewing angle density (Miller et al, 2018), which is directly related to scattering angle coverage. The location of the supernumerary peaks in scattering angle encode CDR in the 0.67 µm example shown in Fig.…”

“…Polarimeters with coarser viewing angle separation for a single wavelength (i.e., > 3 • at 0.67 µm for typical droplets < 15 µm; Miller et al, 2018) may not distinguish these cloudbow oscilla-tions. Instruments like POLDER, which samples at 14 unique viewing angles separated by 10 • , do not provide enough native angular resolution (Shang et al, 2015) and, as a consequence, may not be able to identify wide versus narrow DSD clouds at specific geometries (Miller et al, 2018). Only when sampling all native 6×7 km POLDER pixels inside a 150 km superpixel can they access the full scattering angle coverage in Fig.…”