Effects of cloud horizontal inhomogeneity and drizzle on remote sensing of cloud droplet effective radius: Case studies based on large‐eddy simulations

Abstract

[1] This study investigates effects of drizzle and cloud horizontal inhomogeneity on cloud effective radius (r e ) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS). In order to identify the relative importance of various factors, we developed a MODIS cloud property retrieval simulator based on the combination of large-eddy simulations (LES) and radiative transfer computations. The case studies based on synthetic LES cloud fields indicate that at high spatial resolution ($100 m) 3-D radiative transfer effects, such as illumination and shadowing, can induce significant differences between retrievals of r e based on reflectance at 2.1 mm (r e,2.1 ) and 3.7 mm (r e,3.7 ). It is also found that 3-D effects tend to have stronger impact on r e,2.1 than r e,3.7 , leading to positive difference between the two (Dr e,3.7À2.1 ) from illumination and negative Dr e,3.7À2.1 from shadowing. The cancellation of opposing 3-D effects leads to overall reasonable agreement between r e,2.1 and r e,3.7 at high spatial resolution as far as domain averages are concerned. At resolutions similar to MODIS, however, r e,2.1 is systematically larger than r e,3.7 when averaged over the LES domain, with the difference exhibiting a threshold-like dependence on both r e,2.1 and an index of the sub-pixel variability in reflectance (H s ), consistent with MODIS observations. In the LES cases studied, drizzle does not strongly impact r e retrievals at either wavelength. It is also found that opposing 3-D radiative transfer effects partly cancel each other when cloud reflectance is aggregated from high spatial resolution to MODIS resolution, resulting in a weaker net impact of 3-D radiative effects on r e retrievals. The large difference at MODIS resolution between r e,3.7 and r e,2.1 for highly inhomogeneous pixels with H s > 0.4 can be largely attributed to what we refer to as the "plane-parallel r e bias," which is attributable to the impact of sub-pixel level horizontal variability of cloud optical thickness on r e retrievals and is greater for r e,2.1 than r e,3.7 . These results suggest that there are substantial uncertainties attributable to 3-D radiative effects and plane-parallel r e bias in the MODIS r e,2.1 retrievals for pixels with strong sub-pixel scale variability, and the H s index can be used to identify these uncertainties.Citation: Zhang, Z., A. S. Ackerman, G. Feingold, S. Platnick, R. Pincus, and H. Xue (2012), Effects of cloud horizontal inhomogeneity and drizzle on remote sensing of cloud droplet effective radius: Case studies based on large-eddy simulations,

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

“…Sub-pixel inhomogeneity. The impact of spatial resolution and unresolved sub-pixel cloud inhomogeneity on bispectral retrievals has been well studied (Zhang and Platnick, 2011;Zhang et al, 2012Zhang et al, , 2016). An important conclusion from these studies is that the so-called planeparallel homogenous bias (PPHB) can cause the bispectral technique to significantly overestimate r e .…”

“…The satellite retrieval simulator implemented in this study is built around an LES model (DHARMA) with bin microphysics (Ackerman et al, 2004;Zhang et al, 2012;Miller et al, 2016). The LES provides freely evolving 3-D cloud microphysical properties, which are used as a reference when comparing to numerically simulated retrievals.…”

“…For example, the polarimetric and bispectral methods have different sensitivity to the cloud vertical profile, and at the same time they are also both affected by cloud horizontal inhomogeneity (Zinner et al, 2010;Zhang et al, 2012Zhang et al, , 2016Miller et al, 2016). It is difficult, if not impossible, to disentangle these factors based on observations alone.…”

“…Modeling radiative transfer in an LES scene to obtain total and polarized reflectances opens up the possibility of using the LES to perform synthetic bispectral and polarimetric retrievals. This retrieval simulator framework has proven to be a useful tool in other cloud remotesensing studies (Miller et al, 2016;Zhang et al, 2012). Using this idealized simulation at high spatial resolution, we can attempt to parse the effects of unresolved sub-pixel inhomogeneity, spatial resolution, and angular resolution on the intercomparison of polarimetric and bispectral retrievals.…”

Impact of cloud horizontal inhomogeneity and directional sampling on the retrieval of cloud droplet size by the POLDER instrument

“…Marshak et al (2006) found that ignoring the cloud variability at the sub-pixel scale results in underestimates of the CDR, while ignoring cloud inhomogeneity at scales exceeding the pixel scale can lead to overestimates. It is found that the vertical structure induced by drizzle and 3-D radiative effects operate together to cause dramatic differences between the 1.6, 2.1, and 3.7 µm retrievals (Zhang et al, 2012;Zhang, 2013;Nakajima et al, 2010a, b;Nagao et al, 2013). In addition, the water vapor absorption within a cloud and the presence of an absorbing aerosol layer above a cloud leads to a positive bias in the retrieval (Alexandrov et al, 2012a;Coddington et al, 2010;Haywood et al, 2004).…”