Resolving ice cloud optical thickness biases between CALIOP and MODIS using infrared retrievals

Abstract

Abstract. Despite its importance as one of the key radiative properties that determines the impact of upper tropospheric clouds on the radiation balance, ice cloud optical thickness (IOT) has proven to be one of the more challenging properties to retrieve from space-based remote sensing measurements. In particular, optically thin upper tropospheric ice clouds (cirrus) have been especially challenging due to their tenuous nature, extensive spatial scales, and complex particle shapes and light-scattering characteristics. The lack of independent validation motivates the investigation presented in this paper, wherein systematic biases between MODIS Collection 5 (C5) and CALIOP Version 3 (V3) unconstrained retrievals of tenuous IOT ( < 3) are examined using a month of collocated A-Train observations. An initial comparison revealed a factor of 2 bias between the MODIS and CALIOP IOT retrievals. This bias is investigated using an infrared (IR) radiative closure approach that compares both products with MODIS IR cirrus retrievals developed for this assessment. The analysis finds that both the MODIS C5 and the unconstrained CALIOP V3 retrievals are biased (high and low, respectively) relative to the IR IOT retrievals. Based on this finding, the MODIS and CALIOP algorithms are investigated with the goal of explaining and minimizing the biases relative to the IR. For MODIS we find that the assumed ice single-scattering properties used for the C5 retrievals are not consistent with the mean IR COT distribution. The C5 ice scattering database results in the asymmetry parameter (g) varying as a function of effective radius with mean values that are too large. The MODIS retrievals have been brought into agreement with the IR by adopting a new ice scattering model for Collection 6 (C6) consisting of a modified gamma distribution comprised of a single habit (severely roughened aggregated columns); the C6 ice cloud optical property models have a constant g ≈ 0.75 in the mid-visible spectrum, 5-15 % smaller than C5. For CALIOP, the assumed lidar ratio for unconstrained retrievals is fixed at 25 sr for the V3 data products. This value is found to be inconsistent with the constrained (predominantly nighttime) CALIOP retrievals. An experimental data set was produced using a modified lidar ratio of 32 sr for the unconstrained retrievals (an increase of 28 %), selected to provide consistency with the constrained V3 results. These modifications greatly improve the agreement with the IR and provide consistency between the MODIS and CALIOP products. Based on these results the recently released MODIS C6 optical products use the singlehabit distribution given above, while the upcoming CALIOP V4 unconstrained algorithm will use higher lidar ratios for unconstrained retrievals.

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

“…A MAPE of 100 % might seem high, but one should keep in mind that this translates into small absolute errors for such thin cirrus clouds. For the lower IOT CALIOP range, a similar scatter is observed between IOT CALIOP and modelled IOT from infrared radiances for thin cirrus clouds in Holz et al (2016). Figure 12 shows the density scatter plot with IWP CALIOP on the horizontal axis and IWP CiPS on the vertical axis (Fig.…”

“…When the column optical thickness is derived for all cirrus-covered bins, the relative difference between CALIOP and CPL is only 2.2 % due to cancellation of opposing CALIOP effects. Holz et al (2016) recently showed that the single-layer IOT derived from unconstrained CALIOP retrievals is low biased with respect to a single-channel thermal/infrared IOT retrieval combining CALIOP/MODIS observations and forward radiative transfer modelling. The bias is shown to increase with increasing IOT.…”

Investigation of ice particle habits to be used for ice cloud remote sensing for the GCOM-C satellite mission

“…There is increasing evidence that the ice particle model should contain some degree of surface roughness (Foot, 1988;Baran et al, 2001Baran et al, , 2003Ottaviani et al, 2012;van Diedenhoven et al, , 2013van Diedenhoven et al, , 2014Cole et al, 2013Cole et al, , 2014Holz et al, 2016). In particular, using an ensemble ice particle model, Baran and C.-Labonnote (2007) and Baran et al (2014) showed that featureless phase functions best fitted their multi-angle satellite measurements at solar wavelengths.…”

Degree of ice particle surface roughness inferred from polarimetric observations

“…Application of an unrealistic ice model, e.g., with only smooth (unroughened) surfaces, results in an overall global bias (Macke and Mishchenko, 1996;Yang et al, 2007Yang et al, , 2008bHolz et al, 2016) as well as seasonal biases (Zhang et al, 2009) in cloud property retrievals. The overarching goal of this paper is to gain a better understanding of the constraints in the microphysical parameters of global ice clouds using angular polarimetric observations and state-of-the-art light-scattering computational capabilities.…”

“…Furthermore, the spectral consistency of visible/near-infrared and thermal infrared retrievals (Baran and Francis, 2004) was recently investigated by Liu et al (2014) and Holz et al (2016), who report that retrieved ice cloud optical thicknesses are more consistent when particles are roughened.…”