Both MODIS and CALIOP IOT retrievals require a priori information concerning the ice particle scattering properties that relate the measured reflectance (MODIS) or attenuated backscatter (CALIOP) to the cloud's IOT and potentially the effective particle size. MODIS ice cloud forward radiative calculations in the visible–near-infrared (VNIR) depend directly on the ice particle phase function assumption, and to a first order on the associated asymmetry parameter (g). For CALIOP, an assumed extinction-to-backscatter ratio is required for the unconstrained retrievals where the algorithm is unable to make reliable estimates of cirrus IOT by measuring the attenuated backscatter coefficients in some clear-air region immediately above and below cloud base (Young and Vaughan, 2009). Because solar background signals greatly reduce the signal-to-noise ratio (SNR) of the CALIOP daytime measurements, the vast majority of CALIOP daytime IOT estimates are derived from unconstrained retrievals.

Uncertainties in the ice scattering property assumptions of MODIS and/or CALIOP could account for the biases found in Fig. 1. As will be discussed, an infrared (IR) cirrus IOT retrieval is relatively insensitive to ice particle size and scattering details compared to MODIS and CALIOP VNIR measurements and thus provides an independent means to assess thin to moderately optically thick ice cloud retrievals (IOT ∼ 0–3). In addition, an IR retrieval provides radiative closure with solar reflectance based on MODIS IOT retrievals in the sense that consistency in the two retrieved IOTs also implies forward model consistency with the respective top-of-atmosphere (TOA) VNIR and IR observations.

Using the NASA-funded SSEC Atmosphere Product Evaluation and Test Element (PEATE), now re-named the Suomi-NPP Atmosphere Science Investigator Processing System (SIPS), the sensitivity of MODIS retrievals to ice single-scattering properties are investigated by repeated analyses of collocated January 2010 CALIOP and MODIS observations using a variety of ice crystal habits (Yang et al., 2012) and size distributions. Based on comparisons against IR retrievals, the MODIS MYD06 Collection 6 (C6) ice cloud optical property algorithm uses a single habit – severely roughened aggregated columns (Yang et al., 2012) – instead of the size-dependent multi-habit model (Baum et al., 2005) used for C5. The MYD06 C6 results compare well with a new CALIOP version that uses a modified (larger) extinction-to-backscattering ratio for unconstrained IOT retrievals.

The manuscript is organized as follows. Section 2 presents a detailed description of the algorithms and data sets used in the analysis of the ice cloud optical depths with a focus on the IR retrievals. Section 3 introduces the global intercomparison between the MODIS Collection 5 and CALIPSO V3 ice cloud optical depths, with Sect. 4 presenting the comparison with the collocated IR retrievals (ocean only). Section 5 discusses the impact of the ice model selection (MODIS) and the assumed lidar ratio and multiple-scattering correction (CALIOP) on the ice cloud optical depth and then presents an intercomparison of the MODIS and CALIOP retrievals processed using a modified single-scatter lookup table (severely roughened aggregated columns) and a modified of unconstrained lidar ratio of 31 (instead of 25 for V3). Section 6 summarizes the results and with a focus on the rational for the selection of a single habit for the new single-scattering properties for the MODIS C6 ice cloud retrievals.

The MODIS channel suite includes a range of IR channels extending well into the CO2 absorption region (13–15 µm). The calibration of the IR channels has been extensively validated and proven to have high accuracy, with uncertainties less than 0.5 K across a broad temperature range (Tobin et al., 2006). For ice clouds, the IR radiative transfer is dominated by absorption, and thus is less complex than for the VNIR retrieval. In this section we discuss the IR radiative transfer methodology that is used both to retrieve the IR IOT as well as evaluate the MODIS and CALIOP retrievals.

The goal of radiative closure study is to relate the differences in the CALIOP and MODIS retrieved IOT to the measured TOA channel radiance or brightness temperature (BT) in the MODIS 11 µm channel. To calculate the TOA cloudy radiances requires an accurate radiative transfer model, knowledge of the cloud boundaries, and well-characterized surface temperature/emissivity and atmospheric thermodynamic profiles. LBLDIS (Turner et al., 2003), a cloudy radiative transfer model, is used for this analysis. The model elegantly combines the clear-sky Line-By-Line Radiative Transfer Model (LBLRTM) (Clough and Moncet, 1992) with the Discrete Ordinates Radiative Transfer (DISORT) (Stamnes et al., 1988), a proven and accurate cloudy radiative transfer model. The inputs required for LBLRTM are surface temperature and emissivity, vertically resolved temperature and water vapor profiles, and information regarding trace gas concentrations such as CO2 and O3. For this analysis the surface temperature and thermodynamic profiles are extracted from the NOAA Global Data Assimilation System (GDAS) files that provide profiles at 1∘ spatial resolution every 6 h. For each MODIS and CALIOP field of view (FOV), the closest (in both time and space) GDAS profile is selected. A fixed CO2 concentration of 380 ppm and a climatological O3 profile is used. Given these inputs, LBLRTM is run on the selected FOV filtered using the collocated CALIOP V3 5 km cloud layer products (described in Sect. 3). The results of the clear-sky validation are discussed in Sect. 4.

The cloud microphysics and thermodynamics are defined with a vertical resolution of 500 m within the cloud boundaries defined by the CALIOP layer product. Only FOV where the CALIOP is not attenuated at the surface are used greatly reducing uncertainties in the cloud base determination. For example a cloud with a geometrical thickness of 1.5 km is divided into three layers, with each layer defined by an optical thickness, effective radius, and ice scattering model. For example, for a cloud with a total optical thickness of 1.5, each layer will have an optical thickness of 0.5. Using this methodology the vertical temperature profile is accounted for in the radiative transfer. For daytime IR forward model calculations, the effective radius from the MODIS optical property retrieval is used for all cloud layers. For nighttime CALIOP comparisons, a fixed effective radius of 40 µm is used in the IR calculations. It is important to note that at 11 µm the IOT retrieval is relatively insensitive to the assumed effective radius.

The last remaining variable needed to calculate the TOA IR radiance is IOT. LBLDIS is run independently using either the MODIS or CALIOP retrieved IOT, resulting in high-spectral-resolution TOA radiances with the only differences being the assumed IOT (i.e., MODIS or CALIOP). The spectrally resolved radiances are then integrated over the MODIS Aqua 11 µm channel (band 31) spectral response function, resulting in a simulated TOA radiance that can be directly compared to the measured MODIS 11 µm observations.

In addition to LBLDIS spectral calculations, TOA longwave fluxes are calculated using the Rapid Radiative Transfer Model (RRTM) (Mlawer et al., 1997), which is also based on DISORT and LBLRTM and utilizes a correlated-k method for gas absorption along with broadband ice cloud parameterizations from (Fu et al., 2000). Identical inputs are used for RRTM and the LBLDIS TOA calculations with the only variable being IOT. The TOA fluxes are subsequently used to quantify the impact of the IOT biases on the global characterization of ice cloud radiative forcing.

IR observations provide the independent reference to understand differences between MODIS and CALIOP IOT retrievals. While radiance closure provides valuable information regarding TOA radiances and fluxes, it does not provide a direct assessment of the individual CALIOP and MODIS IOT biases. To convert observed IR TOA radiance to IOT, two different retrieval approaches were used. First, we developed an IR window IOT retrieval that uses the collocated MODIS and CALIOP observations. This “reference” retrieval uses cloud boundary information from CALIOP coupled with the LBLDIS forward model and then retrieves the IR IOT using the MODIS 11 µm window channel observations that are coincident and collocated with CALIOP. A second method uses the spectral emissivity retrieved from the MODIS CO2 emissive cloud-top pressure retrieval that is then related to the IOT and effective radius using a pre-computed lookup table (Heidinger et al., 2015). This method has the advantage of being computationally very efficient, not requiring the CALIOP cloud boundaries, and providing IOT for the entire MODIS swath. Both IR retrieval methods are discussed in more detail in the following subsections.

CALIOP is a two-wavelength elastic backscatter lidar that measures attenuated backscatter components polarized parallel and perpendicular to the transmitted laser light at 532 nm and total attenuated backscatter at 1064 nm (Hunt et al., 2009). Once the received signals have been background-subtracted and calibrated (Powell et al., 2009), a tightly integrated suite of retrieval algorithms is used to detect layer boundaries (Vaughan et al., 2009) and classify layers as either clouds or aerosols (Liu et al., 2009). Layers classified as clouds are further classified according to thermodynamic phase as either ice clouds or water clouds (Hu et al., 2009). Layer optical thickness (including IOT) is then retrieved using one of two techniques: constrained or unconstrained retrievals (Young and Vaughan, 2009). Constrained retrievals are applied whenever the effective two-way transmittance of a layer, Teff2=exp⁡-2ητ=exp⁡-2η∫layer toplayer baseσcrdr, can be directly and reliably measured. In this expression τ is the layer optical depth (IOT for ice clouds), σc(r) is the range-resolved cloud extinction coefficient, and η is a multiple-scattering correction factor whose value depends on the lidar sensing geometry and the scattering characteristics of the particulates being measured. While Teff2 estimates can be obtained from measurements of clear air, opaque water clouds, and ocean surfaces (see Josset et al., 2012; Yongxiang et al., 2007; and Young, 1995, respectively), the CALIOP V3 algorithm only implements the clear-air technique, in which Teff2 can be obtained directly from the ratio of the mean attenuated scattering ratios calculated in regions of clear air located immediately above cloud top and below cloud base (Vaughan et al., 2005). Retrieving IOT from measurements of Teff2 requires knowledge of the appropriate multiple-scattering factor (Winker, 2003). For CALIOP measurements of cirrus clouds, Josset et al. (2012) determined the mean multiple-scattering factor to be 0.61 ± 0.15. In the CALIOP V3 algorithm, η is fixed at 0.6 for all cirrus clouds. More recent results suggest that the multiple-scattering factor is dependent on cloud temperature (Garnier et al., 2015b), which is being considering for the upcoming version 4 products.

Constrained retrievals are the preferred method for retrieving IOT from CALIOP measurements. However, because solar background light significantly degrades the CALIOP SNR during daylight operations, V3 constrained retrievals occur almost exclusively during nighttime observations, thus severely limiting direct comparisons with MODIS IOT retrievals derived from VNIR solar reflectance. For the vast majority of daytime observations, CALIOP IOT retrievals use an unconstrained technique that requires a priori knowledge of the cirrus extinction-to-backscatter ratio (i.e., lidar ratio), Sc=σc(r)βc(r), where σc(r) and βc(r) are, respectively, the cloud extinction and backscatter coefficients. IOT is then obtained by solving the lidar equation using specified values of η and Sc (Young and Vaughan, 2009). Note that while the cloud extinction and backscatter coefficients are explicitly range-dependent, their ratio is assumed to be range-invariant. Although Sc for ice clouds most likely varies depending on crystal habit and size distribution, the CALIOP V3 unconstrained retrievals use a globally constant default value of Sc=25 sr. Based on ground-based lidar observations there can be significant variability in the lidar ratio. The constant value is considered one of the primary sources of uncertainty in the V3 ice cloud extinction retrievals. This value was determined prior to launch from the best information available from numerous ground-based and airborne data sets (e.g., Holz, 2002; Sassen and Comstock, 2001; Yorks et al., 2011).

Errors in lidar ratio selection for unconstrained retrievals generate corresponding errors in the resultant estimates of IOT. In particular, an underestimate of Sc will result in CALIOP underestimating IOT. The selection of the default CALIOP lidar ratio is thus one of the potential major sources of bias in the CALIOP unconstrained retrievals that can be investigated using IR observations from either MODIS or the CALIPSO IIR (Imaging Infrared Radiometer) instrument (Garnier et al., 2015a).

The MODIS imager provides measurements in 36 spectral channels, covering the visible–near-infrared (VNIR), shortwave infrared (SWIR), midwave infrared (MWIR), and thermal IR portions of the spectrum. Spatial resolution is 250 m in two VNIR channels, 500 m in the five VIS/SWIR channels, and 1 km in the remaining channels.

The MODIS cloud optical/microphysical property algorithm is used to generate a single cloud product designated by the NASA Earth Science Data Type (ESDT) names MOD06 and MYD06 for Terra and Aqua MODIS, respectively (hereafter referred to as MYD06 since the algorithms are essentially identical and this study is focused on MODIS Aqua observations). For daytime measurements, the 1 km cloud retrieval algorithm uses multiple spectral channels (primarily six VNIR, SWIR, and MWIR channels, as well as several thermal channels) to simultaneously retrieve cloud optical thickness, effective radius (and derived water path) and thermodynamic phase for liquid- and ice-phase clouds. In addition to the 1 km MODIS Level-1B calibrated radiance product, the algorithm requires the following input: MODIS cloud mask (MYD35) including 250 m mask information (Ackerman et al., 1998), the cloud-top pressure portion of MYD06 (Ackerman et al., 2008; Holz et al., 2008), and a variety of ancillary data sets. Heritage algorithm work is discussed in King et al. (2003), Nakajima and King (1990), Platnick and Twomey (1994), and Platnick et al. (2001).

C5 algorithm-related publications include ice radiative models (Ackerman et al., 2008; Baum et al., 2005; Yang et al., 2007), multilayer detection (Wind et al., 2010), clear-sky restoral filtering (Pincus et al., 2012; Zhang and Platnick, 2011), pixel-level uncertainties, and L3 global gridded statistics (King et al., 2013). An online list of the recent C6 algorithm updates is available from the MODIS Atmosphere Team website (Platnick, 2014). The most relevant update for the current discussion is the adoption of new ice cloud radiative models having an overall smaller asymmetry parameter, as will be discussed in Sect. 5.1. Note that, for consistency with the spherical droplet definition, as well as for use in deriving ice water path, the effective radius of a non-spherical ice particle is defined as 3/4 times the ratio of the average volume of the size distribution to the average cross-sectional area (Yang et al., 2007).

In this section we present the methods used to collocate and merge the CALIOP and MODIS observations providing the foundation for the intercomparisons and analysis presented in the results of Sect. 4.

The analysis is based on 1 month (January 2010) of physically collocated CALIOP and MODIS observations. MODIS is an imaging radiometer, while CALIOP is a near-nadir viewing lidar. Because each instrument has a unique viewing geometry with different spatial resolutions, accurate intercomparisons require collocating the observation FOVs. This analysis uses tools that provide computationally efficient and accurate collocation (Nagle and Holz, 2009). The methodology defines master and follower instruments, with the master typically being the larger FOV and the follower FOV collocated within the master footprint. In this investigation MODIS is defined as the master with CALIOP the follower. The MODIS spatial resolution can be approximated as a rectangular box with a 1 km × 2 km resolution at nadir. The CALIOP IOT retrieval can be performed over horizontal averaging distances ranging from 5 to 80 km, depending on the magnitude of the cloud signal relative to the background noise (Yongxiang et al., 2007). The CALIOP surface footprint is therefore approximated as an 80 m wide swath with the along-track length depending on the amount of spatial averaging. The majority of observations used in this analysis are the 5 km averaged IOT. A more detailed description of the CALIOP and MODIS collocation is presented in Holz et al. (2008).

Leveraging the UW Atmospheric Science Investigator-led Processing System processing capabilities, a month of collocated MODIS and CALIOP collocated observations were processed using the CALIOP and MODIS IOT retrievals with the only difference being incremental changes to the ice cloud parameterizations used in the retrieval algorithms. This approach isolates the impact of the parameterization changes and/or algorithm modifications and provides a direct assessment of the changes in IOT.

Though a primary focus of this investigation is on optimizing C6 ice models to improve IOT intercomparisons, it is understood that ice model crystal habits also affect the particle single-scattering albedo retrieved using the SWIR and MWIR channels that provide effective particle size information. Figure 5a and b show the 2.13 and 3.7 µm channel co-albedo, respectively, as a function of cloud effective radius (CER) for four habit realizations, namely the C5 habit mixture (black line) and the three severely roughened habits, i.e., solid aggregate plates (green line), solid bullet rosettes (red line), and aggregate columns (blue line) (Yang et al., 2012). To the extent that CER retrievals of an asymptotically thick cloud in the SWIR/MWIR are essentially a retrieval of co-albedo, the difference between the aggregated column and C5 model co-albedo implies an effective radius difference of +2 and -8 µm at the 2.1 and 3.7 µm wavelengths, respectively, for a C5 effective radius of about 35 µm; smaller C5 retrieved sizes would result in larger differences.

Figure 6 shows the asymmetry parameter sensitivity to habit for the same four habits shown in Fig. 5. Evidently the habit sensitivity of the asymmetry parameter is also strong in both the 2.1 and 3.7 µm MODIS channels. While the asymmetry parameters of three severely roughened habits are not constant with effective size (though at 2.1 µm the aggregate plates and aggregate columns are nearly constant), the C5 model has much larger size sensitivity at both wavelengths (Cole et al., 2014; van Diedenhoven et al., 2014; Yang et al., 2008). Aggregated columns, with smaller asymmetry parameters relative to C5, will result in a larger retrieved CER estimates. This is because the resulting increase in modeled SWIR reflectance for a given effective size causes the measured reflectance to be associated with a more absorbing (i.e., larger) particle. Therefore, the effect of both co-albedo and asymmetry parameter differences between the severely aggregated column habit and the C5 model act to increase retrieved effective radii at 2.1 µm, while at 3.7 µm some cancellation of effects can be expected.

The single-habit radiative models shown in Figs. 5 and 6 are used to build lookup tables that were integrated into the MODIS C6 cloud retrieval development code. A month of data was processed for each habit. It was found that the habit that provided the best consistency with the IR window retrievals (Sect. 2.1.1) is the severely roughened aggregated column model. The IOT retrieval comparison with the IR window retrievals using this model is shown in Fig. 7a, where the MODIS reflectance-based retrievals using the severely roughened aggregated column model are now clustered around the 1-to-1 line. In addition, this aggregated column model was used to assess the MODIS retrieval swath dependence previously shown in Fig. 4b. The improvement of the aggregated column model (dashed lines) relative to the C5 model (solid lines) is significant. Both results led to the decision to use the severely roughened aggregated column radiative model for the MODIS C6 cloud optical/microphysical property retrievals.

Figure 8 shows an example of ice cloud retrievals for C5 and C6 for typhoon Fung-Wong. The typhoon was located south of Taiwan at the time of the MODIS Aqua data granule acquisition on 20 September 2014 (05:30 UTC). The C5 and C6 ice (cool colors) and liquid (warm colors) cloud optical thickness retrievals are shown in the middle and right panels, respectively. In addition to ice radiative model differences, MYD06 C5 and C6 have different schemes for the cloud thermodynamic phase yielding different ice and liquid phase pixel populations, though the optical thickness spatial patterns are similar for regions having the same phase. Because of the different phase assignments made by these two scheme, quantifying ice model retrieval sensitivities requires that the comparisons be restricted to only those pixels for which both algorithms generate successful retrievals that identify identical cloud phases. With this pixel filtering, the left panel of Fig. 8b shows the normalized IOT distribution for the optical thickness range of the plot. The C6 IOT mode is roughly 27 % smaller than the C5 mode, while the mean is decreased by about 15 %, from 4.16 for C5 to 3.55 for C6. The 2.1 µm ice cloud effective particle radius retrievals are shown in the right panel, with the C6 mode and mean both increasing by about 4 µm (+15 %) for C6 relative to C5.

The MODIS IOT retrieval depends strongly on assumed ice scattering properties that are needed to relate the measured reflectance to the retrieved IOT. The MODIS C5 retrieval used empirically derived habit and size distributions with asymmetry parameters ranging between 0.79 and 0.88 depending on the ice cloud effective radius (Baum et al., 2005). By conducting an infrared closure analysis, we have shown that the C5 parameterization is not representative of the globally averaged ice scattering properties. More recent investigations of the ice cloud asymmetry parameter suggest that most ice clouds have values around 0.75 in the visible spectrum (Cole et al., 2012; van Diedenhoven et al., 2013). Additionally, use of the C5 ice cloud radiative model results in MODIS retrieval biases that are strongly dependent on the viewing angle, as demonstrated in Fig. 4. These findings motivated the investigation of new ice scattering models that have lower asymmetry parameters and weaker dependence on ice effective radius.

Since the MODIS C5 algorithms were finalized, new ice scattering models that incorporate roughened ice crystal parameterizations have been developed (Yang et al., 2012). Experimentation with these new models demonstrates that a modified gamma distribution of severely roughened aggregated columns provides a significantly lower visible asymmetry parameter (∼ 0.75) that shows very little dependence on ice effective radius. For testing purposes, the MODIS cloud retrieval algorithm team implemented these new scattering properties in the MYD06 retrieval algorithm. The updated algorithm was then run on the Atmospheric PEATE and the resulting data were collocated with CALIOP measurements. Simulated TOA cloudy MODIS 11 µm BTs were then computed using the reprocessed MODIS IOT retrievals and are compared to the MODIS measured BT. These new results are presented in Fig. 10b. The updated ice scattering models generate greatly improved IOT estimates that show very close to a one-to-one correspondence with the independently derived IR IOT values (Fig. 7a) and is consistent with the findings of Baum et al. (2014). Additionally the view-angle-dependent bias is largely removed, as presented in Fig. 4b. Based on these results, the recently reprocessed MODIS C6 cloud optical/microphysical property product (now in forward production) uses a modified gamma distribution consisting of a single habit of severely roughened aggregated columns for ice cloud retrievals. An additional benefit of the single habit is that it simplifies the retrieval and increases the reproducibility of the scattering properties by the research community. It is important to note that the selection of the single-habit modified gamma distribution was to provide a radiative consistency with the IR, not a microphysical model.

Figure 10a presents the same filtered 2-D histogram comparing CALIOP and MODIS as Fig. 1 but using the ice radiative model modifications made for MODIS and the updated lidar ratio (32 sr) for CALIOP. Figure 10b presents the IR radiative closure for the updated IOT retrievals for January 2010. Note that the large bias between the MODIS and CALIOP un-constrained IOT is significantly reduced and the IR radiative closure shows very good agreement for all three IOT retrievals. There is still a tendency for the MODIS IOT to be larger than CALIOP in Fig. 10a. The MODIS C6 IR closure in Fig. 10b also demonstrates this bias, with the tail of the distribution weighted to negative BT differences, suggesting the remaining bias is specific to MODIS.

As previously discussed, CALIOP uses one of two methods (i.e., constrained and unconstrained) to retrieve IOT. The constrained method requires high SNR in clear-air regions immediately above and below the cloud. This SNR requirement limits the constrained retrieval primarily to nighttime FOVs, because solar background light severely degrades the clear-air SNR during the daytime. This precludes direct comparison of the constrained retrievals with the MODIS daytime optical property retrievals. The IR retrieval, being day/night independent, allows for direct intercomparisons between the MODIS IR IOT retrievals and both the constrained and un-constrained CALIOP IOT retrievals, providing a means to evaluate the two retrieval methods against a consistent reference.

Figure 3b presents the joint histogram between the unconstrained CALIOP and the MODIS window IR IOT for January 2010 for single-layer cirrus. The filtering criteria are the same as in Fig. 1, except both day and night observations are included. The CALIOP layer optical thickness is filtered using the extinction quality control (QC) flags provided as part of the L2 products. Only QC values of 0 (unconstrained solution, no lidar ratio adjustment), 2 (unconstrained solution, lidar ratio decreased), and 4 (unconstrained solution, lidar ratio increased) were selected. Consistent with the findings of Garnier et al. (2015a), Fig. 3b shows CALIOP unconstrained IOT is significantly low-biased with respect to the IR IOT, with a nonlinear dependence as a function of IOT. Figure 9 compares the CALIOP constrained retrievals (QC = 1) to the MODIS IR COT for the same filtering criteria. This comparison reveals a distinct difference between the CALIOP constrained and unconstrained retrievals (Fig. 3b), as the constrained retrievals demonstrate a significantly smaller bias relative to the IR IOT. While the CALIOP IOT retrieval requires estimates of the multiple-scattering contributions for both the constrained and unconstrained retrievals, the un-constrained method also requires an assumed lidar ratio, whereas the constrained retrieval does not. Because both retrievals use identical fixed multiple-scattering factors, the difference between the constrained and unconstrained retrievals relative to the IR can be attributed to the use of an assumed lidar ratio in the unconstrained retrieval.

To investigate the sensitivity of the CALIOP IOT retrievals to the lidar ratio, a month of CALIOP L2 products was processed (January 2010) with the default lidar ratio increased to 32 sr. This revised value is the mean of all V3 constrained solutions of ice clouds with randomly oriented ice crystals (3 091 952 cases) measured between 28 November 2007 (when CALIPSO permanently changed its pointing angle to 3∘ off nadir) and 30 June 2012. It is important to note that the selection of this new default lidar ratio was based on on-going quality assurance analyses conducted by the CALIOP algorithm team that were wholly independent of the IR intercomparisons with the final value dependent on change to the multiple-scattering correction and calibration. In addition, the CALIOP team is currently investigating a more complex multiple-scattering parameterization that depends on the cloud temperature (Garnier et al., 2015b). The modified CALIOP product was ingested by the Atmospheric PEATE and collocated with both the MODIS C5 and C6 products and the MODIS IR retrievals. The modified CALIOP unconstrained retrievals compared to the reference IR IOT is presented in Fig. 7b. Compared to the standard V3 products (Fig. 3b) the change in the lidar ratio significantly reduced the bias compared to IR IOT, and the nonlinear behavior at large IOT is almost completely removed. This is because optical depth is a nonlinear function of lidar ratio; thus, weakly scattering layers show minimal changes in IOT, while the changes in strongly scattering layers are much more substantial. This result strongly suggests that the current V3 unconstrained lidar ratio of 25 sr should be increased in future versions of the CALIOP data products.

All relevant data can be accessed from the NASA data centers using these DOIs: 10.5067/MODIS/MOD06_L2.006, 10.5067/CALIOP/CALIPSO/CAL_LID_L2_05kmCPro-Prov-V3-01_L2-003.01, and 10.5067/CALIOP/CALIPSO/CAL_LID_L2_05kmCLay-Prov-V3-01_L2-003.01.