Characterizing the information content of cloud thermodynamic phase retrievals from the notional PACE OCI shortwave reflectance measurements

Abstract

We rigorously quantify the probability of liquid or ice thermodynamic phase using only shortwave spectral channels specific to the National Aeronautics and Space Administration's Moderate Resolution Imaging Spectroradiometer, Visible Infrared Imaging Radiometer Suite, and the notional future Plankton, Aerosol, Cloud, ocean Ecosystem imager. The results show that two shortwave-infrared channels (2135 and 2250 nm) provide more information on cloud thermodynamic phase than either channel alone; in one case, the probability of ice phase retrieval increases from 65 to 82% by combining 2135 and 2250 nm channels. The analysis is performed with a nonlinear statistical estimation approach, the GEneralized Nonlinear Retrieval Analysis (GENRA). The GENRA technique has previously been used to quantify the retrieval of cloud optical properties from passive shortwave observations, for an assumed thermodynamic phase. Here we present the methodology needed to extend the utility of GENRA to a binary thermodynamic phase space (i.e., liquid or ice). We apply formal information content metrics to quantify our results; two of these (mutual and conditional information) have not previously been used in the field of cloud studies.

Figure 1.

The final retrieval results after cumulatively ingesting information from all retrieval wavelengths into the GENRA algorithm for a “true” cloud type of τ = 10, r_eff = 10 μm, and phase = liquid. (left column) Results specific to “MODIS-SW” cloud retrieval channels, (middle column) “VIIRS-SW” results, and (right column) “PACE” results (see text). (a, d, and g) The 2-D joint posterior pdf showing contributions from ice thermodynamic phase (pink contours) and liquid thermodynamic phase (blue contours). The marginal pdfs for (b, e, and h) optical thickness and (c, f, and i) effective radius, respectively, where the joint marginal pdf (black) has contributions from liquid (green) and ice (pink) thermodynamic phase. The vertical dashed lines on marginal pdf plots denote the “truth” values.

Figure 2.

Same as in Figure 1 but for a “true” cloud type of of τ = 10, r_eff = 12 μm, and phase = ice.

Figure 3.

Cumulative probability for correctly discriminating ice cloud phase after ingesting information from the “PACE” channel set combination into the GENRA algorithm for a “true” cloud type of τ = 10, r_eff = 12 μm, and phase = ice. Results at final measurement channel correspond to the percent probability of ice phase retrieval reported in Figure 2g.

Figure 4.

Contour plot of the percent probability of correctly retrieving liquid water cloud phase from the joint space spanned by ice and liquid phase solutions when the “true” cloud phase is liquid. Values around 50% indicate an ambiguous phase retrieval (see text). The subplots are specific to specific measurement channel combinations: (a) MODIS-SW, (b) VIIRS-SW, and (c) PACE.

Figure 5.

Same as in Figure 4 but for the ice thermodynamic phase. In Figure 5c, the black point “P” represents the (τ, r_eff) pair discussed in Figure 3.

Figure 6.

The normalized total (black line), marginal (dashed line with circles), and mutual information content (dashed line with squares) derived from the entropy relationships in the (τ, r_eff) cloud optical parameters after ingesting simulated cloud reflectance at 865 nm, 1640 nm, 2135 nm, and 2225 nm into the GENRA algorithm (with no update of the prior). The results are specific to the same cloud case described for Figure 1.

Figure 7.

The normalized Shannon information content and conditional information content of (a) cloud optical thickness and (b) droplet effective radius for the experimental setup and implementation described for Figures 1 and 6.

Figure 8.

(a) Cloud reflectance look-up table at 865 nm and 2135 nm demonstrates near-vertical lines of constant optical thickness and near-horizontal lines of constant effective radius. (b) The mutual information in optical thickness and effective radius from measurements at 865 nm and 2135 nm.

Figure 9.

The 2-D joint posterior pdf for τ = 10, r_eff = 12 μm, and phase = ice assuming measurement uncertainty of 0.3% and the “PACE” measurement channels (Table 1). The impacts of increased radiometric accuracy can be seen by comparing this result with the result shown in Figure 2g.

Figure 10.

The normalized total (black line), marginal (dashed line with circles), and mutual information content (dashed line with squares) derived from the entropy relationships in the (τ, r_eff) cloud optical parameters after ingesting simulated cloud reflectance at 865 nm, 1640 nm, 2135 nm, and 2225 nm into the GENRA algorithm (prior is updated).

Figure 11.

Same as in Figure 8b but for measurement uncertainty equal to 0.3%.