Impact of Rain on Retrieved Warm Cloud Properties Using Visible and Near-Infrared Reflectances Using Markov Chain Monte Carlo Techniques

Estimates of cloud droplet effective radius (
$r_{\mathrm {e}}$
) and optical thickness (
$\boldsymbol {\tau }$
) can be derived using reflected sunlight in a visible non-absorbing channel combined with reflectances from a near IR channel that is absorbing [e.g., the bispectral method (BSM)]. Discrepancies between BSM-estimated
$r_{\mathrm {e}}$
and collocated in situ measurements are commonly attributed to a violation of the assumptions used in the BSM algorithm such as plane parallel geometry, and a single mode droplet size distribution (DSD). This research uses Markov chain Monte Carlo (MCMC) experiments to examine the impact of precipitation on BSM-retrieved
$r_{\mathrm {e}}$
near optical cloud top by comparing the retrievals and associated uncertainties obtained from two types of experiments assuming a unimodal or bimodal drop size distribution. Where rain is present, BSM-retrieved
$r_{\mathrm {e}}$
overestimates the true cloud mode
$r_{\mathrm {e}}$
. Moreover, there is no longer a unique measure of
$r_{\mathrm {e}}$
within the precipitating liquid-phased clouds, resulting in a substantial increase in retrieval uncertainties. This leads to a corresponding loss of information on the total number concentration and liquid water content (LWC) near the cloud top. It is found that
$r_{\mathrm {e}}$
biases are not strongly correlated with properties exclusively pertaining to rain, such as rain water content (RWC) or precipitation rates, but tend to be a function of the ratio between rain and cloud water content (CWC) and the cloud total number concentration. These results highlight the need for additional independent information such as from an active or passive microwave sensor that can identify the presence of precipitation and constrain additional aspects of bimodal droplet distributions.