Droplet concentration (Nd) and liquid water path (LWP) retrievals from passive satellite retrievals of cloud optical depth (τ) and effective radius (re) usually assume the model of an idealized cloud in which the liquid water content (LWC) increases linearly between cloud base and cloud top (i.e. at a fixed fraction of the adiabatic LWC). Generally it is assumed that the retrieved re value is that at the top of the cloud. In reality, barring re retrieval biases due to cloud heterogeneity, the retrieved re is representative of smaller values that occur lower down in the cloud due to the vertical penetration of photons at the shortwave-infrared wavelengths used to retrieve re. This inconsistency will cause an overestimate of Nd and an underestimate of LWP (referred to here as the penetration depth bias), which this paper quantifies via a parameterization of the cloud top re as a function of the retrieved re and τ. Here we estimate the relative re underestimate for a range of idealized modelled adiabatic clouds using bispectral retrievals and plane-parallel radiative transfer. We find a tight relationship between gre = recloud top∕reretrieved and τ and that a 1-D relationship approximates the modelled data well. Using this relationship we find that gre values and hence Nd and LWP biases are higher for the 2.1µm channel re retrieval (re2.1) compared to the 3.7µm one (re3.7). The theoretical bias in the retrieved Nd is very large for optically thin clouds, but rapidly reduces as cloud thickness increases. However, it remains above 20% for τ < 19.8 and τ < 7.7 for re2.1 and re3.7, respectively. We also provide a parameterization of penetration depth in terms of the optical depth below cloud top (dτ) for which the retrieved re is likely to be representative.
The magnitude of the Nd and LWP biases for climatological data sets is estimated globally using 1 year of daily MODIS (MODerate Imaging Spectroradiometer) data. Screening criteria are applied that are consistent with those required to help ensure accurate Nd and LWP retrievals. The results show that the SE Atlantic, SE Pacific and Californian stratocumulus regions produce fairly large overestimates due to the penetration depth bias with mean biases of 32–35% for re2.1 and 15–17% for re3.7. For the other stratocumulus regions examined the errors are smaller (24–28% for re2.1 and 10–12% for re3.7). Significant time variability in the percentage errors is also found with regional mean standard deviations of 19–37% of the regional mean percentage error for re2.1 and 32–56% for re3.7. This shows that it is important to apply a daily correction to Nd for the penetration depth error rather than a time–mean correction when examining daily data. We also examine the seasonal variation of the bias and find that the biases in the SE Atlantic, SE Pacific and Californian stratocumulus regions exhibit the most seasonality, with the largest errors occurring in the December, January and February (DJF) season. LWP biases are smaller in magnitude than those for Nd (−8 to −11% for re2.1 and −3.6 to −6.1% for re3.7).
In reality, and especially for more heterogeneous clouds, the vertical penetration error will be combined with a number of other errors that affect both the re and τ, which are potentially larger and may compensate or enhance the bias due to vertical penetration depth. Therefore caution is required when applying the bias corrections; we suggest that they are only used for more homogeneous clouds.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)