Using passive remote sensing to retrieve the vertical variation of cloud droplet size in marine stratocumulus: An assessment of information content and the potential for improved retrievals from hyperspectral measurements

[1] We present a theoretical study into the information content of solar reflectance measurements at multiple near-infrared wavelengths in the context of deriving the vertical variation of cloud droplet size from marine stratocumulus. We employ a Bayesian optimal estimation approach to retrieve a profile of droplet effective radius as a function of optical depth in the cloud. This allows an assessment of the information content of the measurement of reflectance at different wavelengths and the potential for a new generation of hyperspectral sensors to improve the retrieval. Our results show that using the three absorbing near-infrared channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) centered at 1.6, 2.1 and 3.7 μm a retrieval of droplet sizes lower in the cloud is highly sensitive to small changes in reflectance. Consequently instrument and modeling errors must be reduced to <1% in order to gain a useful uncertainty constraint on droplet size near to cloud base. By introducing many high spectral resolution wavelength channels, such as those available from hyperspectral sensors, we find that the information content pertaining to all retrieval variables increases significantly. This results in a reduction in the uncertainty estimates on all retrieved quantities. We also test the ability of a vertical profile retrieval to provide improved liquid water path estimates by using several in situ profiling measurements of marine stratocumulus in the South-East Pacific region. We find that a vertical profile retrieval significantly improves the liquid water path estimate when compared to a two-band lookup table retrieval.