Abstract
Abstract. A Bayesian algorithm to retrieve profiles of cloud ice water content (IWC), ice particle size (Dme), and relative humidity from millimeter-wave/submillimeter-wave radiometers is presented. The first part of the algorithm prepares an a priori file with cumulative distribution functions (CDFs) and empirical orthogonal functions (EOFs) of profiles of temperature, relative humidity, three ice particle parameters (IWC, Dme, distribution width), and two liquid cloud parameters. The a priori CDFs and EOFs are derived from CloudSat radar reflectivity profiles and associated ECMWF temperature and relative humidity profiles combined with three cloud microphysical probability distributions obtained from in situ cloud probes. The second part of the algorithm uses the CDF/EOF file to perform a Bayesian retrieval with a hybrid technique that uses Monte Carlo integration (MCI) or, when too few MCI cases match the observations, uses optimization to maximize the posterior probability function. The very computationally intensive Markov chain Monte Carlo (MCMC) method also may be chosen as a solution method. The radiative transfer model assumes mixtures of several shapes of randomly oriented ice particles, and here random aggregates of spheres, dendrites, and hexagonal plates are used for tropical convection. A new physical model of stochastic dendritic snowflake aggregation is developed. The retrieval algorithm is applied to data from the Compact Scanning Submillimeter-wave Imaging Radiometer (CoSSIR) flown on the ER-2 aircraft during the Tropical Composition, Cloud and Climate Coupling (TC4) experiment in 2007. Example retrievals with error bars are shown for nadir profiles of IWC, Dme, and relative humidity, and nadir and conical scan swath retrievals of ice water path and average Dme. The ice cloud retrievals are evaluated by retrieving integrated 94 GHz backscattering from CoSSIR for comparison with the Cloud Radar System (CRS) flown on the same aircraft. The rms difference in integrated backscattering is around 3 dB over a 30 dB range. A comparison of CoSSIR retrieved and CRS measured reflectivity shows that CoSSIR has the ability to retrieve low-resolution ice cloud profiles in the upper troposphere.
Highlights
There is ongoing interest in remote sensing of ice clouds due to their importance in radiative cloud feedbacks, precipitation, and upper troposphere water cycling
This paper describes a new Bayesian algorithm that retrieves ice cloud profiles and vertically integrated parameters
Compact Scanning Submillimeter-wave Imaging Radiometer (CoSSIR) is a scanning radiometer, the results shown to this point have been from the nadir brightness temperature data, which allow direct comparison with the Cloud Radar System data and facilitate visualization of retrieved profiles
Summary
There is ongoing interest in remote sensing of ice clouds due to their importance in radiative cloud feedbacks, precipitation, and upper troposphere water cycling. The simpler retrieval algorithms fix any factor that cannot be retrieved, for example, assuming a particular mixture of particle shapes, a fixed size distribution for each effective radius, homogeneous ice cloud, no underlying water clouds, and a specified surface albedo depending on surface type Making these assumptions allows forward radiative transfer modeling to be used to construct a lookup table that, for example, relates two observed radiances to water path and effective radius. Atmospheric parameters that ought to vary according to a prior pdf (because they affect the observations) are often fixed to simplify and speed the solution For these reasons and because the forward function is not linear over the range of retrieval uncertainty, the retrieval errors from optimal estimation are usually substantially underestimated.
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