Determination of Ice Water Path and Mass Median Particle Size Using Multichannel Microwave Measurements

Abstract

The method of simultaneously retrieving ice water path and mass median diameter using microwave data at two frequencies is examined and implemented for tropical nonprecipitating clouds. To develop the retrieval algorithm, the authors first derived a bulk mass?size relation for ice particles in tropical clouds based on microphysical data collected during the Central Equatorial Pacific Experiment. This relation effectively allows ice particle density to decrease with particle size. In implementing the retrieval algorithm, 150- and 220-GHz Millimeter-Wave Imaging Radiometer data collected during the Tropical Ocean and Global Atmosphere Coupled Ocean?Atmosphere Response Experiment were used. Ice water path and mass median diameter are determined based on a lookup table generated by a radiative transfer model. The lookup table depends on cloud type, cloud liquid water path, and atmospheric temperature and humidity profiles. Only nonprecipitating clouds are studied in this paper. Error analyses were performed by a Monte Carlo procedure in which atmospheric profiles, ice cloud height, liquid water content, surface temperature, and instrument noise vary randomly within their uncertainty range through a Latin hypercube sampling scheme. The rms error in the retrievals is then assessed and presented in a two-dimensional diagram of ice water path and mass median diameter. It is shown that the simultaneous retrieval method using 150 and 220 GHz may be used for clouds with ice water path larger than 200 g m?2 and mass median diameter larger than 200 ?m. To obtain meaningful retrievals for ?thinner? clouds, higher microwave frequencies are needed. It is also shown that liquid water clouds that are at the same altitude as ice clouds interfere with the retrievals to a significant degree. To obtain reasonable ice water path and mass median size retrievals, it is necessary first to group clouds into several classes, then to apply separate algorithms to the different classes. The accuracy of the retrievals also depends on cloud type, with the best accuracy for cirrus and the worst for the midtop mixed-phase cloud among the clouds investigated in this study.