Interpretation of Polarization Features in Ground-Based Microwave Observations as Caused by Horizontally Aligned Oblate Raindrops

Abstract

Based on a comparison of ground-based radiometer measurements with microwave radiative transfer calculations, it is shown that raindrops with an oblate shape and a preferred horizontal orientation have a significant effect on microwave polarization signals when compared with spherical particle shape. Measurements with a dual-polarized 19-GHz radiometer reveal a polarization difference of as much as ?18 K in the downwelling microwave radiation at 30° elevation angle. Averaging all rain observations within 19 months leads to a signal of ?6 K. Model calculations covering roughly the same range of weather conditions as that inferred from the meteorological data recorded with the radiometer measurements were carried out with spherical raindrop shape and an oblate particle shape with a fixed horizontal alignment. From the model results, positive polarization difference is expected for spherical particles. This signal was never observed in the recorded data. For oblate drops, the averaged model results lead to a polarization difference of ?8 K, which is in reasonable agreement with the long-term averaged observations. Case studies that compare isolated rain events usually lead to a better match of model and observations. However, there are some major discrepancies in some cases. Possible reasons for the remaining differences are the short-term variations in the cloud microphysics for which the model does not correctly account, such as variations in the melting layer, drop oscillations, or variations in the drop size distribution or angular distribution of the drop alignment. Three-dimensional effects are also important when observing small-scale heavy precipitation. Despite remaining small uncertainties, the comparison presents strong evidence that the oblate raindrop shape, with fixed horizontal alignment, is by far the better choice for accurate radiative transfer calculations than is the spherical shape. The omission of this shape effect can cause significant errors when developing remote sensing algorithms based on model results.