Abstract
Abstract. Radar-based retrievals are often employed to characterize the microphysical properties of cloud hydrometeors, i.e. their phases, habits, densities as well as their respective size and orientation distributions. These techniques are based on a synergetic use of different cloud observation sensor(s) and microphysical model(s) where the information extracted from both sensors and models is combined and converted into microphysical cloud properties. However, the amount of available information is often limited, which forces current microphysical retrieval techniques to base their algorithms on several microphysical assumptions which affect the retrieval accuracy. By simultaneously combining Doppler and polarimetric measurements obtained from fully Doppler polarimetric radars, it is possible to create spectral polarimetric parameters. Although these parameters are easily contaminated with unwanted echoes, this work shows that, from a correct radar signal processing based on filtering and averaging techniques, spectral polarimetric parameters can be correlated to microphysical cloud properties. In particular, preliminary results suggest that particle orientations and habits can be determined from the sole use of such spectral polarimetric parameters. Therefore, such additional spectral polarimetric information offers an opportunity to improve current microphysical retrievals by reducing the number of microphysical assumptions in them.
Highlights
There is a body of literature in atmospheric science that considers cloud microphysical parameterizations as one of the main sources of error in operational climate and weather forecast models (e.g. Cantrell and Heymsfield, 2005; Karcher and Koop, 2005)
In order to improve microphysical retrieval techniques based on spectral polarimetric parameters, further filtering is applied to sZDR(v) using additional clipping and statistical estimators
Most cloud microphysical retrieval techniques suffer from a lack of microphysical information provided by current observations, such as radar sensors, for the microphysical characterization of ice/mixed-phase clouds
Summary
There is a body of literature in atmospheric science that considers cloud microphysical parameterizations as one of the main sources of error in operational climate and weather forecast models (e.g. Cantrell and Heymsfield, 2005; Karcher and Koop, 2005). Typical radar-based retrievals only use the zero, first and second moments of the Doppler reflectivity spectrum, i.e. the reflectivity, the mean Doppler velocity and the Doppler width respectively These radar quantities contain information coming from a mixture of several microphysical parameters (as mentioned above) which are difficult to differentiate without the synergetic use of other sensors and microphysical model information. In Matrosov et al (2000), for example, polarimetric information is employed to infer the shape of cloud particles assuming spheroidal shapes and microphysical homogeneity within the radar resolution volume Notwithstanding their limitations, these findings suggest that Doppler and polarimetric information significantly improve cloud retrieval algorithms mentioned previously by reducing the number of microphysical assumptions.
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