Cloud radar Doppler spectra in drizzling stratiform clouds: 1. Forward modeling and remote sensing applications

• Published in: Journal of Geophysical Research 2011-07, Vol.116 (D13), p.n/a, Article D13201
• Main Authors: Kollias, Pavlos, Rémillard, Jasmine, Luke, Edward, Szyrmer, Wanda
• Format: Article
• Language: English
• Subjects: ASYMMETRY; Atmospheric boundary layer; Atmospheric sciences; CLOUDS; DISTRIBUTION; drizzle; ENVIRONMENTAL SCIENCES; Geophysics; LINE BROADENING; Precipitation; RADAR; REFLECTIVITY; REMOTE SENSING; RESOLUTION; SAMPLING; SENSITIVITY; SIGNAL-TO-NOISE RATIO; SIMULATION; SPECTRA; STATISTICS; stratus; TURBULENCE; VELOCITY
• ISSN: 0148-0227; 2169-897X; 2156-2202; 2169-8996
• DOI: 10.1029/2010JD015237

Several aspects of spectral broadening and drizzle growth in shallow liquid clouds remain not well understood. Detailed, cloud‐scale observations of microphysics and dynamics are essential to guide and evaluate corresponding modeling efforts. Profiling, millimeter‐wavelength (cloud) radars can provide such observations. In particular, the first three moments of the recorded cloud radar Doppler spectra, the radar reflectivity, mean Doppler velocity, and spectrum width, are often used to retrieve cloud microphysical and dynamical properties. Such retrievals are subject to errors introduced by the assumptions made in the inversion process. Here, we introduce two additional morphological parameters of the radar Doppler spectrum, the skewness and kurtosis, in an effort to reduce the retrieval uncertainties. A forward model that emulates observed radar Doppler spectra is constructed and used to investigate these relationships. General, analytical relationships that relate the five radar observables to cloud and drizzle microphysical parameters and cloud turbulence are presented. The relationships are valid for cloud‐only, cloud mixed with drizzle, and drizzle‐only particles in the radar sampling volume and provide a seamless link between observations and cloud microphysics and dynamics. The sensitivity of the five observed parameters to the radar operational parameters such as signal‐to‐noise ratio and Doppler spectra velocity resolution are presented. The predicted values of the five observed radar parameters agree well with the output of the forward model. The novel use of the skewness of the radar Doppler spectrum as an early qualitative predictor of drizzle onset in clouds is introduced. It is found that skewness is a parameter very sensitive to early drizzle generation. In addition, the significance of the five parameters of the cloud radar Doppler spectrum for constraining drizzle microphysical retrievals is discussed. Key Points Description of a new approach of using radar Doppler spectra to study drizzle Provides a new technique for detecting the precipitation onset in stratus Improvement of drizzle quantitative retrievals