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In this article, an unsupervised clustering-based method for identifying rain-contaminated and low-backscatter regions in X-band marine radar images is presented. Rain blurs the wave signatures of radar images, and low-backscatter images caused by calibration errors or too-low wind speed contain little or no wave signatures. In both cases, ocean surface parameter measurement using X-band marine radar will be negatively affected. Four types of features can be extracted based on the distinct difference in texture and pixel intensity distribution between rain-free, rain-contaminated, and low-backscatter regions. Features extracted from each pixel are combined into a feature vector and mapped onto a $10\times 10$ -neuron self-organizing map (SOM). Then, the hierarchical agglomerative clustering algorithm is introduced, which clustered those neurons into three types (i.e., rain-free, rain-contaminated, and low-backscatter). The method is validated using the shipborne marine radar data collected on the East Coast of Canada. The good agreement between the pixel-based clustering results and manually segmented reference images indicates that both rain-contaminated and low-backscatter regions can be identified effectively using the proposed method. [ABSTRACT FROM AUTHOR]

A physically based method is developed to estimate the microphysical structure of the melting layer in stratiform rain using airborne observations by a dual-frequency radar and a 10.7-GHz radiometer. The method employs a nonlinear optimal estimation approach to find two parameters of the gamma drop size distribution (DSD) at each radar range gate from the Ku/Ka-band reflectivities. The DSD profile is used to determine the atmospheric absorption/extinction profile, which enables the surface contribution to the measured brightness temperature to be estimated. The surface wind speed is estimated from the surface emissivity by inverting the forward model, which relates the two. Retrievals in stratiform precipitation require a model to describe the thermodynamic and electromagnetic properties of melting hydrometeors. The melting layer can contribute a majority of the total atmospheric absorption, making it a key component for accurate retrievals in stratiform rain. Several melting layer models were evaluated based on their fit to the dual-frequency reflectivity measurements in the melting layer. A candidate model is selected and tuned to match the radar measurements. The melting layer model is then incorporated into the full forward model for the brightness temperature observed by the radiometer. The surface wind speed assumed in the forward model is forced by the radiometer observations. If the actual surface wind speed is known, this approach provides a powerful constraint on the possible melting layer model. A case study is presented from an airborne campaign over areas of precipitation off the coast of Vancouver Island, British Columbia, Canada. The estimated wind speeds are found to be uncorrelated with the reflectivity and their average value is within 1 m s-1 of that retrieved in a clear area adjacent to the rain. [ABSTRACT FROM AUTHOR]

Turbulence inhomogeneities at 3-m scales can be either isotropic or anisotropic, and the degree of anisotropy can be measured with VHF wind profiler radars. Studies over a period of two years in Montreal, Quebec, Canada, have shown that for this site during nonwinter months, the occurrence of isotropic turbulence at 3-m scales serves as a useful diagnostic for the occurrence of rainfall. Turbulent eddies are expected to be most isotropic when wind shears are weak and the atmosphere is either unstable or close to instability. The measurements show that when the turbulence is most isotropic, rainfall is very common. Furthermore, the development of quasi-isotropic turbulence usually occurs before the onset of precipitation. Lead times are typically of the order of 1 to 6 h. Correlation coefficients between the occurrence of strong isotropy and the development of rain generally exceed 0.6, and can be as high as 0.7. Further studies will be required to determine whether this phenomenon is specific to the Montreal area, or can be applied to other locations as well. [ABSTRACT FROM AUTHOR]