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Using the unprecedented observational capabilities deployed during the Cooperative Atmosphere-Surface Exchange Study-99 (CASES-99), we found three distinct turbulence events on the night of 18 October 1999, each of which was associated with different phenomena: a density current, solitary waves, and downward propagating waves from a low-level jet. In this study, we focus on the first event, the density current and its associated intermittent turbulence. As the cold density current propagated through the CASES-99 site, eddy motions in the upper part of the density current led to periodic overturning of the stratified flow, local thermal instability and a downward diffusion of turbulent mixing. Propagation of the density current induced a secondary circulation. The descending motion following the head of the density current resulted in strong stratification, a sharp reduction in the turbulence, and a sudden increase in the wind speed. As the wind surge propagated toward the surface, shear instability generated upward diffusion of turbulent mixing. We demonstrate in detail that the height and sequence of the local thermal and shear instabilities associated with the dynamics of the density current are responsible for the apparent intermittent turbulence.

Abstract-The recent use of along-track interferometry (ATI) in synthetic aperture radar (SAR) has shown promise for synoptic measurement of ocean surface currents. ATI-SARs have been used to estimate wave fields, currents, and current features. This paper describes and analyzes a dual-beam along-track interferometer to provide spatially resolved vector surface velocity estimates with a single pass of an aircraft. The design employs a pair of interferometer beams, one squinted forward and one squinted aft. Each interferometric phase is sensitive to the component of surface Doppler velocity in the direction of the beam. Therefore, a proper combination of these measurements provides a vector surface velocity estimate in one pass of the aircraft. We find that precise measurements dictate widely spaced beams and that the spatial resolution for the squinted SAR is essentially identical to the sidelooking case. Practical instrument design issues are discussed, and an airborne system currently in development is described. Through computer simulation, we observe the azimuthal displacement of interferometric phases by moving surfaces identical to those of conventional SAR and find that such displacement can bias the estimated surface velocity.

X-band and shorter radar wavelengths are preferable for mobile radar systems because a narrow beam can be realized with a moderately sized antenna. However, attenuation by precipitation becomes progressively more severe with decreasing radar wavelength. As a result, X band has become a popular choice for meteorological radar systems that balances these two considerations. Dual-polarization provides several methods by which this attenuation (and differential attenuation) can be detected and corrected, mitigating one of the primary disadvantages of X-band radars. The dynamics of severe convective storms depend, to some extent, on the distribution and type of hydrometeors within the storm. To estimate the three-dimensional distribution of hydrometeors using X-band radar data, it is necessary to correct for attenuation before applying commonly used hydrometeor classification algorithms. Since 2002, a mobile dual-polarized Doppler weather radar designed at the University of Massachusetts, Amherst has been used to collect high-resolution data in severe convective storms in the plains. This study tests several attenuation correction procedures using dual-polarization measurements, along with a dual-frequency method using S-band Weather Surveillance Radar-1988 Doppler (WSR-88D) and KOUN data. After correcting for attenuation and differential attenuation, a fuzzy logic hydrometeor classification algorithm, modified for X band with KOUN data as a reference, is used to attempt a retrieval of hydrometeor types in observed severe convective storms.

A new fuzzy logic hydrometeor classification algorithm is proposed that takes into account data-based membership functions, measurement conditions, and three-dimensional temperature information provided by a high-resolution nonhydrostatic numerical weather prediction model [the Application of Research to Operations at Mesoscale model (AROME)]. The formulation of the algorithm is unique for X-, C-, and S-band radars and employs wavelength-adapted bivariate membership functions for (Z H , Z DR ), (Z H , K DP ), and (Z H , r HV ) that were established by using real data collected by the French polarimetric radars and T-matrix simulations. The distortion of membership functions caused by deteriorating measurement conditions (e.g., precipitation-induced attenuation, signal-to-clutter ratio, signal-to-noise ratio, partial beam blocking, and distance) is documented empirically and subsequently parameterized in the algorithm. The result is an increase in the amount of overlapping between the membership functions of the different hydrometeor types. The relative difference between the probability function values of the first and second choice of the hydrometeor classification algorithm is analyzed as a measure of the quality of identification. Semiobjective scores are calculated using an expert-built validation dataset to assess the respective improvements brought by using ''richer'' temperature information and by using more realistic membership functions. These scores show a significant improvement in the detection of wet snow.