Comparisons are made between the reflectivity Z, median volume diameter D0, and rain rate R from a dual-frequency profiler and the C-band polarimetric radar (C-POL), which are both located near Darwin, Australia. Examples from the premonsoon “buildup” regime and the monsoon (oceanic) regime are used to illustrate the excellent agreement between the dual-profiler retrievals and the polarimetric radar-based retrievals. This work builds on similar works that were limited in scope to shallow tropical showers and predominantly stratiform rain events. The dual-frequency profiler retrievals of D0 and R herein are based on ensemble statistics, whereas the polarimetric radar retrievals are based on algorithms derived by using one season of disdrometer data from Darwin along with scattering simulations. The latest drop shape versus D relation is used as well as the canting angle distribution results obtained from the 80-m fall bridge experiment in the scattering simulations. The scatterplot of D0 from dual-frequency profiler versus Zdr measurements from C-POL is shown to be consistent not only with the theoretical simulations and prior data but also within prior predicted error bars for both stratiform rain as well as convective rain. Based on dual-frequency profiler–retrieved gamma drop size distribution parameters, a new smoothly varying “separator” indexing scheme has been developed that classifies between stratiform and convective rain types, including a continuous “transition” region between the two. This indexing technique has been applied on a number of low-elevation-angle plan position indicator (PPI) sweeps with the C-POL from the two regime examples, to construct “unconditioned” histograms of D0 in stratiform and convective rain (to within the sensitivity of the radar). With reference to the latter, it is demonstrated that the distribution of D0 is different in the buildup example than in the monsoon example, because of the differences in both the microphysical and kinematic features between the two regimes. In particular, (i) the mean D0 is significantly larger in the buildup example than in the monsoon example, irrespective of rain type; (ii) the histogram width (or standard deviation) is much larger for the buildup example than the monsoon example, irrespective of rain type; and (iii) the histogram skewness is negative for the monsoon regime example because of a lack of larger D0 values, whereas the buildup histogram is positively skewed irrespective of rain type.

An algorithm has been developed that classifies precipitating clouds into either stratiform, mixed stratiform/convective, deep convective, or shallow convective clouds by analyzing the vertical structure of reflectivity, velocity, and spectral width derived from measurements made with the vertical beam of a 915-MHz Doppler wind profiler. The precipitating clouds classified as stratiform and convective clouds match the physical and radar properties deduced by Doppler weather radars in the GATE and EMEX programs. The mixed stratiform/convective cloud category is a hybrid regime containing a melting-layer signature associated with stratiform clouds yet is turbulent above the melting level similar to convective clouds. Shallow convective clouds have hydrometeors confined entirely below the melting level implying that warm rain processes are occurring exclusively. The algorithm is illustrated by classifying precipitating clouds from 10 months of observations at Manus Island (2°S, 147°E) in the western... Abstract An algorithm has been developed that classifies precipitating clouds into either stratiform, mixed stratiform/convective, deep convective, or shallow convective clouds by analyzing the vertical structure of reflectivity, velocity, and spectral width derived from measurements made with the vertical beam of a 915-MHz Doppler wind profiler. The precipitating clouds classified as stratiform and convective clouds match the physical and radar properties deduced by Doppler weather radars in the GATE and EMEX programs. The mixed stratiform/convective cloud category is a hybrid regime containing a melting-layer signature associated with stratiform clouds yet is turbulent above the melting level similar to convective clouds. Shallow convective clouds have hydrometeors confined entirely below the melting level implying that warm rain processes are occurring exclusively. The algorithm is illustrated by classifying precipitating clouds from 10 months of observations at Manus Island (2°S, 147°E) in the western...

The Midlatitude Continental Convective Clouds Experiment (MC3E), a field program jointly led by the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Program and the National Aeronautics and Space Administration’s (NASA) Global Precipitation Measurement (GPM) mission, was conducted in south-central Oklahoma during April–May 2011. MC3E science objectives were motivated by the need to improve our understanding of midlatitude continental convective cloud system life cycles, microphysics, and GPM precipitation retrieval algorithms. To achieve these objectives, a multiscale surface- and aircraft-based in situ and remote sensing observing strategy was employed. A variety of cloud and precipitation events were sampled during MC3E, of which results from three deep convective events are highlighted. Vertical structure, air motions, precipitation drop size distributions, and ice properties were retrieved from multiwavelength radar, profiler, and aircraft observations for a mesoscale convective system (MCS) on 11 May. Aircraft observations for another MCS observed on 20 May were used to test agreement between observed radar reflectivities and those calculated with forward-modeled reflectivity and microwave brightness temperatures using in situ particle size distributions and ice water content. Multiplatform observations of a supercell that occurred on 23 May allowed for an integrated analysis of kinematic and microphysical interactions. A core updraft of 25 m s⁻¹ supported growth of hail and large raindrops. Data collected during the MC3E campaign are being used in a number of current and ongoing research projects and are available through the ARM and NASA data archives.

This work presents two different forms of dense, high-resolution radar data from two frequency modulated continuous wave radar sensors, along sparse radar pointclouds produced by one of the radar sensors. In addition, all datasets include 3D lidar and inertial measurements, and a lidar-based simultaneous localization and mapping pose estimation. Over 2 h of 6D pose data was generated across 52 datasets collected in highly diverse 3D environments including lab spaces, outside and inside large buildings, urban walkways, and a mine. One dataset, from the ASPEN Lab, also includes precision groundtruth generated from a motion capture system. Intrinsic radar calibration and measured extrinsic sensor position calibrations are also provided along with python based development tools to interact with the various datasets. This data is designed to assist with generating radar based localization algorithms and calibrations between radar and other sensors.