Search for: All records

Abstract The Precipitation Occurrence Sensor System (POSS) is a small X-band Doppler radar that measures the Doppler velocity spectra from precipitation falling in a small volume near the sensor. The sensor records a 2D frequency of occurrence matrix of the velocity and power at the mode of each spectrum measured over 1 min. The centroid of the distribution of these modes, along with other spectral parameters, defines a data vector input to a multiple discriminant analysis (MDA) for classification of the precipitation type. This requires the a priori determination of a training set for different types, particle size distributions (PSDs), and wind speed conditions. A software model combines POSS system parameters, a particle scattering cross section, and terminal velocity models, to simulate the real-time Doppler signal measured by the system for different PSDs and wind speeds. This is processed in the same manner as the system hardware to produce bootstrap samples of the modal centroid distributions for the MDA training set. MDA results are compared to images from the Multi-Angle Snowflake Camera (MASC) at the MASCRAD site near Easton, Colorado, and to the CSU–CHILL X-band radar observations from Greeley, Colorado. In the four case studies presented, POSS successfully identified precipitation transitions through a range of types (rain, graupel, rimed dendrites, aggregates, unrimed dendrites). Also two separate events of hail were reported and confirmed by the images. 

Abstract. The lower-order moments of the drop size distribution (DSD) have generally been considered difficult to retrieve accurately from polarimetric radar data because these data are related to higher-order moments. For example, the 4.6th moment is associated with a specific differential phase and the 6th moment with reflectivity and ratio of high-order moments with differential reflectivity. Thus, conventionally, the emphasis has been to estimate rain rate (3.67th moment) or parameters of the exponential or gamma distribution for the DSD. Many double-moment “bulk” microphysical schemes predict the total number concentration (the 0th moment of the DSD, or M0) and the mixing ratio (or equivalently, the 3rd moment M3). Thus, it is difficult to compare the model outputs directly with polarimetric radar observations or, given the model outputs, forward model the radar observables. This article describes the use of double-moment normalization of DSDs and the resulting stable intrinsic shape that can be fitted by the generalized gamma (G-G) distribution. The two reference moments are M3 and M6, which are shown to be retrievable using the X-band radar reflectivity, differential reflectivity, and specific attenuation (from the iterative correction of measured reflectivity Zh using the total Φdp constraint, i.e., the iterative ZPHI method). Along with the climatological shape parameters of the G-G fit to the scaled/normalized DSDs, the lower-order moments are then retrieved more accurately than possible hitherto. The importance of measuring the complete DSD from 0.1 mm onwards is emphasized using, in our case, an optical array probe with 50 µm resolution collocated with a two-dimensional video disdrometer with about 170 µm resolution. This avoids small drop truncation and hence the accurate calculation of lower-order moments. A case study of a complex multi-cell storm which traversed an instrumented site near the CSU-CHILL radar is described for which the moments were retrieved from radar and compared with directly computed moments from the complete spectrum measurements using the aforementioned two disdrometers. Our detailed validation analysis of the radar-retrieved moments showed relative bias of the moments M0 through M2 was <15 % in magnitude, with Pearson’s correlation coefficient >0.9. Both radar measurement and parameterization errors were estimated rigorously. We show that the temporal variation of the radar-retrieved mass-weighted mean diameter with M0 resulted in coherent “time tracks” that can potentially lead to studies of precipitation evolution that have not been possible so far. 

Abstract The intensity of deep convective storms is driven in part by the strength of their updrafts and cold pools. In spite of the importance of these storm features, they can be poorly represented within numerical models. This has been attributed to model parameterizations, grid resolution, and the lack of appropriate observations with which to evaluate such simulations. The overarching goal of the Colorado State University Convective CLoud Outflows and UpDrafts Experiment (C 3 LOUD-Ex) was to enhance our understanding of deep convective storm processes and their representation within numerical models. To address this goal, a field campaign was conducted during July 2016 and May–June 2017 over northeastern Colorado, southeastern Wyoming, and southwestern Nebraska. Pivotal to the experiment was a novel “Flying Curtain” strategy designed around simultaneously employing a fleet of uncrewed aerial systems (UAS; or drones), high-frequency radiosonde launches, and surface observations to obtain detailed measurements of the spatial and temporal heterogeneities of cold pools. Updraft velocities were observed using targeted radiosondes and radars. Extensive datasets were successfully collected for 16 cold pool–focused and seven updraft-focused case studies. The updraft characteristics for all seven supercell updraft cases are compared and provide a useful database for model evaluation. An overview of the 16 cold pools’ characteristics is presented, and an in-depth analysis of one of the cold pool cases suggests that spatial variations in cold pool properties occur on spatial scales from O (100) m through to O (1) km. Processes responsible for the cold pool observations are explored and support recent high-resolution modeling results. 

Abstract Observations of the air vertical velocities ( w air ) in supercell updrafts are presented, including uncertainty estimates, from radiosonde GPS measurements in two supercells. These in situ observations were collected during the Colorado State University Convective Cloud Outflows and Updrafts Experiment (C 3 LOUD-Ex) in moderately unstable environments in Colorado and Wyoming. Based on the radiosonde accelerations, instances when the radiosonde balloon likely bursts within the updraft are determined, and adjustments are made to account for the subsequent reduction in radiosonde buoyancy. Before and after these adjustments, the maximum estimated w air values are 36.2 and 49.9 m s −1 , respectively. Radar data are used to contextualize the in situ observations and suggest that most of the radiosonde observations were located several kilometers away from the most intense vertical motions. Therefore, the radiosonde-based w air values presented likely underestimate the maximum values within these storms due to these sampling biases, as well as the impacts from hydrometeors, which are not accounted for. When possible, radiosonde-based w air values were compared to estimates from dual-Doppler methods and from parcel theory. When the radiosondes observed their highest w air values, dual-Doppler methods generally produced 15–20 m s −1 lower w air for the same location, which could be related to the differences in the observing systems’ resolutions. In situ observations within supercell updrafts, which have been limited in recent decades, can be used to improve our understanding and modeling of storm dynamics. This study provides new in situ observations, as well as methods and lessons that could be applied to future field campaigns.