Abstract

Data from the Collaborative Adaptive Sensing of the Atmosphere (CASA) Integrated Project I (IP1) network of polarimetric X-band radars are used to observe a convective storm. A fuzzy logic hydrometeor identification algorithm is employed to study microphysical processes. Dual-Doppler techniques are used to analyze the 3D wind field. The scanning strategy, sensitivity, and low-level scanning focus of the radars are investigated for influencing bulk hydrometeor identification and dual-Doppler wind retrievals. Comparisons are made with the nearby S-band polarimetric Next Generation Weather Radar (NEXRAD) prototype radar (KOUN), for consistency. Lightning data are used as an independent indicator of storm evolution for comparison with radar observations.

A new methodology for retrieving the vertical wind utilizing upward and variational integration techniques is employed and shown to illustrate trends in mean wind, with particularly good results at low levels. IP1 observations of a case on 10 June 2007 show the development of the updraft, subsequent graupel echo volume evolution, and a descending downdraft preceded by significant graupel in the midlevels, with updraft and graupel volumes leading the onset of lightning. Many of these trends are corroborated by KOUN. The high temporal resolution of three minutes and near-ground sampling provided by IP1 is integral to resolving up- and downdrafts, as well as hydrometeor evolution. IP1 coverage of the upper levels is diminished compared to KOUN, impacting the quality of the dual-Doppler derived vertical winds and ice echo volumes, although the low-level coverage helps to mitigate some errors. However, IP1 coverage of the low- to midlevels is demonstrated to be comparable or better than coverage by KOUN for this storm location.

Abstract

Although much work has been done at S band to automatically identify hydrometeors by using polarimetric radar, several challenges are presented when adapting such algorithms to X band. At X band, attenuation and non-Rayleigh scattering can pose significant problems. This study seeks to develop a hydrometeor identification (HID) algorithm for X band based on theoretical simulations using the T-matrix scattering model of seven different hydrometeor types: rain, drizzle, aggregates, pristine ice crystals, low-density graupel, high-density graupel, and vertical ice. Hail and mixed-phase hydrometeors are excluded for the purposes of this study. Non-Rayleigh scattering effects are explored by comparison with S-band simulations. Variable ranges based on the theoretical simulations are used to create one-dimensional fuzzy-logic membership beta functions that form the basis of the new X-band HID. The theory-based X-band HID is applied to a case from the Collaborative Adaptive Sensing of the Atmosphere (CASA) Integrated Project 1 (IP1) network of X-band radars, and comparisons are made with similar S-band hydrometeor identification algorithms applied to data from the S-band polarimetric Next Generation Weather Radar (NEXRAD) prototype radar, KOUN. The X-band HID shows promise for illustrating bulk hydrometeor types and qualitatively agrees with analysis from KOUN. A simple reflectivity- and temperature-only HID is also applied to both KOUN and CASA IP1 data to reveal benefits of the polarimetric-based HID algorithms, especially in the classification of ice hydrometeors and oriented ice crystals.

Abstract

Polarimetric Doppler radars provide valuable information about the kinematic and microphysical structure of storms. However, in-depth analysis using radar products, such as Doppler-derived wind vectors and hydrometeor identification, has been difficult to achieve in (near) real time, mainly because of the large volumes of data generated by these radars, lack of quick access to these data, and the challenge of applying quality-control measures in real time. This study focuses on modifying and automating several radar-analysis and quality-control algorithms currently used in postprocessing and merging the resulting data from several radars into an integrated analysis and display in (near) real time. Although the method was developed for a specific network of four Doppler radars: two Weather Surveillance Radar-1988 Doppler (WSR-88D) radars (KFTG and KCYS) and two Colorado State University (CSU) research radars [Pawnee and CSU–University of Chicago–Illinois State Water Survey (CSU–CHILL)], the software is easily adaptable to any radar platform or network of radars. The software includes code to synthesize radial velocities to obtain three-dimensional wind vectors and includes algorithms for automatic quality control of the raw polarimetric data, hydrometeor identification, and rainfall rate. The software was successfully tested during the summers of 2004 and 2005 at the CSU–CHILL radar facility, ingesting data from the four-radar network. The display software allows users the ability to view mosaics of reflectivity, wind vectors, and rain rates, to zoom in and out of radar features easily, to create vertical cross sections, to contour data, and to archive data in real time. Despite the lag time of approximately 10 min, the software proved invaluable for diagnosing areas of intense rainfall, hail, strong updrafts, and other features such as mesocyclones and convergence lines. A case study is presented to demonstrate the utility of the software.

Abstract

Isolated warm-rain cells are an important feature over the tropical oceans. Although warm rain is typically associated with relatively small raindrops, large raindrops (>4.5 mm in diameter) have been observed in some cases. Previous studies have examined warm rain cells with large drops on a case-study basis, but they have yet to be investigated in a broader, statistical sense. During the recent Propagation of Intraseasonal Oscillations (PISTON) field campaign, a C-band polarimetric radar routinely measured extreme values of differential reflectivity in small, isolated convection, indicating the presence of large drops. Using an objective feature identification and tracking algorithm, this study offers new insights to the structure and frequency of cells containing large drops. Cells with high differential reflectivity (>3.5 dB) were present in 24% of all radar scans. The cells were typically small (8-km² mean area), short lived (usually <10 min), and shallow (3.7-km mean height). High differential reflectivity was more often found on the upwind side of the cells, suggesting a size sorting mechanism was operating establishing a low concentration of large drops on the upwind side. Differential reflectivity also tended to increase at lower altitudes, which is hypothesized to be due to continued drop growth and increasing temperature (increasing the dielectric constant of water). Rapid vertical cross-section radar scans, as well as transects made by a Learjet aircraft with onboard particle probes, are also used to analyze these cells, and support the conclusions drawn from statistical analysis.

Abstract

The issuance of timely warnings for the occurrence of severe-class hail (hailstone diameters of 2.5 cm or larger) remains an ongoing challenge for operational forecasters. This study examines the application of two remotely sensed data sources between 0100 and 0400 UTC 14 July 2011 when pulse-type severe thunderstorms occurred in the jurisdiction of the Denver/Boulder National Weather Service (NWS) Forecast Office in Colorado. First, a developing hailstorm was jointly observed by the dual-polarization Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) research radar and by the operational, single-polarization NWS radar at Denver/Front Range (KFTG). During the time period leading up to the issuance of the initial severe thunderstorm warning, the dual-polarization radar data near the 0 °C altitude contained a positive differential reflectivity Z _DR column (indicating a strong updraft lofting supercooled raindrops above the freezing level). Correlation coefficient ρ _HV reductions to ~0.93, probably due to the presence of growing hailstones, were observed above the freezing level in portions of the developing >55-dBZ echo core. Second, data from the National Lightning Detection Network (NLDN), including the locations and polarity of cloud-to-ground (CG) discharges produced by several of the evening’s storms, were processed. Some association was found between the prevalence of positive CGs and storms that produced severe hail. The analyses indicate that the use of the dual-polarization data provided by the upgraded Weather Surveillance Radar-1988 Doppler (WSR-88D), in combination with the NLDN data stream, can assist operational forecasters in the real-time identification of thunderstorms that pose a severe hail threat.

Abstract

Two-dimensional video disdrometer (2DVD) data were analyzed from two equatorial Indian (Gan) and west Pacific Ocean (Manus) islands where precipitation is primarily organized by the intertropical convergence zone and the Madden–Julian oscillation (MJO). The 18 (3.5) months of 2DVD data from Manus (Gan) Island show that 1) the two sites have similar drop size distribution (DSD) spectra of liquid water content, median diameter, rain rate R, radar reflectivity z, normalized gamma number concentration N _w , and other integral rain parameters; 2) there is a robust N _w -based separation between convective (C) and stratiform (S) DSDs at both sites that produces consistent separation in other parameter spaces.

The 2DVD data indicate an equatorial, maritime average C/S rainfall accumulation fraction (frequency) of 81/19 (41/59) at these locations. It is hypothesized that convective fraction and frequency estimates are slightly higher than previous radar-based studies, because the ubiquitous weak, shallow convection (<10 mm h⁻¹) characteristic of the tropical warm pool is properly resolved by this high-resolution DSD dataset and identification method. This type of convection accounted for about 30% of all rain events and 15% of total rain volume. These rain statistics were reproduced when newly derived C/S R(z) equations were applied to 2DVD-simulated reflectivity. However, the benefits of using separate C/S R(z) equations are only realizable when C/S partitioning properly classifies each rain type. A single R(z) relationship fit to all 2DVD data yielded accurate total rainfall amounts but overestimated (underestimated) the stratiform (convective) rain fraction by ±10% and overestimated (underestimated) stratiform (convective) rain accumulation by +50% (−15%).

Abstract

Orographic precipitation results from complex interactions between terrain, large-scale flow, turbulent motions, and microphysical processes. This study appeals to polarimetric radar data in conjunction with surface-based disdrometer observations, airborne particle probes, and reanalysis data to study these processes and their interactions as observed during the Olympic Mountain Experiment (OLYMPEX). Radar and disdrometer observations from OLYMPEX, which was conducted over the Olympic Peninsula in the winter of 2015, revealed 3 times as much rain fell over elevated sites compared to those along the ocean and coast. Several events were marked by significant water vapor transport and strong onshore flow. Detailed analysis of four cases demonstrated that the warm sector, which previous authors noted to be a period of strong orographic enhancement over the terrain, is associated not only with deeper warm cloud regions, but also deeper cold cloud regions, with the latter supporting the growth of dendritic ice crystals between 4 and 6 km. This dendritic growth promotes enhanced aggregation just above the melting layer, which then seeds the warm cloud layer below, allowing additional drop growth via coalescence. Periods of subsynoptic variability associated with mesoscale boundaries and low-level jets are shown to locally modify both the ice microphysics as well as surface drop-size distributions. This study explores the spatial and temporal variability of precipitation, cloud microphysics, and their relationship over the complex terrain of the Olympic Peninsula.

Abstract

Dual-polarization radar rainfall estimation relationships have been extensively tested in continental and subtropical coastal rain regimes, with little testing over tropical oceans where the majority of rain on Earth occurs. A 1.5-yr Indo-Pacific warm pool disdrometer dataset was used to quantify the impacts of tropical oceanic drop-size distribution (DSD) variability on dual-polarization radar variables and their resulting utility for rainfall estimation. Variables that were analyzed include differential reflectivity Z _dr; specific differential phase K _dp; reflectivity Z _h ; and specific attenuation A _h . When compared with continental or coastal convection, tropical oceanic Z _dr and K _dp values were more often of low magnitude (<0.5 dB, <0.3° km⁻¹) and Z _dr was lower for a given K _dp or Z _h , consistent with observations of tropical oceanic DSDs being dominated by numerous, small, less-oblate drops. New X-, C-, and S-band R estimators were derived: R(K _dp), R(A _h ), R(K _dp, ζ _dr), R(z, ζ _dr), and R(A _h , ζ _dr), which use linear versions of Z _dr and Z _h , namely ζ _dr and z. Except for R(K _dp), convective/stratiform partitioning was unnecessary for these estimators. All dual-polarization estimators outperformed updated R(z) estimators derived from the same dataset. The best-performing estimator was R(K _dp, ζ _dr), followed by R(A _h , ζ _dr) and R(z, ζ _dr). The R error was further reduced in an updated blended algorithm choosing between R(z), R(z, ζ _dr), R(K _dp), and R(K _dp, ζ _dr) depending on Z _dr > 0.25 dB and K _dp > 0.3° km⁻¹ thresholds. Because of these thresholds and the lack of hail, R(K _dp) was never used. At all wavelengths, R(z) was still needed 43% of the time during light rain (R < 5 mm h⁻¹, Z _dr < 0.25 dB), composing 7% of the total rain volume. As wavelength decreased, R(K _dp, ζ _dr) was used more often, R(z, ζ _dr) was used less often, and the blended algorithm became increasingly more accurate than R(z).

Abstract

This study contrasts midlatitude continental and tropical maritime deep convective cores using polarimetric radar observables and retrievals from selected deep convection episodes during the MC3E and TWPICE field campaigns. The continental convective cores produce stronger radar reflectivities throughout the profiles, while maritime convective cores produce more positive differential reflectivity Z _dr and larger specific differential phase K _dp above the melting level. Hydrometeor identification retrievals revealed the presence of large fractions of rimed ice particles (snow aggregates) in the continental (maritime) convective cores, consistent with the Z _dr and K _dp observations. The regional cloud-resolving model simulations with bulk and size-resolved bin microphysics are conducted for the selected cases, and the simulation outputs are converted into polarimetric radar signals and retrievals identical to the observational composites. Both the bulk and the bin microphysics reproduce realistic land and ocean (L-O) contrasts in reflectivity, polarimetric variables of rain drops, and hydrometeor profiles, but there are still large uncertainties in describing Z _dr and K _dp of ice crystals associated with the ice particle shapes/orientation assumptions. Sensitivity experiments are conducted by swapping background aerosols between the continental and maritime environments, revealing that background aerosols play a role in shaping the distinct L-O contrasts in radar reflectivity associated with raindrop sizes, in addition to the dominant role of background thermodynamics.

Abstract

The purpose of this study is to demonstrate the use of polarimetric observations in a radar-based winter hydrometeor classification algorithm. This is accomplished by deriving bulk electromagnetic scattering properties of stratiform, cold-season rain, freezing rain, sleet, dry aggregated snowflakes, dendritic snow crystals, and platelike snow crystals at X-, C-, and S-band wavelengths based on microphysical theory and previous observational studies. These results are then used to define the expected value ranges, or membership beta functions, of a simple fuzzy-logic hydrometeor classification algorithm. To test the algorithm’s validity and robustness, polarimetric radar data and algorithm output from four unique winter storms are investigated alongside surface observations and thermodynamic soundings. This analysis supports that the algorithm is able to realistically discern regions dominated by wet snow, aggregates, plates, dendrites, and other small ice crystals based solely on polarimetric data, with guidance from a melting-level detection algorithm but without external temperature information. Temperature is still used to distinguish rain from freezing rain or sleet below the radar-detected melting level. After appropriate data quality control, little modification of the algorithm was required to produce physically reasonable results on four different radar platforms at X, C, and S bands. However, classification seemed more robust at shorter wavelengths because the specific differential phase is heavily weighted in ice crystal classification decisions. It is suggested that parts, or all, of this algorithm could be applicable in both operational and research settings.