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The CSU-CHILL radar is a dual-wavelength, dual-polarization weather radar system operating at S and X bands with coaxial beams. One of the capabilities of this radar system is the possibility of developing and/or validating algorithms across dual wavelengths and dual polarizations. This paper presents one such instance, showing how the rainfall field can be estimated either from the S- and X-band reflectivities or from the differential propagation phase at X band. To do so, the paper first presents a dual-wavelength attenuation correction method that uses the reflectivity measured at S band, as the constraint for the correction of the reflectivity measured at X band, and it describes how Mie scattering regions at X band may be detected from the retrieved path-integrated attenuation field. Then, the paper describes how the resulting specific attenuation field relates to rainfall and specific phase at X band, which can be obtained from dual-polarization data at a single wavelength as well, and shows examples. Finally, the paper looks at the relation between attenuation and the differential phase as a function of elevation angle for a few cases, which may be related to the drop size distribution and mean diameter, as well as temperature. [ABSTRACT FROM AUTHOR]

This article presents the data collected and analyzed using the University of Iowa's X-band polarimetric (XPOL) radars that were part of the spring 2013 hydrology-oriented Iowa Flood Studies (IFloodS) field campaign, sponsored by NASA's Global Precipitation Measurement (GPM) Ground Validation (GV) program. The four mobile radars have full scanning capabilities that provide quantitative estimation of the rainfall at high temporal and spatial resolutions over experimental watersheds. IFloodS was the first extensive test of the XPOL radars, and the XPOL radars demonstrated their field worthiness during this campaign with 46 days of nearly uninterrupted, remotely monitored, and controlled operations. This paper presents detailed postcampaign analyses of the high-resolution, research-quality data that the XPOL radars collected. The XPOL dual-polarimetric products and rainfall are compared with data from other instruments for selected diverse meteorological events at high spatiotemporal resolutions from unprecedentedly unique and vast data generated during IFloodS operations. The XPOL data exhibit a detailed, complex structure of precipitation viewed at multiple range resolutions (75 and 30 m). The inter-XPOL comparisons within an overlapping scanned domain demonstrate consistency across different XPOL units. The XPOLs employed a series of heterogeneous scans and obtained estimates of the meteorological echoes up to a range oversampling of 7.5 m. A finer-resolution (30 m) algorithm is described to correct the polarimetric estimates for attenuation at the X band and obtain agreement of attenuation-corrected products with disdrometers and NASA S-band polarimetric (NPOL) radar. The paper includes hardware characterization of Iowa XPOL radars conducted prior to the deployment in IFloodS following the GPM calibration protocol. [ABSTRACT FROM AUTHOR]

This paper reports a high-speed volumetric observation of a wet microburst event using X-band phased array weather radar (PAWR) in Japan. On 10 September 2014, PAWR observed the three-dimensional structure of a convection cell, which had a vertical extent of 5-6 km and a horizontal dimension of 2-10 km, moving toward the east-northeast. At 2310 Japan standard time (JST), a precipitation core with a radar reflectivity of >40 dB Z appeared at 3-5 km above ground level. The core then increased in size and intensity and rapidly descended to the ground. During this time, a reflectivity notch associated with midlevel inflow was initially formed near the top of the precipitation core and, subsequently, at lower altitudes. A strong low-level outflow with a radial divergence of >4 × 10−3 s−1 appeared just below the notch at around 2321 JST. The outflow lasted for approximately 13 min and eventually disappeared after 2333 JST along with dissipation of the causative storm cell. These results suggest that, in addition to hydrometeor loading, evaporative cooling due to the entrainment of midlevel relatively dry air played an additional role in driving a strong downdraft. The preceding signatures including descending precipitation core, reflectivity notch, and midlevel convergence observed by PAWR are useful precursors to forecast the occurrence of low-level wind shear 5-10 min ahead, which is important for safe air traffic operation. [ABSTRACT FROM AUTHOR]

By fitting a parametric vortex model directly to aliased radar radial velocities scanned from a hurricane, the maximum tangential velocity and its radial distance from the hurricane vortex center can be estimated by the recently developed alias-robust vortex analysis. This vortex analysis can be refined to produce a suitable reference radial velocity field on each tilt of a radar scan for the reference check in the first main step of dealiasing. This paper presents the techniques developed for the refinements and shows how and to what extent the refined vortex analysis can improve the reference check and thus enhance the dealiased data coverage, especially over severely aliased data areas around the vortex core of a hurricane or typhoon. In addition, stringent threshold conditions are used in the reference check and the subsequent continuity check to ensure the accepted data are free of alias or almost so. The robustness and improved performance of the method are exemplified by the results tested with severely aliased radial velocities scanned by operational WSR-88D radars from hurricanes in the United States and by operational China New Generation Weather Radar (CINRAD) base data format SA radars from typhoons in China. [ABSTRACT FROM AUTHOR]

The Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR) algorithms consist of modules. This paper describes version 4 (V4) of GPM DPR level 2 (L2) classification (CSF) modules, which consist of two single-frequency (SF) modules-that is, Ku-only and Ka-only modules-and a dual-frequency (DF) module. Each CSF module detects bright band (BB) and classifies rain into three major types, that is, stratiform, convective, and other. The Ku-only and Ka-only CSF modules use algorithms that are similar to the Tropical Rainfall Measuring Mission (TRMM) rain type classification algorithm 2A23. The DF CSF module uses a new method called the measured dual-frequency ratio (DFRm) method for the rain type classification and the detection of BB. It is shown that the Ku-only CSF module and the DF CSF module produce almost indistinguishable rain type counts in a statistical sense. It is also shown that the DFRm method in the DF CSF module improves the detection of BB. [ABSTRACT FROM AUTHOR]

Classic supercell storms occur in a generally well understood environment characterized by instability and vertical wind shear. Within this broadly favorable environment, large day-to-day variability in environmental parameters may lead to widely varying radar presentation of storms. Of interest here is whether specific storm structures exhibit repeatable characteristics in similar environments and whether radar presentation can be predicted with knowledge of environmental characteristics. Specifically, this paper focuses on (i) updraft characteristics inferred using differential reflectivity ZDR columns, (ii) characteristics of storm-relative inflow inferred using ZDR arcs, and (iii) areal extent and cyclicality of polarimetrically inferred hailfall at low levels. Variability of these radar features is compared among storms in similar environments and among a larger subset of storms across highly varying environments. The similarity of storms in similar and different environments is quantified, and tornadic and nontornadic storms are compared. Associations between inferred updraft, inflow, and hailfall characteristics and environmental variables are discussed. Storm features generally exhibit greater similarity among storms in similar environments than across environments, although exceptions occur. The results indicate that many radar features of classic supercells may be useful to learn about microphysical variability across environments. [ABSTRACT FROM AUTHOR]

Natural small-scale microphysical and dynamical mechanisms are identified in a winter orographic snowstorm over the Sierra Madre Range of Wyoming during an intensive observational period (IOP) from the AgI Seeding Cloud Impact Investigation (ASCII; January-March 2012). A suite of high-resolution radars, including a ground-based scanning X-band dual-polarization Doppler on Wheels radar, vertically pointing Ka-band Micro Rain Radar (MRR), and airborne W-band Wyoming Cloud Radar (WCR), and additional in situ and remote sensing instruments are used in the analysis. The analysis focuses on a deep postfrontal period on 16 January 2012 (IOP2) when clouds extended throughout the troposphere and cloud liquid water was absent following the passage of a baroclinic front. A turbulent shear layer was observed in this postfrontal environment that was created by a midlevel cross-barrier jet riding over a decoupled Arctic air mass that extended above mountaintop. MRR and WCR observations indicate additional regions of turbulence aloft that favor rapid particle growth at upper levels of the cloud. Plunging flow in the lee of the Sierra Madre was also observed during this case, which caused sublimation of snow up to 20 km downwind. The multi-instrument analysis in this paper suggests that 1) shear-induced turbulent overturning cells do exist over cold continental mountain ranges like the Sierra Madre, 2) the presence of cross-barrier jets favors these turbulent shear zones, 3) this turbulence is a key mechanism in enhancing snow growth, and 4) snow growth enhanced by turbulence primarily occurs through deposition and aggregation in these cold (<−15°C) postfrontal continental environments. [ABSTRACT FROM AUTHOR]

Storm-scale and mesocyclone-scale processes occurring contemporaneously with a tornado in the Goshen County, Wyoming, supercell observed on 5 June 2009 during the second Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2) are examined using ensemble analyses produced by assimilating mobile radar and in situ observations into a high-resolution convection-resolving model. This paper focuses on understanding the evolution of the vertical structure of the storm, the outflow buoyancy, and processes affecting the vertical vorticity and circulation within the mesocyclone that correspond to changes in observed tornado intensity. Tornadogenesis occurs when the low-level mesocyclone is least negatively buoyant relative to the environment, possesses its largest circulation, and is collocated with the largest azimuthally averaged convergence during the analysis period. The average buoyancy, circulation, and convergence within the near-surface mesocyclone (on spatial scales resolved by the model) all decrease as the tornado intensifies and matures. The tornado and its parent low-level mesocyclone both dissipate surrounded by a weakening rear-flank downdraft. The decreasing buoyancy of parcels within the low-level mesocyclone may partly be responsible for the weakening of the updraft surrounding the tornado and decoupling of the mid- and low-level circulation. Although the supply of horizontal vorticity generated in the forward flank of the storm increases throughout the life cycle of the tornado, it is presumably less easily tilted and stretched on the mesocyclone-scale during tornado maturity owing to the disruption of the low-level updraft/downdraft structure. Changes in radar-measured tornado intensity lag those of ensemble Kalman filter (EnKF) mesocyclone vorticity and circulation. [ABSTRACT FROM AUTHOR]

This paper aims to test the validity, utility, and limitations of the lateral eddy diffusivity concept in a coastal environment through analyzing data from coupled drifter and dye releases within the footprint of a high-resolution (800 m) high-frequency radar south of Martha's Vineyard, Massachusetts. Specifically, this study investigates how well a combination of radar-based velocities and drifter-derived diffusivities can reproduce observed dye spreading over an 8-h time interval. A drifter-based estimate of an anisotropic diffusivity tensor is used to parameterize small-scale motions that are unresolved and underresolved by the radar system. This leads to a significant improvement in the ability of the radar to reproduce the observed dye spreading. [ABSTRACT FROM AUTHOR]

This paper outlines an approach for estimating latent heating, surface rainfall rate, and liquid water path in warm rain from downward-viewing W-band radar observations using a Bayesian Monte Carlo algorithm. The algorithm utilizes observed vertical and path-integrated characteristics of precipitating liquid clouds to identify the most appropriate hydrometeor and latent heating structures in a large database of profiles generated using a cloud-resolving model. These characteristics are selected by applying multiple performance metrics to synthetic retrievals. Analysis of the retrievals suggests that a combination of cloud-top, rain-top, and maximum reflectivity heights; vertically integrated reflectivity and attenuation; and a measure of near-surface intensity is sufficient to constrain bulk properties and the vertical structure of warm rain systems. When applied to observations at CloudSat resolution, biases in retrieved liquid water path and surface rainfall rate are small (less than 10%). The algorithm also captures the vertical structure of latent heating, although the magnitudes of integrated heating and cooling exhibit nearly compensating low biases. Random errors are larger owing to the limitations of single-frequency radar observations in constraining drop size distributions. Uncertainties in the altitudes of peak heating and cooling at the pixel scale are typically less than one vertical level, while uncertainties in vertically resolved estimates of heating and cooling rates are on the order of a factor of 2. The utility of the technique is illustrated through application to case studies from airborne radar data from the VAMOS Ocean-Cloud-Atmosphere-Land Study field campaign and satellite observations from CloudSat. [ABSTRACT FROM AUTHOR]

The sun is known to be a good target for weather radar calibration. In this paper high-resolution raster scans of the sun at high elevations will be used to derive the antenna pattern of weather radar, without being affected by beam propagation effects and reflections close to the earth's surface. It is shown that this pattern matches well to pattern measurements using a point source. Hence, a good estimation of the real antenna pattern can be derived using the sun. Furthermore, formulas to extract undistorted antenna patterns from the sun, even at high-elevation angles, are derived. The signal processing required to achieve high sensitivity for the antenna pattern measurements will be described. Important parts of the antenna pattern-for example, sidelobes-become visible when using long integration times. The polarimetric receiver channel cross-correlation coefficient is proposed as a figure of merit of the cross-polar isolation of the antenna and hence the cross-polar pattern. The results are also compared to point source measurements. This illustrates how an unpolarized signal source like the sun can be used to derive polarimetric variables. [ABSTRACT FROM AUTHOR]

This paper documents the study of the radar signature of the sea surface on the images collected by an X-band weather radar and its application to remote sensing of the coastal zone. The main radar parameters considered here are the reflectivity factor [which was converted into a normalized radar cross section (NRCS)], the differential reflectivity, and the mean radial velocity. Measurements of the NRCS in the vicinity of an instrumented offshore buoy allowed for identifying its variations with wind speed and relative direction, which are found to be consistent with the Georgia Institute of Technology (GIT) model and other measurements. A more accurate empirical model for the NRCS is developed. For moderate to strong winds, the mean radial velocity is linearly related to the radial component of the wind speed, showing the potentiality of weather radars to map the radial surface wind speed at the scale of a basin. The influence of surface currents is weak here but should be likely taken into account in other places with stronger currents. Measurements of the differential reflectivity are also consistent with other measurements made elsewhere for the polarization ratio. The analysis of radar data over the entire coverage could also be performed using surface wind estimates from a meteorological model. Similar but less accurate results are obtained for the NRCS and the mean radial velocity. The extra spatial variability not due to wind speed variations is identified. An inversion procedure is proposed to recover the wind vector field from radar measurements after calibration of the NRCS values. Results are promising and pave the way to the concept of weather radar for ocean remote sensing. [ABSTRACT FROM AUTHOR]

Detection of meteorological radar signals is often carried out using power averaging with noise subtraction either in the time domain or the spectral domain. This paper considers the relative signal processing gain of these two methods, showing a clear advantage for spectral-domain processing when normalized spectral width is less than ~0.1. A simple expression for the optimal discrete Fourier transform (DFT) length to maximize signal processing gain is presented that depends only on the normalized spectral width and the time-domain weighting function. The relative signal processing gain between noncoherent power averaging and spectral processing is found to depend on a variety of parameters, including the radar wavelength, spectral width, available observation time, and the false alarm rate. Expressions presented for the probability of detection for noncoherent and spectral-based processing also depend on these same parameters. Results of this analysis show that DFT-based processing can provide a substantial advantage in signal processing gain and probability of detection, especially when the normalized spectral width is small and when a large number of samples are available. Noncoherent power estimation can provide superior probability of detection when the normalized spectral width is greater than ~0.1, especially when the desired false alarm rate exceeds 10%. [ABSTRACT FROM AUTHOR]

This paper demonstrates the impact of the enhancement in detectability by the dual-frequency precipitation radar (DPR) on board the Global Precipitation Measurement (GPM) core observatory. By setting two minimum detectable reflectivities-12 and 18 dB Z-artificially to 6 months of GPM DPR measurements, the precipitation occurrence and volume increase by ~21.1% and ~1.9%, respectively, between 40°S and 40°N. GPM DPR is found to be able to detect light precipitation, which mainly consists of two distinct types. One type is shallow precipitation, which is most significant for convective precipitation over eastern parts of subtropical oceans, where deep convection is typically suppressed. The other type is probably associated with lower parts of anvil clouds associated with organized precipitation systems. While these echoes have lower reflectivities than the official value of the minimum detectable reflectivity, they are found to mostly consist of true precipitation signals, suggesting that the official value may be too conservative for some sort of meteorological analyses. These results are expected to further the understanding of both global energy and water budgets and the diabatic heating distribution. [ABSTRACT FROM AUTHOR]

The creation of a 3D mosaic is often the first step when using the high-spatial- and temporal-resolution data produced by ground-based radars. Efficient yet accurate methods are needed to mosaic data from dozens of radar to better understand the precipitation processes in synoptic-scale systems such as tropical cyclones. Research-grade radar mosaic methods of analyzing historical weather events should utilize data from both sides of a moving temporal window and process them in a flexible data architecture that is not available in most stand-alone software tools or real-time systems. Thus, these historical analyses require a different strategy for optimizing flexibility and scalability by removing time constraints from the design. This paper presents a MapReduce-based playback framework using Apache Spark's computational engine to interpolate large volumes of radar reflectivity and velocity data onto 3D grids. Designed as being friendly to use on a high-performance computing cluster, these methods may also be executed on a low-end configured machine. A protocol is designed to enable interoperability with GIS and spatial analysis functions in this framework. Open-source software is utilized to enhance radar usability in the nonspecialist community. Case studies during a tropical cyclone landfall shows this framework's capability of efficiently creating a large-scale high-resolution 3D radar mosaic with the integration of GIS functions for spatial analysis. [ABSTRACT FROM AUTHOR]

The 2013 Phased Array Radar Innovative Sensing Experiment (PARISE) investigated the impacts of higher-temporal-resolution radar data on National Weather Service forecasters' warning decision processes during severe hail and wind events. In total, 12 forecasters participated in the 2013 PARISE over a 6-week period during the summer of 2013. Participants were assigned to either a control [5-min phased-array radar (PAR) updates] or experimental (1-min PAR updates) group, and worked two cases in simulated real time. This paper focuses on the qualitative retrospective reports of participants' warning decision processes that were collected using the recent case walk-through method. Timelines of participants' warning decision process were created for both cases, which were then thematically coded according to a situational awareness framework. Coded themes included perception, comprehension, and projection. It was found that the experimental group perceived significantly more information during both cases than the control group (case 1 p = 0.045 and case 2 p = 0.041), which may have improved the quality of their comprehensions and projections. Analysis of timelines reveals that 1-min PAR updates were important to the experimental group's more timely and accurate warning decisions. Not only did the 1-min PAR updates enable experimental participants to perceive precursor signatures earlier than control participants, but through monitoring trends in radar data, the experimental group was able to better detect storm motion, more accurately identify expected weather threats from severe thunderstorms, more easily observe strengthening and diminishing trends in storms, and make more correct tornado-related warning decisions. [ABSTRACT FROM AUTHOR]

A raindrop size distribution (DSD) retrieval method for a weather radar network consisting of several X-band dual-polarization radars is proposed. An iterative maximum likelihood (ML) estimator for DSD retrieval in a single radar was developed in the authors' previous work, and the proposed algorithm in this paper extends the single-radar retrieval to radar-networked retrieval, where ML solutions in each single-radar node are integrated based on a Bayesian scheme in order to reduce estimation errors and to enhance accuracy. Statistical evaluations of the proposed algorithm were carried out using numerical simulations. The results with eight radar nodes showed that the bias and standard errors are −0.05 and 0.09 in log( N w); and N w (mm−1 m−3) and 0.04 and 0.09 in D0 (mm) in an environment with fluctuations in dual-polarization radar measurements (normal distributions with standard deviations of 0.8 dB Z, 0.2 dB, and 1.5° in ZHm, ZDRm, and ΦDPm, respectively). Further error analyses indicated that the estimation accuracy depended on the number of radar nodes, the ranges of varying μ, the raindrop axis ratio model, and the system bias errors in dual-polarization radar measurements. [ABSTRACT FROM AUTHOR]

Two new algorithms for hydrometeor classification using polarimetric radar observations are developed based on prototypes derived by applying clustering techniques (Part I of this two-part paper). Each prototype is defined as a probability distribution of the polarimetric variables and ambient temperature corresponding to a hydrometeor type. The first algorithm is a maximum prototype likelihood classifier that uses all prototypes attributed to the different hydrometeor types in Part I. The hydrometeor type is assigned as the prototype with the highest likelihood when comparing the polarimetric variables and temperature with each prototype. The second algorithm is a Bayesian classifier that uses the probability density functions (PDFs) as derived from the prototype set associated with the identical hydrometeor type. The posteriori probability in the Bayesian method is calculated from a combination of the PDFs and the prior probability, the maximum of which corresponds to the most likely hydrometeor type. The respective merits of the two techniques are discussed. The two classifiers are applied to CP-2 S-band radar observations of two hailstorms that occurred between 16 and 20 November 2008, including the so-called Gap storm, which produced a devastating microburst and large hail at the ground. Results from the classifiers are compared with those derived using the well-established National Center for Atmospheric Research fuzzy logic classifier. In general, good agreement is found, yielding overall confidence in the robustness of the new classifiers. However, large differences are found for the melting ice and ice crystal categories, which will need to be studied further. [ABSTRACT FROM AUTHOR]

Observations and recent high-resolution numerical model simulations indicate that liquid water and partially frozen hydrometeors can be lofted considerably above the environmental 0°C level in the updrafts of convective storms owing to the warm thermal perturbation from latent heating within the updraft and to the noninstantaneous nature of drop freezing. Consequently, upward extensions of positive differential reflectivity (i.e., ZDR ≥ 1 dB)-called ZDR columns-may be a useful proxy for detecting the initiation of new convective storms and examining the evolution of convective storm updrafts. High-resolution numerical simulations with spectral bin microphysics and a polarimetric forward operator reveal a strong spatial association between updrafts and ZDR columns and show the utility of examining the structure and evolution of ZDR columns for assessing updraft evolution. This paper introduces an automated ZDR column algorithm designed to provide additional diagnostic and prognostic information pertinent to convective storm nowcasting. Although suboptimal vertical resolution above the 0°C level and limitations imposed by commonly used scanning strategies in the operational WSR-88D network can complicate ZDR column detection, examples provided herein show that the algorithm can provide operational and research-focused meteorologists with valuable information about the evolution of convective storms. [ABSTRACT FROM AUTHOR]