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The ground-based-radar-derived raindrop size distribution (DSD) parameters—mass-weighted drop diameter Dmass and normalized intercept parameter NW—are the sole resource for direct validation of the National Aeronautics and Space Administration (NASA) Global Precipitation Measurement (GPM) mission Core Observatory satellite-based retrieved DSD. Both Dmass and NW are obtained from radar-measured reflectivity ZH and differential reflectivity ZDR through empirical relationships. This study uses existing relationships that were determined for the GPM ground validation (GV) program and directly compares the NASA S-band polarimetric radar (NPOL) observables of ZH and ZDR and derived Dmass and NW with those calculated by two-dimensional video disdrometer (2DVD). The joint NPOL and 2DVD datasets were acquired during three GPM GV field campaigns conducted in eastern Iowa, southern Appalachia, and western Washington State. The comparative study quantifies the level of agreement for ZH, ZDR, Dmass, and log(NW) at an optimum distance (15–40 km) from the radar as well as at distances greater than 60 km from radar and over mountainous terrain. Interestingly, roughly 10%–15% of the NPOL ZH–ZDR pairs were well outside the envelope of 2DVD-estimated ZH–ZDR pairs. The exclusion of these pairs improved the comparisons noticeably. [ABSTRACT FROM AUTHOR]

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 Zdr and larger specific differential phase Kdp 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 Zdr and Kdp 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 Zdr and Kdp 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 FROM AUTHOR]

The potential future installation of a multifunction phased-array radar (MPAR) network will provide capabilities of case-specific adaptive scanning. Knowing the impacts adaptive scanning may have on short-term forecasts will influence scanning strategy decision-making in hopes to produce the most optimal ensemble forecast while also benefiting human severe weather warning decision-making. An ensemble-based targeted observation algorithm is applied to an observing system simulation experiment (OSSE) where the impacts of synthetic idealized supercell radial velocity observations are estimated before the observations are "collected" and assimilated. The forecast metric of interest is the low-level rotation forecast metric (0–1-km updraft helicity), a surrogate for tornado prediction. It is found that the ensemble-based targeted observation approach can reasonably estimate the true error variance reduction when an effective method that treats sampling error is applied, the period of model forecast is associated with less degrees of nonlinearity, and the observation information content relative to the background forecast is larger. In some scenarios, a subset of a full-volume scan assimilation produces better forecasts than all observations within the full volume. Assimilating the full-volume scan increases the number of potential spurious correlations arising between the forecast metric and radial velocity observation induced state perturbations, which may degrade the forecast metric accuracy. [ABSTRACT FROM AUTHOR]

Severe weather nowcasting is a crucial mission of atmospheric science for the betterment of society to save life, limb, and property. In this study, composite radar data from the Central Weather Bureau of 16 typhoons are collected to examine the statistical performance of the McGill Algorithm for Precipitation nowcasting using Lagrangian Extrapolation (MAPLE) over Taiwan, an extrapolation algorithm that predicts future precipitation based on current radar echoes. In addition, instead of mixing the precipitation between radar extrapolation and numerical model forecast as in previous studies, a blending system is formed by synthesizing the wind information from model forecast with the echo extrapolation motion field via a variational algorithm to improve the nowcasting system. The statistical results of the radar echo extrapolation for 16 typhoon cases show that while the quantitative precipitation nowcasting skill can persist for up to 2 h, significant distortion for the rotational system is found after 2 h. On the other hand, the blending system helps to capture and maintain the rotation of typhoon rainband structures. The blending system extends the nowcasting skill by 1 h to a total of 3 h. Furthermore, the blending scheme performs especially well after the typhoon makes landfall in Taiwan. For disaster prevention and mitigation, this blending nowcasting technique may provide effective weather information immediately. [ABSTRACT FROM AUTHOR]

The performance of a numerical weather prediction model using convective-scale ensemble data assimilation with ground-based global navigation satellite systems-zenith total delay (ZTD) and radar data is investigated on a heavy rainfall event that occurred in Taiwan on 10 June 2012. The assimilation of ZTD and/or radar data is performed using the framework of the WRF local ensemble transform Kalman filter with a model grid spacing of 2 km. Assimilating radar data is beneficial for predicting the rainfall intensity of this local event but produces overprediction in southern Taiwan and underprediction in central Taiwan during the first 3 h. Both errors are largely overcome by assimilating ZTD data to improve mesoconvective-scale moisture analyses. Consequently, assimilating both the ZTD and radar data show advantages in terms of the location and intensity of the heavy rainfall. Sensitivity experiments involving this event indicate that the impact of ZTD data is improved by using a broader horizontal localization scale than the convective scale used for radar data assimilation. This optimization is necessary in order to consider more fully the network density of the ZTD observations and the horizontal scale of the moisture transport by the southwesterly flow in this case. [ABSTRACT FROM AUTHOR]

Radar retrievals of drop size distribution (DSD) parameters are developed and evaluated over the mountainous Olympic Peninsula of Washington State. The observations used to develop retrievals were collected during the 2015/16 Olympic Mountain Experiment (OLYMPEX) and included the NASA S-band dual-polarimetric (NPOL) radar and a collection of second-generation Particle Size and Velocity (PARSIVEL2) disdrometers over the windward slopes of the barrier. Nonlinear and random forest regressions are applied to the PARSIVEL2 data to develop retrievals for median volume diameter, liquid water content, and rain rate. Improvement in DSD retrieval accuracy, defined by the mean error of the retrieval relative to PARSIVEL2 observations, was achieved when using the random forest model when compared with nonlinear regression. Evaluation of disdrometer observations and the retrievals from NPOL indicate that the radar retrievals can accurately reproduce observed DSDs in this region, including the common wintertime regime of small but numerous raindrops that is important there. NPOL retrievals during the OLYMPEX period are further evaluated using two-dimensional video disdrometers (2DVD) and vertically pointing Micro Rain Radars. Results indicate that radar retrievals using random forests may be skillful in capturing DSD characteristics in the lowest portions of the atmosphere. [ABSTRACT FROM AUTHOR]

A potential replacement candidate for the aging operational WSR-88D infrastructure currently in place is the phased array radar (PAR) system. The current WSR-88Ds take ~5 min to produce a full volumetric scan of the atmosphere, whereas PAR technology allows for full volumetric scanning of the same atmosphere every ~1 min. How this increase in temporal frequency of radar observations might affect the National Severe Storms Laboratory's (NSSL) Warn-on-Forecast system (WoFS), which is a storm-scale ensemble data assimilation and forecast system for severe convective weather, is unclear. Since radar data assimilation is critical for the WoFS, this study explores the optimal temporal frequency of PAR observations for storm-scale data assimilation using the 31 May 2013 El Reno, Oklahoma, tornadic supercell event. The National Severe Storms Laboratory's National Weather Radar Testbed PAR in Norman, Oklahoma, began scanning this event more than an hour before the first (and strongest) tornado developed near El Reno, and scanned most of the tornadic supercell's evolution. Several experiments using various cycling and data frequencies to synchronously and asynchronously assimilate these PAR observations are conducted to produce analyses and very short-term forecasts of the El Reno supercell. Forecasts of low-level reflectivity and midlevel updraft helicity are subjectively evaluated and objectively verified using spatial and object-based techniques. Results indicate that assimilating more frequent PAR observations can lead to more accurate analyses and probabilistic forecasts of the El Reno supercell at longer lead times. Hence, PAR is a promising radar platform for WoFS. [ABSTRACT FROM AUTHOR]

In radar observations of hydrometeors, the 0°C isotherm in the atmosphere (i.e., the freezing level) usually appears as a region of enhanced reflectivity. This region is known as the bright band (BB). In this study, observations over 12 months from a vertically pointing 35-GHz radar and a collocated disdrometer at the Natural Environment Research Council (NERC) Facility for Atmospheric and Radio Research (NFARR) are used to identify and compare microphysical differences between BB and non-brightband (NBB) periods. From these observations, the relationship between radar reflectivity Z and rainfall intensity R is found to be Z = 772R0.57 for BB periods and Z = 108R0.99 for NBB periods. Additionally, the brightband strength (BBS) was calculated using a novel method derived from the Michelson contrast equation in an attempt to explain the observed variability in BB precipitation. A series of Z–R relationships are computed with respect to BBS. The coefficients increase with increasing BBS from 227 to 926, while the exponents decrease with increasing BBS from 0.85 to 0.38. The results also indicate that NBB periods identified in the presence of a 0°C isotherm in other studies may be misclassified due to their inability to identify weak brightband periods. As such, it is hypothesized that NBB periods are solely due to warm rain processes. [ABSTRACT FROM AUTHOR]

This study utilizes data collected by the University of Oklahoma Advanced Radar Research Center's Polarimetric Radar for Innovations in Meteorology and Engineering (OU-PRIME) C-band radar as well as the federal KTLX and KOUN WSR-88D S-band radars to study a supercell that simultaneously produced a long-track EF-4 tornado and an EF-2 landspout tornado (EF indicates the enhanced Fujita scale) near Norman, Oklahoma, on 10 May 2010. Contrasting polarimetric characteristics of two tornadoes over similar land cover but with different intensities are documented. Also, the storm-scale sedimentation of debris within the supercell is investigated, which includes observations of rotation and elongation of a tornadic debris signature with height. A dual-wavelength comparison of debris at S and C bands is performed. These analyses indicate that lofted debris within the tornado was larger than debris located outside the damage path of the tornado and that debris size outside the tornado increased with height, likely as the result of centrifuging. Profiles of polarimetric variables were observed to become more vertically homogeneous with time. [ABSTRACT FROM AUTHOR]

A dataset of convection initiation (CI) is of great value in studying the triggering mechanisms of deep moist convection and evaluating the performances of numerical models. In recent years, the data quality of the operationally generated radar mosaics over China has been greatly improved, which provides an opportunity to retrieve a CI dataset from that region. In this work, an attempt is made to reveal the potential of applying a simple framework of objective CI detection for the study of CI climatology in China. The framework was tested using radar mosaic maps in South China that were accessible online. The identified CI events were validated in both direct and indirect ways. On the basis of a direct manual check, nearly all of the identified CI cells had an organized motion. The precipitation echoes of the cells had a median duration of approximately 2.5 h. The CI occurrences were further compared with rainfall estimates to ensure physical consistency. The diurnal cycle of CI occurrence exhibits three major modes: a late-night-to-morning peak at the windward coasts and offshore, a noon-to-late-afternoon peak on the coastal land, and an evening-to-early-morning peak over the northwestern highland. These spatial modes agree well with those of rainfall, indirectly suggesting the reliability of the CI statistics. By processing radar mosaic maps, such a framework could be applied for studying CI climatology over China and other regions. [ABSTRACT FROM AUTHOR]

We investigate the practical predictability limits of deep convection in a state-of-the-art, high-resolution, limited-area ensemble prediction system. A combination of sophisticated predictability measures, namely, believable and decorrelation scale, are applied to determine the predictable scales of short-term forecasts in a hierarchy of model configurations. First, we consider an idealized perfect model setup that includes both small-scale and synoptic-scale perturbations. We find increased predictability in the presence of orography and a strongly beneficial impact of radar data assimilation, which extends the forecast horizon by up to 6 h. Second, we examine realistic COSMO-KENDAsimulations, including assimilation of radar and conventional data and a representation of model errors, for a convectively active two-week summer period over Germany. The results confirm increased predictability in orographic regions. We find that both latent heat nudging and ensemble Kalman filter assimilation of radar data lead to increased forecast skill, but the impact is smaller than in the idealized experiments. This highlights the need to assimilate spatially and temporally dense data, but also indicates room for further improvement. Finally, the examination of operational COSMO-DE-EPS ensemble forecasts for three summer periods confirms the beneficial impact of orography in a statistical sense and also reveals increased predictability in weather regimes controlled by synoptic forcing, as defined by the convective adjustment time scale. [ABSTRACT FROM AUTHOR]

This case study analyzes a nocturnal mesoscale convective system (MCS) that was observed on 25-26 June 2015 in northeastern Kansas during the Plains Elevated Convection At Night (PECAN) project. Over the course of the observational period, a broken line of elevated nocturnal convective cells initiated around 0230UTCon the cool side of a stationary front and subsequently merged to form a quasi-linearMCSthat later developed strong, surface-based outflow and a trailing stratiform region. This study combines radar observations with mobile and fixed mesonet and sounding data taken during PECAN to analyze the kinematics and thermodynamics of the MCS from 0300 to 0630 UTC. This study is unique in that 38 consecutive multi- Doppler wind analyses are examined over the 3.5 h observation period, facilitating a long-duration analysis of the kinematic evolution of the nocturnal MCS. Radar analyses reveal that the initial convective cells and linear MCS are elevated and sustained by an elevated residual layer formed via weak ascent over the stationary front. During upscale growth, individual convective cells develop storm-scale cold pools due to pockets of descending rear-to-front flow that are measured by mobile mesonets. By 0500 UTC, kinematic analysis and mesonet observations show that the MCS has a surface-based cold pool and that convective line updrafts are ingesting parcels from below the stable layer. In this environment, the elevated system has become surface based since the cold pool lifting is sufficient for surface-based parcels to overcome the CIN associated with the frontal stable layer. [ABSTRACT FROM AUTHOR]

Descriptions of the experimental design and research highlights obtained from a series of four multiagency field projects held near Cape Canaveral, Florida, are presented. The experiments featured a 3 MW, dual-polarization, C-band Doppler radar that serves in a dual capacity as both a precipitation and cloud radar. This duality stems from a combination of the radar's high sensitivity and extremely small-resolution volumes produced by the narrow 0.22° beamwidth and the 0.543 m along-range resolution. Experimental highlights focus on the radar's real-time aircraft tracking capability as well as the finescale reflectivity and eddy structure of a thin nonprecipitating stratus layer. Examples of precipitating storm systems focus on the analysis of the distinctive and nearly linear radar reflectivity signatures (referred to as "streaks") that are caused as individual hydrometeors traverse the narrow radar beam. Each streak leaves a unique radar reflectivity signature that is analyzed with regard to estimating the underlying particle properties such as size, fall speed, and oscillation characteristics. The observed along-streak reflectivity oscillations are complex and discussed in terms of diameter-dependent drop dynamics (oscillation frequency and viscous damping time scales) as well as radar-dependent factors governing the near-field Fresnel radiation pattern and inferred drop–drop interference. [ABSTRACT FROM AUTHOR]

Remote sensing observations, especially those from ground-based radars, have been used extensively to discriminate between severe and nonsevere storms. Recent upgrades to operational remote sensing networks in the United States have provided unprecedented spatial and temporal sampling to study such storms. These networks help forecasters subjectively identify storms capable of producing severe weather at the ground; however, uncertainties remain in how to objectively identify severe thunderstorms using the same data. Here, three large-area datasets (geostationary satellite, ground-based radar, and ground-based lightning detection) are used over 28 recent events in an attempt to objectively discriminate between severe and nonsevere storms, with an additional focus on severe storms that produce tornadoes. Among these datasets, radar observations, specifically those at mid- and upper levels (altitudes at and above 4 km), are shown to provide the greatest objective discrimination. Physical and kinematic storm characteristics from all analyzed datasets imply that significantly severe [≥2-in. (5.08 cm) hail and/or ≥65-kt (33.4 m s-1) straight-line winds] and tornadic storms have stronger upward motion and rotation than nonsevere and less severe storms. In addition, these metrics are greatest in tornadic storms during the time in which tornadoes occur. [ABSTRACT FROM AUTHOR]

Radar and rawinsonde data from four ground-based observing stations in the tropical Indo-Pacific warm pool were used to identify possible associations of environmental state variables and their vertical profiles with radar-derived rain rate inside a mesoscale radar domain when the column-integrated relative humidity (CRH) exceeds 80%. At CRH exceeding 80%, a wide range--from near 0 to ;50mmday21--in rain rate is observed; therefore, tropospheric moisture was a necessary but insufficient condition for deep convection. This study seeks to identify possible factors that inhibit rainfall when the atmosphere is sufficiently moist to support large precipitation rates. The domain-mean rain rate was highly sensitive to the areal coverage of intense, convective rainfall that occurs. There were two fundamentally different instances in which convective area was low. One was when the radar domain is primarily occupied by weakly precipitating, stratiform echoes. The other was when the radar domain contained almost no precipitating echoes of any type. While the former was dependent upon the stage of the convective life cycle seen by radar, the latter was probably dependent upon the convective environment. Areal coverage of convective echoes was largely determined by the number of individual convective echoes rather than their sizes, so changes in the clear-air environment of updrafts might have governed how many updrafts grew into deep cumulonimbi. The most likely environmental influence on convective rainfall identified using rawinsonde data was 900--700-hPa lapse rate; however, processes occurring on spatial scales smaller than a radar domain were probably also important but not investigated. [ABSTRACT FROM AUTHOR]

The statistical properties of the radar echoes from biological, precipitation, and ground targets observed with the McGill S-band dual-polarization radar have been used to devise a polarimetric and a nonpolarimetric fuzzy logic algorithm for pixel-by-pixel target identification. Radar observations of migrating birds show distinctly different polarimetric features during their relative approach and departure from the radar site illustrating the dependency of radar parameters on the canting angle and scattering cross section. The devised algorithms have been tested with two independent events, each consisting of 2 h of radar observations with a 5-min temporal resolution. One event consisted of precipitation without birds while the other contained only birds. The misclassifications were 10.12% and 9.6%, respectively, for the two cases for the nonpolarimetric algorithm, and 1.99% and 0.92% for the polarimetric algorithm. The results indicate that even though nonpolarimetric radar membership functions may be considered adequate for separating radar echo returns from birds, precipitation, and ground targets, they are not sufficiently skilled if a greater accuracy is required. Target identification without polarimetric variables especially fails in the region of zero isodop and in precipitation with an echo top below 4 km. [ABSTRACT FROM AUTHOR]

Hurricane Harvey hit the Texas Gulf Coast as a major hurricane on 25 August 2017 before exiting the state as a tropical storm on 29 August 2017. Left in itswake was historic flooding, with some locations measuringmore than 60 in. (150 cm) of rain over a 5-day period. TheWSR-88D radar (KHGX)maintained operations for the entirety of the event. Rain gauge data fromtheHarris County Flood Warning System (HCFWS) was used for validation with the full radar dataset to retrieve daily and event-total precipitation estimates for the period 25-29 August 2017. The KHGX precipitation estimates were then compared with the HCFWS gauges. Three different hybrid polarimetric rainfall retrievals were used, alongwith attenuation-based retrieval that employs the radar-observed differential propagation. An advantage of using a attenuation-based retrieval is its immunity to partial beam blockage and calibration errors in reflectivity and differential reflectivity. All of the retrievals are susceptible to changes in the observed drop size distribution (DSD). No in situDSDdata were available over the study area, so changes in the DSD were interpreted by examining the observed radar data.We examined the parameter space of two key values in the attenuation retrieval to test the sensitivity of the rain retrieval. Selecting a value of a 5 0.015 and b 5 0.600 provided the best overall results, relative to the gauges, but more work needs to be done to develop an automated technique to account for changes in the ambient DSD. [ABSTRACT FROM AUTHOR]

A Bayesian Markov chain Monte Carlo (MCMC) algorithm is utilized to compare the skill of an A-Trainlike observing system with a cloud, convection, and precipitation (CCP) observing system like that contemplated for the 2020s by the 2017 National Academy of Sciences Decadal Survey. The main objective is to demonstrate a framework for observational trade space studies. This initial work focuses on weakly precipitating warm shallow cumulus constructed from in situ data. Radiative computations are based on Mie theory with spherical assumptions. Simulated measurements in the CCP configuration consist of W- and Kaband radar reflectivity and path-integrated attenuation, 31 and 94 GHz brightness temperatures (Tb), and visible and near-infrared reflectances. The collection of measurements in the CloudSat configuration is identical, but includes a single 94 GHz radar frequency, and the uncertainty in the 94 GHz microwave brightness temperature is increased to mimic the CloudSat Tb product. The experiments demonstrate that it remains a challenge to diagnose cloud properties in the presence of light rain because of the tendency of microwave remote sensing to respond to the higher moments of the hydrometeor populations. Rain properties are significantly better constrained than cloud properties, even in the optimal CCP configuration. The addition of Ka-band measurements places substantial constraints on the precipitation rain effective radius and rain rates. The Tb offers important information regarding the column-integrated condensate mass, the measurement accuracy of which appears more likely to affect the retrievals of clouds with low liquid water path. The constraints provided by reflectances are largely restricted to regions near the cloud top, particularly in the raining cases. [ABSTRACT FROM AUTHOR]

A variational retrieval of rain microphysics from polarimetric radar data (PRD) has been developed through the use of S-band parameterized polarimetric observation operators. Polarimetric observations allow for the optimal retrieval of cloud and precipitation microphysics for weather quantification and data assimilation for convective-scale numerical weather prediction (NWP) by linking PRD to physical parameters. Rain polarimetric observation operators for reflectivity ZH, differential reflectivity ZDR, and specific differential phase KDP were derived for S-band PRD using T-matrix scattering amplitudes. These observation operators link the PRD to the physical parameters of water contentWand mass-/volume-weighted diameter Dm for rain, which can be used to calculate other microphysical information. The S-band observation operators were tested using a 1D variational retrieval that uses the (nonlinear) Gauss--Newton method to iteratively minimize the cost function to find an optimal estimate of Dm and W separately for each azimuth of radar data, which can be applied to a plan position indicator (PPI) radar scan (i.e., a single elevation). Experiments on two-dimensional video disdrometer (2DVD) data demonstrated the advantages of including FDP observations and using the nonlinear solution rather than the (linear) optimal interpolation (OI) solution. PRDcollected by the Norman, Oklahoma(KOUN)WSR-88D on 15 June 2011 were used to successfully test the retrieval method on radar data. The successful variational retrieval from the2DVDand the radar data demonstrate the utility of the proposed method. [ABSTRACT FROM AUTHOR]

This study explores the benefits of assimilating infrared (IR) brightness temperature (BT) observations from geostationary satellites jointly with radial velocity (Vr) and reflectivity (Z) observations from Doppler weather radars within an ensemble Kalman filter (EnKF) data assimilation system to the convection-allowing ensemble analysis and prediction of a tornadic supercell thunderstorm event on 12 June 2017 across Wyoming and Nebraska. While radar observations sample the three-dimensional storm structures with high fidelity, BT observations provide information about clouds prior to the formation of precipitation particles when instorm radar observations are not yet available and also provide information on the environment outside the thunderstorms. To better understand the strengths and limitations of each observation type, the satellite and Doppler radar observations are assimilated separately and jointly, and the ensemble analyses and forecasts are compared with available observations. Results show that assimilating BT observations has the potential to increase the forecast and warning lead times of severe weather events compared with radar observations and may also potentially complement the sparse surface observations in some regions as revealed by the probabilistic prediction of mesocyclone tracks initialized from EnKF analyses as various times. Additionally, the assimilation of both BT and Vr observations yields the best ensemble forecasts, providing higher confidence, improved accuracy, and longer lead times on the probabilistic prediction of midlevel mesocyclones. [ABSTRACT FROM AUTHOR]

Time series of quasi--vertical profiles (QVPs) from 52 stratiform precipitation events observed with the polarimetric X-band radar in Bonn, Germany (BoXPol), between 2013 and 2016 have been statistically analyzed to infer microphysical processes shaping the dendritic-growth-layer (DGL) and melting-layer (ML) signatures including surface rainfall. Specific differential phase KDP in the ML shows an average correlation of 0.65 with surface rainfall for these cases. Radar reflectivity decreases below the ML by about 2 dB on average while differential reflectivity ZDR is hardly affected, which suggests rain evaporation as the dominating effect. Estimated ice water content or snow water equivalent precipitation rate S in the DGL is correlated with surface rain rates with lead times of 30 min and longer, which opens a pathway for radar-based nowcasting of stratiform precipitation tendencies. Trajectories of snow generated aloft down to the surface are constructed from wind profiles derived both from the nearest radiosounding and radar-based velocity azimuth displays (VAD) to narrow down the location at which the DGL-generated snow reaches the surface as rain. The lagged correlation analysis between KDP in the DGL and reflectivity ZH at that location demonstrates the superiority of the VAD information. Correlation coefficients up to 0.80 with lead times up to 120 min provide a proof of concept for future nowcasting applications that are based on DGL monitoring. Statistical relations found in our QVP dataset provide a database for estimating intrinsic polarimetric variables from the usual azimuth and elevation scans within and in the vicinity of the ML. [ABSTRACT FROM AUTHOR]

This study uses a dense rain gauge network, radar observations, and an upslope model to document the detailed aspects of precipitation over Da-Tun Mountain (DT) of northern Taiwan under prevailing northeasterly monsoonal flow. DT is one of the most concentrated areas of heavy rainfall in Taiwan, with a size of ~15 km and a terrain peak of ~1 km (MSL), and it constitutes a concave ridge with two windward ridge arms that encompass a funnel-shaped valley. Twenty-one rainfall events identified over DT from January 2011 to February 2015 are chosen for analysis. More than half of the studied cases exhibit two local maxima of rainfall over the ridge arms, and asymmetric characteristics between these two maxima are evident. The other frequent rainfall pattern, observed as upstream winds are more from the east-northeast, is characterized by a local maximum of precipitation inside the valley. Analyses from the upslope model confirm that the occurrence of the windward maxima of rainfall is primarily caused by upslope lifting, and their asymmetric characteristics are closely related to the difference in the azimuthal variations of slope steepness between the two ridge arms. The observed characteristics of rainfall intensities inside the valley, however, cannot be well described by the upslope model. It is found that the lateral flow confluence induced by the deflected flows over the ridge arms may play an essential role in intensifying upslope-forced precipitation within the valley. This effect emerges as upstream winds are roughly parallel to the central axis of funneling regions located between the ridge arms. [ABSTRACT FROM AUTHOR]

During spring 2016 and spring 2017, a vertically pointing, S-band Frequency Modulated Continuous Wave radar (UMass FMCW) was deployed in northern Alabama under the auspices of the Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX)-Southeast. In total, ~14 weeks of data were collected, in conditions ranging from quiescent clear skies to severe thunderstorms. The principal objective of these deployments was to characterize the boundary layer evolution near the VORTEX-Southeast domain. In this paper, we describe intermediate results in service of this objective. Specifically, we describe updates to the UMass FMCW system, document its deployments for VORTEX-Southeast, and apply four automated algorithms: 1) a dealiasing algorithm to the Doppler velocities, 2) a fuzzy logic scatterer classification scheme to separate precipitation from nonprecipitation observations, 3) a brightband/melting-layer identification algorithm for stratiform precipitation, and 4) an extended Kalman filter--based convective boundary layer depth (mixing height) measurement algorithm for nonprecipitation observations. Results from the latter two applications are qualitatively verified against retrieved soundings from a collocated thermodynamic profiling system. [ABSTRACT FROM AUTHOR]

This is a study of a tornadic supercell in Kansas on 14 May 2018 in which data of relatively high spatiotemporal resolution from a mobile, polarimetric, X-band, Doppler radar were integrated with GOES-16 geosynchronous satellite imagery, and with fixed-site, surveillance, S-band polarimetric Doppler radar data. The data-collection period spanned the early life of the storm from when it was just a series of ordinary cells, with relatively low cloud tops, through its evolution into a supercell with much higher cloud tops, continuing through the formation and dissipation of a brief tornado, and ending after the supercell came to a stop and reversed direction, produced another tornado, and collided with a quasi-linear convective system. The main goal of this study was to examine the relationship between the overshooting tops and radar observed features prior to and during tornadogenesis. The highest radar echo top was displaced about 10 km, mainly to the north or northeast of the main updraft and cloud top, from the supercell phase through the first tornado phase of the supercell phase, after which the updraft and the cloud top became more closely located and then jumped ahead; this behavior is consistent with what would be expected during cyclic mesocyclogenesis. The change in direction of the supercell later on occurred while the nocturnal low-level jet was intensifying. No relationship was apparent between changes in the highest cloud-top height and tornadogenesis, but changes in cloud-top heights (rapid increases and rapid decreases) were related to two phases in multicell evolution and to supercell formation. [ABSTRACT FROM AUTHOR]

The assimilation of water vapor mass mixing ratio derived from total lightning data from the Geostationary Lightning Mapper (GLM) within a three-dimensional variational (3DVAR) system is evaluated for the analysis and short-term forecast (≤6 h) of a high-impact convective event over the northern Great Plains in the United States. Building on recent work, the lightning data assimilation (LDA) method adjusts water vapor mass mixing ratio within a fixed layer depth above the lifted condensation level by assuming nearly water-saturated conditions at observed lightning locations. In this algorithm, the total water vapor mass added by the LDA is balanced by an equal removal outside observed lightning locations. Additional refinements were also devised to partially alleviate the seasonal and geographical dependence of the original scheme. To gauge the added value of lightning, radar data (radial velocity and reflectivity) were also assimilated with or without lightning. Although the method was evaluated in quasi–real time for several high-impact weather events throughout 2018, this work will focus on one specific, illustrative severe weather case wherein the control simulation—which did not assimilate any data—was eventually able to initiate and forecast the majority of the observed storms. Given a relatively reasonable forecast in the control experiment, the GLM and radar assimilation experiments were still able to improve the short-term forecast of accumulated rainfall and composite radar reflectivity further, as measured by neighborhood-based metrics. These results held whether the simulations made use of one single 3DVAR analysis or high-frequency (10 min) successive cycling over a 1-h period. [ABSTRACT FROM AUTHOR]

An important objective in scatterometry is the estimation of near-surface wind speed and direction in the presence of rain. We investigate an attenuation correction method using data from the High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) dual-frequency scatterometer, which operates at Ku and Ka band with dual conical scans at incidence angles of 30° and 40°. The method relies on the fact that the differential normalized surface cross section, δσ0 = σ0(Ka) − σ0(Ku), is relatively insensitive to wind speed and direction and that this quantity is closely related to the magnitude of the differential path attenuation, δA = A(Ka) − A(Ku), arising from precipitation, cloud, and atmospheric gases. As the method relies only on the difference between quantities measured in the presence and absence of rain, the estimates are independent of radar calibration error. As a test of the method's accuracy, we make use of the fact that the radar rain reflectivities just above the surface, as seen along different incidence angles, are approximately the same. This yields constraint equations in the form of differences between pairs of path attenuations along different lines of sight to the surface. A second validation method uses the dual-frequency radar returns from the rain just above the surface where it can be shown that the difference between the Ku- and Ka-band-measured radar reflectivity factors provide an estimate of differential path attenuation. Comparisons between the path attenuations derived from the normalized surface cross section and those from these surface-independent methods generally show good agreement. [ABSTRACT FROM AUTHOR]

While land-based high-frequency (HF) radars are the only instruments capable of resolving both the temporal and spatial variability of surface currents in the coastal ocean, recent high-resolution views suggest that the coastal ocean is more complex than presently deployed radar systems are able to reveal. This work uses a hybrid system, having elements of both phased arrays and direction finding radars, to improve the azimuthal resolution of HF radars. Data from two radars deployed along the U.S. East Coast and configured as 8-antenna grid arrays were used to evaluate potential direction finding and signal, or emitter, detection methods. Direction finding methods such as maximum likelihood estimation generally performed better than the well-known multiple signal classification (MUSIC) method given identical emitter detection methods. However, accurately estimating the number of emitters present in HF radar observations is a challenge. As MUSIC's direction-of-arrival (DOA) function permits simple empirical tests that dramatically aid the detection process, MUSIC was found to be the superior method in this study. The 8-antenna arrays were able to provide more accurate estimates of MUSIC's noise subspace than typical 3-antenna systems, eliminating the need for a series of empirical parameters to control MUSIC's performance. Code developed for this research has been made available in an online repository. [ABSTRACT FROM AUTHOR]

To detect wake vortices in all weather conditions, lidar and radar sensors are complementary. It is important to determine accurately the limitations of the radar detection in clear air and to determine the parameters influencing the detection. This paper is the first study to present the simulation and analysis of the radar signatures of wake vortices on the basis of state-of-the-art 3D large-eddy simulations. The setup of the large-eddy simulations is described and the evolution of the wake vortices is illustrated for the three simulated cases. A modified version of the model developed by Muschinski et al. is used here for the calculation of the radar-backscattered signal and the second moment of the Doppler spectrum that is used to estimate the wake vortex circulation. The simulator uses two different radar configurations for the retrieval of the circulation, which is compared with the circulation obtained directly from the large-eddy simulations. The retrieval of the circulation is fairly accurate if the level of backscattered signal is high enough above the detection threshold of the radar. [ABSTRACT FROM AUTHOR]

Narrow regions of intense, banded snowfall present hazardous travel conditions due to rapid onset, high precipitation rates, and lowered visibility. Despite their importance, there are few verification studies of snowbands in operational forecast models. The objective of this study is to evaluate the ability of the High-Resolution Rapid Refresh (HRRR) model to predict snowbands in the United States east of the Rocky Mountains. An automated band-detection algorithm was applied to a 3-yr period of simulated and observed radar reflectivity to compare snowband climatologies. This algorithm uses the distributions of reflectivities in contiguous precipitation regions to determine a band intensity threshold. The predictability of snowbands on a case-by-case basis was also evaluated using an object-oriented approach. The distribution of HRRR forecast banding resembles that of the observations, but with a significant positive frequency bias. This may partially be due to underrepresentation of observed bands in our verification dataset due to limited radar coverage in portions of the central United States. On a case-by-case basis, traditional skill metrics indicate limited predictability, but allowing for small timing discrepancies dramatically improves scores. Object-oriented verification yields mixed results, with 30% of forecasts receiving a score indicative of a well-predicted event. However, 69% of cases have at least one forecast lead demonstrating skill, suggesting the HRRR is successful in depicting environments conducive to band formation. These results suggest adopting a probabilistic, ensemble approach, and indicate that the deterministic HRRR is best suited for the identification of regions of elevated snowband risk and not precise timing or location information. [ABSTRACT FROM AUTHOR]

Machine learning algorithms are trained on a 10-yr archive of composite weather radar images in the Swiss Alps to nowcast precipitation growth and decay in the next few hours in moving coordinates (Lagrangian frame). The hypothesis of this study is that growth and decay is more predictable in mountainous regions, which represent a potential source of practical predictability by machine learning methods. In this paper, artificial neural networks (ANN) are employed to learn the complex nonlinear dependence relating the growth and decay to the input predictors, which are geographical location, mesoscale motion vectors, freezing level height, and time of the day. The average long-term growth and decay patterns are effectively reproduced by the ANN, which allows exploring their climatology for any combination of predictors. Due to the low intrinsic predictability of growth and decay, its prediction in real time is more challenging, but is substantially improved when adding persistence information to the predictors, more precisely the growth and decay and precipitation intensity in the immediate past. The improvement is considerable in mountainous regions, where, depending on flow direction, the root-mean-square error of ANN predictions can be 20%–30% lower compared with persistence. Because large uncertainty is associated with precipitation forecasting, deterministic machine learning predictions should be coupled with a model for the predictive uncertainty. Therefore, we consider a probabilistic perspective by estimating prediction intervals based on a combination of quantile decision trees and ANNs. The probabilistic framework is an attempt to address the problem of conditional bias, which often characterizes deterministic machine learning predictions obtained by error minimization. [ABSTRACT FROM AUTHOR]

A new thermodynamic retrieval scheme is developed by which one can use the wind fields synthesized from multiple-Doppler radars to derive the three-dimensional thermodynamic fields over complex terrain. A cost function consisting of momentum equations and a simplified thermodynamic equation is formulated. By categorizing the analysis domain into flow and terrain regions, the variational technique is applied to minimize this cost function only within the flow region, leading to the solutions for the three-dimensional pressure and temperature perturbations immediately over terrain. Using idealized datasets generated by a numerical model, an experiment is first conducted to assess the accuracy of the proposed algorithm. The retrieval scheme is then applied to a real case that occurred during the 2008 Southwestern Monsoon Experiment (SoWMEX) conducted in Taiwan. The retrieved thermodynamic fields, verified by radiosonde data, reveal the structure of a prefrontal squall line as it approaches a mountain. The retrieved three-dimensional high-resolution pressure and temperature along with the wind fields not only allow us to better understand the thermodynamic and kinematic structure of a heavy rainfall system, but can also be assimilated into a numerical model to improve the forecast. [ABSTRACT FROM AUTHOR]

This work describes developments to improve the Doppler radial wind data assimilation scheme used in the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) data assimilation system with a focus on convection-permitting, 0–18-h forecasts of a heavy precipitation single case study. This work focuses on two aspects: 1) the extension of the radial wind observation operator to include vertical velocity and 2) a refinement of the radial wind super-observation processing. The refinement includes reducing the magnitude of observation smoothing and allowing observations from higher scan angles into the analysis with the intent to improve the assimilation of the radar data for operational, convection-permitting models. The results of this study demonstrate that there is sensitivity to the refinement in super-observation settings. The inclusion of vertical velocity in the observation operator is shown to have a neutral to slightly positive impact on the forecast. Results from this study are suggested to be used as a foundation to prioritize future research into the effective assimilation of radial winds in an operational setting. [ABSTRACT FROM AUTHOR]

The influence of ice hydrometeor shape on the dual-wavelength ratio (DWR) of radar reflectivities at millimeter-wavelength frequencies is studied theoretically and on the basis of observations. Data from dual-frequency (Ka–W bands) radar show that, for vertically pointing measurements, DWR increasing trends with reflectivity Ze are very pronounced when Ka-band Ze is greater than about 0 dBZ and that DWR and Ze values are usually well correlated. This correlation is explained by strong relations between hydrometeor characteristic size and both of these radar variables. The observed DWR variability for a given level of reflectivity is as large as 8 dB, which is in part due to changes in mean hydrometeor shape as expressed in terms of the particle aspect ratio. Hydrometeors with a higher degree of nonsphericity exhibit lower DWR values when compared with quasi-spherical particles because of near-zenith reflectivity enhancements for particles outside the Rayleigh-scattering regime. When particle mass–size relations do not change significantly (e.g., for low-rime conditions), DWR can be used to differentiate between quasi-spherical and highly nonspherical hydrometeors because (for a given reflectivity value) DWR tends to increase as particles become more spherical. Another approach for differentiating among different degrees of nonsphericity for larger scatterers is based on analyzing DWR changes as a function of radar elevation angle. These changes are more pronounced for highly nonspherical particles and can exceed 10 dB. Measurements of snowfall spatiotemporally collocated with spaceborne CloudSat W-band radar and ground-based S-band operational weather radars also indicate that DWR values are generally smaller for ice hydrometeors with higher degrees of nonsphericity, which, for the same level of S-band reflectivity, exhibit greater differential reflectivity values. [ABSTRACT FROM AUTHOR]

Using three years of observations from the Dual-Frequency Precipitation Radar (DPR) aboard the Global Precipitation Measurement (GPM) Core Observatory, properties of the cores of deep convection are examined. First, deep convective systems are selected, defined as GPM precipitation features with maximum 20-dBZ echo-top heights above 10 km. The cores of deep convection are described by the profiles of Ku- and Ka-band radar reflectivity at the location of the highest echo top in each deep convective system. Then the dual-frequency ratio (DFR) profile is derived by subtracting Ka-band from Ku-band radar reflectivity. It is found that values of DFR are larger over land than over ocean in general near the top of the convection, which is consistent with larger ice particles in stronger updrafts in continental convection. The magnitude of DFR at 12 km is positively correlated with the convection intensity indicated by 20- and 30-dBZ echo tops. The microphysical properties including volume-weighted mean diameter, ice water content, and total ice particle number concentration are derived using a simple lookup table approach. Under the same particle size distribution assumption, the cores of deep convection over land have larger ice particle size, higher ice water content, and lower particle concentration than those over ocean at levels above 10 km, but with some distinct regional variations. [ABSTRACT FROM AUTHOR]

A method named BTREC, which is in fact an extension of tracking a radar echo by cross-correlation (TREC) method, is proposed based on the Barnes filter in this paper. BTREC is an efficient objective analysis method for smoothing the motion vectors of radar echo patterns. A comparative analysis of the BTREC vectors and the COTREC (another extension of TREC, which is restricted by the 2D continuity equation) vectors is conducted. The results show that the BTREC method corrects the noise and inconsistencies in TREC vectors (often induced by shielding, ground clutter, and rapid morphological changes of the radar patterns) more thoroughly than COTREC. Then the BTREC, COTREC, and TREC methods are applied to extrapolate the real radar echo pattern over Jiangxi Province in China for two cases (1900–2100 UTC 4 June and 0900–1100 UTC 30 June 2018). Results show that the BTREC method performs the best out of the three methods to forecast the radar echo patterns in the following 1 and 2 h, and has the least distortion. To further confirm that, 892 radar composite reflectivity mosaic images with precipitation in summer (June–August) over Jiangxi are collected to test the three methods. The results show that in the 1- and 2-h extrapolations, the mean differences of the threat score (TS), correlation (CORR), and probability of detection (POD) between BTREC and TREC are obviously more than 0, while that of the false alarm ratio (FAR) is remarkably less than 0 (the threshold to identify whether a grid is correctly predicted is set to 10 dBZ). Although the mean difference of TS, CORR, POD, and FAR between COTREC and TREC have similar variation, their magnitudes are obviously decreased (especially for the 2-h extrapolation). This further indicates that the BTREC method obviously improves the forecast of the radar echo pattern within the next 2 h compared to the COTREC and TREC methods. [ABSTRACT FROM AUTHOR]

The Program of the Antarctic Syowa Mesosphere–Stratosphere–Troposphere/Incoherent Scatter (PANSY) radar is a large atmospheric radar located at the Antarctic Syowa Station (69.01°S, 39.59°E). The PANSY radar performed the first incoherent scatter (IS) measurements in the Antarctic region in 2015. Several specific observations were undertaken in 2017 including a 24-h observation of the ionosphere using a peripheral antenna array to suppress interference from the field-aligned irregularities (FAIs). This paper presents the preliminary results derived from the IS measurements using the PANSY radar and the adaptive signal processing techniques to suppress FAIs. The norm-constrained and directionally constrained minimization of power (NC-DCMP) algorithm was applied to the 24-h ionosphere observations by the PANSY radar with a weighting applied to the directional constraint based on the gain differences of the subarrays. When compared with the conventional nonadaptive approach, the number of usable power profiles was increased by about 24% by the gain-weighted NC-DCMP algorithm, suggesting its effectiveness for FAI clutter suppression in ionosphere observations. Furthermore, detection of FAIs using the dedicated antenna array was found valuable in assessing the reliability of estimations of electron density based on VHF-band IS radar data. [ABSTRACT FROM AUTHOR]

Wind profiling radars are usually not calibrated with respect to reflectivity because such calibrations are both unnecessary for good wind measurements and costly. However, reflectivity from calibrated profilers can reveal many atmospheric attributes beyond winds. Establishing ways to calibrate these radars even after they have been taken out of service would expand the utility of archived profiler data. We have calibrated one operating mode of a 915-MHz profiler deployed at Manus, Papua New Guinea (1992–2001), using two methods. The first method adjusts a radar parameter until the profiler's estimate of rainfall during stratiform events closely matches surface observations. The second adjusts the parameter so that mean brightband heights observed by the profiler (July 1992–August 1994) match the mean brightband reflectivities over the profiler as observed by the TRMM Precipitation Radar (January 1998–July 2001). The results differ by about 5% and yield very similar precipitation errors during tested stratiform events. One or both of these methods could be used on many other wind profilers, whether they have been decommissioned or are currently operational. Data from such calibrated profilers will enable research employing the equivalent reflectivity factor observed by profilers to be compared with that from other radars, and will also enable turbulent studies with Cn2. [ABSTRACT FROM AUTHOR]

Motor vehicle crashes remain a leading cause of accidental death in the United States, and weather is frequently cited as a contributing factor in fatal crashes. Previous studies have investigated the link between these crashes and precipitation typically using station-based observations that, while providing a good estimate of the prevailing conditions on a given day or hour, often fail to capture the conditions present at the actual time and location of a crash. Using a multiyear, high-resolution radar reanalysis and information on 125,012 fatal crashes spanning the entire continental United States over a 6-yr period, we find that the overall risk of a fatal crash increases by approximately 34% during active precipitation. The risk is significant in all regions of the continental United States, and it is highest during the morning rush hour and during the winter months. [ABSTRACT FROM AUTHOR]

Attenuation of the W-band (95 GHz) radar signal by atmospheric ice particles has long been neglected in cloud microphysics studies. In this work, 95-GHz airborne multibeam cloud radar observations in tropical stratiform ice anvils are used to estimate vertical profiles of 95-GHz attenuation. Two techniques are developed and compared, using very different assumptions. The first technique examines statistical reflectivity differences between repeated aircraft passes through the same cloud mass at different altitudes. The second technique exploits reflectivity differences between two different pathlengths through the same cloud, using the multibeam capabilities of the cloud radar. Using the first technique, the two-way attenuation coefficient produced by stratiform ice particles ranges between 1 and 1.6 dB km−1 for reflectivities between 13 and 18 dBZ, with an expected increase of attenuation with reflectivity. Using the second technique, the multibeam results confirm these high attenuation coefficient values and expand the reflectivity range, with typical attenuation coefficient values of up to 3–4 dB km−1 for reflectivities of 20 dBZ. The potential impact of attenuation on precipitating-ice-cloud microphysics retrievals is quantified using vertical profiles of the mean and the 99th percentile of ice water content derived from noncorrected and attenuation-corrected reflectivities. A large impact is found on the 99th percentile of ice water content, which increases by 0.3–0.4 g m−3 up to 11-km height. Finally, T-matrix calculations of attenuation constrained by measured particle size distributions, ice crystal mass–size, and projected area–size relationships are found to largely underestimate cloud radar attenuation estimates. [ABSTRACT FROM AUTHOR]

The recent dual-polarization upgrade to the National Weather Service radar network provides forecasters with new information to use during operations, yet currently this information is not routinely used to explicitly make warning decisions. One potential way to increase operational use is to link new radar signatures and products to existing forecaster conceptual models and the warning decision process. Over the past several years, a unique dataset consisting of rapid-update (<2-min volumes) radar data of storms over central Oklahoma has been collected to examine possible links between ZDR columns and forecaster conceptual models. In total, over 1400 volume scans from 42 storms—ranging from tornadic supercells to nonsevere multicells—are used to relate ZDR column depth to storm reports and radar signatures typically used to issue warnings, such as −20°C reflectivity core and low-level mesocyclone evolution. After completing the analysis, the following key operational findings emerged: 1) no clear differences exist between the ZDR column depth of tornadic and nontornadic mesocyclones, but statistically significant differences do exist between severe and nonsevere storms, 2) the lead time in advance of severe hail and wind reports provided by peaks in ZDR column depth is greater than that provided by peaks in −20°C reflectivity cores, 3) increases in ZDR column size precede increases in −20°C reflectivity core size by about 3.5–9.0 min, and 4) rapid-update volumetric data captures signature evolution several minutes earlier than conventional-update data therefore providing forecasters more time to anticipate hazards and issue warnings. [ABSTRACT FROM AUTHOR]

To improve severe thunderstorm prediction, a novel pseudo-observation and assimilation approach involving water vapor mass mixing ratio is proposed to better initialize NWP forecasts at convection-resolving scales. The first step of the algorithm identifies areas of deep moist convection by utilizing the vertically integrated liquid water (VIL) derived from three-dimensional radar reflectivity fields. Once VIL is obtained, pseudo–water vapor observations are derived based on reflectivity thresholds within columns characterized by deep moist convection. Areas of spurious convection also are identified by the algorithm to help reduce their detrimental impact on the forecast. The third step is to assimilate the derived pseudo–water vapor observations into a convection-resolving-scale NWP model along with radar radial velocity and reflectivity fields in a 3DVAR framework during 4-h data assimilation cycles. Finally, 3-h forecasts are launched every hour during that period. The performance of this method is examined for two selected high-impact severe thunderstorm events: namely, the 24 May 2011 Oklahoma and 16 May 2017 Texas and Oklahoma tornado outbreaks. Relative to a control simulation that only assimilated radar data, the analyses and forecasts of these supercells (reflectivity patterns, tracks, and updraft helicity tracks) are qualitatively and quantitatively improved in both cases when the water vapor information is added into the analysis. [ABSTRACT FROM AUTHOR]

Diabatic cooling from hydrometeor phase changes in the stratiform melting layer is of great interest to both operational forecasters and modelers for its societal and dynamical consequences. Attempts to estimate the melting-layer cooling rate typically rely on either the budgeting of hydrometeor content estimated from reflectivity Z or model-generated lookup tables scaled by the magnitude of Z in the bright band. Recent advances have been made in developing methods to observe the unique polarimetric characteristics of melting snow and the additional microphysical information they may contain. However, to date no work has looked at the thermodynamic information available from the polarimetric radar brightband signature. In this study, a one-dimensional spectral bin model of melting snow and a coupled polarimetric operator are used to study the relation between the polarimetric radar bright band and the melting-layer cooling rate. Simulations using a fixed particle size distribution (PSD) and variable environmental conditions show that the height and thickness of the bright band and the maximum brightband Z and specific differential phase shift K DP are all sensitive to the ambient environment, while the differential reflectivity Z DR is relatively insensitive. Additional simulations of 2700 PSDs based on in situ observations above the melting layer indicate that the maximum Z, Δ Z , and Z DR within the melting layer are poorly correlated with the maximum cooling rate while K DP is strongly correlated. Finally, model simulations suggest that, in addition to riming, concurrent changes in aggregation and precipitation intensity and the associated cooling may plausibly cause observed sagging brightband signatures. [ABSTRACT FROM AUTHOR]

Dual high-frequency (HF) radar systems are often used to provide measurements of waves, winds, and currents. In this study, the accuracy of wave measurements using a single HF radar system (OS081H-A) was explored using datasets obtained during 5–27 January 2014 in the southwestern Taiwan Strait. We selected the study region as an area with >90% coverage (i.e., the range was <100 km). Qualitative and quantitative intercomparison of wave measurements (by the radar and five buoys) and wave model products [from the Simulating Wave Nearshore (SWAN) model] were conducted. Intercomparison of the modeled and in situ significant wave height Hs showed that the model-predicted Hs could be considered to be acceptable for use as "sea truth" to evaluate the radar-derived Hs, with mean bias from −0.45 to −0.16 m, mean absolute error (MAE) of 0.24–0.45 m, and root-mean-square error of 0.31–0.54 m. It was found that the MAE of radar-derived Hs was ≤ 1 m for 86% of the sector (except at the edge of sector) when the model-predicted Hs was ≥ 1.5 m. In particular, the MAE was less than 0.6 m for 63% of the sector, which was mainly distributed in the area with a bearing from −50° to +70° and a range of 20–70 km. The results are promising, but more work is needed. We employed a spatial distribution function for the MAE of the radar-derived Hs over the sample duration based on range, bearing, and mean radar-derived Hs. [ABSTRACT FROM AUTHOR]

The CSU–CHILL radar is a dual-wavelength, dual-polarization weather radar system operating at S and X band with coaxial beams. This radar system offers a unique environment to develop and/or validate algorithms that cut across its wavelengths and polarizations. This paper presents a method to retrieve resonance scattering regions from the difference in intrinsic reflectivities after attenuation correction, which is performed using measured reflectivity fields only. The algorithm to retrieve these regions dominated by non-Rayleigh scattering is applied to different storm events, and the obtained data field capturing the difference in S- and X-band reflectivities due to resonance effects (which we will call Mie signal for convenience) is compared to the collocated dual-polarization fields. The obtained Mie signal is also compared to hail reports. In both cases, the retrieved Mie signal is found to be consistent with the rest of the dual-polarization data fields, and in some situations, it is shown to bring information not directly discernible from the usual dual-polarization radar variables. [ABSTRACT FROM AUTHOR]

Real polarimetric radar observations are directly assimilated for the first time using the ensemble Kalman filter (EnKF) for a supercell case from 20 May 2013 in Oklahoma. A double-moment microphysics scheme and advanced polarimetric radar observation operators are used together to estimate the model states. Lookup tables for the observation operators are developed based on T-matrix scattering amplitudes for all hydrometeor categories, which improve upon previous curved-fitted approximations of T-matrix scattering amplitudes or the Rayleigh approximation. Two experiments are conducted: one assimilates reflectivity (Z) and radial velocity (Vr) (EXPZ), and one assimilates in addition differential reflectivity (ZDR) below the observed melting level at ~2-km height (EXPZZDR). In the EnKF analyses, EXPZZDR exhibits a ZDR arc that better matches observations than EXPZ. EXPZZDR also has higher ZDR above 2 km, consistent with the observed ZDR column. Additionally, EXPZZDR has an improved estimate of the model microphysical states. Specifically, the rain mean mass diameter (Dnr) in EXPZZDR is higher in the ZDR arc region and the total rain number concentration (Ntr) is lower downshear in the forward flank than EXPZ when compared to values retrieved from the polarimetric observations. Finally, a negative gradient of hail mean mass diameter (Dnh) is found in the right-forward flank of the EXPZZDR analysis, which supports previous findings indicating that size sorting of hail, as opposed to rain, has a more significant impact on low-level polarimetric signatures. This paper represents a proof-of-concept study demonstrating the value of assimilating polarimetric radar data in improving the analysis of features and states related to microphysics in supercell storms. [ABSTRACT FROM AUTHOR]

The ability of in situ snowflake microphysical observations to constrain estimates of surface snowfall accumulations derived from coincident, ground-based radar observations is explored. As part of the High-Latitude Measurement of Snowfall (HiLaMS) field campaign, a Micro Rain Radar (MRR), Precipitation Imaging Package (PIP), and Multi-Angle Snow Camera (MASC) were deployed to the Haukeliseter Test Site run by the Norwegian Meteorological Institute during winter 2016/17. This measurement site lies near an elevation of 1000 m in the mountains of southern Norway and houses a double-fence automated reference (DFAR) snow gauge and a comprehensive set of meteorological observations. MASC and PIP observations provided estimates of particle size distribution (PSD), fall speed, and habit. These properties were used as input for a snowfall retrieval algorithm using coincident MRR reflectivity measurements. Retrieved surface snowfall accumulations were evaluated against DFAR observations to quantify retrieval performance as a function of meteorological conditions for the Haukeliseter site. These analyses found differences of less than 10% between DFAR- and MRR-retrieved estimates over the field season when using either PIP or MASC observations for low wind "upslope" events. Larger biases of at least 50% were found for high wind "pulsed" events likely because of sampling limitations in the in situ observations used to constrain the retrieval. However, assumptions of MRR Doppler velocity for mean particle fall speed and a temperature-based PSD parameterization reduced this difference to +16% for the pulsed events. Although promising, these results ultimately depend upon selection of a snowflake particle model that is well matched to scene environmental conditions. [ABSTRACT FROM AUTHOR]

Polarimetric radar measurements provide information about ice particle growth and offer the potential to evaluate and better constrain ice microphysical models. To achieve these goals, one must map the ice particle physical properties (e.g., those predicted by a microphysical model) to electromagnetic scattering properties using a radar forward model. Simplified methods of calculating these scattering properties using homogeneous, reduced-density spheroids produce large errors in the polarimetric radar measurements, particularly for low-aspect-ratio branched planar crystals. To overcome these errors, an empirical method is introduced to more faithfully represent branched planar crystal scattering using scattering calculations for a large number of detailed shapes. Additionally, estimates of the uncertainty in the scattering properties, owing to ambiguity in the crystal shape given a set of bulk physical properties, are also incorporated in the forward model. To demonstrate the utility of the forward model developed herein, the radar variables are simulated from microphysical model output for an Arctic cloud case. The simulated radar variables from the empirical forward model are more consistent with the observations compared to those from the homogeneous, reduced-density-spheroid model, and have relatively low uncertainty. [ABSTRACT FROM AUTHOR]

A hybrid, empirical radar wave inversion technique that treats swell and wind waves separately is presented and evaluated using a single 48-MHz radar unit and in situ wave measurements. This hybrid approach greatly reduces errors in radar wave inversion during swell seas. Our analysis suggests that, prior to the inversion, the second-order spectrum should be normalized using Barrick's weighting function because this process removes harmonic and corner reflection peaks from the inversion and improves the results. In addition, the resulting calibration constants for the wind wave component are not wave-frequency dependent and are similar in magnitude to those found in previous studies using different operating-frequency radars. This result suggests radar frequency independence, although additional experimental verification is required. The swell component of the model presented here ignores the effect of swell's propagation direction on the radar signal. Although this approach has several limitations and may only be useful near the coast (where swell propagates close to perpendicular to the coastline), the resulting wave inversion is accurate even when swell is propagating close to perpendicular to the radar beam direction. RMS differences relative to in situ wave height measurements range from 0.16 to 0.25 m as the radar beam angle increases from 22° to 56°. [ABSTRACT FROM AUTHOR]

Interpolation of ground-based radar measurements is required when mapping data from their native spherical coordinates to a Cartesian grid. For reflectivity the question arises as to whether this processing should be performed in units of Z (mm6 m−3) or dBZ. This study addresses this question using one year of data from three radars, operating in diverse climates across Australia. For each radar, a subset of 800 volume scans is processed to identify "triads"—groups of three consecutive gates with valid data—in each of the three coordinate directions: range, azimuth, and elevation. For every triad, the reflectivity at the central gate is estimated by linearly interpolating between the outer two gates in both Z and dBZ. The resulting values are then compared with the true reflectivity at the central gate to quantify the interpolation errors. For all three sites and in all three coordinate directions, we find that interpolation in Z is more accurate on average, especially in regions of high reflectivity and strong reflectivity gradient (i.e., convective cores). However, interpolation in dBZ is better in regions of low and monotonically increasing/decreasing reflectivity. It is therefore recommended that reflectivities be converted from dBZ to Z prior to interpolation except when identifying echo-top height or other low-reflectivity boundaries. [ABSTRACT FROM AUTHOR]

By using long pulses with extended dwell time, radar sensitivity is enhanced such that more clear-air returns are usable. This enhanced capability can increase clear-air data coverage and provide additional information for radar wind analysis and data assimilation. To explore this potential capability and related benefits, a new scan mode with long pulses and low antenna rotation rate is derived from the existing clear-air scan mode and applied to the named KOUN S-band test bed radar at NSSL. Velocities collected using this newly modified scan mode are significantly enhanced in data coverage compared to those collected with the unmodified scan mode from the nearby test bed radar (named KCRI). However, transmitter duty cycle limits the pulse repetition frequency (PRF) to 455 Hz for the long pulses, which reduces the Nyquist velocity to 12.53 m s−1 for the new scan mode. Such a small Nyquist interval leads to significantly worse problems with aliased velocities. A recently developed dealiasing algorithm is modified by using external reference velocities from KCRI to overcome increased difficulties in dealiasing, making the velocities from KOUN useful over increased range and area. This implies that similar increases in velocity data coverage can be achieved without using external reference velocities if the newly modified scan is paired with an unmodified scan into dual-PRF scans. This dual-PRF approach is proposed for future development. [ABSTRACT FROM AUTHOR]