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Geostationary Operational Environmental Satellites (GOES) Radar Estimation via Machine Learning to Inform NWP (GREMLIN) is a machine learning model that outputs composite reflectivity using GOES-R Series Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) input data. GREMLIN is useful for observing severe weather and initializing convection for short-term forecasts, especially over regions without ground-based radars. This study expands the evaluation of GREMLIN's accuracy against the Multi-Radar Multi-Sensor (MRMS) System to the entire contiguous United States (CONUS) for the entire annual cycle. Regional and temporal variation of validation metrics are examined over CONUS by season, day of year, and time of day. Since GREMLIN was trained with data in spring and summer, root-mean-square difference (RMSD) and bias are lowest in the order of summer, spring, fall, and winter. In summer, diurnal patterns of RMSD follow those of precipitation occurrence. Winter has the highest RMSD because of cold surfaces mistaken as precipitating clouds, but some of these errors can be removed by applying the ABI clear-sky mask product and correcting biases using a lookup table. In GREMLIN, strong echoes are closely related to the existence of lightning and corresponding low brightness temperatures, which result in different error distributions over different regions of CONUS. This leads to negative biases in cold seasons over Washington State, lower 30-dBZ critical success index caused by high misses over the Northeast, and higher false alarms over Florida that are due to higher frequency of lightning. [ABSTRACT FROM AUTHOR]

We use dual-polarization C-band data collected in the Southern Ocean to examine the properties of snow observed during a voyage in the austral summer of 2018. Using existing forward modeling formalisms based on an assumption of Rayleigh scattering by soft spheroids, an optimal estimation algorithm is implemented to infer snow properties from horizontally polarized radar reflectivity, the differential radar reflectivity, and the specific differential phase. From the dual-polarization observables, we estimate ice water content qi, the mass-mean particle size Dm, and the exponent of the mass–dimensional relationship bm that, with several assumptions, allow for evaluation of snow bulk density, and snow number concentration. Upon evaluating the uncertainties associated with measurement and forward model errors, we determine that the algorithm can retrieve qi, Dm, and bm within single-pixel uncertainties conservatively estimated in the range 120%, 60%, and 40%, respectively. Applying the algorithm to open-cellular convection in the Southern Ocean, we find evidence for secondary ice formation processes within multicellular complexes. In stratiform precipitation systems we find snow properties and infer processes that are distinctly different from the shallow convective systems with evidence for riming and aggregation being common. We also find that embedded convection within the frontal system produces precipitation properties consistent with graupel. Examining 5 weeks of data, we show that snow in open-cellular cumulus has higher overall bulk density than snow in stratiform precipitation systems with implications for interpreting measurements from space-based active remote sensors. [ABSTRACT FROM AUTHOR]

Two spaceborne radars currently in orbit enable the sampling of snowfall near the surface and throughout the atmospheric column, namely, CloudSat's Cloud Profiling Radar (CPR) and the Global Precipitation Measurement mission's Dual-Frequency Precipitation Radar (GPM-DPR). In this paper, a direct comparison of the CPR's 2C-SNOW-PROFILE (2CSP), the operational GPM-DPR algorithm (2ADPR) and a neural network (NN) retrieval applied to the GPM-DPR data is performed using coincident observations between both radars. Examination of over 3500 profiles within moderate to strong precipitation (Ka band ≥ 18 dBZ) show that the NN retrieval provides the closest retrieval of liquid equivalent precipitation rate R immediately above the melting level to the R retrieved just below the melting layer, agreeing within 5%. Meanwhile, 2CSP retrieves a maximum value of R at −15°C, decreases by 35% just above the melting layer, and is about 50% smaller than the GPM-DPR retrieved R below the melting layer. CPR-measured reflectivity shows median reduction of 2–3 dB from −15° to −2.5°C, likely the reason for the 2CSP retrieval reduction of R. Two case studies from NASA field campaigns [i.e., Olympic Mountains Experiment (OLYMPEX) and Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS)] provide analogs to the type of precipitating systems found in the comparison between retrieval products. For the snowfall events that GPM-DPR can observe, this work suggests that the 2CSP retrieval is likely underestimating the unattenuated reflectivity, resulting in a potential negative, or low, bias in R. Future work should investigate how frequently the underestimated reflectivity profiles occur within the CPR record and quantify its potential effects on global snowfall accumulation estimation. [ABSTRACT FROM AUTHOR]

A robust and computationally efficient object tracking algorithm is developed by incorporating various tracking techniques. Physical properties of the objects, such as brightness temperature or reflectivity, are not considered. Therefore, the algorithm is adaptable for tracking convection-like features in simulated data and remotely sensed two-dimensional images. In this algorithm, a first guess of the motion, estimated using the Fourier phase shift, is used to predict the candidates for matching. A disparity score is computed for each target–candidate pair. The disparity also incorporates overlapping criteria in the case of large objects. Then the Hungarian method is applied to identify the best pairs by minimizing the global disparity. The high-disparity pairs are unmatched, and their target and candidate are declared expired and newly initiated objects, respectively. They are tested for merger and split on the basis of their size and overlap with the other objects. The sensitivity of track duration is shown for different disparity and size thresholds. The paper highlights the algorithm's ability to study convective life cycles using radar and simulated data over Darwin, Australia. The algorithm skillfully tracks individual convective cells (a few pixels in size) and large convective systems. The duration of tracks and cell size are found to be lognormally distributed over Darwin. The evolution of size and precipitation types of isolated convective cells is presented in the Lagrangian perspective. This algorithm is part of a vision for a modular platform [viz., TINT is not TITAN (TINT) and Tracking and Object-Based Analysis of Clouds (tobac)] that will evolve into a sustainable choice to analyze atmospheric features. [ABSTRACT FROM AUTHOR]

With the launch of the Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM-DPR) in 2014, renewed interest in retrievals of snowfall in the atmospheric column has occurred. The current operational GPM-DPR retrieval largely underestimates surface snowfall accumulation. Here, a neural network (NN) trained on data that are synthetically derived from state-of-the-art ice particle scattering models and measured in situ particle size distributions (PSDs) is used to retrieve two parameters of the PSD: liquid equivalent mass-weighted mean diameter D m l and the liquid equivalent normalized intercept parameter N w l . Evaluations against a test dataset showed statistically significantly improved ice water content (IWC) retrievals relative to a standard power-law approach and an estimate of the current GPM-DPR algorithm. Furthermore, estimated median percent errors (MPE) on the test dataset were −0.7%, +2.6%, and +1% for D m l , N w l , and IWC, respectively. An evaluation on three case studies with collocated radar observations and in situ microphysical data shows that the NN retrieval has MPE of −13%, +120%, and +10% for D m l , N w l , and IWC, respectively. The NN retrieval applied directly to GPM-DPR data provides improved snowfall retrievals relative to the default algorithm, removing the default algorithm's ray-to-ray instabilities and recreating the high-resolution radar retrieval results to within 15% MPE. Future work should aim to improve the retrieval by including PSD data collected in more diverse conditions and rimed particles. Furthermore, different desired outputs such as the PSD shape parameter and snowfall rate could be included in future iterations. [ABSTRACT FROM AUTHOR]

Several studies in the literature have developed approaches to diagnose hail storms from satellite-borne passive microwave imagery and build nearly global climatologies of hail. This paper uses spaceborne Ku-band radar measurements from the Global Precipitation Measurement (GPM) mission Dual-Frequency Precipitation Radar (DPR) to validate several passive microwave approaches. We assess the retrievals on the basis of how tightly they constrain the radar reflectivity at −20°C and how this measured radar reflectivity aloft varies geographically. The algorithm that combines minimum 19-GHz polarization corrected temperature (PCT) with a 37-GHz PCT depression normalized by tropopause height constrains the radar reflectivity most tightly and gives the least appearance of regional biases. A retrieval that is based on a 19-GHz PCT threshold of 261K also produces tightly clustered profiles of radar reflectivity, with little regional bias. An approach using regionally adjusted minimum 37-GHz PCT performs relatively well, but our results indicate it may overestimate hail in some subtropical and midlatitude regions. A threshold applied to the minimum 37-GHz PCT (≤230 K), without any scaling by region or probability of hail, overestimates hail in the tropics and underestimates beyond the tropics. For all retrieval approaches, storms identified as having hail tended to have radar reflectivity profiles that are consistent with general expectations for hailstorms (reflectivity > 50 dBZ below the 0°C level, and > 40 dBZ extending far above 0°C). Profiles from oceanic regions tended to have more rapidly decreasing reflectivity with height than profiles from other regions. Subtropical, high-latitude, and high-terrain land profiles had the slowest decreases of reflectivity with height. [ABSTRACT FROM AUTHOR]

Two power-law relations linking equivalent radar reflectivity factor Ze and snowfall rate S are derived for a K-band Micro Rain Radar (MRR) and for a W-band cloud radar. For the development of these Ze–S relationships, a dataset of calculated and measured variables is used. Surface-based video-disdrometer measurements were collected during snowfall events over five winters at the high-latitude site in Hyytiälä, Finland. The data from 2014 to 2018 include particle size distributions (PSD) and their fall velocities, from which snowflake masses were derived. The K- and W-band Ze values are computed using these surface-based observations and snowflake scattering properties as provided by T-matrix and single-particle scattering tables, respectively. The uncertainty analysis shows that the K-band snowfall-rate estimation is significantly improved by including the intercept parameter N0 of the PSD calculated from concurrent disdrometer measurements. If N0 is used to adjust the prefactor of the Ze–S relationship, the RMSE of the snowfall-rate estimate can be reduced from 0.37 to around 0.11 mm h−1. For W-band radar, a Ze–S relationship with constant parameters for all available snow events shows a similar uncertainty when compared with the method that includes the PSD intercept parameter. To demonstrate the performance of the proposed Ze–S relationships, they are applied to measurements of the MRR and the W-band microwave radar for Arctic clouds at the Arctic research base operated by the German Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI) and the French Polar Institute Paul Emile Victor (IPEV) (AWIPEV) in Ny-Ålesund, Svalbard, Norway. The resulting snowfall-rate estimates show good agreement with in situ snowfall observations while other Ze–S relationships from literature reveal larger differences. [ABSTRACT FROM AUTHOR]

A spatial mismatch between radar-based hail swaths and surface hail reports is commonly noted in meteorological literature. The discrepancy is partly due to hailstone advection and melting between detection aloft and observation at the ground. This study aims to mitigate this problem by introducing a model named HailTrack, which estimates hailfall at the surface using radar observations. The model operates by detecting, tracking, and collating hailstone trajectories using dual-polarized, dual-Doppler radar retrievals. Notable improvements in hailfall forecasts were observed through the use of HailTrack, and initializing the model with radar retrievals of hail differential reflectivity HDR was found to produce the most accurate hailfall estimates. The analysis of a case study in Brisbane, Australia, demonstrated that trajectory modeling significantly improved the correlation between hail swaths and hail-related insurance losses, increasing Heidke skill scores from 0.48 to 0.58. The accumulated kinetic energy of hailstone impacts also showed some skill in identifying areas that were exposed to particularly severe hailfall. Other unique impact estimates are presented, such as hailstone advection information and hailstone impact angle statistics. The potential to run the model in real time and produce short-term (10–15 min) nowcasts is also introduced. Model applications include improving radar-based hail climatologies, validating hail detection techniques and insurance claims data, and providing real-time hail impact maps to improve public awareness of hail risk. [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]

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]

Using cyclone-centered compositing and a database of extratropical-cyclone locations, the distribution of precipitation frequency and rate in oceanic extratropical cyclones is analyzed using satellite-derived datasets. The distribution of precipitation rates retrieved using two new datasets, the Global Precipitation Measurement radar--microwave radiometer combined product (GPM-CMB) and the Integrated Multisatellite Retrievals for GPM product (IMERG), is compared with CloudSat, and the differences are discussed. For reference, the composites of AMSR-E, GPCP, and two reanalyses are also examined. Cyclone-centered precipitation rates are found to be the largest with the IMERG and CloudSat datasets and lowest with GPM-CMB. A series of tests is conducted to determine the roles of swath width, swath location, sampling frequency, season, and epoch. In all cases, these effects are less than ~0.14 mm h-1 at 50-km resolution. Larger differences in the composites are related to retrieval biases, such as ground-clutter contamination in GPM-CMB and radar saturation in CloudSat. Overall the IMERGproduct reports precipitationmore often, with larger precipitation rates at the center of the cyclones, in conditions of high precipitable water (PW). The CloudSat product tends to report more precipitation in conditions of dry or moderate PW. The GPM-CMB product tends to systematically report lower precipitation rates than the other two datasets. This intercomparison provides 1) modelers with an observational uncertainty and range (0.21-0.36mm h-1 near the cyclone centers) when using composites of precipitation for model evaluation and 2) retrieval-algorithm developers with a categorical analysis of the sensitivity of the products to PW. [ABSTRACT FROM AUTHOR]

The interactions between a katabatic flow on a plain and a circular basin cut into the plain and surrounded by an elevated rim were examined during a 5-h steady-state period during the Second Meteor Crater Experiment (METCRAX II) to explain observed disturbances to the nocturnal basin atmosphere. The approaching katabatic flow split horizontally around Arizona's Meteor Crater below a dividing streamline while, above the dividing streamline, an ~50-m-deep stable layer on the plain was carried over the 30-50-m rim of the basin. A flow bifurcation occurred over or just upwind of the rim, with the lowest portion of the stable layer having negative buoyancy relative to the air within the crater pouring continuously over the crater's upwind rim and accelerating down the inner sidewall. The cold air intrusion was deepest and coldest over the direct upwind crater rim. Cold air penetration depths varied around the inner sidewall depending on the temperature deficit of the inflow relative to the ambient environment inside the crater. A shallow but extremely stable cold pool on the crater floor could not generally be penetrated by the inflow and a hydraulic jump-like feature formed on the lower sidewall as the flow approached the cold pool. The upper nonnegatively buoyant portion of the stable layer was carried horizontally over the crater, forming a neutrally stratified, low-wind speed cavity or wake in the lee of the upwind rim that extended downward into the crater over the upwind sidewall. [ABSTRACT FROM AUTHOR]

Recent interest in interpreting polarimetric radar observations of ice and evaluating microphysical model output with these observations has highlighted the importance of accurately computing the scattering of microwave radiation by branched planar ice crystals. These particles are often represented as spheroids with uniform bulk density, reduced from that of solid ice to account for the complex, nonuniform structure of natural branched crystals. In this study, the potential errors that arise from this assumption are examined by comparing scattering calculations of branched planar crystals with those of homogeneous, reduced-density plate crystals and spheroids with the same mass, aspect ratio, and maximum dimension. The results show that this assumption leads to significant errors in backscatter cross sections at horizontal and vertical polarization, specific differential phase (KDP), and differential reflectivity (ZDR), with the largest ZDR errors for ice crystals with the most extreme aspect ratios (,0.01) and effective densities <250 kg m-3. For example, the maximum errors in X-band ZDR are 4.5 dB for 5.6-mm branched planar crystals. However, substantial errors are present at all weather radar frequencies, with resonance scattering effects at Ka and W band amplifying the lowfrequency errors. The implications of these results on the interpretation of polarimetric radar observations and forward modeling of the polarimetric radar variables from microphysical model output are discussed. [ABSTRACT FROM AUTHOR]

The vertical refractivity gradient (VRG) is critical to weather radar beam propagation. The most common method of calculating beam paths uses the 4/3 Earth radius model, which corresponds to standard refraction conditions. In the present work, to better document propagation conditions for radar electromagnetic waves, which is essential for hydrology and numerical weather forecast models to more fully benefit from observations taken from the new-generation weather radar network in China,VRGspatial and temporal variations in the first kilometers above the surface are explored using 6-yr sounding observations. Under the effects of both regional climatic and topographic conditions, VRG values for most of the radars are generally smaller than those of the standard conditions for much of the year. There are similar or slightly larger values at only a few radar sites. Smaller VRG values are more frequent and widespread, especially during rainy seasons when weather radar observations are important. In such conditions, beam heights estimated using standard atmospheric refraction are overestimated relative to actual heights for most of the radars. Underestimates are much less common and of much shorter duration. However, height deviations are acceptable for being well within the uncertainty of radar echo height owing to the ~1° beamwidth. In coastal areas and the middle and lower reaches of the Yangtze River, radar observations should be applied with much more caution because of the greater risk of beam blockage and clutter contamination. [ABSTRACT FROM AUTHOR]

The accuracy of rain-rate estimation using polarimetric radar measurements has been improved as a result of better characterization of radar measurement quality and rain microphysics. In the literature, a variety of power-law relations between polarimetric radar measurements and rain rate are described because of the dynamic or varying nature of rain microphysics. A variational technique that concurrently takes into account radar observational error and dynamically varying rain microphysics is proposed in this study. Rain-rate estimation using the variational algorithm that uses event-based observational error and background rain climatological values is evaluated using observing system simulation experiments (OSSE), and its performance is demonstrated in the case of an epic Colorado flood event. The rain event occurred between 11 and 12 September 2013. The results from OSSE show that the variational algorithm with event-based observational error consistently estimates more accurate rain rate than does the ' R( ZHH, ZDR)' power-law algorithm. On the contrary, the usage of ad hoc or improper observational error degrades the performance of the variational method. Furthermore, the variational algorithm is less sensitive to the observational error of differential reflectivity ZDR than is the R( ZHH, ZDR) algorithm. The variational quantitative precipitation estimation (QPE) retrieved more accurate rainfall estimation than did the power-law dual-polarization QPE in this particular event, despite the fact that both algorithms used the same dual-polarization radar measurements from the Next Generation Weather Radar (NEXRAD). [ABSTRACT FROM AUTHOR]

This study is the third part of a paper series investigating the polarimetric radar properties of melting hail and application of those properties for operational polarimetric hail detection and determination of its size. The results of theoretical simulations in Part I were used to develop a hail size discrimination algorithm (HSDA) described in Part II. The HSDA uses radar reflectivity Z, differential reflectivity ZDR, and cross-correlation coefficient ρhv along with melting-level height within a fuzzy-logic scheme to distinguish among three hail size classes: small hail (with diameter D < 2.5 cm), large hail (2.5 < D < 5.0 cm), and giant hail ( D > 5.0 cm). The HSDA validation is performed using radar data collected by numerous WSR-88D sites and more than 3000 surface hail reports obtained from the Severe Hazards Analysis and Verification Experiment (SHAVE). The original HSDA version was modified in the process of validation, and the modified algorithm demonstrates probability of detection of 0.594, false-alarm ratio of 0.136, and resulting critical success index (CSI) equal to 0.543. The HSDA outperformed the current operational single-polarization hail detection algorithm, which only provides a single hail size estimate per storm and is characterized by CSI equal to 0.324. It is shown that HSDA is particularly sensitive to the quality of ZDR measurements, which might be affected by possible radar miscalibration and anomalously high differential attenuation. [ABSTRACT FROM AUTHOR]

Scattering properties of a large collection of pristine ice crystals at millimeter and centimeter wavelengths are calculated using the generalized multiparticle Mie method. Millimeter- and centimeter-wavelength radar observables are also calculated by employing particle size distributions (PSDs) that ensure the bulk properties (e.g., ice water content and total number concentration) fall within physically realistic ranges. The relationships between radar reflectivity Z and ice water content (IWC) are shown to be sensitive (from one to two orders of magnitude in variability) to the PSDs used and are thus not recommended for IWC retrievals. The relationships between IWC and specific differential phase KDP are less dependent on PSDs. Simple relationships between IWC and KDP at different radar elevation angles and wavelengths are given. If only the general crystal type is known (i.e., planar vs columnar), IWC retrieval errors based on KDP mostly fall within 30%. If more detailed ice crystal type is known, the retrieval errors are reduced to mostly within 10%. These results are similar to earlier reports in the literature but are based on a more extensive collection of model ice crystals and electromagnetic-scattering computations at four wavelengths: X, Ku, Ka, and W bands. The applicability of KDP in retrieving IWC is limited by the measurement accuracy of KDP, which usually requires averaging over several kilometers in range. Given the same noise level, the shorter wavelengths may have relatively smaller fractional errors than the longer wavelengths in KDP-based IWC retrievals and are promising wavelengths for further investigation. [ABSTRACT FROM AUTHOR]

Active remote sensing instruments such as lidar and radar allow one to accurately detect the presence of clouds and give information on their vertical structure and phase. To better address cloud radiative impact over the Arctic area, a combined analysis based on lidar and radar ground-based and A-Train satellite measurements was carried out to evaluate the efficiency of cloud detection, as well as cloud type and vertical distribution, over the Eureka station (80°N, 86°W) between June 2006 and May 2010. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO) and CloudSat data were first compared with independent ground-based cloud measurements. Seasonal and monthly trends from independent observations were found to be similar among all datasets except when compared with the weather station observations because of the large reported fraction of ice crystals suspended in the lower troposphere in winter. Further investigations focused on satellite observations that are collocated in space and time with ground-based data. Cloud fraction occurrences from ground-based instruments correlated well with both CALIPSO operational products and combined CALIPSO- CloudSat retrievals, with a hit rate of 85%. The hit rate was only 77% for CloudSat products. The misdetections were mainly attributed to 1) undetected low-level clouds as a result of sensitivity loss and 2) missed clouds because of the distance between the satellite track and the station. The spaceborne lidar-radar synergy was found to be essential to have a complete picture of the cloud vertical profile down to 2 km. Errors are quantified and discussed. [ABSTRACT FROM AUTHOR]

A framework based on measured raindrop size distribution (DSD) data has been developed to assess uncertainties in DSD models employed in Ku- and Ka-band dual-wavelength radar retrievals. In this study, the rain rates and attenuation coefficients from DSD parameters derived by dual-wavelength algorithms are compared with those directly obtained from measured DSD spectra. The impact of the DSD gamma parameterizations on rain estimation from the Global Precipitation Measurement mission (GPM) Dual-Frequency Precipitation Radar (DPR) is examined for the cases of a fixed shape factor μ as well as for a constrained μ-that is, a μ-Λ relation (a relationship between the shape parameter and slope parameter Λ of the gamma DSD)-by using 11 Particle Size and Velocity (Parsivel) disdrometer measurements with a total number of about 50 000 one-minute spectra that were collected during the Iowa Flood Studies (IFloodS) experiment. It is found that the DPR-like dual-wavelength techniques provide fairly accurate estimates of rain rate and attenuation if a fixed- μ gamma DSD model is used, with the value of μ ranging from 3 to 6. Comparison of the results reveals that the retrieval errors from the μ-Λ relations are generally small, with biases of less than ±10%, and are comparable to the results from a fixed- μ gamma model with μ equal to 3 and 6. The DSD evaluation procedure is also applied to retrievals in which a lognormal DSD model is used. [ABSTRACT FROM AUTHOR]

In this study, methods of convective/stratiform precipitation classification and surface rain-rate estimation based on the Atmospheric Radiation Measurement Program (ARM) cloud radar measurements were developed and evaluated. Simultaneous and collocated observations of the Ka-band ARM zenith radar (KAZR), two scanning precipitation radars [NCAR S-band/Ka-band Dual Polarization, Dual Wavelength Doppler Radar (S-PolKa) and Texas A&M University Shared Mobile Atmospheric Research and Teaching Radar (SMART-R)], and surface precipitation during the Dynamics of the Madden-Julian Oscillation/ARM MJO Investigation Experiment (DYNAMO/AMIE) field campaign were used. The motivation of this study is to apply the unique long-term ARM cloud radar observations without accompanying precipitation radars to the study of cloud life cycle and precipitation features under different weather and climate regimes. The resulting convective/stratiform classification from KAZR was evaluated against precipitation radars. Precipitation occurrence and classified convective/stratiform rain fractions from KAZR compared favorably to the collocated SMART-R and S-PolKa observations. Both KAZR and S-PolKa radars observed about 5% precipitation occurrence. The convective (stratiform) precipitation fraction is about 18% (82%). Collocated disdrometer observations of two days showed an increased number concentration of small and large raindrops in convective rain relative to dominant small raindrops in stratiform rain. The composite distributions of KAZR reflectivity and Doppler velocity also showed distinct structures for convective and stratiform rain. These evidences indicate that the method produces physically consistent results for the two types of rain. A new KAZR-based, two-parameter [the gradient of accumulative radar reflectivity Z e (GAZ) below 1 km and near-surface Z e] rain-rate estimation procedure was developed for both convective and stratiform rain. This estimate was compared with the exponential Z-R (reflectivity-rain rate) relation. The relative difference between the estimated and surface-measured rainfall rates showed that the two-parameter relation can improve rainfall estimation relative to the Z-R relation. [ABSTRACT FROM AUTHOR]