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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]

This article describes a concept whereby future operational polarimetric phased array radars (PPAR) routinely monitor ice crystal alignment regions caused by thundercloud electric fields with volume scan updates (∼12 min−1) sufficient to resolve the temporal variation due to lightning and subsequent rapid electric field regeneration in nonsevere thunderstorms. Routine observations of crystal alignment regions may enhance thunderstorm nowcasting through comparison of their temporal and spatial structure with other polarimetric signatures, integration with lightning detection data, and assimilation into convection resolving numerical weather prediction models. If crystal alignment observations indicate strong electrification well in advance of the first lightning strike and likewise reliably indicate the decay of strong electric fields at the end of a storm, this capability may improve warning for lightning-sensitive activities such as airport ramp operations and space launch. Experimental observations of crystal alignment volumes in central Oklahoma severe storms and their relation to those storms' structures are presented and used to motivate discussion of possible PPAR architectures. In one case—a tornadic supercell—these observations illustrate an important limitation. Even the hypothesized 12 min−1 volume scan update rate would not resolve the temporal variation of the crystal alignment regions in such storms, suggesting that special, adaptive scanning methods may be appropriate for such storms. We describe how future operational phased array radars could support a crystal alignment measurement mode via parallel, time-multiplexed processing and discuss potential impacts on the radar's primary weather observation mission. We conclude by discussing research needed to better understand technical challenges and operational benefits. [ABSTRACT FROM AUTHOR]

Recent studies from the Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE) demonstrated definitive radar evidence of seeding signatures in winter orographic clouds during three intensive operation periods (IOPs) where the background signal from natural precipitation was weak and a radar signal attributable to seeding could be identified as traceable seeding lines. Except for the three IOPs where seeding was detected, background natural snowfall was present during seeding operations and no clear seeding signatures were detected. This paper provides a quantitative analysis to assess if orographic cloud seeding effects are detectable using radar when background precipitation is present. We show that a 5-dB change in equivalent reflectivity factor Ze is required to stand out against background natural Ze variability. This analysis considers four radar wavelengths, a range of background ice water contents (IWC) from 0.012 to 1.214 g m−3, and additional IWC introduced by seeding ranging from 0.012 to 0.486 g m−3. The upper-limit values of seeded IWC are based on measurements of IWC from the Nevzorov probe employed on the University of Wyoming King Air aircraft during SNOWIE. This analysis implies that seeding effects will be undetectable using radar within background snowfall unless the background IWC is small, and the seeding effects are large. It therefore remains uncertain whether seeding had no effect on cloud microstructure, and therefore produced no signature on radar, or whether seeding did have an effect, but that effect was undetectable against the background reflectivity associated with naturally produced precipitation. Significance Statement: Operational glaciogenic seeding programs targeting wintertime orographic clouds are funded by a range of stakeholders to increase snowpack. Glaciogenic seeding signatures have been observed by radar when natural background snowfall is weak but never when heavy background precipitation was present. This analysis quantitatively shows that seeding effects will be undetectable using radar reflectivity under conditions of background snowfall unless the background snowfall is weak, and the seeding effects are large. It therefore remains uncertain whether seeding had no effect on cloud microstructure, and therefore produced no signature on radar, or whether seeding did have an effect, but that effect was undetectable against the background reflectivity associated with naturally produced precipitation. Alternative assessment methods such as trace element analysis in snow, aircraft measurements, precipitation measurements, and modeling should be used to determine the efficacy of orographic cloud seeding when heavy background precipitation is present. [ABSTRACT FROM AUTHOR]

The use of radial velocity information from the European weather radar network is a challenging task, because of a heterogeneous radar network and the different ways of providing the Doppler velocity information. Preprocessing is therefore needed to harmonize the data. Radar observations consist of a very high resolution dataset, which means that it is both demanding to process as well as that the inherent resolution is much higher than the model resolution. One way of reducing the number of data is to create "super observations" (SO) by averaging observations in a predefined area. This paper describes the preprocessing necessary to use radar radial velocities in the data assimilation where the SO construction is included. Our main focus is to optimize the use of radial velocities in the HARMONIE-AROME numerical weather model. Several experiments were run to find the best settings for first-guess check limits as well as a tuning of the observation error value. The optimal size of the SO and the corresponding thinning distance for radar radial velocities was also studied. It was found that the radial velocity information and the reflectivity from weather radars can be treated differently when it comes to the size of the SO and the thinning. A positive impact was found when adding the velocities together with the reflectivity using the same SO size and thinning distance, but the best results were found when the SO and thinning distance for the radial velocities are smaller than the corresponding values for reflectivity. [ABSTRACT FROM AUTHOR]

The Dual-Frequency Precipitation Radar (DPR), which consists of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR) on board the GPM Core Observatory, cannot observe precipitation at low altitudes near the ground contaminated by surface clutter. This near-surface region is called the blind zone. DPR estimates the clutter-free bottom (CFB), which is the lowest altitude not included in the blind zone, and estimates precipitation at altitudes higher than the CFB. High CFBs, which are common over mountainous areas, represent obstacles to detection of shallow precipitation and estimation of low-level enhanced precipitation. We compared KuPR data with rain gauge data from Da-Tun Mountain of northern Taiwan acquired from March 2014 to February 2020. A total of 12 cases were identified in which the KuPR missed some rainfall with intensity of .10 mm h21 that was observed by rain gauges. Comparison of KuPR profile and ground-based radar profile revealed that shallow precipitation in the KuPR blind zone was missed because the CFB was estimated to be higher than the lower bound of the range free from surface echoes. In the original operational algorithm, CFB was estimated using only the received power data of the KuPR. In this study, the CFB was identified by the sharp increase in the difference between the received powers of the KuPR and the KaPR at altitude affected by surface clutter. By lowering the CFB, the KuPR succeeded in detection and estimation of shallow precipitation. SIGNIFICANCE STATEMENT: The Dual-Frequency Precipitation Radar (DPR) on board the GPMCore Observatory cannot capture precipitation in the low-altitude region near the ground contaminated by surface clutter. This region is called the blind zone. The DPR estimates the clutter-free bottom (CFB), which is the lower bound of the range free from surface echoes, and uses data higher than CFB. DPR consists of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR). KuPR missed some shallow precipitation more than 10 mm h21 in the blind zone over Da-Tun Mountain of northern Taiwan because of misjudged CFB estimation. Using both the KuPR and the KaPR, we improved the CFB estimation algorithm, which lowered the CFB, narrowed the blind zone, and improved the capability to detect shallow precipitation. [ABSTRACT FROM AUTHOR]

The accuracy and uncertainty of radar echo-top heights estimated by ground-based radars remain largely unknown despite their critical importance for applications ranging from aviation weather forecasting to severe weather diagnosis. Because the vantage point of space is more suited than that of ground-based radars for the estimation of echo-top heights, the use of spaceborne radar observations is explored as an external reference for cross comparison. An investigation has been carried out across the conterminous United States by comparing the NOAA/National Severe Storms Laboratory Multi-Radar Multi-Sensor (MRMS) system with the space-based radar on board the NASA--JAXA Global Precipitation Measurement satellite platform. No major bias was assessed between the two products. An annual cycle of differences is found, driven by an underestimation of the stratiform cloud echo-top heights and an overestimation of the convective ones. The investigation of the systematic biases for different radar volume coverage patterns (VCP) shows that scanning strategies with fewer tilts and greater voids as VCP 21/121/221 contribute to overestimations observed for high MRMS tops. For VCP 12/212, the automated volume scan evaluation and termination (AVSET) function increases the radar cone of silence, causing overestimations when the echo top lies above the highest elevation scan. However, it seems that for low echo tops the shorter refresh rates contribute to mitigate underestimations, especially in stratiform cases. [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]

Coincident radar data with Doppler radar measurements at X, Ku, Ka, and W bands on the NASA ER-2 aircraft overflying the NASA P-3 aircraft acquiring in situ microphysical measurements are used to characterize the relationship between radar measurements and ice microphysical properties. The data were obtained from the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS). Direct measurements of the condensed water content and coincident Doppler radar measurements were acquired, facilitating improved estimates of ice particle mass, a variable that is an underlying factor for calculating and therefore retrieving the radar reflectivity Ze, median mass diameter Dm, particle terminal velocity, and snowfall rate S. The relationship between the measured ice water content (IWC) and that calculated from the particle size distributions (PSDs) using relationships developed in earlier studies, and between the calculated and measured radar reflectivity at the four radar wavelengths, are quantified. Relationships are derived between the measured IWC and properties of the PSD, Dm, Ze at the four radar wavelengths, and the dual-wavelength ratio. Because IWC and Ze are measured directly, the coefficients in the mass--dimensional relationship that best match both the IWC and Ze are derived. The relationships developed here, and the mass--dimensional relationship that uses both the measured IWC and Ze to find a best match for both variables, can be used in studies that characterize the properties of wintertime snow clouds. [ABSTRACT FROM AUTHOR]

This study evaluates ice particle size distribution and aspect ratio φ Multi-Radar Multi-Sensor (MRMS) dual-polarization radar retrievals through a direct comparison with two legs of observational aircraft data obtained during a winter storm case from the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. In situ cloud probes, satellite, and MRMS observations illustrate that the often-observed Kdp and ZDR enhancement regions in the dendritic growth layer can either indicate a local number concentration increase of dry ice particles or the presence of ice particles mixed with a significant number of supercooled liquid droplets. Relative to in situ measurements, MRMS retrievals on average underestimated mean volume diameters by 50% and overestimated number concentrations by over 100%. IWC retrievals using ZDR and Kdp within the dendritic growth layer were minimally biased relative to in situ calculations where retrievals yielded −2% median relative error for the entire aircraft leg. Incorporating φ retrievals decreased both the magnitude and spread of polarimetric retrievals below the dendritic growth layer. While φ radar retrievals suggest that observed dendritic growth layer particles were nonspherical (0.1 ≤ φ ≤ 0.2), in situ projected aspect ratios, idealized numerical simulations, and habit classifications from cloud probe images suggest that the population mean φ was generally much higher. Coordinated aircraft radar reflectivity with in situ observations suggests that the MRMS systematically underestimated reflectivity and could not resolve local peaks in mean volume diameter sizes. These results highlight the need to consider particle assumptions and radar limitations when performing retrievals. significance statement: Developing snow is often detectable using weather radars. Meteorologists combine these radar measurements with mathematical equations to study how snow forms in order to determine how much snow will fall. This study evaluates current methods for estimating the total number and mass, sizes, and shapes of snowflakes from radar using images of individual snowflakes taken during two aircraft legs. Radar estimates of snowflake properties were most consistent with aircraft data inside regions with prominent radar signatures. However, radar estimates of snowflake shapes were not consistent with observed shapes estimated from the snowflake images. Although additional research is needed, these results bolster understanding of snow-growth physics and uncertainties between radar measurements and snow production that can improve future snowfall forecasting. [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 vertically pointing Ka-band radar (Metek MIRA-35C) installed at the Instituto Geof'ısico del Per'u, Atmospheric Microphysics and Radiation Laboratory (LAMAR) Huancayo Observatory, which is located at an elevation of 3.3 km MSL in the Andes Mountains of Peru, is used to investigate the effects of terrain on satellite-based precipitation measurement in the Andes. We compare the vertical structure of precipitation observed by theMIRA-35C with Ka-band radar measurements from the Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) mission core satellite using an approach based on Taylor's hypothesis of frozen turbulence that attempts to reduce the impact of spatiotemporal offsets between these two radar measurements. From 3 April 2014 to 20 May 2018, the DPR measured precipitation near LAMAR during 15 of its 157 coincident overpasses. There were six simultaneous observations with MIRA-35C. We found that the average of the DPR's lowest clutter-free bin is 1.62 km AGL, but the presence of precipitation worsens the situation, causing a 0.4-km-deeper algorithm-detected blind zone for the DPR at the Huancayo Observatory. In the study area, the depth of the clutter layer observed with DPR often extends above the melting layer but can be highly variable, extending even as high as 5 km AGL. These results suggest that DPR estimates of stratiform precipitation over the AndesMountains are likely underestimated because of the terrain effects on the satellite measurements and problems in its blind zone detection algorithms, highlighting the difficulty in estimating precipitation in mountainous terrain from spaceborne radar. [ABSTRACT FROM AUTHOR]

Fine-scale characteristics of summer precipitation over Cang Mountain, a long and narrow mountain with a quasi-north–south orientation in Southwest China, are studied using station and radar data. Three kinds of rainfall processes are classified according to the initial stations of regional rainfall events (RREs) by utilizing minute-scale rain gauge data. RREs initiating in the western part of Cang Mountain exhibit eastward evolution and tend to reach their maximum rainfall intensity on the mountaintop. The results indicate differences in the precipitation evolution characteristics between short-duration (1–3 h) and long-duration (at least 6 h) events. Short-duration events begin farther from the mountaintop and then propagate eastward, whereas long-duration events remain longer around the mountaintop. RREs that initiate from the eastern part of Cang Mountain display westward propagation and frequently reach their maximum rainfall intensity over the eastern slope of the mountain. Among them, short-duration events tend to propagate farther west of Cang Mountain at high speeds, but the westward evolution of long-duration events is mainly confined to the eastern part of Cang Mountain. For mountaintop-originated RREs, precipitation quickly reaches its maximum intensity after it starts and then continues for a long time around the mountaintop during the period from late afternoon to early morning. These findings provide references for the fine-scale prediction of precipitation evolution in small-scale mountainous areas. [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]

Fully polarimetric scanning and vertically pointing Doppler spectral data from the state-of-the-art Stony Brook University Ka-band Scanning Polarimetric Radar (KASPR) are analyzed for a long-duration case of ice pellets over central Long Island in New York from 12 February 2019. Throughout the period of ice pellets, a classic refreezing signature was present, consisting of a secondary enhancement of differential reflectivityZDR beneath the melting layer within a region of decreasing reflectivity factor at horizontal polarization ZH and reduced copolar correlation coefficient rhv. The KASPR radar data allow for evaluation of previously proposed hypotheses to explain the refreezing signature. It is found that, upon entering a layer of locally generated columnar ice crystals and undergoing contact nucleation, smaller raindrops preferentially refreeze into ice pellets prior to the complete freezing of larger drops. Refreezing particles exhibit deformations in shape during freezing, leading to reduced rhv, reduced co-to-cross-polar correlation coefficient rxh, and enhanced linear depolarization ratio, but these shape changes do not explain the ZDR signature. The presence of columnar ice crystals, though apparently crucial for instigating the refreezing process, does not contribute enough backscattered power to affect the ZDR signature, either. [ABSTRACT FROM AUTHOR]

The sensitivity of microwave brightness temperatures (TBs) to hydrometeors at frequencies between 89 and 190 GHz is investigated by comparing Fengyun-3C(FY-3C)MicrowaveHumidity Sounder-2 (MWHS-2) measurements with radar reflectivity profiles and retrieved products from the Global Precipitation Measurement mission's Dual-Frequency Precipitation Radar (DPR). Scattering-induced TB depressions (DTBs), calculated by subtracting simulated cloud-free TBs from bias-corrected observed TBs for each channel, are compared withDPR-retrieved hydrometeor water path (HWP) and vertically integrated radar reflectivity ZINT. We also account for the number of hydrometeors actually visible in each MWHS-2 channel by weightingHWP with the channel's cloud-free gas transmission profile and the observation slant path. We denote these transmission-weighted, slant-path-integrated quantities with a superscript asterisk (e.g., HWP*). The so-derived linear sensitivity of DTB with respect to HWP* increases with frequency roughly to the power of 1.78. A retrieved HWP* of 1 kgm22 at 89GHz on average corresponds to a decrease in observed TB, relative to a cloud-free background, of 11 K. At 183 GHz, the decrease is about 34-53K. We perform a similar analysis using the vertically integrated, transmission-weighted slant-path radar reflectivity Z*INT and find that DTB also decreases approximately linearly with (Z*INT)0:58. The exponent of 0.58 corresponds to the one we find in the purely DPR-retrieval-based ZINT-HWP relation. The observed sensitivities of DTB with respect to Z*INT and HWP* allow for the validation of hydrometeor scattering models. [ABSTRACT FROM AUTHOR]

In a 2018 paper by Bukovčić et al., polarimetric bivariate power-law relations for estimating snowfall rate S and ice water content (IWC), S ⁡ (K DP , Z) = γ K DP α Z β and IWC ⁡ (K DP , Z) = γ 2 K DP α 2 Z β 2 , were developed utilizing 2D video disdrometer snow measurements in Oklahoma. Herein, these disdrometer-based relations are generalized for the range of particle aspect ratios from 0.5 to 0.8 and the width of the canting angle distribution from 0° to 40° and are validated via analytical/theoretical derivations and simulations. In addition, a novel S(KDP, Zdr) polarimetric relation utilizing the ratio between specific differential phase KDP and differential reflectivity Zdr, K DP / ⁡ (1 − Z dr − 1 ) , is derived. Both KDP and (1 − Z dr − 1 ) are proportionally affected by the ice particles' aspect ratio and width of the canting angle distribution; therefore, the variables' ratio tends to be almost invariant to the changes in these parameters. The S(KDP, Z) and S(KDP, Zdr) relations are applied to the polarimetric S-band WSR-88D data obtained from three geographical locations in Virginia, Oklahoma, and Colorado, and their performance is compared with estimations from the standard S(Z) relations and ground snow measurements. The polarimetric estimates of snow accumulations from the three cases exhibit smaller bias in comparison with the S(Z), indicating good potential for more reliable radar snow measurements. [ABSTRACT FROM AUTHOR]

Typhoon Bolaven caused significant damage with severe rainfall all over South Korea, including Cheju Island, which received more than 250 mm in 2 days in August 2012. It was regarded as the most powerful storm to strike the Korean Peninsula in nearly a decade. The rainfall-rate datasets were obtained from S-band radar operated by the Korea Meteorological Administration to be analyzed and compared with the mesoscale Cloud Resolving Storm Simulator (CReSS) model simulation. Multifractal analysis was conducted to understand the structure of the rainfall rate with height in the typhoon system. The radar rainfall data presented with strong intermittency across scales at lower altitudes (1 and 2 km) and a more homogeneous rainfall field at high altitude (5 km) with two parameters (fractal codimension and multifractality index). The statistical scaling moment function and maximal singularities show clear significant differences between radar and the CReSS model. [ABSTRACT FROM AUTHOR]

Quasi-vertical profiles (QVPs) obtained from a database of U.S. WSR-88D data are used to document polarimetric characteristics of the melting layer (ML) in cold-season storms with high vertical resolution and accuracy. A polarimetric technique to define the top and bottom of the ML is first introduced. Using the QVPs, statistical relationships are developed to gain insight into the evolution of microphysical processes above, within, and below the ML, leading to a statistical polarimetric model of the ML that reveals characteristics that reflectivity data alone are not able to provide, particularly in regions of weak reflectivity factor at horizontal polarization ZH. QVP ML statistics are examined for two regimes in the ML data: ZH ≥ 20 dBZ and ZH < 20 dBZ. Regions of ZH ≥ 20 dBZ indicate locations of MLs collocated with enhanced differential reflectivity ZDR and reduced copolar correlation coefficient ρhv, while for ZH < 20 dBZ a well-defined ML is difficult to discern using ZH alone. Evidence of large ZDR up to 4 dB, backscatter differential phase δ up to 8°, and low ρhv down to 0.80 associated with lower ZH (from −10 to 20 dBZ) in the ML is observed when pristine, nonaggregated ice falls through it. Positive correlation is documented between maximum specific differential phase KDP and maximum ZH in the ML; these are the first QVP observations of KDP in MLs documented at S band. Negative correlation occurs between minimum ρhv in the ML and ML depth and between minimum ρhv in the ML and the corresponding enhancement of ZH (ΔZH = ZHmax − ZHrain). [ABSTRACT FROM AUTHOR]

To fulfill the evolving observational needs of the National Weather Service (NWS), future weather radar systems will have to meet demanding requirements. Designing such systems will likely involve trade-offs between system cost and operational performance. A potential cost driver for future weather radars that could cause significant data-quality impacts on forecasters is the required angular resolution and sidelobe performance, which are mainly dictated by the antenna radiation pattern. Typical antenna radiation patterns can be characterized by the width of the main lobe and their sidelobe levels, which are traditionally measured across the azimuthal and elevation dimensions. In this work, we study the impact of increasing sidelobe levels on NWS forecasters' data interpretation during warning operations. The resulting impact model can be used by decision-makers to better understand the cost–benefit trade-offs inherent in any radar system design. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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]

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]

Hail-bearing storms produce substantial socioeconomic impacts each year, yet challenges remain in forecasting the type of hail threat supported by a given environment and in using radar to estimate hail sizes more accurately. One class of hail threat is storms producing large accumulations of small hail (SPLASH). This paper presents an analysis of the environments and polarimetric radar characteristics of such storms. Thirteen SPLASH events were selected to encompass a broad range of geographic regions and times of year. Rapid Refresh model output was used to characterize the mesoscale environments associated with each case. This analysis reveals that a range of environments can support SPLASH cases; however, some commonalities included large precipitable water (exceeding that day's climatological 90th-percentile values), CAPE < 2500 J kg−1, weak storm-relative wind speeds (<10 m s−1) in the lowest few kilometers of the troposphere, and a weak component of the storm-relative flow orthogonal to the 0–6-km shear vector. Most of the storms were weak supercells that featured distinctive S-band radar signatures, including compact (<200 km2) regions of reflectivity factor > 60 dBZ, significant differential attenuation evident as negative differential reflectivity extending downrange of the hail core, and anomalously large specific differential phase KDP. The KDP values often approached or exceeded the operational color scale's upper limit (10.7° km−1); reprocessing the level-II data revealed KDP >17° km−1, the highest documented in precipitation at S band. Electromagnetic scattering calculations using the T-matrix method confirm that large quantities of small melting hail mixed with heavy rain can plausibly explain the observed radar signatures. [ABSTRACT FROM AUTHOR]

There is growing interest in combining microphysical models and polarimetric radar observations to improve our understanding of storms and precipitation. Mapping model-predicted variables into the radar observational space necessitates a forward operator, which requires assumptions that introduce uncertainties into model–observation comparisons. These include uncertainties arising from the microphysics scheme a priori assumptions of a fixed drop size distribution (DSD) functional form, whereas natural DSDs display far greater variability. To address this concern, this study presents a moment-based polarimetric radar forward operator with no fundamental restrictions on the DSD form by linking radar observables to integrated DSD moments. The forward operator is built upon a dataset of >200 million realistic DSDs from one-dimensional bin microphysical rain-shaft simulations, and surface disdrometer measurements from around the world. This allows for a robust statistical assessment of forward operator uncertainty and quantification of the relationship between polarimetric radar observables and DSD moments. Comparison of "truth" and forward-simulated vertical profiles of the polarimetric radar variables are shown for bin simulations using a variety of moment combinations. Higher-order moments (especially those optimized for use with the polarimetric radar variables: the sixth and ninth) perform better than the lower-order moments (zeroth and third) typically predicted by many bulk microphysics schemes. [ABSTRACT FROM AUTHOR]

To overcome a deficiency in the standard Ku- and Ka-band dual-wavelength radar technique, a modified version of the method is introduced. The deficiency arises from ambiguities in the estimate of the mass-weighted diameter Dm of the raindrop size distribution (DSD) derived from the differential frequency ratio (DFR), defined as the difference between the radar reflectivity factors (dB) at Ku and Ka band ZKu − ZKa. In particular, for DFR values less than zero, there are two possible solutions of Dm, leading to ambiguities in the retrieved DSD parameters. It is shown that the double solutions to Dm are effectively eliminated if the DFR is modified from ZKu − ZKa to ZKu − γZKa (dB), where γ is a constant with a value less than 0.8. An optimal radar algorithm that uses the modified DFR for the retrieval of rain and Dm profiles is described. The validity and accuracy of the algorithm are tested by applying it to radar profiles that are generated from measured DSD data. Comparisons of the rain rates and Dm estimated from the modified DFR algorithm to the same hydrometeor quantities computed directly from the DSD spectra (or the truth) indicate that the modified DFR-based profiling retrievals perform fairly well and are superior in accuracy and robustness to retrievals using the standard DFR. [ABSTRACT FROM AUTHOR]

This is a feasibility study on the use of dual-polarized radars to infer icing in terminal airspaces (TASs) of commercial airports. The amount and quality of radar coverage in each TAS is quantified as a function of its location, traffic, and vulnerability to icing. No airport has 100% of the TAS covered, but most high-traffic or high-icing airports have comparatively good coverage (between 70% and 90%). A common occurrence during icing is anomalous propagation as 79% of events had an inversion within the TAS. This leads to overestimates in the elevations of icing layers and can cause significant ground-clutter contamination, which can overwhelm the echo produced by precipitation. The effects of beam broadening were also considered. Typical dendrite growth and melting layers can only be resolved in part of the TAS part of the time, or not at all, as these layers are often shallower than the radar beam. Because most airports have coverage from multiple radars, use of a three-dimensional mosaic was investigated. This allows for an increase in the TAS coverage (generally between 5% and 15%) and partly mitigates some of the resolution issues, but the maxima within individual layers are somewhat reduced in the interpolation process. A series of recommendations is made to address the concerns raised by this investigation. These include using only icing tops (not bottoms) to identify areas of icing, use of data mining to retrieve precipitation echo in the presence of ground clutter, and including the beamwidth in radar mosaics. [ABSTRACT FROM AUTHOR]

Severe (>2.5 cm) hail causes >$5 billion in damage annually in the United States. However, radar sizing of hail remains challenging. Typically, spheroids are used to represent hailstones in radar forward operators and to inform radar hail-sizing algorithms. However, natural hailstones can have irregular shapes and lobes; these details significantly influence the hailstone's scattering properties. The high-resolution 3D structure of real hailstones was obtained using a laser scanner for hail collected during the 2016–17 Insurance Institute for Business and Home Safety (IBHS) Hail Field Study. Plaster casts of several record hailstones (e.g., Vivian, South Dakota, 2010) were also scanned. The S-band scattering properties of these hailstones were calculated with the discrete dipole approximation (DDA). For comparison, scattering properties of spheroidal approximations of each hailstone (with identical maximum and minimum dimensions and mass) were calculated with the T matrix. The polarimetric radar variables have errors when using spheroids, even for small hail. Spheroids generally have smaller variations in the polarimetric variables than the real hailstones. This increased variability is one reason why the correlation coefficient tends to be lower in observations than in forward-simulated cases using spheroids. Backscatter differential phase δ also is found to have large variance, particularly for large hailstones. Irregular hailstones with a thin liquid layer produce enhanced and more variable values for reflectivity factor at horizontal polarization ZHH, differential reflectivity ZDR, specific differential phase KDP, linear depolarization ratio (LDR), and δ compared with dry hailstones; is also significantly reduced. [ABSTRACT FROM AUTHOR]

This study aims to characterize the background physical processes in the development of those heavy precipitation clouds that contribute to the Tropical Rainfall Measuring Mission (TRMM) active and passive sensor differences. The combined global observation data from TRMM, CloudSat, and European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) from 2006 to 2014 were utilized to address this issue. Heavy rainfall events were extracted from the top 10% of the rain events from the Precipitation Radar (PR) and TRMM Microwave Imager (TMI) rain-rate climatology. Composite analyses of CloudSat and ERA-Interim were conducted to identify the detailed cloud structures and the background environmental conditions. Over tropical land, TMI tends to preferentially detect deep isolated precipitation clouds for relatively drier and unstable environments, while PR identifies more organized systems. Over the tropical ocean, TMI identifies heavy rainfall events with notable convective organization and clear regional gradients between the western and eastern Pacific Ocean, while PR fails to capture the eastward shallowing of convective systems. The PR–TMI differences for the moist and stable environments are reversed over tropical land. [ABSTRACT FROM AUTHOR]

Several months of regional convection-permitting forecasts using two microphysical schemes (WSM6 and Thompson) are evaluated to determine the accuracy of the simulated convective structure and convective depth and the impact of microphysical scheme on simulated convective properties and biases. Forecasts are evaluated by using concepts from object-based approaches to compare the three-dimensional simulated reflectivity field with the reflectivity field as observed by radar. Results from analysis of both schemes reveals that forecasts generally perform well near the surface but differ considerably aloft both from observations and from each other. Forecasts are found to contain too many convective cores that are individually larger than in the observations, with at least double the number of observed convective cores reaching the midtroposphere (i.e., 4–8 km). Although the number of cores is overpredicted, WSM6 cores typically contain lower simulated reflectivity values than the observations, and the regions of highest reflectivity values do not extend far enough vertically. Conversely, Thompson cores are found to have significantly higher reflectivity values within cores, with the strongest intensities extending higher than in the observations and having magnitudes higher than any observed cores. Forecast reflectivity distributions within convective cells are found to contain more spread than in the observations. The study also assessed the uncertainty in simulated reflectivity calculations by using a second commonly utilized method to calculate simulated reflectivity. The sensitivity analysis reveals that the primary conclusions with each method are similar but the variability generated by using different simulated reflectivity calculations can be as pronounced as when using different microphysical schemes. [ABSTRACT FROM AUTHOR]

Two of the "Doppler on Wheels" facility radars (DOW6 and DOW7) have been upgraded to dual-polarization capabilities and operate at two closely spaced X-band frequencies. For particles with sizes that are large relative to the wavelength, resonance scattering effects may lead to differences in the backscattered radiation between these two frequencies. This study investigates the utility of dual-frequency, dual-polarization DOW radars for hail detection and sizing. T-matrix scattering calculations at the two X-band DOW7 frequencies reveal that dual-frequency differences in the radar reflectivity factors at horizontal polarization (ΔλZH) and differential reflectivities (ΔλZDR) exist for hailstones, whereas negligible differences exist for raindrops. These differences are enhanced for wet or melting hailstones. Further, these dual-frequency differences may be positive or negative, thereby defining four distinct quadrants in the ΔλZH–ΔλZDR parameter space that occur for narrow bands of hail sizes. DOW7 data from two hail-bearing storms are analyzed: one produced only small hail, and the other produced severe hail up to ~3.8 cm in diameter. The analysis reveals dual-frequency signals that are consistent with the scattering calculations for those sizes, including consistent changes in the signatures below the melting layer in the first storm as hailstones acquire more liquid meltwater and a shift in the ΔλZH–ΔλZDR parameter space over time as the second storm grew upscale and hail sizes decreased. Implications for further applications and suggestions about closely spaced dual-frequency observations at other wavelengths are discussed. [ABSTRACT FROM AUTHOR]

In modeling the radar rainfall uncertainty, rain gauge measurement is generally regarded as the areal "true" rainfall. However, the inconsistent scales between radar and gauge may introduce a new uncertainty (also known as gauge representative uncertainty), which is erroneously identified as radar rainfall uncertainty and therefore called pseudouncertainty. It is crucial to comprehend what percentage of the estimated radar rainfall uncertainty actually stems from such pseudouncertainty rather than radar rainfall itself. For this reason, based on a fully formulated radar rainfall uncertainty model, this study aims to explore how the gauge representative error affects the distribution, spatial dependence, and temporal dependence of hourly accumulated radar rainfall uncertainty, and consequently affects the produced radar rainfall uncertainty band. Three group scenarios that delineate various degrees of gauge representative errors were designed to configure and run the uncertainty model. In the setting of a long-term analysis (almost 7 years) of the Brue catchment in the United Kingdom, we found that the gauge representative error affected the simulation of the marginal distribution of radar rainfall error, and had a considerable effect on temporal dependence estimation of radar rainfall uncertainty. The spread of the rainfall uncertainty band decreased with the growth of the gauge density in a radar pixel. The scenario with the lowest representative error only had 78% uncertainty spread of the scenario that has the largest error. This indicated there was a large impact of the representative error on radar rainfall uncertainty models. It is hoped that more catchments with diverse climate and geographical conditions and more radar data with various spatial scales could be explored by the research community to further investigate this crucial issue. [ABSTRACT FROM AUTHOR]

This research is Part II of a two-part study that evaluates the ability of image-processing and select machine-learning algorithms to detect, classify, and track midlatitude mesoscale convective systems (MCSs) in radar-reflectivity images for the conterminous United States. This paper focuses on the tracking portion of this framework. Tracking is completed through a two-step process using slice (snapshots of instantaneous MCS intensity) data generated in Part I. The first step is to perform spatiotemporal matching, which associates slices through temporally adjacent radar-reflectivity images to generate swaths, or storm tracks. When multiple slices are found to be matches, a difference-minimization procedure is used to associate the most similar slice with the existing swath. Once this step is completed, a second step combines swaths that are spatiotemporally close. Tracking performance is assessed by calculating select metrics for all available swath-building perturbations to determine the optimal approach in tracking. Frequency maps and time series generated from the swaths suggest that the spatiotemporal occurrence of these swaths is reasonable as determined from previous work. Further, these events exhibit a diurnal cycle that is distinct from that of overall convection for the conterminous United States. Last, machine-learning predictions are found to limit areas of high MCS frequency to the central and eastern Great Plains. [ABSTRACT FROM AUTHOR]

This research evaluates the ability of image-processing and select machine-learning algorithms to identify midlatitude mesoscale convective systems (MCSs) in radar-reflectivity images for the conterminous United States. The process used in this study is composed of two parts: segmentation and classification. Segmentation is performed by identifying contiguous or semicontiguous regions of deep, moist convection that are organized on a horizontal scale of at least 100 km. The second part, classification, is performed by first compiling a database of thousands of precipitation clusters and then subjectively assigning each sample one of the following labels: 1) midlatitude MCS, 2) unorganized convective cluster, 3) tropical system, 4) synoptic system, or 5) ground clutter and/or noise. The attributes of each sample, along with their assigned label, are used to train three machine-learning algorithms: random forest, gradient boosting, and ''XGBoost.'' Results using a testing dataset suggest that the algorithms can distinguish between MCS and non-MCS samples with a high probability of detection and low probability of false detection. Further, the trained algorithm predictions are well calibrated, allowing reliable probabilistic classification. The utility of this two-step procedure is illustrated by generating spatial frequency maps of automatically identified precipitation clusters that are stratified by using various reflectivity and probabilistic prediction thresholds. These results suggest that machine learning can add value by limiting the amount of false-positive (non-MCS) samples that are not removed by segmentation alone. [ABSTRACT FROM AUTHOR]

This study used the first detailed radar measurements of the vertical structure of precipitation obtained in the central Andes of southern Peru and Bolivia to investigate the diurnal cycle and vertical structure of precipitation and melting-layer heights in the tropical Andes. Vertically pointing 24.1-GHz Micro Rain Radars in Cusco, Peru (3350m MSL, August 2014-February 2015), and La Paz, Bolivia (3440m MSL, October 2015-February 2017), provided continuous 1-min profiles of reflectivity and Doppler velocity. The time-height data enabled the determination of precipitation timing, melting-layer heights, and the identification of convective and stratiform precipitation features. Rawinsonde data, hourly observations of meteorological variables, and satellite and reanalysis data provided additional insight into the characteristics of these precipitation events. The radar data revealed a diurnal cycle with frequent precipitation and higher rain rates in the afternoon and overnight. Short periods with strong convective cells occurred in several storms. Longer-duration events with stratiform precipitation structures were more common at night than in the afternoon. Backward air trajectories confirmed previous work indicating an Amazon basin origin of storm moisture. For the entire dataset, median melting-layer heights were above the altitude of nearby glacier termini approximately 17%of the time in Cusco and 30%of the time in La Paz, indicating that some precipitation was falling as rain rather than snow on nearby glacier surfaces. During the 2015-16 El Niño, almost half of storms in La Paz had melting layers above 5000m MSL. [ABSTRACT FROM AUTHOR]

Chaff is a radar countermeasure typically used by military branches in training exercises around the United States. Chaff within view of the S-band WSR-88D beam can appear prominently on radar users' displays. Knowledge of chaff characteristics is useful for radar users to discriminate between chaff and weather echoes and for automated algorithms to do the same. The WSR-88D network provides dual-polarimetric capabilities across the United States, leading to the collection of a large database of chaff cases. This database is analyzed to determine the characteristics of chaff in terms of the reflectivity factor and polarimetric variables on large scales. Particular focus is given to the dynamics of differential reflectivity ZDR in chaff and its dependence on height. In contrast to radar observations of chaff for a single event, this study is able to reveal a repeatable and new pattern of radar chaff observations. A discussion about the observed characteristics is presented, and hypotheses for the observed ZDR dynamics are put forth. [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]

Accurate measurements of snow amounts by radar are very difficult to achieve. The inherent uncertainty in radar snow estimates that are based on the radar reflectivity factor Z is caused by the variability of snow particle size distributions and snow particle density as well as the large diversity among snow growth habits. In this study, a novel method for snow quantification that is based on the joint use of radar reflectivity Z and specific differential phase KDP is introduced. An extensive dataset of 2D-video-disdrometer measurements of snow in central Oklahoma is used to derive polarimetric relations for liquid-equivalent snowfall rate S and ice water content IWC in the forms of bivariate power-law relations S = γ1Kγ1 DPZβ1and IWC5γ2Kγ2 DPZβ2, along with similar relations for the intercept N0s and slope Ls of the exponential snow size distribution. The physical basis of these relations is explained. Theirmultipliers are sensitive to variations in the width of the canting angle distribution and to a lesser extent the particles' aspect ratios and densities, whereas the exponents are practically invariant. This novel approach is tested against the S(Z) relation using snow disdrometermeasurements in three geographical regions (Oklahoma,Colorado, and Canada). Significant improvement in snowestimates relative to the traditional Z-based methods is demonstrated. [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]

Precipitation events in which rainfall is generated primarily below the freezing level via warm-rain processes have traditionally presented a significant challenge for radar and satellite quantitative precipitation estimation (QPE) algorithms. It is possible to improve QPE in warm-rain events if they are correctly identified/classified as warm rain prior to precipitation estimation. Additionally, it is anticipated that classification schemes incorporating polarimetric radar data will be able to leverage precipitation microphysical information to better identify warm-rain precipitation events. This study lays the groundwork for the development of a polarimetric warm-rain classification algorithm by documenting the typical three-dimensional polarimetric characteristics associated with midlatitude warm-rain precipitation events. These characteristics are then compared with those observed in non-warm-rain events. Nearly all warm-rain precipitation events were characterized by lower median values of Z, ZDR, and KDP relative to the non-warm-rain convective cases. Furthermore, droplet coalescence was determined to be the dominant microphysical process in the majority of warm-rain events, while in non-warm-rain stratiform events, evaporation and breakup appeared to be the dominant (warm) microphysical processes. Most warm-rain events were also associated with sharp decreases in reflectivity, with height above the freezing level coincident with low echo-top heights and freezing-level ZDR values near 0, indicating limited ice- and mixed-phase precipitation growth processes. These results support the feasibility of a future polarimetric warm-rain identification algorithm. [ABSTRACT FROM AUTHOR]