Polarimetric Radar Observations Research Articles

Polarimetric radar observations of the Jersey tornadic supercell on 1–2 November 2023

AbstractA large tornado and severe hailstorm struck Jersey, Channel Islands, just before midnight on 1–2 November 2023. Radar data are analysed to explore the morphology of the parent supercell thunderstorm. A tornado debris cloud was evident in polarimetric fields, collocated with a reflectivity ‘debris ball’. The debris cloud comprised a small region of exceptionally low correlation coefficient (<0.8) collocated with differential reflectivity ≤0dB. In vertical section, the debris signature comprised a column of low correlation coefficient and high reflectivity that tilted north‐northeast with height and extended to at least 2km above ground level.

Raindrop size distribution (DSD) retrieval from polarimetric radar observations using neural networks

As a key component of rainfall estimation, the understanding of raindrop size distribution (DSD) is a long-standing goal in meteorology and hydrology. Given that weather radar can observe the precipitation microphysics over large spatial and temporal scales, it has been broadly applied in DSD estimation. Traditional polynomial regression algorithms that correlate DSD parameters and radar signatures are still widely applied due to their simple structure and acceptable accuracy. This study proposes a new DSD retrieval model using dual-polarization radar observations based on long short-term memory (LSTM) network techniques. Three schemes able to retrieve the parameters of a normalized gamma DSD (LSTM-D0, LSTM-Nw, and LSTM-μ) are proposed with different combinations of polarimetric radar measurement inputs. All LSTM estimators exhibit better performance than the polynomial regression method. The Nash-Sutcliffe efficiency coefficient for estimates of drop median diameter (D0) and intercept parameter (Nw) increases from 0.93 and 0.70 to 0.95 and 0.93 respectively at Chilbolton station. Poor estimates of the shape parameter (μ) using the polynomial regression estimator complicate real applications, whereas the remarkable improvement of LSTM model estimation facilitates practical applications. The temporal and spatial predictability is then estimated to investigate long-term estimator performance for various radars, or at least for all radar pixels of a single radar. The predictability, measured by the Nash coefficient, increases temporally by 0.08, 0.31, and 0.39 and spatially by 0.03, 0.19, and 0.23 for the parameters D0, log10Nw, and μ respectively. This study contributes to improving quantitative precipitation estimates from radar polarimetry, enabling a better understanding of precipitation microphysics.

Improving Explainability of Deep Learning for Polarimetric Radar Rainfall Estimation

AbstractMachine learning‐based approaches demonstrate a significant potential in radar quantitative precipitation estimation (QPE) applications. In contrast to conventional methods that depend on local raindrop size distributions, deep learning (DL) can establish an effective mapping from three‐dimensional radar observations to ground rain rates. However, the lack of transparency in DL models poses challenges toward understanding the underlying physical mechanisms that drive their outcomes. This study aims to develop a DL‐based QPE system and provide a physical explanation of radar precipitation estimation process. This research is designed by employing a deep neural network consisting of two modules. The first module is a quantitative precipitation estimation network that has the capability to learn precipitation patterns and spatial distribution from multidimensional polarimetric radar observations. The second module introduces a quantitative precipitation estimation shapley additive explanations method to quantify the influence of each radar observable on the model estimate across various precipitation intensities.

Comparison of Simulated Warm‐Rain Microphysical Processes in a Record‐Breaking Rainfall Event Using Polarimetric Radar Observations

AbstractDuring 6–7 May 2017, a record‐breaking nocturnal rainfall event occurred in Guangzhou, China, and it was a typical warm‐sector heavy rainfall event under weak synoptic forcing. A prior observational study by the authors revealed that warm‐rain microphysical processes were dominant and responsible for the record‐breaking precipitation. In this study, the double‐moment Morrison, Thompson and NSSL microphysics schemes in WRF are evaluated against polarimetric radar observations in their ability of reproducing observed microphysical characteristics. The Thompson scheme shows the greatest fidelity to the observed raindrop size distribution (RSD) median value, corresponding to the most amount of precipitation forecast. While the Morrison and NSSL simulations overestimate (underestimate) the raindrop size (number concentration), exhibiting continental‐type convective precipitation. The three experiments slightly overestimate differential reflectivity (ZDR), but significantly underestimate specific differential phase (KDP) and liquid water content by about 30%–50%, implying the undervaluation of number of medium‐sized raindrops. Examinations of the occurrence frequencies of ZDR, KDP, mass‐weighted diameter, and logarithmic normalized intercept parameter for rain suggest that all three schemes fail to reproduce the full variability of observed RSD for the extreme rainfall. The vertical variations of RSD parameters and the Kumjian‐Ryzhkov parameter space suggest that the collision–coalescence is the dominant warm‐rain microphysical process but the simulated process is too weak. This may be attributed to the misrepresented RSD near the melting layer, where the raindrops with lower number concentration and larger sizes cannot grow through the collision–coalescence process as actively.

Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: a comprehensive long-term dataset of remote sensing observations

Abstract. We present a comprehensive quality-controlled 15-month dataset of remote sensing observations of low-level mixed-phase clouds (LLMPCs) taken at the high Arctic site of Ny-Ålesund, Svalbard, Norway. LLMPCs occur frequently in the Arctic region and extensively affect the energy budget. However, our understanding of the ice microphysical processes taking place in these clouds is incomplete. The dual-wavelength and polarimetric Doppler cloud radar observations, which are the cornerstones of the dataset, provide valuable fingerprints of ice microphysical processes, and the high number of cases included allows for the compiling of robust statistics for process studies. The radar data are complemented with thermodynamic retrievals from a microwave radiometer, liquid base height from a ceilometer, and wind fields from large-eddy simulations. All data are quality controlled, especially the cloud radar data, which are accurately calibrated, matched, and corrected for gas and liquid-hydrometeor attenuation, ground clutter, and range folding. We finally present an analysis of the temperature dependence of Doppler, dual-wavelength, and polarimetric radar variables, to illustrate how the dataset can be used for cloud microphysical studies. The dataset has been published in Chellini et al. (2023) and is freely available at: https://doi.org/10.5281/zenodo.7803064.

Assimilation of 3D polarimetric microphysical retrievals in a convective-scale NWP system

Abstract. This study assimilates for the first time polarimetric C-band radar observations from the German meteorological service (DWD) into DWD's convective-scale model ICON-D2 using DWD's ensemble-based KENDA assimilation framework. We compare the assimilation of conventional observations (CNV) with the additional assimilation of radar reflectivity Z (CNV + Z), with the additional assimilation of liquid or ice water content (LWC or IWC) estimates below or above the melting layer instead of Z where available (CNV + LWC/Z or CNV + IWC/Z respectively). Hourly quantitative precipitation forecasts (QPF) are evaluated for two stratiform and one convective rainfall events in the summers of 2017 and 2021. With optimized data assimilation settings (e.g., observation errors), the assimilation of LWC mostly improves first-guess QPF compared with the assimilation of Z alone (CNV + Z), whereas the assimilation of IWC does not, especially for convective cases, probably because of the lower quality of the IWC retrieval in these situations. Improvements are, however, notable for stratiform rainfall in 2021, for which the IWC estimator profits from better specific differential phase estimates owing to a higher radial radar resolution than the other cases. The assimilation of all radar data sets together (CNV + LWC + IWC + Z) yields the best first guesses. All assimilation configurations with radar information consistently improve deterministic 9 h QPF compared with the assimilation of only conventional data (CNV). Forecasts based on the assimilation of LWC and IWC retrievals on average slightly improve Fraction Skill Score (FSS) and Frequency Bias (FBI) compared with the assimilation of Z alone (CNV + Z), especially when LWC is assimilated for the 2017 convective case and when IWC is assimilated for the high-resolution 2021 stratiform case. However, IWC assimilation again degrades forecast FSS for the convective cases. Forecasts initiated using all radar data sets together (CNV + LWC + IWC + Z) yield the best FSS. The development of IWC retrievals that are more adequate for convection constitutes one next step to further improving the exploitation of ice microphysical retrievals for radar data assimilation.

The Dynamics and Microphysical Characteristics of the Convection Producing the Record-Breaking Hourly Precipitation on 20 July 2021 in Zhengzhou, China

An hourly rainfall of 201.9 mm fell in Zhengzhou on 20 July 2021, breaking the hourly rainfall record of mainland China and causing severe urban flooding and human casualties. This observation-based study investigates the associated convective-scale and mesoscale dynamics and microphysical processes using disdrometer and polarimetric radar observations aided by retrievals from the Variational Doppler Radar Analysis System. The synoptic flow forcing brought abundant moisture from the oceans and converged at Zhengzhou; then, the extreme rainfall was produced by a slow-moving convective storm that persisted throughout the hour over Zhengzhou. Unusually high concentrations of raindrops of all sizes (showing combined properties of maritime and continental convection) are revealed by the disdrometer data, whereas the polarimetric radar data suggest that both ice-based and warm rain processes were important contributors to the total rainfall. High precipitation efficiency was achieved with an erect updraft at the low levels, whereas enhanced easterly inflows kept the storm moving slowly. The interaction between convective-scale and mesoscale dynamics and microphysical processes within the favorable synoptic conditions led to this extremely heavy rainfall.

Polarimetric Radar Observations of a Long-Lived Supercell and Associated Tornadoes on 10–11 December 2021

Abstract We present environmental and polarimetric radar observations of a long-lived December supercell that tracked approximately 750 km from Arkansas to northern Kentucky. The storm was associated with two long-track EF4 tornadoes, one of which was among the longest-tracked tornadoes recorded in the United States. The supercell’s life cycle is documented from 2000 UTC 10 to 0700 UTC 11 December 2021, using data from five operational polarimetric weather radars. After convection initiation in central Arkansas, it took nearly 4 h for a supercell to develop. Afterward, the storm’s ZDR column and arc became anomalously large leading up to genesis of the first EF4 tornado. During this time, the storm’s environment had moderate convective available potential energy (CAPE) and strong deep-layer shear. A cell interaction at about 0200 UTC disrupted the supercell updraft, weakening the ZDR arc and column, and initiating the largest radar-implied hailfall event observed with this storm. The remnant circulation associated with the first EF4 tornado did not fully dissipate, and it appeared to merge with the low-level mesocyclone on the nose of a rear-flank downdraft surge likely initiated by the hailfall. It is hypothesized that this merger was important to the intensification of the storm’s second EF4 tornado, which lasted nearly 3 h and traveled approximately 267 km. During the second EF4 tornado the storm experienced decreasing CAPE and increasing storm relative helicity. Increasing interactions with other cells eventually weakened the storm, and its original updraft was obscured before the storm’s remnants dissipated in northern Kentucky. Significance Statement In December 2021, a long-lived supercell thunderstorm produced two long-track, violent tornadoes, including one that produced historic damage across western Kentucky. Radar observations indicate that, once the storm became a supercell, its updraft became anomalously large relative to similar storms studied prior. Simultaneously its storm-relative inflow strengthened markedly, supporting the first long-lived tornado. Interactions with a developing thunderstorm disrupted the supercell’s updraft, leading to hail fallout and updraft weakening. The remnant circulation associated with the first strong tornado merged with the supercell’s updraft and became a rotation source for the second long-track tornado, which persisted for nearly 3 h. Eventually interactions with other thunderstorms weakened the supercell.

Polarimetric Radar Observations of Biological Scatterers in the Eye of Typhoon Lekima (2019)

AbstractTropical cyclones not only cause strong winds and heavy rainfall, but they can also facilitate the transport of birds and insects from tropical regions to areas along their paths. Before super Typhoon Lekima made landfall in 2019, an operational polarimetric radar in Wenzhou City observed biological scatterers in the typhoon's eye. These scatterers were likely birds and insects that were trapped in the calm center of the typhoon by strong winds and heavy rain. The polarimetric variables of these biological scatterers had specific characteristics: low reflectivity factor (ZH) values with a median of 7.5 dBZ, low cross‐correlation coefficient (ρhv) values with a median of 0.65, large differential reflectivity (ZDR) values with a median of 2.8 dB and a maximum of ∼7.8 dB, and widely‐distributed differential phase (ΦDP) values with 25th and 75th percentiles ranging from 1.8° to 36.0° and a median of 20.1°. When the edge of the eye reached the coastline, the birds and insects landed, with the biological scatterer signature changing from a circular shape to a band shape. We further compared Lekima with Atlantic Hurricane Irene which happened in 2011, and both storms had similar polarimetric characteristics contributed by both birds and insects. However, the biological echoes in Lekima better exhibited the characteristics of birds with a larger proportion of samples with ZDR lower than 5 dB as well as large ΦDP and low ρhv values. This finding could help to understand the role of typhoons in driving biological migration between oceans and/or islands and continents.

Estimation of extreme precipitation events in Estonia and Italy using dual-polarization weather radar quantitative precipitation estimations

Abstract. Evaluating extreme rainfall for a certain location is commonly considered when designing stormwater management systems. Rain gauge data are widely used to estimate rainfall intensities for a given return period. However, the poor spatial and temporal resolution of operational gauges is the main limiting factor. Several studies have used rainfall estimates based on weather radar horizontal reflectivity (Zh), but they come with a great caveat: while proven reliable for low or moderate rainfall rates, they are subject to major errors in extreme rainfall and convective cases. It is widely known that C-band weather radar can underestimate precipitation intensity due to signal attenuation or overestimate it due to hail and clutter contamination. From the late 1990s, dual-polarization weather radar started to become operational in the national surveillance radar network in Europe, providing innovative quantitative precipitation estimation (QPE) based on polarimetric variables. This study circumvents Zh shortcomings by using specific differential-phase (Kdp) data from operational dual-polarization C-band weather radars. The rain intensity estimates based on a specific differential-phase data are immune to attenuation and less affected by hail contamination. In this study, for the first time, QPEs based on polarimetric observations by operational C-band weather radars and without any rain gauge adjustments are analyzed. The purpose is to estimate return periods for 1 h rainfall total computed from polarimetric weather radar data using non-adjusted QPEs based on R(Zh,Kdp) data and to compare the results with those derived using R(Zh) and rain gauge data. Only the warm period during the year is considered here, as most of the extreme precipitation events for such a duration occur for both places studied (Italy and Estonia) at this time. Limiting the dataset to warm periods also allows us to use the radar-based rainfall quantitative precipitation estimations, which are more reliable than the snowfall ones. Data from operational dual polarimetric C-band weather radar sites are used from both Italy and Estonia. Given climatologically homogeneous regions, this study demonstrates that polarimetric weather radar observations can provide reliable QPEs compared to single-polarization estimates with respect to rain gauges and that they can provide a reliable estimation of return periods of 1 h rainfall total, even for relatively short time series.

Influence of cloud microphysics schemes on weather model predictions of heavy precipitation

Abstract. Cloud microphysics is one of the major sources of uncertainty in numerical weather prediction models. In this work, the ability of a numerical weather prediction model to correctly predict high-impact weather events, i.e., hail and heavy rain, using different cloud microphysics schemes is evaluated statistically. Polarimetric C-band radar observations over 30 convection days are used as the observation dataset. Simulations are made using the regional-scale Weather Research and Forecasting (WRF) model with five microphysics schemes of varying complexity (double moment, spectral bin (SBM), and Predicted Particle Properties (P3)). Statistical characteristics of heavy-rain and hail events of varying intensities are compared between simulations and observations. All simulations, regardless of the microphysics scheme, predict heavy-rain events (15, 25, and 40 mm h−1) that cover larger average areas than those observed by radar. The frequency of these heavy-rain events is similar to radar-measured heavy-rain events but still scatters by a factor of 2 around the observations, depending on the microphysics scheme. The model is generally unable to simulate extreme hail events with reflectivity thresholds of 55 dBZ and higher, although they have been observed by radar during the evaluation period. For slightly weaker hail/graupel events, only the P3 scheme is able to reproduce the observed statistics. Analysis of the raindrop size distribution in combination with the model mixing ratio shows that the P3, Thompson two-moment (2-mom), and Thompson aerosol-aware schemes produce large raindrops too frequently, and the SBM scheme misses large rain and graupel particles. More complex schemes do not necessarily lead to better results in the prediction of heavy precipitation.

Fine Resolution Classification of New Ice, Young Ice, and First-Year Ice Based on Feature Selection from Gaofen-3 Quad-Polarization SAR

A new method of sea ice classification based on feature selection from Gaofen-3 polarimetric Synthetic Aperture Radar (SAR) observations was proposed. The new approach classifies sea ice into four categories: open water (OW), new ice (NI), young ice (YI), and first-year ice (FYI). Seventy parameters that have previously been applied to sea ice studies were re-examined for sea ice classification in the Okhotsk Sea near the melting point on 28 February 2020. The ‘separability index (SI)’ was used for the selection of optimal features for sea ice classification. Full polarization parameters (the backscatter intensity contains the horizontal transmit-receive intensity (σhh0), Shannon entropy (SEi), the spherical scattering component of Krogager decomposition (Ks)), and hybrid polarization parameters (horizontal receive intensity(σrh0), hybrid-pol Shannon entropy (CPSEi), the correlation coefficient (ρrh−rv) between the σrh0 and σrv0, and the surface scattering component of m − α decomposition αs) were determined as the optimal parameters for the different work modes of SAR. The selected parameters were used to classify sea ice by the random forest classifier (RFC), and classification results were validated by manually interpreted ice maps derived from Landsat-8 data. The classification accuracy of OW, NI, YI and FYI reached 95%, 96%, 98% and 85%, respectively.

Evaluating Simulated Microphysics of Stratiform and Convective Precipitation in a Squall Line Event Using Polarimetric Radar Observations

Recent upgrades to China’s radar network now allow for polarimetric measurements of convective systems in central China, providing an effective data set with which to evaluate the microphysics schemes employed in local squall line simulations. We compared polarimetric radar variables derived by Weather Research and Forecasting (WRF) and radar forward models and the corresponding hydrometeor species with radar observations and retrievals for a severe squall line observed over central China on 16 March 2022. Two microphysics schemes were tested and were able to accurately depict the contrast between convective and stratiform regions in terms of the drop size distribution (DSD) and reproduce the classical polarimetric signatures of the observed differential reflectivity (ZDR) and specific differential phase (KDP) columns. However, for the convective region, the simulated DSDs in both schemes exhibited lower proportions of large drops and lower liquid water content; by contrast, for the stratiform region, the proportion of large drops was found to be too high in the Morrison (MORR) scheme. The underprediction of ice-phase processes in the convective region, particularly the riming processes associated with graupel and hail, was likely responsible for the bias toward large raindrops at low levels. In the stratiform region, raindrop evaporation in the WRF Double-Moment 6-Class (WDM6) scheme, which partially offsets the overestimation of ice-phase processes, produced ground DSDs that more closely matched the observational data, and did not exhibit the overly strong warm-rain collisional growth processes of MORR.

Refreezing of Partially Melted Hydrometeors: Polarimetric Radar Observations and Microphysical Model Simulations

Abstract The discovery of a polarimetric radar signature indicative of hydrometeor refreezing has shown promise in its utility to identify periods of ice pellet production. Uniquely characterized well below the melting layer by locally enhanced values of differential reflectivity (ZDR) within a layer of decreasing radar reflectivity factor at horizontal polarization (ZH), the signature has been documented in cases where hydrometeors were completely melted prior to refreezing. However, polarimetric radar features associated with the refreezing of partially melted hydrometeors have not been examined as rigorously in either an observational or microphysical modeling framework. Here, polarimetric radar data—including vertically pointing Doppler spectral data from the Ka-band Scanning Polarimetric Radar (KASPR)—are analyzed for an ice pellets and rain mixture event where the ice pellets formed via the refreezing of partially melted hydrometeors. Observations show that no such distinct localized ZDR enhancement is present, and that values instead decrease directly beneath enhanced values associated with melting. A simplified, explicit bin microphysical model is then developed to simulate the refreezing of partially melted hydrometeors, and coupled to a polarimetric radar forward operator to examine the impacts of such refreezing on simulated radar variables. Simulated vertical profiles of polarimetric radar variables and Doppler spectra have similar features to observations, and confirm that a ZDR enhancement is not produced. This suggests the possibility of two distinct polarimetric features of hydrometeor refreezing: ones associated with refreezing of completely melted hydrometeors, and those associated with refreezing of partially melted hydrometeors. Significance Statement There exist two pathways for the formation of ice pellets: refreezing of fully melted hydrometeors, and refreezing of partially melted hydrometeors. A polarimetric radar signature indicative of fully melted hydrometeor refreezing has been extensively documented in the past, yet no study has documented the refreezing of partially melted hydrometeors. Here, observations and idealized modeling simulations are presented to show different polarimetric radar features associated with partially melted hydrometeor refreezing. The distinction in polarimetric features may be beneficial to identifying layers of supercooled liquid drops within transitional winter storms.

The Retrieval of Drop Size Distribution Parameters Using a Dual-Polarimetric Radar

The raindrop size distribution (DSD) is vital for applications such as quantitative precipitation estimation, understanding microphysical processes, and validation/improvement of two-moment bulk microphysical schemes. We trace the history of the DSD representation and its linkage to polarimetric radar observables from functional forms (exponential, gamma, and generalized gamma models) and its normalization (un-normalized, single/double-moment scaling normalized). The four-parameter generalized gamma model is a good candidate for the optimal representation of the DSD variability. A radar-based disdrometer was found to describe the five archetypical shapes (from Montreal, Canada) consisting of drizzle, the larger precipitation drops and the ‘S’-shaped curvature that occurs frequently in between the drizzle and the larger-sized precipitation. Similar ‘S’-shaped DSDs were reproduced by combining the disdrometric measurements of small-sized drops from an optical array probe and large-sized drops from 2DVD. A unified theory based on the double-moment scaling normalization is described. The theory assumes the multiple power law among moments and DSDs are scaling normalized by the two characteristic parameters which are expressed as a combination of any two moments. The normalized DSDs are remarkably stable. Thus, the mean underlying shape is fitted to the generalized gamma model from which the ‘optimized’ two shape parameters are obtained. The other moments of the distribution are obtained as the product of power laws of the reference moments M3 and M6 along with the two shape parameters. These reference moments can be from dual-polarimetric measurements: M6 from the attenuation-corrected reflectivity and M3 from attenuation-corrected differential reflectivity and the specific differential propagation phase. Thus, all the moments of the distribution can be calculated, and the microphysical evolution of the DSD can be inferred. This is one of the major findings of this article.

Polarimetric radar observation of the melting layer during the pre-summer rainy season over South China

ABSTRACT The geometric and polarimetric characteristics of the melting layer (ML) during the pre-summer rainy season over South China (PRSC) are investigated from 389 S-band polarimetric radar volume scans. The existing ML detection algorithm is slightly improved, in which the ML is automatically detected by using vertical gradients of radar reflectivity factor for horizontal polarizations (Z H), and co-polar cross-correlation coefficient (ρ HV) from quasi-vertical profiles. And these profiles are obtained by azimuthal averaging of polarimetric radar variables at antenna elevation 19.5°. The comparison between the detected ML top and the 0°C isotherm heights from ERA5 demonstrates that the detection algorithm has high accuracy with an offset of 158 m. Due to larger snowflakes, the average ML thickness in PRSC is thicker (i.e. 150 m) than that in other climatic regions. The ML is characterized by large Z H, large differential reflectivity (Z DR) and small ρ HV, with average values of 32 dBZ, 0.91 dB and 0.94. Furthermore, the peak values of Z H and Z DR in the ML are obviously larger than that from rain regions and snow regions, while the opposite is true for ρ HV distribution.

A Conditional Generative Adversarial Network for Weather Radar Beam Blockage Correction

Missing or low-quality data regions usually happen to weather radars. One of the most common situations is beam blockage or partial beam blockage. Therefore, correction of weather radar observations that are partially or fully blocked is an indispensable step in radar data quality control and subsequent quantitative applications, especially in complex terrain environments such as the western United States. In this article, we propose a deep learning framework based on generative adversarial networks (GANs) for restoring partial beam blockage regions in polarimetric radar observations using local and global contextual information. Due to the diverse precipitation types, blockage conditions, and ground information in different areas, two radars deployed in two different regions characterized by different precipitation types are used to demonstrate the proposed methodology. Both are S-band operational Weather Surveillance Radar – 1988 Doppler (WSR-88D): KFWS located in Fort Worth, northern Texas, and KDAX located in Davis, northern California. For training the GAN model, this article simulates the partial beam blockage situations by manually cropping observation sectors of both KDAX and KFWS radar data. The trained models were tested using independent precipitation events in Texas and California to demonstrate the model effectiveness in inpainting “missing" data. In addition, this paper cross-tested the data with different precipitation features to examine the generalization capacity of the beam blockage correction models. The beam blockage correction performance is also compared with a traditional linear interpolation approach. The results show that for both domains the continuity of precipitation observations is greatly improved after applying the deep learning-based inpainting approach. For the KFWS test data, some visible discrepancies exist between the results from models trained based on convective and stratiform precipitation events in Texas and California, respectively, yet both models outperform the traditional interpolation method. For the KDAX test data, both the model trained using the KFWS data from convective precipitation events in Texas and the model trained using KDAX data from stratiform precipitation events in California render a similar performance. Although ground truth is not available for the real blocked radar data, the repaired observations demonstrated a great potential for improved quantitative applications.

Using Polarimetric Radar Observations to Characterize First Echoes of Thunderstorms and Nonthunderstorms: A Comparative Study

AbstractThis work compares the characteristics of the first echoes of thunderstorms and nonthunderstorms retrieved from S‐band polarimetric radar observations. Observations of 57 (39) isolated thunderstorm (nonthunderstorm) cells with roughly equivalent aerosol and water vapor conditions but different convective available potential energy were obtained with a S‐band polarimetric radar and three independent lightning location systems during 2016/2017 in southern China. Storms with the first echoes were divided into three types based on echo top heights, namely, type 1 (below 0°C layer), type 2 (0°C to −10°C), and type 3 (above −10°C layer). Our observations show median values of radar reflectivity (ZH) and differential reflectivity (ZDR) of type 1 and type 2 in warm phase layer (below 0°C layer) are obviously greater in nonthunderstorms than in thunderstorms, but this feature is not significant in type 3 storms. In the mixed 1 phase layer (0°C to −10°C), median ZH in type 2 is greater in nonthunderstorms while median ZDR in type 3 is slightly smaller. In the mixed 2 phase layer (−10°C to −38°C), median ZH is greater in thunderstorms while median ZDR is smaller, and ZDR values in nonthunderstorms are closer to zero. Although results of ZDR comparisons in the mixed phase are likely affected by random errors and/or residual bias errors, these different signatures suggest different characteristics of liquid or ice particles between thunderstorms and nonthunderstorms. This study is expected to advance our understanding of physical processes responsible for the generation of the first flash.

Polarimetric Radar and VHF Lightning Observations in a Significantly Tornadic Supercell

Polarimetric Radar and VHF Lightning Observations in a Significantly Tornadic Supercell

Microphysical Differences in the Concentric Eyewalls of Typhoon Lekima (2019)

AbstractUsing polarimetric radar observations, different microphysical characteristics were found in the asymmetric concentric eyewalls of Typhoon Lekima (2019). In addition to the differential reflectivity (ZDR) column, a typical size‐sorting radar signature was evident in the upright inner eyewall: a cyclonically upwind offset of the ZDR maximum relative to the horizontal reflectivity (ZH) and specific differential phase (KDP) maxima. In contrast, the large values of ZDR were located above the melting layer along the upper edge of the more tilted outer eyewall and did not extend downward to form a ZDR column. In the cyclonic direction, moreover, the large values of the three polarimetric variables in the outer eyewall almost overlapped, without the cyclonic offset of typical size‐sorting radar signature. Thus, more active warm‐cloud (cold‐cloud) processes dominate in the outer (inner) eyewall, particularly for the production of large drops, closely associated with the intrinsic dynamical structure of the concentric eyewalls.