Electrical Alignment Signatures of Ice Particles Before Intracloud Lightning Activity Detected by Dual-Polarized Phased Array Weather Radar

Wang S., Wada Y., Hayashi S., et al. Electrical Alignment Signatures of Ice Particles Before Intracloud Lightning Activity Detected by Dual-Polarized Phased Array Weather Radar. Journal of Geophysical Research: Atmospheres 129, e2023JD039942 (2024); https://doi.org/10.1029/2023jd039942.The cloud electrification process has great significance in understanding the microphysical properties, electrical characteristics, and evolution of thunderstorms. This study employs an X-band dual-polarized multiparameter phased array weather radar (MP-PAWR) to observe the electrical alignment signatures of ice particles before the first intracloud (IC) lightning flash, and to explore the evolution of the upper charge region in the early electrification stage of an isolated thunderstorm. Negative KDP signatures associated with vertically oriented ice particles by strong electric fields in the upper parts of the thunderstorm are analyzed by introducing composite KDP, which is defined as a minimum KDP value observed in a vertical column across all elevation scans at each specific horizontal grid point at and above a designated layer. About 7 min before the first IC lightning flash, the mean canting angle of ice particles in the upper parts of the cloud changed from horizontal to vertical by strong electric fields, and the concentration of vertically aligned ice particles on the top of the cloud reached the maximum 30 s before the first IC lightning flash. These signatures exhibit an early electrification process in the upper parts of the thunderstorm. These results indicate that with the high spatial and temporal resolution, MP-PAWRs have the ability not only to detect the rapid evolution of microphysical structures but also to observe the early electrification of thunderstorms, which will facilitate forecasting IC lightning flash initiation combined with graupel presence signatures in the mixed-phase region in normal operation

Polarimetric radar observations and interpretation of co-cross-polar correlation coefficients

Includes bibliographical references (page 354).Preliminary analysis of all components of the polarimetric radar covariance matrix for precipitation measured with the NCAR S-band dual-polarization Doppler radar (S-Pol) and the Colorado State University-University of Chicago-Illinois State Water Survey (CSU-CHILL) radars is presented. Radar reflectivity at horizontal polarization Zh, differential reflectivity ZDR, linear depolarization ratio LDR, specific differential phase KDP, crosscorrelation coefficient | ρhv | , and two co-cross-polar correlation coefficients, ρxh and ρxv, have been measured and examined for two rain events: the 14 August 1998 case in Florida and the 8 August 1998 case in Colorado. Examination of the coefficients ρxh and ρxv is the major focus of the study. It is shown that hydrometeors with different types of orientation can be better delineated if the coefficients ρxv and ρxv are used. Rough estimates of the raindrop mean canting angles and the rms width of the canting angle distribution are obtained from the co-cross-polar correlation coefficients in combination with other polarimetric variables. Analysis of the two cases indicates that the raindrop net canting angles averaged over the propagation paths near the ground in typical convective cells do not exceed 2.5°. Nonetheless, the mean canting angles in individual radar resolution volumes in rain can be noticeably higher. Although the net canting angle for individual convective cells can deviate by a few degrees from zero, the average over a long propagation path along several cells is close to zero, likely because canting angles in different cells vary in sign. The rms width of the canting angle distribution in rain is estimated to vary mainly between 5° and 15° with the median value slightly below 10°

Evolution of Specific Differential Phase in Squall Lines and Corresponding Lightning Channels

Dual-polarimetric radar products have been used in observing changes and persistence of thunderstorm electric fields in relation to lightning discharges. One such product, specific differential phase (KDP), is valuable for its ability to detect the change in particle orientation. Negative KDP values above the freezing level indicate ice crystals are oriented vertically beyond 45 degrees in response to an electric field. The relationship between negative KDP to electric fields and the evolution of negative KDP values through the life cycle of thunderstorms has not been previously well documented. In this study, one of the Shared Mobile Atmospheric Research and Teaching (SMART) radars was used to sample a small Florida squall line (2012) and a large Oklahoma squall line (2016). Data collected from the Florida event was overlaid with local lightning mapping array (LMA) data. The resulting composites were used to compare lightning channel positions to polarimetric signatures, and to study the evolution of those signatures through the life cycle of the squall line. A charge analysis was performed to examine the locations of charge regions in relation to the polarimetric ice-alignment signatures for the Florida squall line. Polarimetric signatures from the Oklahoma squall line were compared to those found in the Florida squall line. In both cases, a persistent, strongly-negative KDP region was observed above the freezing level on the stratiform side of the reflectivity maximum. This negative KDP region was elongated and sloped downward from the convective region into the stratiform region during later stages of the stratiform region development. A second region of negative KDP also existed on the forward side of the reflectivity maximum associated with mature convective cells, but fluctuated in strength frequently. In the Florida case, LMA radiation points for a given flash tended to follow contours of zero-KDP and would initiate around one of the negative KDP regions. A charge analysis of the flashes found that the negative KDP region tended to be below the positive charge region and above the negative charge region. Given that the location of the negative KDP region in relation to the lightning channels, it can be concluded that radar could be used to monitor the electrification of thunderstorms. However, the application is limited by the scan speed. The use of phased-array technology would be necessary to attempt to predict individual intracloud flashes

A Polarimetric Radar Analysis of Cold- and Warm-Based Supercells

Polarimetric analyses of supercell thunderstorms have been increasingly common within the past decade, since operational polarimetric radar data became available in 2013. Although polarimetric signatures within supercell thunderstorms are well known, few have investigated variability in these signatures in differing environments. Polarimetric signatures can provide vital information regarding the microphysical characteristics and processes in supercell thunderstorms. Specific polarimetric signatures of interest are the differential reflectivity (ZDR) column, the low-level polarimetrically inferred hail core, and the ZDR arc. These signatures provide information regarding updraft characteristics, hailfall characteristics, and size sorting processes in the storm- relative inflow. Previous studies have identified these signatures and their microphysical significance, yet there is much to learn regarding how these characteristics differ between environments. The investigation of these signatures found within supercells characterized by differing cloud base temperatures will be discussed herein. These preliminary results can serve as an operational aid when observing supercell thunderstorms in a severe weather event, as these signatures can help to determine the potential for specific hazards, given specific environments. The environments of each type of supercell, along with the characteristics of their associated polarimetric signatures, can provide information about updraft intensity, hailfall characteristics, or tornado potential. This investigation finds that there are some significant differences, especially within the ZDR columns and the low- level polarimetrically inferred hail core, in the observed polarimetric signatures between different environments. All warm-based supercells exhibited a ZDR column, while many of the cold-based cases did not exhibit any column. Along with more warm-based cases exhibiting columns, they were also deeper than those observed in the cold-based cases. Cold-based supercells also exhibited much larger inferred hail cores than the warm-based supercells, which can be attributed to the cooler environments in which cold-based supercells are found. Finally, the ZDR arcs shown no large statistical differences across environments. This could be a consequence to the different thresholds utilized for identifying the arcs, along with different hailfall characteristics between environments. Advisor: Matthew Van Den Broek

Prediction of total lightning in Colorado and Alabama thunderstorms based on storm dynamical and microphysical variables

2015 Spring.Includes bibliographical references.Thunderstorms impact their environment in a variety of ways, including the production of nitrogen oxides (NOₓ) by lightning (LNOₓ). Accurate prediction of total lightning flash rate in thunderstorms is important to improve estimates of LNOₓ from the storm scale to the global scale. New flash rate parameterization schemes have been developed based on observed relationships between lightning flash rate and storm parameters for Colorado thunderstorms during the Deep Convective Clouds and Chemistry (DC3) experiment. Storm total flash rates are determined using an automated flash counting algorithm that clusters very high frequency (VHF) radiation sources emitted by electrical breakdown in clouds and detected by the northern Colorado lightning mapping array (LMA). Storm parameters such as hydrometeor echo volumes and ice masses are calculated from polarimetric radar retrievals. Measurements of updraft strength are obtained by synthesizing radial velocity retrievals from the CSU-CHILL and CSU-Pawnee radars to determine three-dimensional wind fields. Bulk storm parameters including the graupel echo volume, 30-dBZ echo volume, and precipitating ice mass are found to be robustly correlated to flash rate (R² ~ 0.8). It is shown that simple flash rate parameterization schemes based on these quantities predict gross flash rate behavior reasonably well. Updraft intensity-based flash rate schemes are also developed, but updraft parameters were not as strongly correlated to flash rate as storm volume quantities. The use of multiple storm parameters to predict flash rate is also investigated, since flash rate may be sensitive to multiple processes or characteristics within thunderstorms. A simple approach is found to be most effective: storm-total graupel and reflectivity echo volumes were split up into representative area and height dimensions and regressed against flash rate. The combined quantities predict flash rate variability somewhat better than simpler single-parameter flash rate schemes. All new flash rate schemes are tested against observations of Alabama thunderstorms documented during DC3 to examine their potential regional limitations. The flash rate schemes developed work best for strong Colorado storms with sustained high flash rates. Finally, relationships between total flash rate and flash size are discussed, with implications for the improved prediction of LNOₓ

Precipitation Estimation Using C-Band Dual Polarimetric Weather Radar

Radar Quantitative Precipitation Estimation (QPE) plays an important role in weather forecasting, especially nowcasting, and hydrology. This study evaluates the current QPE algorithm implemented by the Canadian Radar Network of Environment Canada, suggests an improved algorithm, and also evaluates the use of polarimetric radars for estimation of Snow Water Equivalent (SWE), solid snowfall, and rainfall rates. Data from the dual polarimetric C-band King City radar (CWKR) near Toronto, Ontario, SWE and solid snowfall rates from Oakville, Ontario, SWE from the CAN-Now project at Pearson International Airport (CYYZ), Toronto, Ontario, and Mount Pearl, Newfoundland were used in this project. The ground observations show that the polarimetric variables could be used to infer a few of the microphysical processes during snowfall. It is suggested that the co-polar correlation coefficient (hv) could be sensitive to the size ranges of different snow habits within the radar sampled volume. Also, higher differential reflectivity (ZDR) values were measured with large aggregates due to the Mie resonance effect, lower fluttering angles, or induced field transverse. Data from the three sites were used to develop S(ZeH)-based algorithms at 1 hr interval SWE, where ZeH is the radar equivalent reflectivity factor. Similarly, two additional algorithms were developed using SWE at 10 min intervals from CYYZ and Mt. Pearl but they were found to have less skill. A modest difference was found between S(ZeH) and the polarimetric algorithm, S(ZeH, ZDR), in estimating SWE. The 1 hr interval SWE accumulation from the three sites were combined to develop an additional S(ZeH) algorithm which had statistically better results. The results show a severe underestimation of SWE and solid snowfall rates by the current Environment Canada algorithm. The similarity of the S(ZeH) algorithms for CYYZ and Mount Pearl suggests that the same algorithm could be used for many sites. A strong correlation was found between radar reflectivity factor and ground solid snowfall measurement. Accordingly, S(ZeH) and S(ZeH, ZDR) algorithms were established to directly estimate solid snowfall rates on the ground. The S(ZeH) was found to have superior results compared to the S(ZeH, ZDR). Finally, the polarimetric variables were found to be useful in estimating rainfall rates. Thus, three rainfall algorithms (R(ZeH), R(ZeH, ZDR), R(KDP)) were established and compared against the current algorithm employed by the Environment Canada and counterpart algorithms established by Bringi et al. (2010). A logic tree was devised with certain polarimetric thresholds to choose the optimal algorithm among the three established ones. It appears that for rain, unlike for snow, the polarimetric parameters are very useful for quantitative precipitation estimation

Application of Machine Learning to Multiple Radar Missions and Operations

This dissertation investigated the application of Machine Learning (ML) in multiple radar missions. With the increasing computational power and data availability, machine learning is becoming a convenient tool in developing radar algorithms. The overall goal of the dissertation was to improve the transportation safety. Three specific applications were studied: improving safety in the airport operations, safer air travel and safer road travel. First, in the operations around airports, lightning prediction is necessary to enhance safety of the ground handling workers. Information about the future lightning can help the workers take necessary actions to avoid lightning related injuries. The mission was to investigate the use of ML algorithms with measurements produced by an S-band weather radar to predict the lightning flash rate. This study used radar variables, single pol and dual-pol, measured throughout a year to train the machine learning algorithm. The effectiveness of dual-pol radar variables for lighting flash rate prediction was validated, and Pearson's coefficient of about 0.88 was achieved in the selected ML scheme. Second, the detection of High Ice Water Content (HIWC),which impact the jet engine operations at high altitudes, is necessary to improve the safety of air transportation. The detection information help aircraft pilots avoid hazardous HIWC condition. The mission was to detect HIWC using ML and the X-band airborne weather radar. Due to the insufficiency of measured data, radar data was synthesized using an end-to-end airborne weather system simulator. The simulation employed the information about ice crystals' particle size distribution (PSDs), axial ratios, and orientation to generate the polarimetric radar variables. The simulated radar variables were used to train the machine learning to detect HIWC and estimate the IWC values. Pearson's coefficient of about 0.99 was achieved for this mission. The third mission included the improvement of angular resolution and explored the machine learning based target classification using an automotive radar. In an autonomous vehicle system, the classification of targets enhances the safety of ground transportation. The angular resolution was improved using Multiple Input Multiple Output (MIMO) techniques. The mission also involved classifying the targets (pedestrian vs. vehicle) using micro-Doppler features. The classification accuracy of about 94% was achieved

Development of a polarimetric radar based hydrometeor classification algorithm for winter precipitation

2012 Fall.Includes bibliographical references.The nation-wide WSR-88D radar network is currently being upgraded for dual-polarized technology. While many convective, warm-season fuzzy-logic hydrometeor classification algorithms based on this new suite of radar variables and temperature have been refined, less progress has been made thus far in developing hydrometeor classification algorithms for winter precipitation. Unlike previous studies, the focus of this work is to exploit the discriminatory power of polarimetric variables to distinguish the most common precipitation types found in winter storms without the use of temperature as an additional variable. For the first time, detailed electromagnetic scattering of plates, dendrites, dry aggregated snowflakes, rain, freezing rain, and sleet are conducted at X-, C-, and S-band wavelengths. These physics-based results are used to determine the characteristic radar variable ranges associated with each precipitation type. A variable weighting system was also implemented in the algorithm's decision process to capitalize on the strengths of specific dual-polarimetric variables to discriminate between certain classes of hydrometeors, such as wet snow to indicate the melting layer. This algorithm was tested on observations during three different winter storms in Colorado and Oklahoma with the dual-wavelength X- and S-band CSU-CHILL, C-band OU-PRIME, and X-band CASA IP1 polarimetric radars. The algorithm showed success at all three frequencies, but was slightly more reliable at X-band because of the algorithm's strong dependence on specific differential phase. While plates were rarely distinguished from dendrites, the latter were satisfactorily differentiated from dry aggregated snowflakes and wet snow. Sleet and freezing rain could not be distinguished from rain or light rain based on polarimetric variables alone. However, high-resolution radar observations illustrated the refreezing process of raindrops into ice pellets, which has been documented before but not yet explained. Persistent, robust patterns of decreased correlation coefficient, enhanced differential reflectivity, and an inflection point around enhanced reflectivity occurred over the exact depth of the surface cold layer indicated by atmospheric soundings during times when sleet was reported at the surface. It is hypothesized that this refreezing signature is produced by a modulation of the drop size distribution such that smaller drops preferentially freeze into ice pellets first. The melting layer detection algorithm and fall speed spectra from vertically pointing radar also captured meaningful trends in the melting layer depth, height, and mean correlation coefficient during this transition from freezing rain to sleet at the surface. These findings demonstrate that this new radar-based winter hydrometeor classification algorithm is applicable for both research and operational sectors

Multi-scale process studies in the tropics: results from lightning observations

March 1997.Also issued as author's dissertation (Ph.D.) -- Colorado State University, 1997.Includes bibliographical references.Cloud-to-ground (CG) lightning and meteorological observations collected in the tropics were analyzed to address the following question: What do observations of lightning tell us about processes occurring over multiple scales in the tropical atmosphere? An emphasis was placed on the analysis of observations collected over the western Pacific warm-pool during TOGA COARE. Large-scale observations from COARE suggest that the occurrence of lightning over the western Pacific Ocean is a sensitive function of both the magnitude and vertical distribution of convective available potential energy (CAPE). Small variations in the marine boundary layer humidity were highly correlated to variations in the CAPE and/or boundary-layer 8w. In tum, small increases (O[0.5° C]) in the boundary-layer 8w, were associated with disproportionate increases in lightning activity. The diurnal cycle of CG lightning exhibited a pronounced maximum (minimum) around 2 a.m. (12 p.m.) local-time. Diurnal cycles of CAPE, convective and total precipitation exhibited similar diurnal cycles, but were weaker in amplitude. Over cloud-scales, upward-building 30 dBZ reflectivity cores extended to elevations colder than -10°C in lightning-producing tropical oceanic convection. Additionally, mean updraft strengths (when observed) in several Lightning-producing cases exceeded 6 m s-1 near the -10°C level. These observations support the hypothesis that updraft magnitudes between the 0°C and -10°C levels in tropical convection must exceed the terminal fall-speed of millimeter sized liquid and frozen drops in order to provide the requisite hydrometeor mass to electrification processes in the cold regions of the cloud. To investigate the coupling between cloud-scale electrification, kinematics, microphysics, and the large-scale thermodynamic environment, a one-dimensional cloud-model with a four­class bulk-microphysical ice scheme and a parameterization for non-inductive charging processes, was used to simulate tropical convection. In the cloud-simulations, convective heating profiles associated with lightning (non-lightning) producing convection were associated with a more pronounced upper-level (low-level) heating peak and an increased (decreased) contribution by ice-processes to the total surface rainfall. Since the rainfall process and lightning production become increasingly more correlated as contributions from the ice-phase to the total rainfall increase, we investigated the correlation between rainfall and lightning over large spatial and temporal scales for many different rainfall regimes. The results indicate that CG lightning flash density and rainfall are well correlated in warm-season rainfall regimes where highly electrified convection is prolific. In certain situations, it may be possible to use CG-lightning flash density to diagnose warm-season monthly rainfall totals, or differentiate between rainfall regimes.Sponsored by the National Aeronautics and Space Administration under grant NGT-30268