Lightning flash rate nowcasting based on polarimetric radar data and machine learning

The comparative analysis of radar-based hail detection methods presented here, uses C-band polarimetric radar data from Czech territory for 5 stormy days in May and June 2016. The 27 hail events were selected from hail reports of the European Severe Weather Database (ESWD) along with 21 heavy rain events. The hail detection results compared in this study were obtained using a criterion, which is based on single-polarization radar data and a technique, which uses dual-polarization radar data. Both techniques successfully detected large hail events in a similar way and showed a strong agreement. The hail detection, as applied to heavy rain events, indicated a weak enhancement of the number of false detected hail pixels via the dual-polarization hydrometeor classification. We also examined the performance of hail size detection from radar data using both single- and dual-polarization methods. Both the methods recognized events with large hail but could not select the reported events with maximum hail size (diameter above 4 cm).

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Crop classification from C-band polarimetric radar data

International Journal of Remote Sensing ◽

10.1080/01431169408954289 ◽

1994 ◽

Vol 15 (14) ◽

pp. 2871-2885 ◽

Cited By ~ 31

Author(s):

G.M. FOODY ◽

M. B. McCULLOCH ◽

W. B. YATES

Keyword(s):

Radar Data ◽

Polarimetric Radar ◽

Crop Classification

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Fuzzy Logic Classification of S-Band Polarimetric Radar Echoes to Identify Three-Body Scattering and Improve Data Quality

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-13-0358.1 ◽

2014 ◽

Vol 53 (8) ◽

pp. 2017-2033 ◽

Cited By ~ 14

Author(s):

Vivek N. Mahale ◽

Guifu Zhang ◽

Ming Xue

Keyword(s):

Fuzzy Logic ◽

Standard Deviation ◽

Radar Data ◽

Classification Algorithm ◽

Radar Reflectivity ◽

Membership Functions ◽

Polarimetric Radar ◽

Radar Echo ◽

Three Body ◽

Reflectivity Factor

AbstractThe three-body scatter signature (TBSS) is a radar artifact that appears downrange from a high-radar-reflectivity core in a thunderstorm as a result of the presence of hailstones. It is useful to identify the TBSS artifact for quality control of radar data used in numerical weather prediction and quantitative precipitation estimation. Therefore, it is advantageous to develop a method to automatically identify TBSS in radar data for the above applications and to help identify hailstones within thunderstorms. In this study, a fuzzy logic classification algorithm for TBSS identification is developed. Polarimetric radar data collected by the experimental S-band Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma (KOUN), are used to develop trapezoidal membership functions for the TBSS class of radar echo within a hydrometeor classification algorithm (HCA). Nearly 3000 radar gates are removed from 50 TBSSs to develop the membership functions from the data statistics. Five variables are investigated for the discrimination of the radar echo: 1) horizontal radar reflectivity factor ZH, 2) differential reflectivity ZDR, 3) copolar cross-correlation coefficient ρhv, 4) along-beam standard deviation of horizontal radar reflectivity factor SD(ZH), and 5) along-beam standard deviation of differential phase SD(ΦDP). These membership functions are added to an HCA to identify TBSSs. Testing is conducted on radar data collected by dual-polarization-upgraded operational WSR-88Ds from multiple severe-weather events, and results show that automatic identification of the TBSS through the enhanced HCA is feasible for operational use.

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Multilag Correlation Estimators for Polarimetric Radar Measurements in the Presence of Noise

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-11-00010.1 ◽

2012 ◽

Vol 29 (6) ◽

pp. 772-795 ◽

Cited By ~ 24

Author(s):

Lei Lei ◽

Guifu Zhang ◽

Richard J. Doviak ◽

Robert Palmer ◽

Boon Leng Cheong ◽

...

Keyword(s):

Theoretical Analysis ◽

Data Quality ◽

Signal To Noise Ratio ◽

Simulated Data ◽

Radar Data ◽

Polarimetric Radar ◽

Signal To Noise ◽

Spectral Moments ◽

Radar Measurements

Abstract The quality of polarimetric radar data degrades as the signal-to-noise ratio (SNR) decreases. This substantially limits the usage of collected polarimetric radar data to high SNR regions. To improve data quality at low SNRs, multilag correlation estimators are introduced. The performance of the multilag estimators for spectral moments and polarimetric parameters is examined through a theoretical analysis and by the use of simulated data. The biases and standard deviations of the estimates are calculated and compared with those estimates obtained using the conventional method.

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The Analysis and Prediction of Microphysical States and Polarimetric Radar Variables in a Mesoscale Convective System Using Double-Moment Microphysics, Multinetwork Radar Data, and the Ensemble Kalman Filter

Monthly Weather Review ◽

10.1175/mwr-d-13-00042.1 ◽

2014 ◽

Vol 142 (1) ◽

pp. 141-162 ◽

Cited By ~ 43

Author(s):

Bryan J. Putnam ◽

Ming Xue ◽

Youngsun Jung ◽

Nathan Snook ◽

Guifu Zhang

Keyword(s):

Kalman Filter ◽

Ensemble Kalman Filter ◽

Mesoscale Convective System ◽

Radar Data ◽

Cold Pool ◽

Convective System ◽

Polarimetric Radar ◽

Microphysics Scheme ◽

Mesoscale Convective ◽

Double Moment

Abstract Doppler radar data are assimilated with an ensemble Kalman Filter (EnKF) in combination with a double-moment (DM) microphysics scheme in order to improve the analysis and forecast of microphysical states and precipitation structures within a mesoscale convective system (MCS) that passed over western Oklahoma on 8–9 May 2007. Reflectivity and radial velocity data from five operational Weather Surveillance Radar-1988 Doppler (WSR-88D) S-band radars as well as four experimental Collaborative and Adaptive Sensing of the Atmosphere (CASA) X-band radars are assimilated over a 1-h period using either single-moment (SM) or DM microphysics schemes within the forecast ensemble. Three-hour deterministic forecasts are initialized from the final ensemble mean analyses using a SM or DM scheme, respectively. Polarimetric radar variables are simulated from the analyses and compared with polarimetric WSR-88D observations for verification. EnKF assimilation of radar data using a multimoment microphysics scheme for an MCS case has not previously been documented in the literature. The use of DM microphysics during data assimilation improves simulated polarimetric variables through differentiation of particle size distributions (PSDs) within the stratiform and convective regions. The DM forecast initiated from the DM analysis shows significant qualitative improvement over the assimilation and forecast using SM microphysics in terms of the location and structure of the MCS precipitation. Quantitative precipitation forecasting skills are also improved in the DM forecast. Better handling of the PSDs by the DM scheme is believed to be responsible for the improved prediction of the surface cold pool, a stronger leading convective line, and improved areal extent of stratiform precipitation.

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Relationships between Deep Convection Updraft Characteristics and Satellite-Based Super Rapid Scan Mesoscale Atmospheric Motion Vector–Derived Flow

Monthly Weather Review ◽

10.1175/mwr-d-18-0119.1 ◽

2018 ◽

Vol 146 (10) ◽

pp. 3461-3480 ◽

Cited By ~ 10

Author(s):

Jason M. Apke ◽

John R. Mecikalski ◽

Kristopher Bedka ◽

Eugene W. McCaul ◽

Cameron R. Homeyer ◽

...

Keyword(s):

Doppler Radar ◽

Deep Convection ◽

Motion Vector ◽

Three Dimensional ◽

Radar Data ◽

Severe Weather ◽

Lightning Flash ◽

Rapid Scan ◽

Total Lightning ◽

The Relationship

Abstract Rapid acceleration of cloud-top outflow near vigorous storm updrafts can be readily observed in Geostationary Operational Environmental Satellite-14 (GOES-14) super rapid scan (SRS; 60 s) mode data. Conventional wisdom implies that this outflow is related to the intensity of updrafts and the formation of severe weather. However, from an SRS satellite perspective, the pairing of observed expansion and updraft intensity has not been objectively derived and documented. The goal of this study is to relate GOES-14 SRS-derived cloud-top horizontal divergence (CTD) over deep convection to internal updraft characteristics, and document evolution for severe and nonsevere thunderstorms. A new SRS flow derivation system is presented here to estimate storm-scale (<20 km) CTD. This CTD field is coupled with other proxies for storm updraft location and intensity such as overshooting tops (OTs), total lightning flash rates, and three-dimensional flow fields derived from dual-Doppler radar data. Objectively identified OTs with (without) matching CTD maxima were more (less) likely to be associated with radar-observed deep convection and severe weather reports at the ground, suggesting that some OTs were incorrectly identified. The correlation between CTD magnitude, maximum updraft speed, and total lightning was strongly positive for a nonsupercell pulse storm, and weakly positive for a supercell with multiple updraft pulses present. The relationship for the supercell was nonlinear, though larger flash rates are found during periods of larger CTD. Analysis here suggests that combining CTD with OTs and total lightning could have severe weather nowcasting value.

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INVESTIGATION OF RADAR AND ELECTRICAL CHARACTERISTICS OF THUNDERCLOUDS SEEDED WITH A GLACIOGENIC REAGENT IN KARNATAKA, INDIA

Meteorologiya i Gidrologiya ◽

10.52002/0130-2906-2021-8-112-122 ◽

2021 ◽

pp. 112-122

Author(s):

A.A. SIN'KEVICH ◽

◽

B. BOE ◽

S. PAWAR ◽

YU. P. MIKHAILOVSKII ◽

...

Keyword(s):

Network Data ◽

Lightning Flash ◽

Electrical Characteristics ◽

North Caucasus ◽

Flash Rate ◽

Convective Clouds ◽

The North ◽

Lightning Detection ◽

Karnataka State ◽

Order Of Magnitude

Characteristics of developing convective clouds (Cu) in Karnataka state (India) during the thunderstorm formation are analyzed using weather radar and lightning detection network data. It is noted that radar characteristics of Cu which produced lightning, exceed those where lightning does not form. The study has shown that the number of negative cloud-to-ground strokes exceeds the number of positive ones by an order of magnitude. The radar characteristics of clouds in India and the North Caucasus are compared. Significant differences in lightning flash rates over the mentioned regions are registered. A low correlation is found between the supercooled volume and the flash rate of negative lightning. The paper also presents the results of studying the dynamic characteristics of four Cu seeded with a glaciogenic reagent. The thunderstorm risk is estimated for the clouds. It is shown that the seeding increases a probability of lightning events.

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Validation of a Multilag Estimator on NJU-CPOL and a Hybrid Approach for Improving Polarimetric Radar Data Quality

Remote Sensing ◽

10.3390/rs12010180 ◽

2020 ◽

Vol 12 (1) ◽

pp. 180

Author(s):

Shiqing Shao ◽

Kun Zhao ◽

Haonan Chen ◽

Jianjun Chen ◽

Hao Huang

Keyword(s):

Theoretical Analysis ◽

Correlation Coefficient ◽

Hybrid Method ◽

Radar Data ◽

Noise Power ◽

Signal Power ◽

Polarimetric Radar ◽

Moment Estimation ◽

Spectrum Width ◽

Differential Reflectivity

For the estimation of weak echo with low signal-to-noise ratio (SNR), a multilag estimator is developed, which has better performance than the conventional method. The performance of the multilag estimator is examined by theoretical analysis, simulated radar data and some specific observed data collected by a C-band polarimetric radar in previous research. In this paper, the multilag estimator is implemented and verified for Nanjing University C-band polarimetric Doppler weather radar (NJU-CPOL) during the Observation, Prediction and Analysis of Severe Convection of China (OPACC) field campaign in 2014. The implementation results are also compared with theoretical analysis, including the estimation of signal power, spectrum width, differential reflectivity, and copolar correlation coefficient. The results show that the improvement of the multilag estimator is little for signal power and differential reflectivity, but significant for spectrum width and copolar correlation coefficient when spectrum width is less than 2 ms−1, which implies a large correlation time scale. However, there are obvious biases from the multilag estimator in the regions with large spectrum width. Based on the performance analysis, a hybrid method is thus introduced and examined through NJU-CPOL observations. All lags including lag 0 of autocorrelation function (ACF) are used for moment estimation in this algorithm according to the maximum usable lag number. A case study shows that this hybrid method can improve moment estimation compared to both conventional estimator and multilag estimator, especially for weak weather echoes. The improvement will be significant if SNR decreases or the biases of noise power in the conventional estimator increase. In addition, this hybrid method is easy to implement on both operational and non-operational radars. It is also expected that the proposed hybrid method will have a better performance if applied to S-band polarimetric radars which have twice the maximum useable lags in the same conditions with C-band radars.

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Sensitivities of Quantitative Precipitation Forecasts for Typhoon Soudelor (2015) near Landfall to Polarimetric Radar Data Assimilation

Remote Sensing ◽

10.3390/rs12223711 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3711

Author(s):

Chih-Chien Tsai ◽

Kao-Shen Chung

Keyword(s):

Data Assimilation ◽

Radar Data ◽

Convective Precipitation ◽

Polarimetric Radar ◽

Differential Phase ◽

Observation Operator ◽

Quantitative Precipitation Forecasts ◽

Series Of Experiments ◽

Differential Reflectivity ◽

Radar Data Assimilation

Based on the preciousness and uniqueness of polarimetric radar observations collected near the landfall of Typhoon Soudelor (2015), this study investigates the sensitivities of very short-range quantitative precipitation forecasts (QPFs) for this typhoon to polarimetric radar data assimilation. A series of experiments assimilating various combinations of radar variables are carried out for the purpose of improving a 6 h deterministic forecast for the most intense period. The results of the control simulation expose three sources of the observation operator errors, including the raindrop shape-size relation, the limitations for ice-phase hydrometeors, and the melting ice model. Nevertheless, polarimetric radar data assimilation with the unadjusted observation operator can still improve the analyses, especially rainwater, and consequent QPFs for this typhoon case. The different impacts of assimilating reflectivity, differential reflectivity, and specific differential phase are only distinguishable at the lower levels of convective precipitation areas where specific differential phase is found most helpful. The positive effect of radar data assimilation on QPFs can last three hours in this study, and further improvement can be expected by optimizing the observation operator in the future

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Forecasting Lightning Threat Using Cloud-Resolving Model Simulations

Weather and Forecasting ◽

10.1175/2008waf2222152.1 ◽

2009 ◽

Vol 24 (3) ◽

pp. 709-729 ◽

Cited By ~ 96

Author(s):

Eugene W. McCaul ◽

Steven J. Goodman ◽

Katherine M. LaCasse ◽

Daniel J. Cecil

Keyword(s):

Numerical Models ◽

Wrf Model ◽

Phase Region ◽

General Character ◽

Lightning Flash ◽

Flash Rate ◽

Spatial Coverage ◽

Physically Based ◽

Cloud Resolving Model ◽

Near Term

Abstract Two new approaches are proposed and developed for making time- and space-dependent, quantitative short-term forecasts of lightning threats, and a blend of these approaches is devised that capitalizes on the strengths of each. The new methods are distinctive in that they are based entirely on the ice-phase hydrometeor fields generated by regional cloud-resolving numerical simulations, such as those produced by the Weather Research and Forecasting (WRF) model. These methods are justified by established observational evidence linking aspects of the precipitating ice hydrometeor fields to total flash rates. The methods are straightforward and easy to implement, and offer an effective near-term alternative to the incorporation of complex and costly cloud electrification schemes into numerical models. One method is based on upward fluxes of precipitating ice hydrometeors in the mixed-phase region at the −15°C level, while the second method is based on the vertically integrated amounts of ice hydrometeors in each model grid column. Each method can be calibrated by comparing domain-wide statistics of the peak values of simulated flash-rate proxy fields against domain-wide peak total lightning flash-rate density data from observations. Tests show that the first method is able to capture much of the temporal variability of the lightning threat, while the second method does a better job of depicting the areal coverage of the threat. The blended solution proposed in this work is designed to retain most of the temporal sensitivity of the first method, while adding the improved spatial coverage of the second. Simulations of selected diverse North Alabama cases show that the WRF can distinguish the general character of most convective events, and that the methods employed herein show promise as a means of generating quantitatively realistic fields of lightning threat. However, because the models tend to have more difficulty in predicting the instantaneous placement of storms, forecasts of the detailed location of the lightning threat based on single simulations can be in error. Although these model shortcomings presently limit the precision of lightning threat forecasts from individual runs of current generation models, the techniques proposed herein should continue to be applicable as newer and more accurate physically based model versions, physical parameterizations, initialization techniques, and ensembles of forecasts become available.

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