scholarly journals Observations of Ash, Ice, and Lightning within Pyrocumulus Clouds Using Polarimetric NEXRAD Radars and the National Lightning Detection Network

2017 ◽  
Vol 145 (12) ◽  
pp. 4899-4910 ◽  
Author(s):  
Kendell T. LaRoche ◽  
Timothy J. Lang
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Case Studies ◽  
Correlation Coefficient ◽  
Convective Cloud ◽  
Past Research ◽  
Polarimetric Radar ◽  
Dual Polarization ◽  
Lightning Detection ◽  
Differential Reflectivity

A pyrocumulus is a convective cloud that can develop over a wildfire. Under certain conditions, pyrocumulus clouds become vertically developed enough to produce lightning. NEXRAD dual-polarization weather radar and upgraded National Lightning Detection Network (NLDN) data were used to analyze 10 case studies of ash plumes and pyrocumulus clouds from 2013 that either did or did not produce detected lightning. Past research has shown that pyrocumulus cases are most likely to produce lightning when there is a decrease in differential reflectivity (toward 0 dB) and an increase in the correlation coefficient (to >0.8), as measured by polarimetric radar, due to the transition from pure smoke/ash to frozen hydrometeors. All pyrocumulus cases that produced lightning in this study displayed the polarimetric characteristics of rimed ice within their respective clouds. Time series analysis of radar-inferred ash and rimed ice volumes within ash plumes and pyrocumulus clouds showed that NLDN-detected lightning occurred only after the cloud contained significant amounts of precipitation-sized rimed ice. The results suggest that the recently dual-pol-enabled NEXRADs and the more sensitive NLDN network can be used to explore ash plume and pyrocumulus microphysical structure and lightning production.

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

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.

An Automated Python Algorithm to Quantify ZDR Arc and KDP-ZDR Separation Signatures in Supercells

2020 ◽  
Author(s):  
Matthew B. Wilson ◽  
Matthew S. Van Den Broeke
Keyword(s):  
Time Series ◽  
Recent Work ◽  
Radar Data ◽  
Dual Polarization ◽  
Low Level ◽  
Supercell Storms ◽  
Size Sorting ◽  
Supercell Thunderstorms ◽  
Differential Reflectivity

AbstractSupercell thunderstorms often have pronounced signatures of hydrometeor size sorting within their forward flank regions, including an arc-shaped region of high differential reflectivity (ZDR) along the inflow edge of the forward flank known as the ZDR arc and a clear horizontal separation between this area of high ZDP values and and an area of enhanced KDP values deeper into the storm core. Recent work has indicated that ZDR arc and KDP-ZDR separation signatures in supercell storms may be related to environmental storm-relative helicity and low-level shear. Thus, characteristics of these signatures may be helpful to indicate whether a given storm is likely to produce a tornado. Although ZDR arc and KDP-ZDR separation signatures are typically easy to qualitatively identify in dual-polarization radar fields, quantifying their characteristics can be time-consuming and makes research into these signatures and their potential operational applications challenging. To address this problem, this paper introduces an automated Python algorithm to objectively identify and track these signatures in Weather Surveillance Radar-1988 Doppler (WSR-88D) radar data and quantify their characteristics. This paper will discuss the development of the algorithm, demonstrate its performance through comparisons with manually-generated time series of ZDR arc and KDP-ZDR separation signature characteristics, and briefly explore potential uses of this algorithm in research and operations.

Detrended Correlogram Method for Non-Stationary Time-Series Analysis

2021 ◽  
pp. 2250012
Author(s):  
G. F. Zebende ◽  
E. F. Guedes
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Correlation Coefficient ◽  
Memory Effect ◽  
Cross Correlation ◽  
Time Lag ◽  
Stationary Time Series ◽  
Time Lags ◽  
Statistical Tool ◽  
Auto Correlation

A correlogram is a statistical tool that is used to check time-series memory by computing the auto-correlation coefficient as a function of the time lag. If the time-series has no memory, then the auto-correlation must be close to zero for any time lag, otherwise if there is a memory, then the auto-correlations must be significantly different from zero. Therefore, based on the robust detrended cross-correlation coefficient, [Formula: see text], we propose the detrended correlogram method in this paper, which will be tested for some time-series (simulated and empirical). This new statistical tool is able to visualize a complete map of the auto-correlation for many time lags and time-scales, and can therefore analyze the memory effect for any time-series.

scholarly journals Mapping Forest Degradation due to Selective Logging by Means of Time Series Analysis: Case Studies in Central Africa

2014 ◽  
Vol 6 (1) ◽  
pp. 756-775 ◽  
Author(s):  
Manuela Hirschmugl ◽  
Martin Steinegger ◽  
Heinz Gallaun ◽  
Mathias Schardt
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Case Studies ◽  
Central Africa ◽  
Forest Degradation ◽  
Selective Logging ◽  
Series Analysis

scholarly journals Close-Range Observations of Tornadoes in Supercells Made with a Dual-Polarization, X-Band, Mobile Doppler Radar

2007 ◽  
Vol 135 (4) ◽  
pp. 1522-1543 ◽  
Author(s):  
Howard B. Bluestein ◽  
Michael M. French ◽  
Robin L. Tanamachi ◽  
Stephen Frasier ◽  
Kery Hardwick ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Cross Correlation ◽  
Doppler Radar ◽  
Dual Polarization ◽  
Radar Beam ◽  
X Band ◽  
Close Range ◽  
Debris Cloud ◽  
The Cross ◽  
Differential Reflectivity

Abstract A mobile, dual-polarization, X-band, Doppler radar scanned tornadoes at close range in supercells on 12 and 29 May 2004 in Kansas and Oklahoma, respectively. In the former tornadoes, a visible circular debris ring detected as circular regions of low values of differential reflectivity and the cross-correlation coefficient was distinguished from surrounding spiral bands of precipitation of higher values of differential reflectivity and the cross-correlation coefficient. A curved band of debris was indicated on one side of the tornado in another. In a tornado and/or mesocyclone on 29 May 2004, which was hidden from the view of the storm-intercept team by precipitation, the vortex and its associated “weak-echo hole” were at times relatively wide; however, a debris ring was not evident in either the differential reflectivity field or in the cross-correlation coefficient field, most likely because the radar beam scanned too high above the ground. In this case, differential attenuation made identification of debris using differential reflectivity difficult and it was necessary to use the cross-correlation coefficient to determine that there was no debris cloud. The latter tornado’s parent storm was a high-precipitation (HP) supercell, which also spawned an anticyclonic tornado approximately 10 km away from the cyclonic tornado, along the rear-flank gust front. No debris cloud was detected in this tornado either, also because the radar beam was probably too high.

A sentiment democracy?

2017 ◽  
Vol 17 (2) ◽  
pp. 241-257
Author(s):  
Andrea Ceron
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Case Studies ◽  
Social Networking Sites ◽  
The Road ◽  
Positive Effects ◽  
Series Analysis ◽  
Italian Politics

Abstract Does the interaction with the opinions of ‘friends’ and ‘followers’ affect the behavior of politicians? So far, little attention has been devoted to the effect of social networking sites (SNS) on ‘hard politics’ choices. Focusing on two case studies related to Italian politics, namely the debate on the civil unions bill and the ‘Fertility Day’ crisis, in 2016, this paper tries to fill this gap assessing the influence of SNS on the behavior of politicians. For this purpose, supervised aggregated sentiment analysis and time series analysis are used to evaluate whether politicians surrender to the pressure put on them by their followers. The findings highlight some positive effects in terms of accountability/transparency, though in terms of responsiveness politics seems to continue as usual, and the road toward a full ‘sentiment democracy’ is still far ahead.

scholarly journals Polarimetric Tornadic Debris Signature Variability and Debris Fallout Signatures

2015 ◽  
Vol 54 (12) ◽  
pp. 2389-2405 ◽  
Author(s):  
Matthew S. Van Den Broeke
Keyword(s):  
Land Cover ◽  
Correlation Coefficient ◽  
Cross Correlation ◽  
Extreme Values ◽  
Life Cycles ◽  
Areal Extent ◽  
Polarimetric Radar ◽  
Horizontal Polarization ◽  
Differential Reflectivity ◽  
Relative Flow

AbstractValues of polarimetric radar variables may vary substantially between and through tornadic debris signature (TDS) events. Tornadoes with higher intensity ratings are associated with higher average and extreme values of reflectivity factor at horizontal polarization ZHH and lower values of copolar cross-correlation coefficient ρhv. Although values of these variables often fluctuate through reported tornado life cycles, ZHH repeatably decreases and ρhv repeatably increases across the volume scan immediately following reported tornado demise. Land cover has a relatively small effect on values of the polarimetric variables within TDSs, although near-radar urban TDSs may exhibit relatively high ZHH values. TDS areal extent is typically larger aloft than near the surface, although this trend may reverse in the most intense tornadoes. Maximum altitude to which a TDS is visible is more strongly a function of tornado intensity than of land cover or ambient shear and instability. Debris often disappears once lofted but may also be observed to spread out downstream with the storm-relative flow or to fall out along the parent storm’s northwest flank in a debris fallout signature (DFS). DFS characteristics, although variable, most commonly include ZHH values of 30–35 dBZ, ρhv values of 0.60–0.80, and values of differential reflectivity ZDR that are repeatably near 0 dB.

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