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Thunderstorm tracking and monitoring on the basis of three dimensional lightning data and conventional and polarimetric radar data [Elektronische Ressource] / presented by Vera Meyer

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Thunderstorm Tracking and Monitoring onthe Basis of Three-Dimensional LightningData and Conventional and PolarimetricRadar Data A thesis submitted to the Fakultat fur Physik,Ludwig-Maximilians-UniversitatMunchen for the degree of Dr. rer. nat.presented byDipl.-Ing. Vera Meyerfrom Vienna, Austriasubmitted on 30 May 2010Thunderstorm Tracking and Monitoring onthe Basis of Three Dimensional LightningData and Conventional and PolarimetricRadar Data A thesis submitted to the Fakultat fur Physik,Ludwig-Maximilians-UniversitatMunchen for the degree of Dr. rer. nat.presented byDipl.-Ing. Vera Meyerfrom Vienna, Austriasubmitted on 30 May 2010Gutachter der Dissertation:apl. Prof. Dr. habil. U. SchumannProf. Dr. H. D. BetzVerteidigung der Arbeit am 28.06.2010ContentsContents iAbstract v1 Introduction 12 Background of Convection Lifecycles and their Nowcasting 52.1 Cloud Electri cation and Lightning Discharges in Thunderstorms . . . . 52.1.1 Global Electric Circuit . . . . . . . . . . . . . . . . . . . . . . . . 52.1.2 Processes and Electrical Structure in Thunderstorms 52.1.3 Lightning Processes and Their Discharge Characteristics . . . . . 72.2 Conceptual Model for Thunderstorm Development and Evolution . . . . 142.2.1 Classical Life-Cycle Model . . . . . . . . . . . . . . . . . . . . . . 142.2.2 Organization Forms of Thunderstorms . . . . . . . . . . . . . . . 172.3 Data Sources and Measurement Principles . . . . . . . . . . . . . . . . .

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Published 01 January 2010
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Thunderstorm Tracking and Monitoring on
the Basis of Three-Dimensional Lightning
Data and Conventional and Polarimetric
Radar Data
A thesis submitted to the Fakultat fur Physik,
Ludwig-Maximilians-Universitat
Munchen for the degree of Dr. rer. nat.
presented by
Dipl.-Ing. Vera Meyer
from Vienna, Austria
submitted on 30 May 2010Thunderstorm Tracking and Monitoring on
the Basis of Three Dimensional Lightning
Data and Conventional and Polarimetric
Radar Data
A thesis submitted to the Fakultat fur Physik,
Ludwig-Maximilians-Universitat
Munchen for the degree of Dr. rer. nat.
presented by
Dipl.-Ing. Vera Meyer
from Vienna, Austria
submitted on 30 May 2010Gutachter der Dissertation:
apl. Prof. Dr. habil. U. Schumann
Prof. Dr. H. D. Betz
Verteidigung der Arbeit am 28.06.2010Contents
Contents i
Abstract v
1 Introduction 1
2 Background of Convection Lifecycles and their Nowcasting 5
2.1 Cloud Electri cation and Lightning Discharges in Thunderstorms . . . . 5
2.1.1 Global Electric Circuit . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.2 Processes and Electrical Structure in Thunderstorms 5
2.1.3 Lightning Processes and Their Discharge Characteristics . . . . . 7
2.2 Conceptual Model for Thunderstorm Development and Evolution . . . . 14
2.2.1 Classical Life-Cycle Model . . . . . . . . . . . . . . . . . . . . . . 14
2.2.2 Organization Forms of Thunderstorms . . . . . . . . . . . . . . . 17
2.3 Data Sources and Measurement Principles . . . . . . . . . . . . . . . . . 18
2.3.1 Lightning Monitoring Methods . . . . . . . . . . . . . . . . . . . 18
2.3.2 Radar Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.4 Methods and Applications of Thunderstorm Nowcasting . . . . . . . . . . 30
2.4.1 Scale Dependencies . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.4.2 Current Status of Thunderstorm Nowcasting . . . . . . . . . . . . 32
2.4.3 Operational Thunderstorm Nowcasting Tools . . . . . . . . . . . . 34
3 Methodology and Data Sources 37
3.1 Concept ec-TRAM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.1.1 General Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.1.2 Radar-Cell Tracker rad-TRAM . . . . . . . . . . . . . . . . . . . 43
3.1.3 Lightning-Cell Tracker li-TRAM . . . . . . . . . . . . . . . . . . . 45
3.1.4 Tracking Method ec-TRAM . . . . . . . . . . . . . . . . . . . . . 46
3.1.5 Cell Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.2 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.2.1 Lightning detection network - LINET . . . . . . . . . . . . . . . . 49
3.2.2 DWD Radar Furholzen . . . . . . . . . . . . . . . . . . . . . . . . 56
3.2.3 DLR POLDIRAD . . . . . . . . . . . . . . . . . . . . . . . 56
3.2.4 Satellite Data from cell tracking algorithm Cb-TRAM . . . . . . . 57
3.2.5 Sampling Domain and Period . . . . . . . . . . . . . . . . . . . . 58
iii CONTENTS
4 Performance and Veri cation of the Thunderstorm Tracker ec-TRAM 61
4.1 Cell Identi cation with ec-TRAM . . . . . . . . . . . . . . . . . . . . . . 62
4.2 Cell Tracking with . . . . . . . . . . . . . . . . . . . . . . . . 68
4.3 Cell Nowcasting and Nowcast Quality with ec-TRAM . . . . . . . . . . . 73
4.4 Statistical Analyses of Selected Cell Parameters . . . . . . . . . . . . . . 74
5 Evaluation of Convective Life-Cycles from Thunderstorm Cell Tracker
ec-TRAM 81
5.1 Case-studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
5.2 Statistical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
5.2.1 Lightning-Cell Parameter Correlations . . . . . . . . . . . . . . . 89
5.2.2 Time Lagged IC and CG Discharge Activity . . . . . . . . . . . . 95
6 Conclusion and Outlook 97
A Appendix 105
A.1 De nition of Radio Wave Frequency Bands . . . . . . . . . . . . . . . . . 105
A.2 of Box-And-Whisker Plot . . . . . . . . . . . . . . . . . . . . . 105
A.3 Veri cation Methods for Dichotomous (Yes/No) Forecasts . . . . . . . . 106
A.4 SQL Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
A.4.1 Tabular Structure and Linkages . . . . . . . . . . . . . . . . . . . 108
A.4.2 Cell Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
A.5 Linear Lightning-Cell Parameter Correlation Plots with Distribution Spread114
Acknowledgements 119
Bibliography 120iiiiv CONTENTSAbstract
The aim of this work is to assess the bene t of total-lightning information as indepen-
dent data source for thunderstorm tracking and short-term prediction (nowcasting) of
storm evolution. Special focus has been laid on the three-dimensional lightning infor-
mation and the in-cloud and cloud-to-ground discrimination provided by the lightning
detection network LINET. The reliability of the lightning information and its usability
for nowcasting purposes have been tested both separately and in combination with other
data sources which are commonly used for thunderstorm nowcasting.
The new thunderstorm tracker ec-TRAM (tracking and monitoring of electrically
charged cells; Meyer et al. (2009)) has been developed to identify, track, and monitor
thunderstorms in high temporal and spatial resolution by combining the information of
independently tracked convective ground-precipitation cells and lightning-cells to new
cell objects. The algorithm builds on the autonomously operating routines rad-TRAM
(tracking and monitoring of radar cells; Kober and Ta erner (2009)) and li-TRAM
(tracking and monitoring of lightning cells). The latter has also been developed within
this work.
The new tracking algorithm has been tested based on a thunderstorm data set of
more than 500 storm tracks which were recorded by ec-TRAM in southern Germany
during summer 2008. It is found that the newly composed cell objects comprehensively
describe simple as well as complex thunderstorm structures and the cell tracking method
of ec-TRAM proves to be more coherent and stable in comparison with the tracking
performances of rad-TRAM and li-TRAM.
For two selected thunderstorms the time series of cell parameters monitored by ec-
TRAM have been complemented with three-dimensional polarimetric radar data and
satellite data to assess how the temporal evolution and parameter correlation of total-
lightning strokes, hydrometeor formation, ground precipitation patterns, and cloud top
temperature can be used to estimate the storm state and predict its development. The
parameter evolutions are found to be consistent with the current state of knowledge.
A principal life-cycle scheme can be identi ed for the cell parameters on large time
scales. The stronger uctuating short-term parameter evolutions are found to re ect the
momentary storm dynamic. Based on the lifetime diagrams several warning parameters
for subsequent storm events can be suggested.
Signi cant cell parameter correlations, which can be parameterized, are also found in
statistical analyses over the complete data set. Strong positive correlations are found
between cell extension, discharge frequency, and in-cloud discharge height. Two cell-
regimes, sharply separated at a speci c cell characteristic, can clearly be identi ed in all
correlation diagrams. Interpreted on the basis of previous studies and in terms of the
current state of knowledge, it seems most likely that the two cell-regimes re ect the storm
characteristics of di erent storm organization forms. The parameterized correlation
curves could then be used as cell parameterizations in operational nowcasting tools to
predict the dynamic evolution, duration, and danger potential of a storm, provided
that the storm system can be classi ed. Finally, it can be concluded that this study
demonstrates the usability and the promising potential of total-lightning data as reliable
and independent data source for future nowcasting tools.
vvi CHAPTER 0. ABSTRACT