the use of a cost-effective x-band weather radar in alpine region maurizio savina · 2012-03-14 ·...

The Use of a Cost-Effective X-band Weather Radar

in Alpine Region

Maurizio Savina

Diss. ETH No. 20141

Institute of Environmental Engineering ETH Zurich

Diss. ETH No. 20141

The Use of a Cost-Effective X-bandWeather Radar in Alpine Region

A dissertation submitted to the

ETH Zurich

for the degree of

Doctor of Sciences

presented by

MAURIZIO SAVINA

Dipl. Civil Eng. Polytechnic University of Turin

born August 02, 1980

citizen of Italy

accepted on the recommendation of

Prof. Dr. P. Burlando, examiner

Prof. Dr. D. Sempere Torres, co-examiner

Dr. U. Germann, co-examiner

2011

Abstract

Alpine hydrology is driven by orography to the extent that orography affects the space-time

structure of precipitation. Mountain precipitation results from a multitude of processes such as

mechanical lifting, enhancement, shadowing, feeder-seeder effect. Many of these processes are

poorly understood, especially at small spatial and temporal scales. Consequently, this limits

our understanding of the majority of the precipitation-related natural hazards occurring in both

high- and lowlands.

This lack of knowledge is mainly due to the intrinsic limitations of our best measurement

techniques: raingauges and weather radars. Raingauges collect only point observation, weather

radars produce instantaneous 2-3D distributed reflectivity maps. The mountain cause a number

of limitations of weather radar systems, which often are reliable in a limited space-time domain.

A solution to this limitation might be the use of a number of cost-effective short-range X-band

radars as complement to raingauges and conventional, large and expensive weather radars.

This PhD thesis presents the results of a pilot experiment, which aimed to develop and assess

a cost-effective X-band weather radar (LAWR) deployed in an Alpine setting. The LAWR was

deployed between August 2007 and October 2011 on the summit of the Kl. Matterhorn, located

in the Swiss-Italian Alps at 3883 m a.s.l. (Valais, Switzerland). This radar site guaranteed

good radar visibility above the local Alpine area as well as the presence of infrastructure such

as cable-car and a power supply.

The radar site has a characteristic Alpine climate with related difficulties in the operating

conditions. Indeed, much effort went into the improvement of hardware that in its final form was

characterized by a mean time between failure of about two months, a great achievement for such

environmental conditions. The fact that the state-of-the-art LAWR correction and conversion

procedures were not reliable for Alpine applications, a new set of corrections and an Alpine

radar conversion method were developed and tested. The results showed that cost-effective

X-band weather radars can be deployed successfully also in the Alps. The modified LAWR was

able to measure precipitation in all its forms as well as its spatial variability. A raingauge-based

validation revealed uncertainty which, on the other hand, could be reduced by further hardware

and software modifications.

The results of this PhD thesis are highly useful guidelines to be adopted for installation of

cost-effective X-band radar in Alpine and other settings.

i

Sommario

L’effetto orografico prodotto dalle catene montuose influenza sia le precipitazioni che l’idrologia

montana e premontana. L’orografia, infatti, controlla le precipitazioni tramite un insieme

di meccanismi come ad esempio il sollevamento meccanico dell’aria umida che poi, essendo

soggetta ad espansione e raffreddamento contribuisce alla formazione di idrometeore. Questo

insieme di processi agisce a diverse scale spazio-temporali che sono ancor oggi scarsamente

comprese. Di conseguenza, queste lacune si ripercuotono anche sulla comprensione di fenomeni

idrogeologici che, innescati da estreme precipitazioni, producono effetti devastanti sia in ambito

alpino sia più a valle in zone pianeggianti.

Una delle principali limitazioni ad una corretta comprensione dell’effetto orografico sulle pre-

cipitazioni è dovuta alle limitate capacità dei sensori utilizzati per la misurazione della precip-

itazione: pluviometri e radar meteorologici. I pluviometri misurano puntualmente un volume

di precipitazione, i radar meteorologici producono invece misure di riflettività istantanee e dis-

tribuite su 2 o 3D. La presenza di alte cime causa forti limitazioni a queste tecniche di misura

che quindi risultano affidabili su un dominio spazio-temporale molto limitato. Una possibile

soluzione a questo problema potrebbe essere l’impiego di radar meteorologici a corto raggio.

Questi infatti, avendo costi di installazione e gestione più modesti, potrebbero essere usati

come complemento alle misure dei pluviometri e dei radar meteorologici.

Questa tesi di Dottorato presenta i risultati di un progetto pilota finalizzato allo sviluppo e

valutazione di un radar economico in banda X (LAWR) installato in contesto alpino. Il LAWR

è stato installato sulla cima del Piccolo Cervino, nel complesso Alpino Italo-Svizzero ad una

quota pari a 3883 m s.l.m. (Vallese, Svizzera). Questa posizione è stata scelta in quanto

capace di fornire un buona visibilità sull’arco alpino locale e perchè già fornita delle principali

infrastrutture sia per l’accesso che per la fornitura di corrente. L’esperimento è durato cinquanta

mesi, da Agosto 2007 a Ottobre 2011.

Le estreme condizioni ambientali del Piccolo Cervino sono risultate molto limitative e ciò ha

causato diverse difficoltà durante l’esperimento. In particolare, il radar ha necessitato sostanziali

modifiche, che hanno però portato ad un LAWR alpino molto affidabile e caratterizzato da un

tempo medio tra due successivi guasti pari a circa due mesi. Un ottimo risultato viste le critiche

condizioni ambientali d’esercizio. Le misure del LAWR, prima di essere utilizzabili per scopi

meteorologici e idrologici, devono essere corrette e convertite in intensità di precipitazione. La

iii

mancanza di modelli di conversione applicabili in ambienti alpini ha richiesto lo studio e la

validazione di nuovi metodi, sia per la correzione delle misure LAWR sia per la loro conversione

in intensità di precipitazione.

Il LAWR è stato capace di misurare sia precipitazione solida che liquida. La validazione dello

strumento, basata sul confronto con misure pluviometriche, ha indicato una certa incertezza

nelle misurazioni che comunque sarebbe notevolmente ridotta da ulteriori modifiche dello stru-

mento. Il LAWR ha anche permesso lo studio della distribuzione spaziale della precipitazione

alpina, la quale è risultata accadere più frequentemente alle quote comprese tra 1400 e 1950

m s.l.m.

I risultati di questo Dottorato di ricerca sono delle linee guida riguardanti dei metodi innovativi

e affidabili per l’installazione e l’esercizio di sistemi radar simili a quello adoperato in questo

studio, sia per ambienti alpini che pianeggianti.

Zusammenfassung

Die Hydrologie von alpinen Gebieten ist von der Orographie beeinflusst, die die räumlich-

zeitliche Struktur des Niederschlages bestimmt. Orographische Niederschläge sind durch eine

Vielzahl verschiedener Prozesse, wie zum Beispiel Hebungs- und Seeder-Feeder-Prozesse, gekenn-

zeichnet. Viele dieser Prozesse sind vor allem auf kleiner räumlicher und zeitlicher Skala kaum

verstanden, was zu einem ungenügenden Verständis vieler Naturgefahren in Gebirgen aber auch

im Flachland führt.

Die fehlenden Kenntnisse sind hauptsächlich auf Limitierungen in der heutigen Messtechnik

der Niederschlagsmesser und Wetterradare zurückzuführen. Niederschlagsmesser repräsentieren

lediglich kummulative Punktmessungen, während Wetterradare 2-3D Karten liefern. Gebirgsre-

gionen verursachen jedoch eine Reihe von Limitierungen für Wetterradare, die oft in einer

Reduzierung von deren räumliche und zeitliche Skalen resultieren. Eine Lösung dieses Prob-

lems kann der Einsatz von kostengünstigen lokalen X-Band Wetterradaren als Ergänzung zu

Niederschlagsmessern sowie konventionellen und teuren C-Band Wetterradaren sein.

Diese Disseration präsentiert Ergebnisse eines Pilotexperiments, welches ein kostengünstiges

X-Band Wetterradar (LAWR) unter alpinen Bedingungen testete, und dieses für die gegebenen

Bedingungen anpasste und weiterentwickelte. Das LAWR war im Zeitraum von August 2007

bis Oktober 2011 auf dem Gipfel des Klein Matterhorns (Wallis, Schweiz) auf 3883 m ü. NN

in den Schweizer Alpen installiert. Dieser Standort garantierte eine gute Sichtweite über das

regionale alpine Gebiet und bot eine gute Infrastuktur, welche unter anderem eine permanente

Stromversorgung und die Erreichbarkeit mittels einer Seilbahn beinhaltete.

Der gewählte Standort ist durch ein alpines Klima charakterisiert, welches Schwierigkeiten für

den Betrieb eines Radar darstellt. Deshalb wurde ein ehrheblicher Aufwand in die Verbesserung

der Hardware investiert. In ihrer entgültigen Form betrug die mittlere Betriebsdauer zwis-

chen zwei Ausfällen zwei Monate. Dies ist unter den gegebenen Bedingungen ein grosser Er-

folg. Weiterhin führten Standardkorrekturen und -konvertierungenverfahren für LAWR Radare

unter den alpinen Bedingungen zu unzuverlässigen Ergebnissen, weswegen in dieser Arbeit

ein neuer Korrekturansatz sowie eine neue Konvertierungsmethode entwickelt und getestet

wurden. Infolge dieser neu entwickelten Methoden können nun kostengünstige X-Band Nieder-

schlagsradare mit Erfolg in den Alpen eingesetzt werden. Weiter war das modifizierte LAWR

in der Lage, orographische Niederschläge in all ihren verschiedenen Formen zu messen sowie

v

deren räumliche Variabilität zu erfassen. Eine Validierung basierend auf Daten von Niederschal-

gsmessern, zeigte Unsicherheiten auf, welche durch weitere Hardware- sowie Softwaremodifika-

tionen reduziert werden können.

Die Ergebnisse dieser Dissertation sind essentielle Richtlinien für die Installation von kosten-

günstigen X-Band-Radargeräten unter alpinen Bedingungen und bieten ausserdem sehr nützliche

Ansätze für die Verbesserung von deren Leistung unter verschiedensten Standortbedingungen.

vi

Contents

1 Introduction 1

1.1 Precipitation mechanisms in complex terrain . . . . . . . . . . . . . . . . . . 4

1.2 Precipitation measurement techniques . . . . . . . . . . . . . . . . . . . . . . 7

1.3 Project rationale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2 Local Area Weather Radar (LAWR) 17

2.1 General . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.2 Standard LAWR system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.3 Hardware adaptations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3 Project site and radar visibility 35

3.1 Project site . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

3.2 Antenna pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

3.3 Visibility model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

4 Visibility related errors and Data Quality Control 45

4.1 Source of Errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

4.2 Data Quality Control framework . . . . . . . . . . . . . . . . . . . . . . . . . 48

5 Alpine LAWR conversion model 55

5.1 Model development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

5.2 Model implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

5.3 Model performance criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

vii

5.4 LAWR model performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

5.5 LAWR performance validation . . . . . . . . . . . . . . . . . . . . . . . . . . 85

5.6 LAWR application: vertical gradient of Alpine precipitation . . . . . . . . . . . 93

6 Concluding remarks and outlook 97

6.1 Summary of hardware and controlling improvements . . . . . . . . . . . . . . 97

6.2 Summary of Radar performance . . . . . . . . . . . . . . . . . . . . . . . . . 98

6.3 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

7 References 105

A Radar settings 117

B Data transferring and warning 121

C Raingauge characteristics 127

D Some moments on the Kl. Matterhorn 131

Acknowledgments 135

Curriculum Vitae 136

viii

List of Figures

1.1 Mechanisms of orographic rainfall across the Italian-Swiss Alps . . . . . . . . . 7

1.2 Tipping-bucket and weighing raingauges comparison . . . . . . . . . . . . . . 9

1.3 Swiss raingauge RhiresD vs. radar composite RAIN . . . . . . . . . . . . . . . 13

1.4 Swiss C-band radar data quality . . . . . . . . . . . . . . . . . . . . . . . . . 14

1.5 Radar site and KM LAWR . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.1 LAWR measures formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.2 Timing of the main Local Area Weather Radar (LAWR) phases . . . . . . . . 20

2.3 Radar specific attenuation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.4 LAWR Pulse Volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.5 Pulse volume growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.6 Clutter map application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.7 Measurement consolidation . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.8 Experiment setup - Kl. Matterhorn . . . . . . . . . . . . . . . . . . . . . . . 30

2.9 LAWR hardware: rack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

2.10 Antenna icing, problem and solution . . . . . . . . . . . . . . . . . . . . . . . 32

2.11 Alpine LAWR hardware set-up . . . . . . . . . . . . . . . . . . . . . . . . . . 34

3.1 Local Alpine orography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

3.2 3dB antenna pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

3.3 Elevation of the radar beam . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

3.4 Radar visibility across two Alpine valleys . . . . . . . . . . . . . . . . . . . . 40

ix

3.5 Cartesian radar visibility products . . . . . . . . . . . . . . . . . . . . . . . . 42

3.6 Polar radar visibility products . . . . . . . . . . . . . . . . . . . . . . . . . . 43

3.7 Polar representation of geometrical ground clutter . . . . . . . . . . . . . . . 44

4.1 Dry echoes, NCFM and GGC . . . . . . . . . . . . . . . . . . . . . . . . . . 49

4.2 Quality Control decision tree . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

4.3 Quality control mask . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

4.4 Radar echoes along two Alpine transects . . . . . . . . . . . . . . . . . . . . 52

4.5 Radar echoes at 12 km range, all 360 deg around . . . . . . . . . . . . . . . . 53

5.1 Alpine transect and hydrometeors sampling . . . . . . . . . . . . . . . . . . . 57

5.2 Sampling angular elevation and LAWR power . . . . . . . . . . . . . . . . . . 59

5.3 Ground echoes as hardware-state indicator . . . . . . . . . . . . . . . . . . . 64

5.4 Magnetron decay retrieved from ground echoes . . . . . . . . . . . . . . . . . 65

5.5 CDF of LAWR observations and L90 definition . . . . . . . . . . . . . . . . . 66

5.6 Raingauge locations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

5.7 LAWR antenna icing and associated at-antenna-attenuation . . . . . . . . . . 70

5.8 sL-sG scattering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

5.9 Distribution of Pearson’s coefficients . . . . . . . . . . . . . . . . . . . . . . 75

5.10 Curves for Alpine Conversion Factors . . . . . . . . . . . . . . . . . . . . . . 76

5.11 Alpine Conversion Factor Map Season 2009-2010 . . . . . . . . . . . . . . . . 77

5.12 Alpine Conversion Factor Map Season 2011 . . . . . . . . . . . . . . . . . . . 78

5.13 LAWR vs. raingauges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

5.14 Error scores for LAWR.II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

5.15 Error scores for LAWR.III . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

5.16 Correlation of Mean Relative Error . . . . . . . . . . . . . . . . . . . . . . . 82

5.17 Seasonal Alpine Conversion Factor . . . . . . . . . . . . . . . . . . . . . . . 83

5.18 Seasonal Quantitative Precipitation Estimation . . . . . . . . . . . . . . . . . 84

5.19 Error scores of cross-validation for LAWR.II . . . . . . . . . . . . . . . . . . . 87

5.20 Error scores of cross-validation for LAWR.III . . . . . . . . . . . . . . . . . . 88

x

5.21 Mean Relative Error variation D1 . . . . . . . . . . . . . . . . . . . . . . . . 89

5.22 Mean Relative Error variation D2 . . . . . . . . . . . . . . . . . . . . . . . . 89

5.23 Split-sample validation and ACF curves . . . . . . . . . . . . . . . . . . . . 91

5.24 Error scores for split-sample validation . . . . . . . . . . . . . . . . . . . . . 92

5.25 LAWR precipitation map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

5.26 Frequency distribution for wet area and ground elevation . . . . . . . . . . . . 95

5.27 Variation of rz with the ground elevation . . . . . . . . . . . . . . . . . . . . 96

B.1 Data transfer architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

D.1 Moments on the Kl. Matterhorn . . . . . . . . . . . . . . . . . . . . . . . . . 133

xi

List of Tables

2.1 LAWR characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.2 One-way specific attenuation . . . . . . . . . . . . . . . . . . . . . . . . . . 22

5.1 Table of LAWR.II and LAWR.III error scores . . . . . . . . . . . . . . . . . . 81

5.2 Cross-validation LAWR.II . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

5.3 Cross-validation LAWR.III . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

5.4 Split-sample validation for LAWR.II . . . . . . . . . . . . . . . . . . . . . . . 91

5.5 Split-sample validation for LAWR.III . . . . . . . . . . . . . . . . . . . . . . . 91

C.1 Raingauge characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

xiii

Glossary

A/D converter Analogical to Digital converter. 18, 19, 25, 27

clutter map Radar map of the ground clutter recorded during dry condition. 24

count dimensionless output of the LAWR A/D converter. 19

echo top Indication of the highest level with aggregated hydrometers, i.e. measurable with

conventional weather radar. 56–58, 61, 75, 76, 83, 98, 99, 103

MeteoSwiss Swiss Federal Office of Meteorology and Climatology. 5, 9, 13, 14, 20, 68

NASS MeteoSwiss C-band radar product; best estimation of precipitation at the ground from

the composite of the three Swiss C-band weather radars; gauge-corrected. Spatial and

temporal resolution of 1 km and 5 min respectively. 5, 94

NEXRAD NEXt-generation RADar; US network of 159 S-band Doppler weather radars oper-

ated by the National Oceanic and Atmospheric Administration (NOAA). 20

RAIN MeteoSwiss C-band radar product; it represents the best estimation of precipitation at

the ground from the composite of the three Swiss c-band weather radars. It has spatial

and temporal resolution of 1 km and 2.5 min respectively. 13

xv

Acronyms

fdf Frequency Distribution Function. 63, 64, 79, 94, 95

ACFM Alpine Conversion Factor Map. 62, 76, 99

AP Anomalous Propagation. 39, 47–52

APUNCH “Advanced Process UNderstanding and prediction of hydrological extremes and

Complex Hazards”. 4

CASA Collaborative Adaptive Sensing of the Atmosphere. 12

CCES ETH Competence Center for Environment and Sustainability. 4

CDF Cumulative Distribution Function. 37, 61, 65, 79, 94, 99

CE Cumulative Error. 72

CRE Cumulative Relative Error. 72, 79, 86, 99

DEM Digital Elevation Model. 35, 40–43, 48, 50, 76, 94

DHI former Danish Hydraulic Institute. 3, 17, 18, 21, 24, 25, 27–29, 31, 33, 63, 64, 74, 97

DSD Drop Size Distribution. 10, 11, 27, 57, 60, 61, 80, 83, 99–101

ETH-AP ETH-APUNCH weighing raingauge network. 67, 68

ETH-Ar ETH-Arolla weighing raingauge network. 67

FOEN Swiss Federal Office for the Environment. 12

GC Ground Clutter. 24, 25, 39, 41, 46, 48, 50–52, 63–65

xvii

GGC Geometrical Ground Clutter. 41, 47–50

IfU-ETH Zurich Institute of Environmental Engineering at the Swiss Federal Institute of

Technology ETH Zurich. 17

IOP Intensive Observation Period. 1

KM Kl. Matterhorn. 29, 30, 36, 41, 46, 47, 60, 66

LAN Local Area Network. 26

LAWR Local Area Weather Radar. ix, 3, 4, 14, 15, 17–20, 22–41, 44–48, 50–53, 55, 56,

58–64, 66, 67, 69–73, 75, 79, 83, 85, 86, 90, 93, 94, 97–100, 102, 103

LTP Long-Term Persistence. 5, 6, 94

MAE Mean Absolute Error. 71, 72, 86

MARE Mean Absolute Relative Error. 72, 79, 86

ME Mean Error. 71, 90

MRE Mean Relative Error. 71, 79, 80, 87, 88, 90, 99–101

MTBF Mean Time Between Failure. 29, 33

NCFM Normalized Cumulative Frequency Map. 48, 50, 51, 53

NWPs Numerical Weather Prediction models. 13

OK Area with high quality LAWR observations. 48, 55, 67, 77, 78, 93, 94

PC Personal Computer. 17, 26, 32, 34, 97

PCC Pearson’s Correlation Coefficient. 61, 70, 73, 99

PDU Power Distribution Unit. 32, 33

PMM Probability Matching Method. 61

PPI Plan Position Indicator. 24

PSD Particles Size Distribution. 11, 27

xviii

QC Data Quality Control. 46, 48–51, 55, 58, 63, 64, 67, 77, 78, 94

QPE Quantitative Precipitation Estimations. 3, 6, 13, 26, 27, 35, 41, 45, 47, 50, 64, 83, 90,

99, 100

QPF Quantitative Precipitation Forecasts. 3, 6, 14, 26

RadCTR Radar ConTRol Software, designed by DHI. 66, 73

RTC Real Time Control. 29, 33

SH SHielded area. 47, 48, 50–52, 58

SL Side Lobe. 46, 48–52

SU SUspicions area. 48, 51, 52

TB Tipping-Bucket Gauge. 9, 69

TIK-ETH Zurich Computer Engineering and Networks Laboratory at the Swiss Federal In-

stitute of Technology ETH Zurich. 33

UPS Uninterruptible Power Supply. 32, 33

VPR Vertical Profile of Reflectivity. 12, 103

WG Weighing Gauge. 9, 69

WMO World Meteorological Organization. 8

ZBAG Zermatt Bergbahnen AG. 29

xix

List of Symbols

a Scale of the Z −R relation. 10, 11

ACF LAWR Alpine Conversion Factor. xi, 55, 56, 59, 61, 62, 73–79, 83, 85, 90–92, 99–102

αL Lower boundary of the antenna pattern, [deg]. 37

b Exponent of the Z −R relation. 10, 11

C 8-bit LAWR output for file extension *.P00 . 26–28

CF LAWR Conversion Factor. 27, 28

CFDHI DHI LAWR Conversion Factor. 27

Cr Radar constant. 10

D1MRE First factor for the evaluation of MRE score worsening during LAWR validation phase,

[−]. 87, 100, 101

D2MRE Second factor for the evaluation of MRE score worsening during LAWR validation

phase, [−]. 87, 88, 101

E Major temporary errors, [-]. 60

�N Error in antenna Northering, [deg]. 66

L∗ Threshold used to compute NCFM [count]. 48, 49

NCF10 Normalized Cumulative Frequency value for L = 10 count. 50

�X Error in LAWR map positioning, [m]. 66

f Transmitted frequency, [Hz]. 18

xxi

φ Density function of the Gaussian distribution. 37

Fatt DHI attenuation correction factor, [-]. 21

fs Sampling frequency, [MHz]. 19, 22

G Raingauge observation, [mm time−1]. 27, 28, 59, 62, 65, 69, 70, 79, 80, 87, 99

γ Elevation angle, [deg]. 18

Gj,T Time series at raingauge jth (G) integrated on period length T , [mm T−1]. 61

gt Gain of the transmitting antenna, [−]. 10

H∗ Maximum echo-top level, [m or km]. 57–62, 75, 83, 85, 98–100

H Elevation of the beam barycenter, [m a.s.l.]. 39, 40, 99

ha Elevation of the radar antenna, [m a.s.l.]. 39

HB Elevation of the beam bottom, [m a.s.l.]. 39, 40

HT Elevation of the beam top, [m a.s.l.]. 39, 40

j Index for raingauge locations, [-]. 60

K Dielectric constant of the target. 10, 12, 60

L 10-bit output for file extension *.L00 . 26, 27, 49, 59, 62, 65, 69, 70, 79, 99, 103

λ Transmitted wavelength, [cm]. 10, 18, 20

L90 Local q90 value retrieved from CDF (L), [count h−1]. 65

Ldry L value in case of dry weather observation. Sometime computed as seasonal mean value.

48–50, 63–65

Lj,T Time series of LAWR values (L) in coordinate rj, ρj and aggregated at cumulation

length T , [count T−1]. 61

Lp Space pulse length, [m]. 19

η Generic hydrometeors. 10, 23, 27, 36, 41, 56, 57, 59

xxii

n Number of raingauges used during the experiment, [−]. 67, 69, 73, 75, 79, 85

Nj Number of events recorded by both LAWR and raingauge at location jth, [−]. 71

Ns Number of samples per pulse, [MHz]. 19

ω Antenna rotation rate, [rpm]. 18, 25

PRF Pulse Repetition Frequency, [Hz]. 18

Pr Received power, [kW ]. 10, 57

Pt Transmitted power, [kW ]. 10, 38, 57

π Ratio of circumference of circle to its diameter, about 3.14. 10

pV Pulse volume, volume occupied by radar-born electromagnetic waves, [m3]. 22–24, 27,

35, 36, 38, 41, 56

R Radar based precipitation rate, [mm time−1]. 10, 21, 22, 27, 62, 79, 80, 87, 103

r Range, [m or km]. 10, 26, 35, 39–41, 48–50, 58, 61, 62, 66, 69, 70, 75

R∗ Discriminant threshold used for the identification of dry/wet LAWR pixels, [mm h−1]. 94

R2 Coefficient of Determination, [−]. 61, 76

Re Earth’s equatorial radius,[km]. 39, 57

rHB Real elevation of beam bottom, [m a.s.l.]. 41–43

rHgB Real elevation of beam bottom above the ground, [m a.g.]. 41–43, 47, 48, 51, 60, 87,

89, 98, 100–102

ρ Azimuth of radar antenna alignment, [deg]. 26, 35, 40, 41, 48–50, 61, 69, 70, 75

rmax Maximum range of detection, [m or km]. 18, 20, 21, 35, 52, 59, 60, 64

rz Index explaining the dependency of precipitation on the ground elevation. 94, 96

S 10-bit value of samples along a LAWR scanline, [count]. 21, 25

Satt Specific Attenuation, [dB km−1]. 21

SDR Number of Scanned Direction per Rotation, [−]. 18

xxiii

sGj,T Sorted Gj,T , [mm T−1]. 61, 73, 74

sLj,T Sorted version of Lj,T , [count T−1]. 61, 73, 74

sP Fraction of total power used to to sample sV , [%]. 38, 39, 57, 75, 76, 83, 87, 89, 99, 100

sP ∗ Effective sampling power used to sample falling hydrometeros, [%]. 58, 61, 62, 75, 98

sV Sampling volume as fraction of theoretical pulse volume, [%]. 10, 38, 41–43, 47, 50, 52,

56, 57, 73–75

T Time of accumulation, [time]. 27, 60, 62, 67, 70, 74

τ Pulse width, [µs]. 18

θh Beam height, [deg]. 10, 18, 35, 46

θL Lower angular elevation of the sampling volume, [deg]. 38, 57–59

tm Antenna mechanical time, [ms]. 19, 20

tp Time pulse length, [µs]. 10, 19, 20

ts Sampling time length, [ms]. 19, 20

θU Upper angular elevation of the sampling volume, [deg]. 38, 57–59

θw Beam width, [deg]. 10, 18, 25, 35, 46

V 16-bit variance value for LAWR file extension *.V00, [count2]. 26

wpl One-way Wet Path Length, [km]. 21

Z Radar Reflectivity, [mm6 m−3]. 10, 57

Zg Ground elevation, from DEM, [ma.s.l.]. 94–96

Zg Mean ground elevation of wet LAWR pixels, from DEM, [ma.s.l.]. 94, 95

xxiv

...once Tom suggested that I check the NEXRAD before biking home,

I think this was the starting point.

...once Tom suggested that I check the NEXRAD before biking home,

I think this was the starting point

Chapter 1

Introduction

Mountains have a primary role in the global water cycle. They can trigger precipitation events

and modify their space-time evolution (Savina et al., 2011a). Those in turn, together with tem-

perature, influence mountain non-polar capacity to create static temporary fresh water storages

at the base of any continental ecosystem and civilization but which themselves unfortunately

are not rarely subjects of international water conflicts (Gleick, 1994).

Mountains might trigger precipitation by acting as obstacles to the motion of moist-air mass

which, being forced to lift, is subjected to expansion and cooling. As a consequence, the

condensation of water vapour in small droplets, following the collision-coalescence or the Berg-

eron (ice-crystal) process (Ahrens, 2007) might lead to aggregated water drops/snow flakes

and more in general to falling hydrometeors. Mountains can act also as barriers to synop-

tic low-pressure systems, which for instance when attracting cold fronts might trigger frontal

precipitation systems. Mountains modify the space-time structure of precipitation (Savina

et al., 2011a) inducing precipitation enhancement, impinging, and shadowing at space-time

scales as small as the one given by the distances between local geomorphological features (i.e.,

microscale) and the quick timing of low-level streams (less than a few minutes). Some of

these processes cannot be observed and therefore are only guessed. Others have been observed

during Intensive Observation Period (IOP)s but being highly dependent on local geo-climatic

conditions cannot be exported to other geographic settings.

In mountainous areas, the orographic enhancement of rainfall, step flanks and shallow ground-

water can cause a very short response time of the catchment (e.g., Petrascheck and Hegg,

2002) regardless of the contributing area, with consequent downstream slope erosions, land-

slides, floods and other forms of natural hazards (e.g., APUNCH1, Mandapaka et al., 2009)

1Advanced Process UNderstanding and prediction of hydrological extremes and Complex Hazards, at http:

//www.cces.ethz.ch/projects/hazri/apunch

1

http://www.cces.ethz.ch/projects/hazri/apunchhttp://www.cces.ethz.ch/projects/hazri/apunch

2 Chapter 1. Introduction

which, if not promptly forecasted, can generate large economic damages and impacts on social

systems.

The need of improving our understanding of the time-space evolution of mountain precipitation

is important also for lowlands hydrology. This relation will become apparent in the future.

Besides the indication of future weather extremization (e.g., Frei et al., 1998), that in it-self

would dramatically increase flood risk in both high and lowlands, all projections of future climate

scenarios indicate that winter temperatures in middle Europe will increase. As a consequence,

the solid-liquid partition of Alpine precipitation will shift towards higher land and this will

increase flood risk primarily in the lowlands (CH2011, 2011).

The main common goal, which many times creates misunderstanding between meteorologists

and hydrologists, is to generate reliable distributed precipitation information with a time reso-

lution as small as the time-scale of the quickest orographic process. This is the main need of

current hydrological applications, and indirectly the motivation of this work.

The poor knowledge of precipitation processes in complex terrain is mainly due to the lack

of reliable measurements in mountain areas. The more reliable and operative instruments for

precipitation estimation are ground-based and they are raingauges and weather radars. The

good thing is that their advantages and disadvantages are complementary and therefore better

results might be achieved by a combination of the two measurements techniques. The bad

thing is that mountains emphasize both raingauges and radars limitations.

Raingauges provide quantitative time-integrated measurements of precipitation close to the

ground. Weather radars provide time-averaged instantaneous observations of reflectivity backscat-

tered from hydrometeors when they are still aloft, falling and maybe not yet completely formed.

Raingauges provide only point measurements, which are usually assumed to be ground truth,

weather radars give as output very precious precipitation maps but with questionable reliabil-

ity. An up-to-date weighing raingauge installation costs approximately ten thousand Euro, a

conventional C-band weather radar installation costs more than two million Euro. Often radar

maps are corrected by raingauge observations and this brings together the advantages of the

complementary observations to a product that is distributed and hopefully reliable.

Mountains, and more in general mountainous environments, cause limitations in both tech-

niques. Raingauges might underestimate precipitation because of wind-induced perturbation

(Nespor and Sevruk, 1999) and heating-related water loss (Savina et al., 2011b). On the other

hand, weather radars in mountainous regions suffer harsh climatological conditions, their visi-

bility is normally limited by partial or total beam blocking, and often the retrieved precipitation

maps are biased by residual ground clutter. This is in addition to other problems affecting not

only Alpine radars, but all radars: signal attenuation, bright band, overshooting, beam partial

filling, and variation in shape and phase of the weather target (Zawadzki, 1984). Alone, neither

3

raingauges nor weather radars have the characteristics and accuracy needed to produce reliable

Quantitative Precipitation Estimations (QPE) and related Quantitative Precipitation Forecasts

(QPF).

As mentioned in Joss and Germann (2000), an affordable improvement of the knowledge about

mountain precipitation can be achieved by using networks of cost-effective X-band weather

radars. They are smaller, covering shorter ranges and are cheaper. Indeed, in the future their

price might decrease to a cost comparable to that of an expensive raingauge installation. Cost-

effective X-band weather radars might fill the gap between large weather radars and raingauge

networks (Pedersen, 2009), giving spatial and temporal insight into the variability of deposition

processes leading to precipitation in mountainous areas.

Nowadays such X-band systems are commercialized only by few a companies, such as the

former Danish Hydraulic Institute (DHI)2, which produces the LAWR (Jensen, 2002), and

Gematronik3, which produces the so called RAINSCANNER R© (Gekat et al., 2008). In addition,

a number of research institutes are working to develop their own systems like for instance

the Polytechnic University of Turin which is currently testing a network of portable micro-

radars called MicroRadarNet R©4 (Turso et al., 2009) and the ESRL-NOAA5 which deployed

the transportable polarimetric HYDROX (Matrosov et al., 2009). These cost-effective systems

have yet to be tested in Alpine areas.

This work is a complete study of feasibility with regard to the installation and the operation of a

cost-effective X-band weather radar installed in the Alps, on the summit of the Kl. Matterhorn

(Piccolo Cervino, Canton Valais, Switzerland) at 3883 m a.s.l., under severe climatological and

operational conditions. The tested system is a LAWR. The experiment lasted 50 months and

revealed most of the weaknesses not observed by previous lowland LAWR installations, as well

as the potential for use in harsh Alpine environments. During the experiment the hardware of

the radar was improved and new data processing methodologies were developed. The modified

LAWR was able to measure precipitation in all its forms. A raingauge-based validation revealed

high uncertainty which on the other hand could be reduced by further hardware and software

modifications. The results of this study are useful guidelines to be used by any other cost-

effective Alpine, and not-Alpine, installations.

The experiment was co-funded by the Services of Roads and Water Courses, Energy and

Hydraulic Forces of the Canton Valais and by the ETH Zurich through a special credit of

the Vice Presidency for Research. The experiment was also supported by the Swiss Federal

Office for the Environment (FOEN). Some of the experiment-related studies were embedded

2http://www.dhigroup.com3http://www.gematronik.com4http://www.microradarnet.net5http://www.esrl.noaa.gov

http://www.dhigroup.comhttp://www.gematronik.comhttp://www.microradarnet.nethttp://www.esrl.noaa.gov


into the project “Advanced Process UNderstanding and prediction of hydrological extremes

and Complex Hazards” (APUNCH), funded by the ETH Competence Center for Environment

and Sustainability (CCES).

The thesis is structured as follows. The next Sections of this Chapter review precipitation

mechanisms in complex terrain as well as precipitation measurement techniques, and provide

the project rationale. Chapter 2 includes the description of the LAWR sensor and the relative

technological innovations made during the experiment. Chapter 3 presents the project site and

the analysis of radar visibility. The description of the measurement campaigns, of the nature of

possible LAWR errors and their relevance for the experiment are listed in Chapter 4. In order

to convert LAWR observations into precipitation rates it is necessary to perform the so called

radar conversion. The uniqueness of this radar installation, the highest fix X-band installation

in Europe, required the development of new conversion methodologies which are explained,

validated and applied in Chapter 5. Concluding remarks and outlook are given in Chapter 6.

1.1 Precipitation mechanisms in complex terrain

The space-time structure of precipitation in complex terrain is strongly modulated by orography,

mostly through the effect that mountains have on the vertical structure and stability of the

atmosphere and on water vapour fluxes (e.g., Smith, 1979). There are several mechanisms

which can lead to orographic precipitation in mountains, such as stable upslope ascent, blocking

of the air flow, downvalley flow induced by evaporative cooling, lee-side convergence, convection

triggered by solar heating or mechanical lifting above the level of free convection, and the

seeder-feeder mechanism (Roe, 2005). The upslope ascent mechanism is the most common

explanation why windward slopes and higher altitudes receive more precipitation than leeward

slopes, as wind-driven moist air masses are forced to rise over orographic barriers leading to

cooling, condensation, cloud formation and increased precipitation. Depending on the stability

of the atmosphere and wind speed, mountain ranges may also block and divert air flow, leading

to precipitation further windward where topography forces lifting of the moist air.

Observations of orographic precipitation support this view. For example, results from intensive

observation periods within the Mesoscale Alpine Programme (MAP6) generally confirm the

occurrence of the traditional upslope ascent mechanism, at the same time stressing the im-

portance of particular local conditions (e.g., Medina and Houze Jr, 2003; Rotunno and Houze,

2007). Indirect evidence is also provided by sharp changes in vegetation along mountain slopes,

and by studies showing that maximum precipitation intensities are registered upstream with

respect to the Alpine crest (e.g., Roe, 2005; Medina and Houze Jr, 2003). Most importantly it

6http://www.map.meteoswiss.ch

1.1. Precipitation mechanisms in complex terrain 5

has been demonstrated that upslope wind speed, its direction and Froude number (which indi-

cates the likelihood of blocking of the flow) can explain a large part of orographic precipitation

occurrence (e.g., Medina and Houze Jr, 2003; Neiman et al., 2009; Panziera and Germann,

2010). The orientation and shape of the mountain range also plays a role. For example, Ebtehaj

and Foufoula-Georgiou (2010) showed that the maximum precipitation intensity occurs when

the mesoscale wind direction is perpendicular to the mountain barrier, while Schneidereit and

Schaer (2000) showed that the shape of the Alps enhances precipitation in the concavity.

Spatial and temporal scales at which orographic precipitation takes place are also important.

Upslope precipitation enhancement is likely to be valid at a mountain range scale (∼50 kmwith relief higher than ∼1.5 km) (Roe, 2005). When moving from mesoscale to local scales,orography changes wind dynamics which in turn modifies the microphysical growth mechanisms

(Medina and Houze Jr, 2003). At smaller spatial scales the dynamical effects such as blocking,

convective instability and evaporative cooling influence precipitation growth by a series of

processes not yet well known. Moreover, at these scales the distribution of precipitation at the

ground is dominated by the drop size distribution and fall speed as well as the horizontal wind

speed. The effect of orography changes within the year. In warm seasons, the local effects of

thermally forced lifting on sun-facing slopes (convection) lead to cloud formation and triggering

of thunderstorms, which are not necessarily related to wind flow and to impinging moist air

mass. These local effects are less important in cold seasons when the spatial distribution of

precipitation is dominated by larger scale synoptic motion of stratiform clouds and blocking by

orography (e.g., Banta, 1990).

An example of such seasonal dependence of local orographic effect on precipitation patterns

is provided by Savina et al. (2011a), who recently investigated and quantified the seasonal

variation of the orographic influence on precipitation by means of Long-Term Persistence (LTP).

LTP is a characteristic of a continuous process and it evaluates the presence of long-term

memory. In analogy with the Hurst exponent (Hurst, 1951; Rodriguez-Iturbe et al., 1998;

Koutsoyiannis, 2010), high LTP indicates high repeatability and deterministicity of the process,

while low LTP is typical of a random time-uncorrelated process. The analysis was based on the

MeteoSwiss radar product NASS for a two year period within a section of the Swiss-Italian Alps

and showed that generally LTP is higher in winter than in summer. Warm season precipitation

with convective activity has lower values of LTP indicating more randomness in precipitation

deposition in time, while cold season precipitation with more long-lasting synoptic events and a

dominant fraction of solid precipitation has higher LTP indicating more structured deposition.

Similar results were presented in the study carried out by Egli and Jonas (2009), who analysed

the accumulation of snowfall based on snow depth point measurements across the Swiss Alps.

They found very similar LTP values (called β in Egli and Jonas, 2009) within six winter seasons.


Another evidence of differences between convective and stratiform precipitation comes, for

example, from variogram analyses of radar data, which show higher variability in convective

than in stratiform events (Germann and Joss, 2000), or from scaling analyses of high resolution

raingauge data, which find winter events to be smoother, less variable, and with stronger

autocorrelation (Molnar and Burlando, 2008). The relationship of the scaling exponent LTP

with topography may have a similar explanation.

The results of Savina et al. (2011a) indicated that LTP was negatively related to mean altitude

and surface variance in the warm season and positively in the cold season (Figure 6 and Table 2

in the paper). Stratiform precipitation in the cold season is driven by higher altitude wind fields

and the variance of the topographic height at high altitudes can accentuate the singularities in

the deposition process by acting as barriers to synoptic flow (Panziera and Germann, 2010),

thus leading to longer and better correlated events in space and a higher LTP. The suppression

of convection at high altitudes may also be due to more stable air masses in the cold season,

a process which was observed in frontal storms in the Southern Alps in New Zealand by Harris

et al. (1996) and Purdy et al. (2001). Conversely, convective precipitation in the warm season,

which is driven by thermally and orographically-forced lifting in the Alpine forelands at lower

altitudes, may become less localized and more random in space as moist air is moved towards

higher altitudes. Warm rain processes and a higher likelihood of atmospheric instability allow

convective events to develop and progress towards the Alpine barrier, thus leading to lower

LTP at high altitudes. These features were, for example, observed in storms in the Blue

Ridge Mountains, Virginia, USA, where warm rain and leeside orographic forcing led to more

intermittent precipitation fields as they progressed to the main ridge (Nykanen, 2003).

A typical synoptic mechanism of severe orographic precipitation in the Alps is shown in Fig-

ure 1.1 (after Yuh-Lang, 2005). If a low-pressure system occupies middle latitude on the North

of the Alps and a conditionally or potentially unstable (i.e., warm and moist) air-stream coming

from the Mediterranean Sea is impinging on the south side of the mountains, heavy precipi-

tation might be triggered along the windward side of the Alps, which in turn might generate

flooding on the Italian Alpine side (Yuh-Lang, 2005). Then, if part of the leeward Alpine side is

occupied by a quasistationary high-pressure ridge that slows the progress of the orographically

forced convective system over the threatened area, the heavy rainfall might trigger floods also

in the Central/North Swiss Alpine side (FOWG, 2005).

In such cases the availability of correct QPE would help to produce prompt and realiable

QPF which, in turn, would improve flood forecasting and thus the operation of warning and

protection systems.

1.2. Precipitation measurement techniques 7Klein

Matterhorn

Zurich

MilanPadana valley

A L P S

A L P S

30 km

Mat

ter v

alle

y

Zermatt

Aosta valley7°.72 E

45°.

95N

m a.s.l.

A L P S

Zurich

Milan

Po valley

7°.72 E

45°.

95N

120 km

120

km

50 100 km0

Figure 1.1: Schematic for the synoptic element leading to orographic rainfall across the Italian-Swiss Alps.

After Yuh-Lang (2005).

1.2 Precipitation measurement techniques

The two main sources of observations are raingauges and weather radars. The former is usually

considered more realistic - sometimes it is referred to as “ground truth”when compared to

radar measurements - but it gives only point observations, integrated on the raingauge orifice

area that typically measures 200 cm2. On the other hand, weather radars give a distributed

observation of precipitation over a large area (e.g., within 200 km range from the radar antenna)

while the precipitation is still in the atmosphere, falling, or even worse when it is not yet fully

formed. Weather radar maps have spatial resolutions ranging from 1 to 4 km2. Both sensors

typology might provide records up to 1 min resolution in time.

The mean distance between Alpine daily offline raingauges is about 10-15 km (Frei and Schär,

1998), i.e., about 1 station per 100 km2 (Schwarb, 2000). Despite the fact that this is a very

high density if compared to other raingauge networks, it is still too coarse to capture most of

the spatial signatures of orographic precipitation (Roe, 2005), particularly because of the high

sub-daily space-time variability. The latter cannot be captured, moreover, by the Alpine hourly

or sub-hourly online raingauge networks, which are much more coarse, especially above 2000 m

a.s.l. (Schwarb, 2000). Thus, even though under normal operational conditions raingauges are

reliable, their density is inadequate to measure small space-time characteristics of precipitation

in which we are interested (Roe, 2005). The best one can achieve to quantify the space-time

variability of precipitation in mountain areas is through the combination of the spatial extension

of weather radar observations with the higher accuracy of raingauge observations (Joss et al.,

1998; Todini and A, 2001; Mazzetti and Todini, 2004; Velasco-Forero et al., 2009).


1.2.1 Raingauge measurements

The general term raingauge refers to an instrument that, simply accumulating, by tipping-

buckets or with more innovative weighing technology, produces an integration in time of the

precipitation falling inside a circular orifice of size that might range between 200 and 400 cm2.

In addition to the calibration errors and the construction-related shortcomings, wind is likely

the physical variable that most affects measurements of both solid and liquid precipitation.

Wind-induced undercatch is generally considered to be the largest error and it has been quan-

tified in field experiments (for a review see, e.g., Sevruk, 1982), as well as by wind tunnel

testing and numerical modelling (e.g., Nespor and Sevruk, 1999). Based on such experiments,

methods for correcting measured liquid and solid precipitation were developed (e.g., Goodison

et al., 1998; Sevruk, 2004, 2005). Wind effects are particularly strong for solid precipitation,

especially for unshielded gauges (e.g., Yang et al., 1999, 2000; Sevruk, 2004, 2005).

In order to assess the performance of different types of gauges, the World Meteorological Orga-

nization (WMO) has organized several international intercomparisons of gauge performance for

liquid precipitation in the laboratory (e.g., Lanza et al., 2005; Lanza and Stagi, 2009a) and in

the field (e.g., Goodison et al., 1998; Sevruk et al., 2009; Lanza and Vuerich, 2009b). These

intercomparisons have highlighted the need to properly calibrate and correct tipping-bucket

gauges and deal with noise filtering in electronic weighing gauges for rainfall measurements

(Vuerich et al., 2009). On the other hand, other studies proved that the only way to reduce

the magnitude of random errors and instrumental bias is to couple at least two instruments

per observation point (e.g., Krajewski and Ciach, 2003).

Only a few studies analysed the uncertainty related to solid precipitation measurements with

standard can-type raingauges. In particular, snowfall measurement by tipping-bucket gauges

is particularly difficult. The solid precipitation must be melted before it can flow through the

funnel to the tipping mechanism and be recorded. Funnels of tipping-bucket gauges must be

heated and this leads to inevitable evaporation and wetting losses. It has been found that

these heating-related losses may be comparable or even larger than wind-induced undercatch

(e.g., Zweifel and Sevruk, 2002). Heating losses in weighing gauges are much smaller because

only the orifice rim is generally heated to prevent snow accumulation, and the weight of solid

precipitation is recorded directly. The process of melting the snow, together with the coarser

depth resolution of tipping-bucket gauges, also lead to a delay in recording snowfall (e.g.,

Macdonald and Pomeroy, 2007). This delay is most evident in the beginning of snowfall events

when the precipitation intensity is low. The delay depends on the snowfall intensity, the heating

system power, and the tip volume and sampling time resolution of the gauge.

The problem of measuring solid precipitation in the Alps has been recently reviewed by Savina

1.2. Precipitation measurement techniques 9

et al. (2011b). By means of an experiment conducted by IfU-ETHZ7 in Zermatt (Valais,

Switzerland) two years of solid precipitation recorded by a heated Tipping-Bucket Gauge (TB)8

and a heated Weighing Gauge (WG)9 (see Figure 1.2) located at the MeteoSwiss SMN10 site,

showed that heated TB underestimates at least by 23% the real solid precipitation. In addition,

the TB system revealed an average delay in recording the beginning of the solid precipitation

event of about 30 min. In order to overcome this problem, it is recommended to apply data

homogenization (Savina et al., 2011b) and integration in time. But this will inevitably reduce

the utility of datasets which nowadays is highly related to the temporal resolution.

Figure2

Figure3

Figure 1.2: (a) Delay caused by the different precipitation collection paths in TB and WG gauge. (b)

Evaporation from different heated surfaces shown in orange. The scheme is not to scale. After Savina

et al. (2011b).

1.2.2 Weather radar measurements

Weather Radar (RAdio Detection And Ranging) measure precipitation indirectly, by means of

energy backscattered from falling hydrometeors. This capability was noticed during the second

world war when hydrometeors perturbed normal visualization of target echo. Its potentiality

was quickly exploited and the first operative weather radar was developed for the US Army

by David Atlas (Atlas, 1990). The last decades were characterized by a quick evolution that

brought into operation many other weather radar features like the measure of Doppler velocity

spectrum and the emission of a dual polarized signal.

The big advantage of weather radars is that they can locate very precisely the position of falling

hydrometeors within a typical range of about 200 km from the antenna. Thus, the final output

of weather radars is a precipitation map with resolutions ranging between 1 and 4 km2 in space

7Institute of Environmental Engineering at the Swiss Federal Institute of Technology ETH Zurich8Lambrecht Tipping-Bucket Gauge, LTB in Savina et al. (2011b)9MPS Weighing Gauge, MPS in Savina et al. (2011b)

10Swiss Meteorological Network, SwissMetNet, www.meteoswiss.ch

www.meteoswiss.ch


and from 1 to 5 min in time. Today the most common weather radars operate at about 10 cm

(S-band), 5 cm (C-band) or 3 cm (X-band) wavelengths.

As mentioned before, radars observations are indirect measures of precipitation. Nowadays

weather radars can directly measure the phase of the sent and received electromagnetic waves,

the shape of the illuminated hydrometeors η and the backscattered power Pr. The latter is

then converted to radar reflectivity Z with Equation (1.1).

Pr =

(π3 · Pt · g2t · θh · θw · tp

1024 · λ2 · ln (2)

)· |K|

2 · Zr2

(1.1)

The content of the term in brackets, which includes the transmitted power Pt, the antenna

gain gt, the beam height θh and width θw, the time pulse length tp and the wavelength λ, is

only a function of the system and is therefore also called the radar constant Cr. The received

power is a function of η through the dielectric constant of the target, K (e.g., from 0.93 for

water to about 0.2 for snow), the range from the target r and the radar reflectivity factor Z.

The latter can be expressed as a function of the Drop Size Distribution (DSD) with

Z =∑sV

D6 (1.2)

where sV represents a unitary sampled volume expressed in m3 and D is the diameter of the

illuminated η in mm (Rinehart, 2004). For a specific radar (1.1) becomes:

Z =Pr · r2

|K|2 · Cr. (1.3)

Since Z might span several orders of magnitude, it is normally log-transformed with

dBZ = 10 · log (Z)⇔ Z = 10dBZ10 . (1.4)

Finally, R can be computed by means of the Marshall-Palmer Z−R equation (Marshall et al.,1947; Marshall and Palmer, 1948)

Z = a ·Rb , (1.5)

where the parameters a and b are a function of the DSD and the hydrometeor phase. Marshall

et al. (1947) proposed the use of a = 220 and b = 1.6 which, however, should change as a

function of precipitation type (Battan, 1973).

1.2. Precipitation measurement techniques 11

Radar can give incorrect measurements because of many reasons: beam shielding, ground

clutter, detection of the melting layer (i.e., bright-band), signal attenuation (particularly present

in X-band, as pointed out by Berne and Uijlenhoet, 2006), range-dependent errors like beam

broadening and weather overshooting, hardware instabilities, and difficult operating conditions,

especially in mountainous areas (Joss and Waldvogel, 1990; Germann et al., 2006b). But,

while the latter problems are somehow stable in time and quantifiable (e.g. bright-band),

and therefore they can be assumed to be limitations of a specific radar installation, any DSD

variation (e.g., Sempere-Torres et al., 1994) would generate errors in the Z−R conversion. Thelatter is the larger contribution to the uncertainty of apparently reliable radars (e.g., Zawadzki,

1984; Berenguer and Zawadzki, 2008).

The lack of knowledge of small scale precipitation processes does not allow real time adjust-

ments of the a and b parameters of the Z−R relation and the original Marshall-Palmer’s valuesare still commonly used (Lee and Zawadzki, 2005). It is very difficult to measure precisely the

DSD, and the Particles Size Distribution (PSD), (Berne, 2005), but considerable improvements

are ongoing. What was before observable only by very expensive video disdrometers today can

be estimated by much cheaper, and consequently more numerous in space, Particle Size Ve-

locity sensors (Jaffrain and Berne, 2011a; Jaffrain et al., 2011b). Schleiss et al. (2009) showed

that an improvement to the limitation given by point DSD observations is achievable by 2D

simulation of DSDs using multiplicative random functions. This method was found to be con-

sistent with ground observations for scales contained within 20 km. But any operative weather

radar would need real time DSD estimations corresponding to the hydrometeors illuminated

by the radar beam. This measure usually differs from what is observed at the ground (Wüest

et al., 2009). So, the common operative solution is to keep a and b constant with the hope

that by averaging observations in time (i.e., for the few scans which then are averaged in a

single time step) and space (i.e., within the sampled volume sV ) the deviation between real

and assumed DSD would be negligible. This is, however, not the case.

The main problem in assuming a and b as constant is that the DSD changes in space and

time by random time-correlated processes. Its time-organization, which is hardly visible in in-

stantaneous observations, becomes clear at longer integration times (e.g., 1-2 h), especially for

stratiform precipitation events (Berenguer and Zawadzki, 2008, 2009). Stratiform precipita-

tion is characterized by large hydrometeors mixing and its temporal integration would generate

high temporal correlation of DSD. On the other hand, convective precipitation causes less

mixing of hydrometeors, with a contained time-space correlation, and therefore is more easily

representable by standard a and b coefficients.

Last but not least, an additional source of uncertainty affecting weather radar is the variability

of the dielectric constant. It ranges between 0.93 for water and about 0.2 for snow, and it


might even change from radar to radar (Rinehart, 2004; Pedersen, 2009). As frequently hap-

pens, falling hydrometeors change phase and so what is observed aloft does not correspond

to what effectively reaches the ground. Even though there is not a clear solution, significant

improvements against both bright-band contamination and K variation were found by correct-

ing the Vertical Profile of Reflectivity (VPR) (Germann and Joss, 2002; Franco et al., 2006;

Bordoy et al., 2010).

An improvement of observations in complex terrain could be achieved using a network of cost-

effective local X-band radars (Joss and Germann, 2000). Nowadays X-band radars are widely

used to investigate local precipitation processes; their main advantages are a high power reso-

lution, a better weather-to-ground-clutter signal ratio in the Reyleigh region (Germann et al.,

2006b) and lower costs compared to C-band and S-band. Indeed, in 2003 the American National

Science Foundation formed the Collaborative Adaptive Sensing of the Atmosphere (CASA)11

with the aim of developing a dense network of small, low-cost X-band radars (e.g., Trabal and

Mclaughlin, 2007). These systems have already been exploited in urban environments, where

the knowledge of the space-time variability of rainfall is extremely useful in managing real-time

urban drainage systems (see e.g., Delrieu et al., 1997, 1999; Pedersen, 2009). As a matter of

fact, similar advantages might also be achieved in complex terrain. The deployment of a net-

work of local cost-effective X-band radars might be a solution to overcome mountain-induced

large-radar and raingauge limitations, acting as gap-fillers at the catchment (valley) scale. So

far there have been no investigations of such systems in mountain environments comparable

to the Alps, thus suggesting the need for this study.

1.3 Project rationale

The limitations outlined in the previous Sections point at a need for investigating other options

to measure and/or estimate precipitation with improved accuracy, particularly in mountainous

area. A good opportunity in this respect was offered by the interest of the cantonal authorities

of the Canton of Valais (Switzerland) in improving the precipitation monitoring system over

the Rhone river basin. This is part of the mountainous region in south-west Switzerland, which

in the last decades was hit by severe and extremely expensive floods. In particular, the Swiss

Federal Office for the Environment (FOEN) lists, as recent floods, the extreme events that

occurred in August 1987, September 1993, May 1999, October 2000, and August 200512.

Apart from the flood of 1999, which was related to the abundant snowfalls during the winter

1998-1999, the other floods were related to extreme precipitation events. The flood of 2000

11http://socc.caps.ou.edu/faq.html#Why_is_CASA_using_X-band_radars12http://www.bafu.admin.ch/hydrologie/01834/02041/index.html?lang=en

http://socc.caps.ou.edu/faq.html##Why_is_CASA_using_X-band_radarshttp://www.bafu.admin.ch/hydrologie/01834/02041/index.html?lang=en

1.3. Project rationale 13

was the worst: it had a return period above 200 years and it cost the lives of 16 persons in

Switzerland and 23 in Italy. In Switzerland alone, the damages amounted to 670 Million of Swiss

Francs13. The extreme rainfall occurring in 19-22 August 2005 was caused by a combination

of synoptic instability similar to the one described in Section 1.1 and in Figure 1.1.

Weather radar-based remote sensing is the only technique that allows to measure the space-time

distribution of precipitation to a detail needed to feed Numerical Weather Prediction models

(NWPs) and, pending the reliability of precipitation estimates, also distributed hydrological

models. The latter, in turn, generate valuable forecasts of catchment response to meteorolog-

ical forcings, which are used for flood prevention through early warning systems (see e.g., the

project MINERVE, that aims at peak flow management via a multireservoir system of existing

hydropower schemes, as outlined by Hernandez et al., 2010). However, as mentioned in the

previous Section, mountains decrease the quality of weather radar QPEs.

This is illustrated, for instance, by Figure 1.3, which shows the mean annual precipitation from

50 year spatially interpolated raingauge observations (panel a, source of data MeteoSwiss, after

Bordoy and Burlando, 2011) compared to a five year accumulation of the Swiss C-band radar

RAIN (panel b, after Molnar et al., 2011). The discrepancy between the two precipitation fields

in the region of Valais is high and consistent with the map of Swiss radar quality shown in

Figure 1.4 (MeteoSwiss, 2010). The Swiss C-band radars have poor visibility in part of Valais,

Valle d’Aosta, Grisons and part of the western Piedmont regions (Gabella et al., 2005). Because

of this, nowadays the best QPE in Valais are confined to interpolated raingauge observations,

which provide rough estimates of the distributed pattern needed for flood forecasting and

hydropower operation (Tobin et al., 2011).

RAINRAIN

7°E 10°E6°E 8°E 9°E

46°N

46.5°N

47°N

47.5°N

RhiresDRhiresD

250 500 1000 1500 2000 2500 3000 3500 4000

(b)

KM

Abis

Lema

La Dole

Abis

Lema

La Dole

[mm year-1]

(a)

Figure 1.3: Swiss raingauge RhiresD14 vs. radar composite RAIN (elaborated by Roger Bordoy and Athanasios

Paschalis, IfU-ETH Zurich)

13http://www.bafu.admin.ch/hydrologie/01834/02041/02045/index.html?lang=en

http://www.bafu.admin.ch/hydrologie/01834/02041/02045/index.html?lang=en


Figure 1.4: Swiss C-band radar data quality, from MeteoSwiss (2010).

In order to overcome this limitation, in the next years MeteoSwiss will install two new Doppler

C-band radars, which together with the renewal of the existing three weather radars will improve

precipitation estimation in the Swiss Alps (MeteoSwiss, 2010). However, this upgrade will not

prevent that, even at a short range, orography will cause beam blocking, related overshooting

and ground clutter contamination.

An improvement of observations and QPFs in complex terrain such as that of Valais can be

made by using, as a complement to large-scale conventional weather radars, a network of

local-area X-band weather radars that provide local observations at finer space-time resolution.

Accordingly, this provides the rationale for this study, which describes methods and results of a

pilot experiment aiming at the assessment of the performance of a single cost-effective X-band

Local Area Weather Radar (LAWR) in the Alpine environment.

The objective of the study was twofold. On the one hand, it aimed at the development and

assessment of a cost-effective LAWR to be used in orographically complex regions as an oper-

ational tool to complement existing measurement techniques (C-band radars and raingauges).

On the other hand it aimed at testing whether the same radar could lead to better under-

standing of the nature of the precipitation process, e.g. identifying any possible dependence of

precipitation on mountain terrain.

The experiment started on August 2007 and it ended in October 2011. During this period

a LAWR was installed at the summit of the Kl. Matterhorn at 3883 m a.s.l. (Figure 1.5,

Canton of Valais, Switzerland). This was the first time that a cost-effective X-band radar

was installed at such an elevation and could be tested in operation-line conditions, which, in

addition, provided good visibility in almost all directions above the Alpine chain.

1.3. Project rationale 15

A L P S

A L P S

Zurich

Milan

Po valleyPo valley

7°.72 E

45°.9

5N

120 km

120

km

50 100 km0

Lema

Albis

La Dole

KM

KM LAWR

Figure 1.5: LAWR site and the area of investigation within the Swiss-Italian Alps. The red dot indicates

the position of the LAWR (45.95N, 7.72E, 3883 m a.s.l.). The three stars indicate the position of the Swiss

C-band radars (Albis, 47.28N, 8.51E, 928 m a.s.l.; La Dole, 46.43N, 6.10E, 1680 m a.s.l.; Lema, 46.04N,

8.83E, 1625 m a.s.l.). The rectangle indicates the domain of the LAWR (120 x 120 km) while the blue circle

indicates a 30 km range. The right panel shows the LAWR installed during the experiment.

Chapter 2

Local Area Weather Radar (LAWR)

2.1 General

The X-band radar used in this PhD project is a ship-born radar adapted for precipitation

detection by the DHI (Jensen, 2002). It is commonly called Local Area Weather Radar (LAWR)

and at the time of the experiment there were about 30 units installed worldwide. The LAWR was

installed on the basis of a collaboration between the Chair of Hydrology and Water Resources

Management of the Institute of Environmental Engineering at the Swiss Federal Institute of

Technology ETH Zurich (IfU-ETH Zurich) and Canton Valais. It was located on the summit

of the Kl. Matterhorn (3883 m a.s.l.) and it was the highest weather radar in Europe. The

field of observation covered a circle of 60 km radius centered on the Kl. Matterhorn (see

Figure 1.5).

The next Sections of this Chapter describe the standard LAWR system and the hardware and

software improvements needed to support the severe Alpine environmental conditions and the

orography.

2.2 Standard LAWR system

2.2.1 Hardware

The standard LAWR system is based on the ship-born Furuno FR1525 MARK3 radar (DHI,

2009; Furuno, 2002). It includes a 2.5 m long slotted waveguide array antenna, a Furuno

FR1525 MARK3 radar monitor, one Personal Computer (PC) for data processing still working

in the DOS R© operating system and one PC for data storage and communication based on the

17

18 Chapter 2. Local Area Weather Radar (LAWR)

Windows R© operating system (Jensen, 2002; Pedersen, 2009). DHI included a self-developed

A/D converter, which converts the raw analogical signal from the LAWR receiver (i.e., ranging

from 0 to -9 V) into a digital signal with 10 bit resolution. The receiver of the LAWR is

logarithmic.

The LAWR has a 25 kW peak power and the non-coherent transceiver emits electromagnetic

waves in the X-band. The wave frequency, f , is 9.41 GHz and the corresponding wave length

in space, λ, is about 3 cm and in time, τ , is about 0.1 ns (τ≈f−1).

λ =c

f(2.1)

where c ≈ 3· 108 m s−1 is the light speed in a vacuum.

The LAWR has a fixed horizontal scanning elevation (γ = 0 deg) and the antenna scans 360

directions per rotation (SDR= 360). The antenna performs 24 rotations per minute (ω=24

rpm). Beam height, θh, and width, θw, at 3dB decay are assumed to measure respectively

20 deg and 0.95 deg (see Figure 3.2). The LAWR has a Pulse Repetition Frequency, PRF , of

600 Hz. Table 2.1 summarizes these and other LAWR characteristics.

Table 2.1: LAWR characteristics, after DHI (2009).

Radar type X-band

Frequency (f) 9410 ± 30 MHzWavelength (λ) 3 cm

Period (τ) 0.1 ns

Beam height (θh) 20 deg

Beam width (θw) 0.95 deg

Scanning elevation (γ) 0 deg (horizontal)

Antenna rotation rate (ω) 24 rpm

Radar scanned direction per rota-

tion (SDR)

360

Pulse Repetition Frequency (PRF ) 600 Hz

Max ranging (rmax) 60 km

2.2. Standard LAWR system 19

Measure formation strategy – flowchart

Attenuation Correction (AC)

VolumeCorrection (VC)

Grass correction

Primary cluttermap subtraction

bins formation

Measure consolidation

Every 5 min.

24 scan per minute

Spatial interp. on Cart. grid, median filter app.

Single scanline(8000 samples)

Storage of files *.L00 and *.V00 in PC com.

Output (*.L00) and *.V00

Output (*.P00’s)

Storage of files *.P00 with different grid res.

Application of:Secondary clutter map,cutoff and cutoff low.

From counts to precipitation radar conversion

Rainfall intensity maps

(a)

(b)

(c)

(d)Legend

PC Communicator (Windows machine)

PC Processor (DOS machine)

Remote machine

adjustments

measure formation

main steps

LAN / internet internal step in time

Figure 2.1: Flow chart of LAWR measures formation. Division of the operation in three main block represen-

tative of the three different machines in which they are performed.

2.2.2 Emission, reception and signal sampling

LAWR observations result from laborious data processing that merges single 1D scanline obser-

vations into 2D polar and Cartesian fields. The data processing is schematized in Figure 2.1.

Each scan can be divided in three phases. First, the LAWR transmitter emits a pulse for a fixed

time duration, tp, equal to 1.2 µs. This corresponds to a space length, Lp, equal to 360 m.

Second, the receiver waits for the backscattered power within a fixed sampling time, ts, of

0.4 ms. The sampling frequency, fs, of the A/D converter is 20 MHz so that the number of

samples, Ns, produced by each scan is

Ns = ts · fs = 8000 . (2.2)

Hence, each sample is a 10 bit count representative of 7.5 m in range. The third phase consists

of the mechanical rotation to align the antenna along the next scanline. Its length is called

mechanical time, tm, and it measures 6.54 ms. Thus, tp, ts and tm correspond to about 0.02%,

5.76% and 94.22% of the whole time spent on one scanline. Figure 2.2 represents these three


times on a pie chart.

By imposing ts, the maximum range of the LAWR, rmax, becomes fixed and more specifically

is equal to

rmax =ts · c

2= 60 km . (2.3)

The factor 2 in the denominator of (2.3) accounts for for the travelling time from the antenna

to the target and back.

Thus, once the system registers a single scanline made up of 8000 samples - see operation (a)

in Figure 2.1 - it proceeds to the next steps needed for data adjustments and consolidation as

shown in Figure 2.1.

ts tp tmts tp tm

ts tp t_mecc

1 2 3ts tptm

12

3tm tstp

tm = 94.23%

ts = 5.76%

tp = 0.01%

Figure 2.2: Timing of the main LAWR phases, tp, ts and tm.

2.2.3 Attenuation correction

Microwaves are subject to attenuation because of absorption and scattering due to atmospheric

gases, clouds and precipitation. Attenuation due to gases affects all common wave lengths , i.e.,

3 cm of X-band (characterizing the LAWR), 5 cm of C-band (characterizing e.g., MeteoSwiss

radars) and 10 cm of S-band (characterizing e.g., NEXRAD radars). Gases attenuate waves

proportionally to the square of their pressure, and the magnitude ranges between 4 dB for

short λ to 2 dB for long λ. Usually such attenuation is easily compensated for by means of a

correction, which is a function of range and season. The standard LAWR does not account for

this correction.

The magnitude of the attenuation caused by precipitation is a function of number, size and

shape of falling particles. Ice particles attenuate less than water particles, due to lower ab-

sorption. Attenuation from precipitation is very high for X-band and it is almost negligible for

S-band. The use of C-band (5 cm) is acceptable for long range too if applied in mid-latitude

(WMO, 2008).


Table 2.2 provides values of specific attenuation (Satt) found by Burrows and Attwood (1949)

and valid for homogeneous precipitation rates at 18◦C. Figure 2.3 shows the values relevant to

precipitation rates up to 10 mm h−1. Assuming a moderate precipitation rate R = 6 mm h−1

and a one-way wet-path-length, wpl, of 10 km (i.e., a very likely situation during stratiform

precipitation) the specific attenuation would be 0.002 dB km−1, 0.0118 dB km−1 and 0.0874

dB km−1 respectively. Multiplying these values for the total wpl (i.e., two time the one-way

path) and converting from dB to percent, the cumulated attenuation of the weather echo back

to the antenna can be computed as

catt[%] = 100−1

10Satt·10−1·2·wpl· 100 , (2.4)

which gives a value of 0.92% for λ= 10 cm, 5.29% for λ= 5 cm and 33.13% for λ= 3.2 cm.

The resulting magnitude of attenuation of X-band radar signals is very high and indeed, it

represents the main limitation of such systems. Despite this, as mentioned above, there is high

interest in developing networks of 3 cm X-band weather radar to be used at short range (WMO,

2008). Investigating possible strategies to correct for such a high attenuation is therefore a key

issue in making the development of such networks really attractive for operational purposes.

DHI suggests applying the following empirical correction factor, Fatt (DHI, 2009):

Fatt(rk) = 1 +

αk∑i=0

Si

8000 · C1k ∈ [1...8000] (2.5)

Where Fatt(rk) is the forward correction factor for the kth sample and it is a function of the

signal intensities encountered along the path antenna (k = 0) – sample kth, Si is the received

signal for the kth sample and the coefficients α and C1 are empirical, with typical values equal

to 1.5 and 200 respectively (DHI, 2009). For a hypothetical wet-path wpl= rmax = 60 km and

a constant value of S = Sc, at k = 8000, the Fatt(rk=8000) is equal to 1 +Sc·(α/C1). Thus,Fatt would range between 1 (when Sc = 0 count) and 8.60 (when Sc = 1024 counts).

This kind of attenuation correction neither account for the variation in the hardware calibration,

nor for the real precipitation ratio. In other words, the inherent instability of this forward

attenuation correction can lead to misleading results, which might be worse than what is

achievable with a simpler raingauge correction (Hitschfeld and Bordan, 1954).


0 2 4 6 8 10

0.00040.00080.0016

0.0080.004

0.0160.0320.064

0.120.24

R [mm h-1]

Atte

nuat

ion

[dB

km

-1]

10 cm 5 cm 3.2 cm

Figure 2.3: Values of specific attenuation [dB km−1] for S-band (λ= 10 cm), C-band (λ= 5 cm) and X-band

(λ= 3.2 cm) for different precipitation rates (R) ranging from light to intense.

Table 2.2: One-way specific attenuation at 18◦C. R is the precipitation rate in mm h−1. After Burrows and

Attwood (1949); source WMO (2008)

Wavelength [cm] Relation dB km−1

10 (S-band) 0.000343 · R0.97

5 (C-band) 0.0018 · R1.05

3.2 (X-band, e.g., LAWR) 0.01 · R1.21

2.2.4 Volume correction

The radar-based electromagnetic waves propagate away from the antenna. It is assumed that

they homogeneously fill a volume called the“pulse volume”, hereafter indicated with pV . Its

height and width are a function of the range and can be approximated as

Hpv(r) ≈ r · tan (θh) = 0.364 · r (2.6a)Wpv(r) ≈ r · tan (θw) = 0.0166 · r (2.6b)

The depth is fixed by the radial resolution and, for this stage of LAWR data processing, it is

given by fs, thus equalling 7.5 m in range. The resulting pV at r = rmax is about 21.8 km

high, 1 km wide and 7.5 m deep, thus measuring about 163.5·106 m3. Figure 2.4 shows thepV within the LAWR beam.

Because of the large, range-dependent, pV broadening it is likely to have spatial gradient of the


portion of pV filled by η, which in turn would result in incorrect spatial precipitation patterns

(e.g., see Figure 2.5). Hence, a range-dependent correction must be implemented. DHI (2009)

called this correction “volume correction”and it is given by the volume correction factor FV C(DHI, 2009)

FV C(k) = C2 · er·C3 k ∈ [1...8000] , (2.7)

where C2 and C3 are empirical parameters that are estimated by assuming homogeneity of

the radar coverage area over an accumulation period of a few months (Pedersen et al., 2010).

Usually it is assumed C2 = 1 and C3 = 0.03 (DHI, 2009).

The approach is very simplistic: precipitation is a process that clusters in space and time

(Austin and Houze, 1972; Jameson and Kostinski, 2001) and it is CLEARLY not homogeneous

in space, especially when precipitation is influenced by orography (Savina et al., 2011a). In

addition, in the context of applications in complex terrain, the sampled volume is not only a

function of the range, but it is also highly dependent on the beam-blocking and therefore a

function of the orography. This issue will be discussed and addressed in following Chapters.

pV

r (or k)

θhθw

beam

Hpv(r)

7.5 mWpv(r)

LAWRLAWR

r (or k)

θhθw

beam

Hpv(r)

7.5 mWpv(r)

LAWRLAWR

pV

Figure 2.4: Scheme of the LAWR beam, light grey, and theoretical Pulse Volume (pV ).

2.2.5 Radar noise and clear air echoes

It happens often that, even though the LAWR waves do not intercept weather echoes, the

output shows minor echoes due to receiver noise and clear air echoes (WMO, 2008). The

receiver noise is a small additive error mainly due to the thermal motion of electrons in resistive

hardware components. The receiver noise is proportional to the bandwidth and generally small

for X-band radars. Clear air echo is often due to a strong gradient of refractive index in the

atmosphere, insects, birds, and any other flying objects such as airplanes, etc.

The standard version of the LAWR accounts for this noise by the so called “grass correc-

tion”(DHI, 2009), which is applied just after the attenuation and volume corrections. It consists


Signal Processing

15

5.2 Volume Correction

The relatively large vertical opening angle of 10° results in a rapid increasing beam volume with range as illustrated in Figure 5.3. The consequence of this is that a small number of drops in the sample volume at very close range, where the beam volume is very small, is most often observed, while the same number of drops at further ranges will result in a value below the cut-off as result of the integration over a larger volume.

Figure 5.3 Illustration of the beam filling issue which is handled by the volume correction.

The volume correction is applied to the attenuation corrected signal Zrin order to get the adjusted signal Zrv:

32egv CrexpC

1ZZ (5.2)

where:: Volume corrected reflectivity at range r Adjusted reflectivity at range r from eq. 5.1

: range : Empirical constants that are location dependent.

Initial value guess: 1 and -0.03

The volume correction is constraint to a maximum correction value of 4 in order to avoid over correction. The applied volume correction can be seen on the GUI.

5.3 Noise cut-off

There are two parameters for adjusting the noise cut-off level, both are given in the radar.cfg file. The Grass parameter should be adjusted to

Figure 2.5: LAWR pV growth in case of total blockage of the lower beam fraction. After DHI (2009).

of a constant filtering which uses two parameters, the “Grass”and the “Cut off min”. These are

tuned to have, in a dry situation, a clean map plus some small “spiders”(DHI, 2009). Typical

values for Grass and Cut off min are 6 counts and 8 counts respectively.

2.2.6 Ground clutter and primary clutter map subtraction

Fix opaque objects such as buildings and terrain generate radar echoes called Ground Clutter

(GC). In principle, with the new radar technologies, the ground clutter is easily detected and

eliminated from further data uses. Doppler-radar clutter recognition is based on the analysis

of the Doppler spectrum, i.e., the spectrum of all detected Doppler velocity (WMO, 2008).

Objects generating GC do not move and therefore have null Doppler speed. Thus, once GC

is localized, the relative reflectivity values will be ruled out and replaced by values measured

at a higher Plan Position Indicator (PPI) angle or eventually interpolated from clutter-free

neighboring cells (Joss et al., 1998).

Because the LAWR is a non-coherent radar, it does not allow phase measurements that can be

used to compute Doppler velocity. Accordingly, the standard approach proposed by DHI for GC

detection and elimination is based on the use of static clutter maps. A clutter map is derived

from radar observations made during dry weather condition. The main assumption here is that

the reflectivity of the ground (or any other immobile object illuminated by the radar beam)

and the power emitted by the radar are constant in time. These assumptions are realistic if a

new clutter map is generated often enough to capture both the variation of ground reflectivity,

which can be caused by snow deposition/melt, change in trees foliage, ground wetting, and the

variation of the power emitted, caused for instance by magnetron decay (see Section 5.2.1).

The clutter map subtraction is applied on the route (i.e., in real time, before storing the data)


at each single scanline of the analogical LAWR (0 – -9 V) video signal (see Figure 2.1). Finally,

the DHI method labels as precipitation any signal value greater than the local GC given by the

clutter map.

the use of a cost-effective x-band weather radar in alpine region maurizio savina · 2012-03-14 ·...

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