Quantifying solar radiation at the earth surface with meteorological and satellite data

QUANTIFYING SOLAR RADIATION
AT THE EARTH SURFACE
WITH METEOROLOGICAL AND SATELLITE DATA

Jedrzej
S. Bojanowski
֒


Examining committee:
Prof. dr. ing. W. Verhoef

University of Twente

Prof. dr. Z. Su

University of Twente

Prof. dr. K. Dabrowska-Zieli´
nska
֒
Prof. dr. G. de Leeuw

Institute of Geodesy and Cartography

Dr. B. Baruth

European Commission Joint Research Centre

Finnish Meteorological Institute

ITC dissertation number 242
ITC, P.O. Box 217, 7500 AE Enschede, The Netherlands
ISBN: 978-90-6164-371-5
Cover design by Zosia Dzier˙zawska
Printed by ITC Printing Department
Copyright c 2013 by Jedrzej
S. Bojanowski
֒
This book was composed and typeset using LATEX by Jedrzej
S. Bojanowski
֒


QUANTIFYING SOLAR RADIATION
AT THE EARTH SURFACE
WITH METEOROLOGICAL AND SATELLITE DATA

DISSERTATION

to obtain
the degree of doctor at the University of Twente,
on the authority of the rector magnificus,
prof. dr. H. Brinksma,
on account of the decision of the graduation committee,
to be publicly defended
on Wednesday 15 January 2014 at 14.45 hrs

by
Jedrzej
Stanislaw Bojanowski

֒
born on 7 February 1982
in Warsaw, Poland


This dissertation is approved by
Prof. dr. A. K. Skidmore, promoter
Dr. ir. A. Vrieling, assistant promoter


To Zosia,
my family and friends



Acknowledgements

One could imagine that carrying out a PhD research is but an intellectual challenge,
backed by a good deal of hard work, and that what one learns during the PhD
project is specialized knowledge of a relatively narrow discipline. This was my
conviction, at least, when I decided to start a PhD study funded by the European
Commission’s Joint Research Centre and academically supervised by the Faculty
of Geo-Information Science and Earth Observation (ITC) of the University of
Twente. Four years later, as I am writing these acknowledgements, I know that
writing a PhD is a truly transformative experience; to such an extent that I feel
a different person now. For this transformation, I am indebted to all the great,
knowledgeable and kind people I was honoured to work with during these last four
years in the Netherlands, Italy and Switzerland.
First and foremost, my deepest appreciation goes to my supervisors from ITC:
Andrew Skidmore and Anton Vrieling. If someone would ask me to describe perfect

supervision, I would simply explain the way how Andrew and Anton supervised
my PhD project. I am proud and grateful for having had the possibility to work
with them, and to learn from them. Moreover, I truly hope that I will be able to
employ a similar professionalism while developing my own research career. I would
look forward to continue working with Andrew and Anton in the future, since it
was not only scientifically exciting, but also immensely satisfying on a personal
level.
I owe my gratitude to Andrew for all his enthusiasm and help when, many
years before I started this PhD thesis, we examined opportunities for carrying out
a PhD at ITC. Working under Andrew’s supervision had for long been a dream
start of a research career for me. Consequently I was very happy when eventually
we found an opportunity to realize this. During my PhD project, Andrew always
watched over me, making sure that my research was going in the right direction;

vii


viii

steering me if necessary, but usually not directly and explicitly. I often needed
quite some time and reflection to value his advice, and to realize how apt it was.
I truly appreciated this stimulating way of supervision: the kind without ready,
final answers, but where I was expected to find a solution myself.
I will always be grateful to Anton for the tremendous work he put in writing
research papers with me. He revised each paper several times and patiently taught
me how to present my results properly. It is from him that I learned conscientiousness and attention to detail, two things I cannot imagine my research work
without today. I would also like to thank Anton for rescuing me, often perhaps
unknowingly, in moments of despair, when I was convinced that my PhD project
was heading in the wrong direction.
The other persons at ITC that I wish to thank are Kees de Bie and Valentijn

Venus, for their kind help in defining the research plan of my PhD project, and
Esther Hondebrink-Lopez and Loes Colenbrander for their outstanding help with
the formalities during the project, and in the preparation of the final thesis.
I am indebted to Bettina Baruth for giving me the opportunity to join the
AGRI4CAST team at the Joint Research Centre. Bettina dedicated a lot of her
time to me at the beginning of the PhD project, and helped me in defining the
research goals that formed the basis of my project. I truly appreciate her readiness
to share knowledge and expertise with me. I hope that the results of my PhD
project will prove beneficial to the AGRI4CAST team.
I would like to thank Marcello Donatelli for the discussions we had; they were
not limited to solar radiation modelling, but touched on a lot of scientific issues
in general. Our discussions were extremely inspiring and stimulating for me, and
resulted in the joint research paper that is presented as Chapter 2 of this thesis.
Working with Marcello was not only enlightening, but also very enjoyable.
My work in the Joint Research Centre was made pleasant in large part due to
my excellent colleagues from the AGRI4CAST team. A special thanks to Gregory
Duveiller, Lorenzo Seguini, and my office-mate Andrea Maiorano, for scientific
discussions, their support, and patience in listening to my thoughts and complaints.
I am grateful to Allard de Wit and Gerbert Roerink for the collaboration at the
beginning of my project. The conclusions of the joint paper (Chapter 3) prepared
a solid ground for my further research.
I would like to express a special word of thanks to J¨org Trentmann and Christine
Traeger-Chatterjee for introducing me in the CM SAF community. Our discussions
during the CM SAF meetings definitely enriched my thesis and, moreover, assured
me that satellite climatology is a discipline I want to explore further (as I am
currently doing).


ix

I also express my sincere gratitude to Katarzyna Dabrowska-Zieli´
nska, a member
֒
of the examining committee of this thesis. Although she was not involved in my
PhD project, she provided a great contribution to starting up my research career,
without which I would have never achieved what I have.
I am grateful for all the support I received from Heike Kunz, Reto St¨ockli and
Christof Appenzeller, as well as from all my esteemed colleagues at MeteoSwiss
during the last months of my PhD project. Finishing the thesis was a tough period
for me, and I truly appreciate the encouragement they all gave me.
Regrettably, I cannot acknowledge by name all my fantastic friends in Italy,
because the list would not fit in here. Particular appreciation goes out to my
friends from the volleyball and basketball teams: Pallavolo Ispra, Ispra Lakers and
I Trigliceridi. They all know that without training several times per week, I would
not have found the energy and stamina to proceed with my thesis.
I will be forever indebted to my beloved friends in Warsaw, whom I had left when
I decided to study abroad, and who make Warsaw a place that I am determined to
come back to some day. Regardless of how long and of how far away from Warsaw
I am, I always feel their support and friendship, and it motivates me to carry on
with what I am doing.
Lastly, from the bottom of my heart, I would like to thank my whole family
for all their love and encouragement: especially both my parents, who raised me
giving me work ethic and humility, and supported me all my life with remarkable
steadfastness. My mother, whose moral support has been indispensable. My father,
who drove a truck for thousands of kilometres to help me relocate between Poland,
Italy, and Switzerland. My brother Michal, who shared with me his own PhD
experience, and cheered me up when I got lost in my research. My grandmother
Konstancja, professor of biochemistry, whose interest and academic advice have
been much appreciated. My aunt Agnieszka, who taught me mathematics and
(perhaps more importantly) analytical thinking. And most of all my loving wife

Zosia, to whom I dedicate this book, who is the best person in the world, and who
makes every place where we live feel like home.

Z¨
urich, October 2013



Contents

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1

1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2 Solar radiation data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1
3

1.2.1 Measuring solar radiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4

1.2.2 Empirical models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2.3 Physically-based models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5

6

1.2.4 Satellite observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3 Scope and objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7
8

1.4 Outline of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2

Auto-calibration of solar radiation models . . . . . . . . . . . . . . . . . . . . . 13
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.1 Data from weather stations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.2 Meteosat Second Generation data . . . . . . . . . . . . . . . . . . . . . . . 15
2.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.3.1 Temperature-based solar radiation models . . . . . . . . . . . . . . . . 17
2.3.2 Clear-sky transmissivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.3.3 Auto-calibration procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.3.4 Evaluation of the auto-calibrated models . . . . . . . . . . . . . . . . . 22
2.3.5 Simulation of evapotranspiration . . . . . . . . . . . . . . . . . . . . . . . . 23
2.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.4.1 Evapotranspiration simulation . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

xi

xii

3

Contents

Evaluation of MSG-derived solar radiation . . . . . . . . . . . . . . . . . . . . . 37
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.2 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.2.1 CarboEurope flux tower radiation measurements . . . . . . . . . . 40
3.2.2 Weather stations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.2.3 MCYFS gridded global radiation estimates . . . . . . . . . . . . . . . 42
3.2.4 ERA-Interim radiation product . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.2.5 DSSF derived from MSG by the LSA-SAF . . . . . . . . . . . . . . . . 44
3.3 Validation and intercomparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.3.1 Validation with observed radiation from CarboEurope . . . . . 46
3.3.2 Comparison with observed radiation from operational
weather stations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.3.3 Validation and intercomparison of DSSF, MCYFS and
ECMWF products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.3.4 Trend analysis of DSSF data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.3.5 Impact on simulated crop yields . . . . . . . . . . . . . . . . . . . . . . . . . 56
3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
3.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

4

Calibration of solar radiation models using MSG data . . . . . . . . . 65
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.2 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

4.2.1 Surface solar radiation from MSG . . . . . . . . . . . . . . . . . . . . . . . 67
4.2.2 Selection of weather stations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3.1 Estimating solar radiation from sunshine hours . . . . . . . . . . . .
4.3.2 Estimating solar radiation from cloud coverage . . . . . . . . . . . .
4.3.3 Estimating solar radiation from air temperature . . . . . . . . . . .
4.3.4 Calibration of the models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

69
69
70
70
71

4.3.5 Spatial interpolation of model coefficients . . . . . . . . . . . . . . . . . 71
4.3.6 Evaluation of solar radiation model coefficients . . . . . . . . . . . . 72
4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.4.1 Evaluation of the models employing Meteosat Second
Generation-based calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.4.2 Interpolation of model coefficients . . . . . . . . . . . . . . . . . . . . . . . 77
4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
4.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83


Contents

5

xiii

Comparison of solar radiation data sources . . . . . . . . . . . . . . . . . . . . 85
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
5.2 Solar radiation datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
5.2.1 The CM-SAF’s Meteosat First Generation data . . . . . . . . . . .
5.2.2 The LSA-SAF’s Meteosat Second Generation data . . . . . . . . .
5.2.3 ERA-Interim reanalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2.4 Interpolated weather station data . . . . . . . . . . . . . . . . . . . . . . .
5.2.5 Ground measurements used for validation . . . . . . . . . . . . . . . .
5.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

90
90
91
91
93
94

5.3.1 Comparison with ground measurements . . . . . . . . . . . . . . . . . . 94
5.3.2 Comparison of the solar radiation datasets . . . . . . . . . . . . . . . . 95
5.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
5.4.1 Comparison with ground measurements . . . . . . . . . . . . . . . . . . 98
5.4.2 Comparison of the solar radiation datasets . . . . . . . . . . . . . . . . 104
5.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
5.5.1 Quality of solar radiation measurements . . . . . . . . . . . . . . . . . . 110
5.5.2 Concatenating satellite-based products . . . . . . . . . . . . . . . . . . . 112
5.5.3 Backup solution for the merged satellite-based dataset . . . . . 114
5.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
6

Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
6.2 Improvements in solar radiation modelling . . . . . . . . . . . . . . . . . . . . . . 120
6.3 Accuracy of satellite-derived solar radiation . . . . . . . . . . . . . . . . . . . . . 122
6.4 Merging solar radiation datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
6.5 Key practical outcomes of this thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
6.6 Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

A

Performance statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
Samenvatting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
Curriculum vitae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
ITC Dissertations Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151



List of Tables

2.1

Performance of the Bristow and Campbell, and Hargreaves solar

2.2

radiation models for the ground- and auto-calibration methods . . . . . 25
Performance of evapotranspiration estimates from the
Penman-Monteith equation depending on solar radiation input data

30

3.1

Data sources for daily solar radiation in Europe . . . . . . . . . . . . . . . . . . 39

3.2

Characteristics of CarboEurope flux tower sites . . . . . . . . . . . . . . . . . . 41

3.3

Error statistics of DSSF solar radiation measured against
CarboEurope measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

3.4

Error statistics of all solar radiation products measured against
CarboEurope data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

3.5

Statistical analysis of global radiation data from MCYFS, DSSF

3.6

and ECMWF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
Seasonal Kendall test on the dekadal DSSF global radiation estimates 56

4.1

Performance of the solar radiation models depending on the

4.2

calibration method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
Significance of the differences in RRMSE of the ˚
Angstr¨om-Prescott,
Supit-Van Kappel, and Hargreaves models depending on the
calibration method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

5.1

Data sources for daily solar radiation in Europe . . . . . . . . . . . . . . . . . . 96

5.2

Performance statistics of SIS, DSSF and ERA-Interim solar

5.3

radiation calculated against measured solar radiation . . . . . . . . . . . . . 100
Comparison between SIS, DSSF, ERA-Interim and JRC-MARS
solar radiation estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

xv

List of Figures

1.1

Schematic diagram of the global mean energy balance of the Earth .

3

1.2
1.3

Crop growth processes simulated by WOFOST . . . . . . . . . . . . . . . . . . .
Geographic distribution of potential climatic constraints to plant

4

growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5

2.1

Flowchart of the auto-calibration method . . . . . . . . . . . . . . . . . . . . . . . . 21

2.2

Performance of the Bristow and Campbell model for ground- and

2.3

auto-calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Performance of the Hargreaves model for ground- and auto-calibration 27

2.4

Map of performance statistics of the Bristow and Campbell model
for ground- and auto-calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.5

Map of performance statistics of the Hargreaves model for ground-

2.6

and auto-calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Clear-sky transmissivity map derived from MSG . . . . . . . . . . . . . . . . . 30

2.7

Comparison of solar radiation estimates from the Bristow and

2.8

Campbell model and measured solar radiation . . . . . . . . . . . . . . . . . . . 31
Comparison of solar radiation estimates from the Hargreaves
model against measured solar radiation . . . . . . . . . . . . . . . . . . . . . . . . . 32

3.1
3.2

Overview of the study area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
LSA-SAF distribution regions for MSG-derived products . . . . . . . . . . 45

3.3

Validation of DSSF against CarboEurope in situ measurements . . . . 47

3.4

Time series of CarboEurope and DSSF solar radiation for Cabauw . . 48

3.5

Spatial distribution of RMSE between DSSF and observed

3.6

radiation at operational weather stations . . . . . . . . . . . . . . . . . . . . . . . . 50
Box plot of MBE and RMSE between observed and DSSF-estimated
radiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
xvii


xviii

List of Figures

3.7

Temporal evolution of the performance of DSSF measured against

observed radiation at operational weather stations . . . . . . . . . . . . . . . . 52

3.8

Annual average global radiation estimated by three products . . . . . . . 54

3.9

Differences in annual average global radiation estimates between
MCYFS, DSSF and ECMWF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

3.10 Potential total above-ground biomass of maize as calculated by
MCYFS using standard MCYFS and DSSF global radiation . . . . . . . 58
3.11 Water-limited total above-ground biomass of maize as calculated
by MCYFS using standard MCYFS and DSSF global radiation . . . . 59
4.1

Spatial distribution of differences in RRMSE of the ˚
Angstr¨om-

4.2

Prescott, Supit-Van Kappel and Hargreaves models depending on
the calibration method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
Box plot of the MBE and RRMSE of the of ˚
Angstr¨om-Prescott,

4.4

Supit-Van Kappel and Hargreaves models . . . . . . . . . . . . . . . . . . . . . . . 78

˚
Interpolated Angstr¨
om-Prescott model coefficients . . . . . . . . . . . . . . . . 79
Interpolated Supit-Van Kappel model coefficients . . . . . . . . . . . . . . . . . 80

4.5

Interpolated Hargreaves model coefficients . . . . . . . . . . . . . . . . . . . . . . . 81

5.1

Weather stations from the JRC-MARS database . . . . . . . . . . . . . . . . . 92

5.2

Number of weather stations reporting solar radiation . . . . . . . . . . . . . 94

5.3

Comparison of SIS, DSSF and ERA-Interim solar radiation with

4.3

measured data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
5.4

RMSE of SIS, DSSF and ERA-Interim solar radiation . . . . . . . . . . . . . 101

5.5

MBE and standard deviation of bias error of SIS, DSSF and
ERA-Interim solar radiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

5.6

Comparison of SIS and measured solar radiation for each Meteosat
First Generation mission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

5.7

The monthly-aggregated MBE of SIS, DSSF and ERA-Interim
solar radiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

5.8

Annual average (2005) solar radiation from SIS, DSSF,
ERA-Interim and JRC-MARS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

5.9

Standard deviation (2005) of solar radiation from SIS, DSSF,
ERA-Interim and JRC-MARS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

5.10 Kernel density plot of the differences between SIS, DSSF,
ERA-Interim and JRC-MARS solar radiation . . . . . . . . . . . . . . . . . . . . 108
5.11 Spatio-temporal distribution of differences between SIS, DSSF,
ERA-Interim and JRC-MARS solar radiation . . . . . . . . . . . . . . . . . . . . 109


List of Figures

xix

5.12 RRMSE and MBE derived from a comparison between SIS, DSSF,
ERA-Interim and JRC-MARS solar radiation . . . . . . . . . . . . . . . . . . . . 110
5.13 Per-grid bias removal between SIS and DSSF solar radiation . . . . . . . 114



List of Acronyms

AVHRR

Advanced Very High Resolution Radiometer

BSRN

Baseline Surface Radiation Network

CGMS

Crop Growth Monitoring System

CLARA-A1

Cloud, Albedo and Radiation dataset (AVHRR-based)

CM-SAF

Satellite Application Facility on Climate Monitoring

DSSF

Down-welling Surface Short-wave Radiation Flux

EC

European Commission

ECMWF

European Centre for Medium-Range Weather Forecasts

ECV

Essential Climate Variable

ERA

ECMWF Reanalysis

EUMETSAT

European Organisation for the Exploitation of Meteorological

FTP

File Transfer Protocol

GCOS

GEBA

Global Climate Observing System
Global Energy Balance Archive

GMT

Greenwich Mean Time

GOES

American Geostationary Operational Environmental Satellites

HIMAWARI

Japanese Geostationary Meteorological Satellite

Satellites

HDF

Hierarchical Data Format

H-SAF

Satellite Application Facility on Support to Operational Hydrology
and Water Management

IDW

Inverse Distance Weighting

INSAT

Indian National Satellite System

IPCC

Intergovernmental Panel on Climate Change

JRC

Joint Research Centre

LSA-SAF

Satellite Application Facility on Land Surface Analysis

xxi


xxii

List of Acronyms

MAE
MARS
MBE
MCYFS
MVIRI

MSG
NOAA
NWC-SAF

Mean Absolute Error
Monitoring Agricultural Resources Unit
Mean Bias Error
MARS Crop Yield Forecasting System
Meteosat Visible and Infra-red Imager
Meteosat Second Generation
National Oceanic and Atmospheric Administration
Satellite Application Facility on Support to Nowcasting & Very
Short Range Forecasting

NWP
OSI-SAF
PATMOS-x
RMAE
RMBE
RMSE
RRMSE
SAF
SEVIRI
SIS
TIROS
TOMS
UNFCCC
WGS-84
WMO
WOFOST

Numerical Weather Prediction
Ocean and Sea Ice Satellite Application Facility
Pathfinder Atmospheres Extended
Relative Mean Absolute Error
Relative Mean Bias Error
Root Mean Square Error
Relative Root Mean Square Error
EUMETSAT’s Satellite Application Facility
Spinning Enhanced Visible and Infrared Imager
Surface Incoming Short-wave Radiation
Television Infrared Observation Satellite
Total Ozone Mapping Spectrometer
United Nations Framework Convention on Climate Change
World Geodetic System 1984
World Meteorological Organization
World Food Studies crop growth model


List of Symbols

a
Aa , Ab
Bb , Bc

Albedo or canopy reflection coefficient
˚
Angstr¨
om-Prescott model coefficients
Bristow-Campbell model coefficients

C
Cw

Annual mean cloud fractional cover
Mean total cloud cover during daytime observations

d

Correction factor for Earth-Sun distance

dc
df

Distance to coast
Number of degrees of freedom

δ

Slope vapour pressure curve

∆Ti
∆Tm

Daily air temperature range
Average monthly air temperature range

ea

Actual vapour pressure

es
Ei

Saturation vapour pressure
Estimated value for day i

ET0

Daily grass reference evapotranspiration

ǫ

Reduction constant to Ipot (in auto-calibration procedure)

F

Down-welling surface short-wave radiation flux

F0
γ

Solar constant
Psychometric constant

γ(h)
h

Semivariogram
Lag distance (in semivariogram calculation)

Ha , Hb

Hargreaves model coefficients

i
Ipot

Day of the year
Daily potential surface short-wave solar radiation

Is

Daily surface short-wave solar radiation at the earth’s surface

Ix

Daily extra-terrestrial short-wave solar radiation

xxiii


xxiv

List of Symbols

Ix,hr
Mi

Hourly extra-terrestrial short-wave solar radiation

Measured value for day i

M

Average value of all measured values

n
nd

Number of observations
Daily sunshine duration

Nd
p

Day length
p-value (in statistical significance testing)

P
θhr

Percent of clear-sky days per year
Solar irradiance angle relative to the normal to the land surface

θs
R2

Solar zenith angle
Coefficient of determination

Rn

Net radiation

Rns
Rnl

Net short-wave radiation
Net long-wave radiation

Sa , Sb , Sc

Supit-Van Kappel model coefficients

σ
t

Stefan-Boltzmann constant
t-statistic (in statistical significance testing)

T

Effective transmittance of the atmosphere

Tn , Tx
τ

Minimum and maximum daily air temperature
Clear-sky atmospheric transmissivity

τi
u2

Atmospheric transmissivity for day i
Wind speed measured at 2 metres above the ground

z(si )

Value of the model coefficient at location si (in semivariogram

z(si + h)

calculation)
Value of the model coefficient at location si + h (in semivariogram calculation)


Chapter 1

Introduction

1.1 Background
The energy of the sun is the main energy source on the earth (Mavi and Tupper,
2004). Only minor quantities not exceeding one percent of total energy is available
from other sources including geothermal energy, tidal energy, and cosmic radiation (Nordell and Gervet, 2009). The sun’s energy drives physical, chemical and
biological processes taking place in the earth-atmosphere system, thereby determining the earth’s climate (Bonan, 2002) and allowing organic life on the earth
(Mavi and Tupper, 2004). Solar radiation provides the energy required by plants
for their growth and maintenance (Hall, 2001). The visible part of solar radiation
plays a central role in plant growth through the process of photosynthesis (Mavi
and Tupper, 2004). Seasonal variation in day length and incoming solar radiation,
together with air temperature and precipitation, results in the periodic events in

the lives of plant, i.e. plant phenology. For agricultural crops, the amount of solar
radiation determines the upper productivity limit (Hall, 2001; Mavi and Tupper,
2004; Rodriguez et al., 1999). In this context, knowledge about the spatial distribution and temporal variation of solar radiation reaching the earth surface is
important for crop growth monitoring and yield forecasting.
According to the recent earth energy budget estimates by Wild et al. (2013), 22
percent (76 W m−2 ) of solar radiation reaching the top of the earth’s atmosphere
is reflected back to space. Twenty-three percent (79 W m−2 ) is absorbed by the
clouds, gases and aerosols in the atmosphere. Only 54 percent (185 W m−2 ) of
solar radiation reaches the earth’s surface (Fig. 1.1). The solar energy reaching
the earth surface, referred throughout this thesis as surface solar radiation, global
radiation or simply solar radiation, depends on the geographic location, orientation of the surface, time of the day, time of the year, and atmospheric composition

1