Comparing satellite derived rainfall with ground based ...€¦ · precipitation estimates are widely used to measure global rainfall on near real-time and monthly timescales for

Comparing satellite derived rainfall

with ground based radar for

North-western Europe

Hojjat Seyyedi

January, 2010

Course Title: Geo-Information Science and Earth Observation

for Environmental Modelling and Management

Level: Master of Science (Msc)

Course Duration: April 2008 - January 2010

Consortium partners: University of Southampton (UK)

Lund University (Sweden)

University of Warsaw (Poland)

International Institute for Geo-Information Science

and Earth Observation (ITC) (The Netherlands)

GEM thesis number: 2010-04

Comparing satellite derived rainfall with ground based radar for North-western

Europe

by

HOJJAT SEYYEDI

Thesis submitted to the International Institute for Geo-information Science and Earth

Observation in partial fulfilment of the requirements for the degree of Master of

Science in Geo-information Science and Earth Observation for Environmental

Modelling and Management

Thesis Assessment Board

Chairman: Prof.Dr. Z. (Bob) Su

External Examiner: Prof T.P. Dawson

Internal Examiner: Dr. B.H.P. (Ben) Maathuis

Supervisor: Dr.Ir. C.M.M. (Chris) Mannaerts

Supervisor: M.Sc. A. (Andre) Kooiman

International Institute for Geo-Information Science and Earth Observation

Enschede, The Netherlands

Disclaimer

This document describes work undertaken as part of a programme of study at

the International Institute for Geo-information Science and Earth Observation.

All views and opinions expressed therein remain the sole responsibility of the

author, and do not necessarily represent those of the institute.

i

Abstract

The primary reason of implementing meteorological satellites is avoid the coverage

and time gap of conventional ground-based rainfall data for a number of

applications, above all hydrology and weather forecasting and finally enhanced

identification and quantification of rainfall at the time scales. Uses of global and

local data have significant limitation because of lack of precipitation measurements

over the oceans as well as uneven distribution of rain gauges and weather radars

over land. Precipitation has also a direct impact on human life that other atmospheric

phenomena seldom have: an example is represented by heavy rain events and flash

floods (Levizzani et al., 2002). According to above background satellite

precipitation estimates are widely used to measure global rainfall on near real-time

and monthly timescales for climate studies, numerical weather prediction (NWP)

data assimilation, now-casting and flash flood warning, tropical rainfall potential,

and water resources monitoring. Therefore similar to any observational data,

investigating their accuracy and limitations is crucial. This is done by verifying the

satellite estimates against independent data from rain gauges and radars (Levizzani,

et al., 2007). The study utilized two METEOSAT products that are currently freely

available to the scientific community: multi sensor precipitation estimate (MPE)

from METEOSAT8 with 5 minutes temporal resolution and MPE from

METEOSAT9 with 15 min temporal resolution. The gauge adjusted radar

precipitation measurements with 5 minutes temporal resolution from the royal

Netherlands meteorological institute (KNMI) used as reference data. All data series

were collected and processed for two different rainfall events over the Netherlands

and some parts of Belgium. Two types of statistical analysis have been applied,

categorical statistics to assess the special accuracy of products as well as continuous

statistics to assess errors in magnitude. The statistical analysis has been done based

on three different temporal resolutions, instantaneous, one hour accumulated and

three hours accumulated. The categorical statistical scores for the MPE products

from METEOSAT8 and METEOSAT9 have been improved by decreasing temporal

resolution from 15 min to 3 hours accumulated. These values for the first event are

better than the second event. This indicates these products have some difficulty in

estimating severe events. However, the METEOSAT8 MPE product has relatively

better performance in case of second event. May be this is related to shorter

accumulation time for the co-location with the SSM/I which leads to higher

performance in severe events. In case of the first event the MPE product from

METEOSAT9 shows higher scores. It is easy to infer that in normal rainfall events

using MPE product from METEOSAT9 is more reliable. The null hypothesis of

ii

there is no difference in spatial accuracy of EUMETSAT MPE products from

METEOSAT8 and METEOSAT9 rejected. Regression analysis for mean rainfall

values indicates in case of first event the r² is roughly 0.90 with 95% confidence

level. This shows very strong correlation between MPE products and ground based

radar data. However, this value for the second event is roughly 60% with 95%

confidence level. These results confirm the weakness of MPE products in estimating

severe events. It is worth to mention that the mean rainfall values from

METEOSAT9 MPE product has relatively higher correlation with ground based

radar data in all cases. The time series graphs related to ME, MAE and RMSE shows

overestimation in MPE products. The overestimation is almost the same for both

products. The magnitude of overestimation in the second event is drastically more

than the first event. These graphs also are in the same direction with the previous

results, and verify the limitation of MPE products in estimating strong events. The

null hypothesis of There is no difference in estimated values by EUMETSAT MPE

products from METEOSAT8 and METEOSAT9 rejected.

iii

Acknowledgements

Foremost, I would like to express my sincere gratitude to European Union, Erasmus

Mundus consortium and GEM MSc course organizers from the University of

Southampton, the Lund University, Warsaw University and the ITC, for awarding

me the scholarship to undertake this course. It has been a lifetime experience for me.

I owe my deepest gratitude to my first supervisor Dr. Ben Maathuis. Thank you very

much for your continuous support, guidance and useful suggestions during my

research. I gained a lot of scientific experience from him. I was overwhelmed by his

patience and the encouragement that he gave me from the beginning of the work

until production of the thesis. I am most grateful to my second supervisor Dr. Chris

Mannaerts for his continuous supports, enthusiasm, and immense knowledge.

My special thanks to Prof Beth Ebert, Chief Scientist, The Centre for Australian

Weather and Climate Research, Dr. Thomas Heinemann, Meteorological Product

Expert, EUMETSAT, Germany, dr. I. Holleman and Dr. Rob Roebeling, Chief

Scientist, Royal Netherlands Meteorological Institute, Dr. Angel Luque, Universitat

de les Illes Balears, and Mr. Bas Retsios, ITC, for their invaluable support and

suggestions on data sets processing and statistical analysis.

My sincere thanks also goes to Prof. T.P. Dawson, Prof. Andrew Skidmore, Prof.

Katarzyna Dobrowska, Prof. Petter Pilesjö, Dr. Cecilia Akselsson, Mr. Andre

Kooiman, Ms. Karin Larsson, Ms. Steff Webb and Ms. Monique Romarck for their

support and hospitality during my stay in four hosting countries.

My best wishes to my GEM classmates from around the globe and the lovely people

whom I met in the four countries for sharing their friendship and knowledge during

the past 18 months.

Last but not the least; I would like to thank my family: my parents Esmaeil Seyyedi

and Iran Badri for giving birth to me at the first place and supporting me spiritually

throughout my life. Behnam, Maryam, and Fatima, thanks for being supportive and

caring siblings.

iv

Table of contents

1. Introduction .......................................................................................... 13

1.1. Background and the problem........................................................ 13

1.2. Research objective ........................................................................ 15

1.2.1. General objective .................................................................. 15

1.2.2. Specific objectives ................................................................ 15

1.3. Research questions ....................................................................... 15

1.4. Research hypotheses ..................................................................... 15

1.5. Outline of the thesis ...................................................................... 17

2. Literature Review ................................................................................. 18

2.1. Satellite based precipitation estimation ........................................ 18

2.1.1. Visible and Infrared .............................................................. 18

2.1.2. Passive Microwave ............................................................... 19

2.1.3. Active sensors ....................................................................... 20

2.1.4. Blended techniques ............................................................... 21

2.2. Statistical methods for verifying estimated precipitation by

satelltes ..................................................................................................... 24

2.2.1. Validation of near real-time vs. climate scale precipitation . 24

2.2.2. Validation data ..................................................................... 25

2.2.3. Standard statistical methods for verifying precipitation

estimated by satellite ............................................................................ 25

3. Materials and methods .......................................................................... 30

3.1. General description of data ........................................................... 30

3.1.1. Description of EUMETSAT Multi Sensor Precipitation

Estimation (MPE) products .................................................................. 30

3.1.2. Description of ground based radar data ................................ 33

3.2. Methodology for comparision the satellite estimates against

independent data from radars ................................................................... 37

3.2.1. Data preparation ................................................................... 37

3.2.2. Categorical verification statistics ......................................... 39

3.2.3. Continuous verification statistics ......................................... 39

4. Results .................................................................................................. 43

4.1. The first event ............................................................................... 43

4.1.1. Instantaneous comparison .................................................... 45

v

4.1.2. One hour accumulated rainfall comparison .......................... 50

4.1.3. Three hours accumulated rainfall comparison...................... 55

4.2. The second event .......................................................................... 61

4.2.1. Instantaneous comparison .................................................... 61

4.2.2. One hour accumulated rainfall comparison .......................... 67

4.2.3. Three hours accumulated rainfall comparison...................... 72

5. Discussion ............................................................................................ 77

5.1. Spatial Accuracy........................................................................... 77

5.2. Assessing errors in magnitude ...................................................... 80

6. Conclusions and Recommendations ..................................................... 83

6.1. Conclusions .................................................................................. 83

6.2. Recommendations ........................................................................ 84

7. References ............................................................................................ 85

8. Appendix .............................................................................................. 89

vi

List of figures

Figure 1 the electromagnetic spectrum, from gamma rays to radio waves, on

satellite images of particular wavelengths .................................................... 18

Figure 2 Block diagram for both the real time and research product

algorithms, showing input data (left side), processing (center), output data

(right side), data flow (thin arrows), and processing control (thick arrows) 22

Figure 3 Look-up tables (LUT) derived from a 6h period on Aug. 19th, 2001

for the 5°x5° box number 229, over West Africa ......................................... 24

Figure 4 (a) EUMETSAT MPE product from METEOSAT8 (26th May 2009,

02:45 UTC). (b) EUMETSAT MPE product from METEOSAT9 (26th May

2009, 02:45 UTC) ........................................................................................ 32

Figure 5 Map shows KNMI weather radars location ................................... 33

Figure 6 (a) Radar reflectivity factor(dBZ) map and (b) Radar rainfall

intensity (mm/h) map for 26 May 2009, 00:00 UTC ................................... 36

Figure 7 the research methodology .............................................................. 37

Figure 8 processed images (a) MPE_METEOSAT9, (b)

MPE_RSS_METEOSAT8 and (c) RADAR (26 May 2009, 04:00 UTC) ... 39

Figure 9 flow diagram of methodology for categorical verification statistics

calculation .................................................................................................... 41

Figure 10 flow diagram of methodology for continuous verification statistics

calculation .................................................................................................... 42

Figure 11 reported rainfall for May 2009 ..................................................... 43

Figure 12 the cloud analysis image for the first event .................................. 44

Figure 13 mean rainfall values (Event1, instantaneous) .............................. 48

Figure 14 Mean Error (ME) values (Event1, instantaneous)........................ 48

Figure 15 Mean Absolute Error values (Event1, instantaneous) .................. 49

Figure 16 Root Mean Square Error (RMSE) values (Event1, instantaneous)

...................................................................................................................... 49

Figure 17 mean rainfall values (Event1, 1Hour accumulated) ..................... 53

Figure 18 Mean Error (ME) values (Event1, 1Hour accumulated) .............. 54

Figure 19 Mean Absolute Error values (Event1, 1Hour accumulated) ........ 54

Figure 20 Root Mean Square Error (RMSE) values (Event1, 1Hour

accumulated) ................................................................................................ 55

Figure 21 mean rainfall values (Event1, 3Hours accumulated) ................... 59

vii

Figure 22 Mean Error (ME) values (Event1, 3Hours accumulated) ............ 59

Figure 23 Mean Absolute Error values (Event1, 3Hours accumulated) ....... 60

Figure 24 Root Mean Square Error (RMSE) values (Event1, 3Hours

accumulated) ................................................................................................ 60

Figure 25 the cloud analysis image for the second event ............................. 61

Figure 26 mean rainfall values (Event2, instantaneous) .............................. 65

Figure 27 Mean Error (ME) values (Event2, instantaneous)........................ 65

Figure 28 Mean Absolute Error (MAE) values (Event2, instantaneous) ..... 66


...................................................................................................................... 66

Figure 30 mean rainfall values (Event2, 1 Hour accumulated) .................... 70

Figure 31 Mean Error (ME) values (Event2, 1 Hour accumulated) ............. 70

Figure 32 Mean Absolute Error (MAE) values (Event2, 1 Hour accumulated)

...................................................................................................................... 71

Figure 33 Root Mean Square Error (RMSE) values (Event2, 1 Hour

accumulated) ................................................................................................ 71

Figure 34 mean rainfall values (Event2, 3 Hours accumulated) .................. 75

Figure 35 Mean Error (ME) values (Event2, 3 Hours accumulated) ........... 75

Figure 36 Mean Absolute Error (MAE) values (Event2, 3Hours

accumulated) ................................................................................................ 76


accumulated) ................................................................................................ 76

Figure 38 Scatter graph for MPE products, instantaneous comparison,

second event ................................................................................................. 81

viii

List of tables

Table 1 2*2 contingency table ..................................................................... 26

Table 2 MSG SERVI Channel ..................................................................... 30

Table 3 geographical projection parameters of the KNMI radar images.

Modified from .............................................................................................. 34

Table 4 the corners of KNMI radar image ................................................... 34

Table 5 Derived empirical constants a and b using the typical Z = aRb

relation, for several precipitation types ........................................................ 35

Table 6 calculated categorical statistics scores between METEOSAT8_RSS

and ground based radar (Event1, instantaneous) .......................................... 46

Table 7 calculated categorical statistics scores between METEOSA9 and

ground based radar (Event1, instantaneous) ................................................. 47


and ground based radar (Event1, 1 Hour accumulated) ............................... 51


ground based radar (Event1, 1 Hour accumulated) ...................................... 53


and ground based radar (Event1, 3 Hours accumulated) .............................. 57


ground based radar (Event1, 3Hours accumulated) ..................................... 58


and ground based radar (Event2, instantaneous) .......................................... 63


ground based radar (Event2, instantaneous) ................................................. 64


and ground based radar (Event2, 1 Hour accumulated) ............................... 68


ground based radar (Event2, 1 Hour accumulated) ...................................... 69


and ground based radar (Event2, 3Hours accumulated) ............................... 73


ground based radar (Event2, 3Hours accumulated) ..................................... 74

Table 18 results for categorical statistics analysis (instantaneous) .............. 78

Table 19 results for categorical statistical analysis (one hour accumulated) 79

ix

Table 20 results for categorical statistical analysis (three hours accumulated)

...................................................................................................................... 79

Table 21 Regressions for mean rainfall values in MPE products,

instantaneous comparison, α=0.05 ............................................................... 81

Table 22 Regressions for mean rainfall values in MPE products, one hour

accumulated, α=0.05 .................................................................................... 82

Table 23 Regressions for mean rainfall values in MPE products, three hours

accumulated, α=0.05 .................................................................................... 82

x

Abbreviations

AMSR-E Advance Microwave Scanning Radiometer - Earth Observing

System

AMSU-B Aqua and Advance Microwave Sounding Unit – B

CAMS Climate Assesment and Monitoring System

CLAI Cloud Analysis Image

CPC Climate Prediction Center

CSI Critical Success Index

DMSP Defence Meteorological Satellite Program

DSD Drop Size Distribution

EBBT Equivalent Blackbody Temperature

ESA European Space Agency

ETS Equitable Threat Score

EUMETSAT European Organization for the Exploitation of Meteorological

Satellites

FAR False Alarm Ratio

FRC Fraction Correct

GDAL Geospatial Data Abstraction Library

GEO Geosynchronous

GMS Geostationary Meteorological Satellite

GOES Geostationary Operational Environmental Satellite

GPCC Global Precipitation Climatological Center

GPCP Global Precipitation Climatology Project

GPI GOES Precipitation Index

GPROF Goddard Profiling

GRIB GRIdded Binary

HDF5 Hierarchical Data Format 5

HRV High Resolution Visible

ILWIS Integrated Land and Water Information System

IR Infrared

ITC International Institute for Geo-Information Science and Earth

Observation

JAXA Japan Aerospace Exploration Agency

KNMI Royal Netherlands Meteorological Institute

LEO Low Earth Orbit

LUT Look up Table

MAE Mean Absolute Error

xi

ME Mean Error

METEOSAT Meteorological Satellite

MPE Multi-Sensor Precipitation Estimate

MSG Meteosat Second Generation

NIR Near Infrared

NOAA National Oceanic and Atmospheric Administration

NWP Numerical Weather Prediction

PERSIANN Precipitation Estimation from Remotely Sensed Information using

Artificial Neural Networks

PMW Passive Microwave

POD Probability of Detection

PR Precipitation Radar

pseudoCAPPI pseudo-Constant Altitude Plan Position Indicator

QPF Quantitative Precipitation Forecasting

RADAR Radio Detection and Ranging

RMSE Root Mean Square Error

RSS Rapid Scanning Service

SEVIRI Spinning Enhanced Visible and Infrared Imager

SI International System of Units

SSM/I Special Sensor Microwave/Imager

TMI TRMM Microwave Imager

TMPA TRMM Multi-satellites Precipitation Analysis

TRMM Tropical Rainfall Measuring Mission

UTC Universal Time Coordinate

UTM Universal Transverse Mercator

VIS Visible

WGS World Geodetic System

WMO World Meteorological Organization

WV Water Vapor

WWRP World Weather Research Program

13

1. Introduction

1.1. Background and the problem

The primary reason of implementing meteorological satellites is avoid the coverage

and time gap of conventional ground-based rainfall data for a number of

applications, above all hydrology and weather forecasting and finally enhanced

identification and quantification of rainfall at the time scales. Uses of global and

local data have significant limitation because of lack of precipitation measurements

over the oceans as well as uneven distribution of rain gauges and weather radars

over land. There is similar problem for the determination of wind, pressure,

temperature, and humidity fields. Precipitation has also a direct impact on human

life that other atmospheric phenomena seldom have: an example is represented by

heavy rain events and flash floods (Levizzani et al., 2002).

According to above background satellite precipitation estimates are widely used to

measure global rainfall on near real-time and monthly timescales for climate studies,

numerical weather prediction (NWP) data assimilation, now-casting and flash flood

warning, tropical rainfall potential, and water resources monitoring. Therefore

similar to any observational data, investigating their accuracy and limitations is

crucial. This is done by verifying the satellite estimates against independent data

from rain gauges and radars (Levizzani et al. 2007).

Continuous verification statistics including MEAN, MEAN ERROR, MEAN

ABSOLUTE ERROR, RMSE, MSE, Standard Deviation, BIAS, STANDARD

DEVIATION DIFFERENCES (SDD), Correlation Coefficient (CORR), and SKILL

SCORE are very useful in quantifying the errors of satellite precipitation estimates.

Categorical verification statistics such as Accuracy, BIAS, Probability of Detection

(POD), False Alarm Ratio (FAR), Critical Success Index (CSI) or Threat Score,

Equitable Threat Score (ETS), Fraction Correct (FRC), Probability of false

detection, and Hansen and Kuipers discrimination (true Skill Statistics) reveals more

specific information on expected errors in rain location, type, mean and maximum

intensities which is required by users of near real-time precipitation estimates

(Luque, et al., 2006 and 2008 ; Levizzani et al. 2007; Roebeling, R., and Holleman,

I., 2009; The Centre for Australian Weather and Climate Research, Forecast

Verification – Issues, Methods and FAQ, 2009).

14

The main sources for validating precipitation estimated by satellite are observations

from rain gauges and radar rainfall estimates. Each source has separate advantages

and disadvantages.

The most important advantage for rain gauges is giving direct measurement of rain

accumulation. However, they are unrepresentative of timescale and areal value

estimated by satellite respectively because gauge accumulation over several minutes

to several hours versus a satellite “snapshot” and they are point measurements. In

addition; gauges are unable to verify oceanic satellites rainfall estimations because

they are limited to land regions and islands (Ebert, E. E. and J. L. McBride, 2000

and Levizzani et al. 2007).

The main advantages for radar images are high temporal (5-10 min) and spatial (1

km) resolution. Radar observations are representative of timescale and areal value

estimated by satellite in that they give “snapshots”. However, radar images are

themselves indirect measurements of rainfall and are prone to errors of various

kinds. Careful quality control of the data and bias correction using nearby rain-gauge

observations can correct most of the error in radar rainfall estimates. Radars

measurements are limited to land regions and near-coastal regions (Levizzani et al.

2007).

Until now, few studies have compared the potential of METEOSAT 8 and

METEOSAT 9 MPE products with ground radar data and/or gauge in estimating

rainfall with long accumulation time or wide spatial grid. In this study we have

sought to address this topic by validating EUMETSAT MPE products over the

Netherlands with high temporal (5 and 15min) and spatial (3km) resolution rain

radar data from The Royal Netherlands Meteorological Institute (KNMI).

15

1.2. Research objective

1.2.1. General objective

The general objective of this research is evaluating EUMETSAT MPE products

form METEOSAT8 and METEOSAT9 in comparison with ground based rain radar

data.

1.2.2. Specific objectives

To achieve the general objective, two steps are necessary to be implemented

according to the categorical and continuous verification methods. They are described

as following:

- Implementing categorical verification statistics for assessing how the

spatial distribution of the MPE products from METEOSAT8 and

METEOSAT9 differ from the reference data.

- Implementing continuous verification statistics for assessing how the values

of the MPE products from METEOSAT8 and METEOSAT9 differ from

the reference data.

1.3. Research questions

To achieve the main objective, the following research question must be answered:

- What are the differences in spatial distribution of EUMETSAT MPE

products from METEOSAT 8 (5 min temporal resolution) and

METEOSAT 9 (15 min temporal resolution) in comparison with reference

data?

- What are the differences in estimated values by EUMETSAT MPE

products from METEOSAT 8 (5 min temporal resolution) and

METEOSAT 9 (15 min temporal resolution) in comparison with reference

data?

1.4. Research hypotheses

According to the main objective in this research, the hypotheses for testing aiming to

the comparison EUMETSAT MPE products from METEOSAT8 and METEOSAT9

with ground based radar data. Therefore, the hypothesis can be given as following:

16

For assessing spatial distribution of EUMETSAT MPE products from METEOSAT

8 and METEOSAT9 in comparison with ground-based radar data

H0: There is no difference in spatial distribution of EUMETSAT MPE

products from METEOSAT8 and METEOSAT9

H1: There is difference in spatial distribution of EUMETSAT MPE


For assessing EUMETSAT MPE products from METEOSAT 8 and METEOSAT9

accuracy in magnitude in comparison with ground-based radar data

H0: There is no difference in estimated values by EUMETSAT MPE


H1: There is difference in estimated values by EUMETSAT MPE products

from METEOSAT8 and METEOSAT9

17

1.5. Outline of the thesis

Chapter 2: describes and summarizes the literature with respect to the applied

blending algorithms for producing MPE products, verifying MPE products with

ground based radar data as well as applicable standard statistical methods.

Chapter 3: Generally describes the characteristics of data used in this study including

Multi sensor Precipitation Estimation_ Rapid Scanning Service (MPE_RSS) product

from METEOSAT 8 with 5 min temporal resolution, MPE product from

METEOSAT 9 with 15 min temporal resolution, gauge adjusted ground based radar

data as reference data with 5 min temporal resolution, principle and framework of

the general methodology of this study, and the algorithm used to achieve objectives.

Chapter 4: Presents the results derived from data processing, categorical statistical

analysis as well as continuous statistical analysis.

Chapter 5: Discussion

Chapter 6: Conclude the findings of this study, and give some recommendation and

opportunities for future studies.

18

2. Literature Review

2.1. Satellite based precipitation estimation

Satellite-based precipitation estimation has quite long story and became one of the

more intense research topics in the discipline of satellite meteorology. The major

problem of all methods is the in-direct relation between the precipitation on the

ground and the measured satellite signal; therefore various studies have been carried

out to address and solve these issues. This part will give a brief explanation of the

main types of rainfall retrievals from space using VIS/IR, PMW, active sensors and

blending techniques. Figure 1 illustrates the electromagnetic spectrum, from gamma

rays to radio waves, on satellite images of particular wavelengths.

Figure 1 the electromagnetic spectrum, from gamma rays to radio waves, on

satellite images of particular wavelengths ,Source: (El-Baz 2008)

2.1.1. Visible and Infrared

Observations in VIS/IR spectral bands allow for measuring reflecting or emission

from cloud top or near cloud top and thus they generally cannot be exploited to

directly estimate precipitation intensities(Lensky and Levizzani 2008).

19

There is no specific separation point between rain and no rain based on cloud top

radiometric temperature, usually referred to as equivalent blackbody temperature

(EBBT), therefore screening of geosynchronous (GEO) IR (10.8 µm) images for

precipitating clouds is less tractable. However, a number of researchers have

developed GEO IR rain algorithms based on the use of EBBT threshold. These are

best demonstrated by the GOES Precipitation Index (GPI) which uses an invariant

235 K EBBT threshold to map out precipitating cloud area. The GPI technique

assumes that all pixels colder than 235 K precipitate at a constant rate of 3 mm/h.

Two main issues in screening IR images for precipitating clouds are:

- Clouds supposed cold enough to produce rain according to an EBBT

threshold may have evolved beyond the rain stage (e.g., inactive optically

thick cirrus anvils)

- Clouds not supposed cold enough may be experiencing pre-ice-phase rain

microphysics (e.g., frontal rain)

In spite of these disadvantages, GEO IR data provide important physical

characteristics of precipitation producing storms and, furthermore, are available over

the daily cycle (Grose et. al., 2002). The use of geostationary satellite IR data is even

more in-direct approach. Moreover, the issues related to GEO IR data are valid for

geostationary satellite IR data. In the face of these disadvantages the high temporal

and spatial resolution of geostationary satellite data makes the use of IR brightness

temperatures useful for both, the near real estimation of rainfall in areas with a

sparse radar network (e.g. Central Africa) as well as for the long-term monitoring

(Heinemann and Kerényi, 2003; Ebert et. al., 1998).

The RAINSAT is a bispectral method for rain area estimation. This method uses

data from GOES calibrated with radar data to provide both real-time and near real

time analysis of rain area. It does not show any significant rainfall for cloud albedos

less than 0.5. It shows rapidly increasing rain rates above cloud albedos of 0.7 when

only the VIS rain rate relationship is considered. The IR-only relationship shows a

rapid increase in rain rates for temperatures colder than 220 K. Considerable

improvements, especially a decrease in false alarm rate, can be achieved when VIS

data is included. The VIS data is especially helpful in filtering out cirrus clouds.

These clouds have cold cloud top temperatures, but appear transparent in the VIS

images. However, during the night this VIS data is not available(Henken et al.,

2009).

2.1.2. Passive Microwave

PMW frequencies have been used for rain retrieval for about 25 years and the

techniques that have been developed and refined in time rely on the emission signal

of rain drops over the ocean at frequencies at or below 37 GHz and the scattering

20

signal of ice particles in the precipitation layer over land at frequencies at or above

85 GHz(Lensky and Levizzani 2008). The most recent and widely used algorithm

for precipitation retrieval from PMW frequencies is the Goddard Profiling (GPROF)

technique. GPROF retrieves the instantaneous rainfall and the rainfall vertical

structure using the response functions for different channels peaking at different

depths within the raining column. There are, however, more independent variables

within raining clouds than there are channels in the observing system and this

requires additional assumptions or constraints( Strangeways, 2007).

The most of available PMW precipitation retrieval have been optimized for the

corresponding satellite sensors and each algorithm has its own advantages and

disadvantages related to the specific application it was designed for. Therefore, none

of them appears to be universally better than the other. Therefore a translucent,

parametric algorithm for ensuring uniform rainfall products across all available

sensors especially in case of the Global Precipitation Measurement (GPM) mission

is needed(Kummerow, et al. 2007).

Note that, PMW-based retrieval methods are strongly linked to the physics of

precipitation formation, are still performing much better in heavy rain convective

conditions over the ocean and often perform poorly in light rain. However, all

algorithms engaged with some problems, including:

- Snow detection

- Diffraction, which limits the ground resolution for a given satellite PMW

antenna

- PMW sensors at present are available only on Low Earth Orbit (LEO)

Satellites and this greatly limits the time resolution of observations(Lensky

and Levizzani 2008).

2.1.3. Active sensors

The history of precipitation retrieval from space using active sensors started in

November 1997 with the launch of Tropical Rainfall Measuring Mission (TRMM)

(the first and so far only). Designed by Japan Aerospace Exploration Agency

(JAXA), the Precipitation Radar (PR) uses a single frequency of 13.8 GHz, giving a

resolution of 4 km diameter and a swath width of 215 km, using across-track

scanning. It measures vertical profiles of precipitation from the ground up to 20 km

in 250 m altitude steps when looking vertically down. The quality and resolution of

PR products are undisputable and data usage has grown in time steadily. it is safe to

say that the PR has become a sort of „truth‟ against which all other products are

compared and evaluated, e.g. investigating the performance of TRMM Microwave

Imager (TMI) rain estimation using the TRMM precipitation radar (PR)(Furuzawa

and Nakamura 2005).

21

However, there are some limitations for PR. TRMM data is limited to the 35S – 35N

degree latitude belt, attenuation, relatively narrow swath (215 km) (Lensky and

Levizzani 2008), and the PR is limited to measuring rainfall more intense than 0.7

mm per hour, below which it is not detected( Strangeways, 2007).

2.1.4. Blended techniques

The wide variety of sensors in orbit suggests that their combined use could in

principle help alleviate some of the deficiencies of a single sensor method by using

data obtained from another sensor. This kind of strategy is also instrumental in

creating global rainfall datasets for which space-time coverage is crucial.

Vicente and Anderson (1993) mixed MW and IR rainfall estimation methods for

instantaneous estimation for first time. This approach needs effective combination of

MW and IR geostationary temporal and spatial resolution. Vicente and Anderson

(1994) introduced a new rain retrieval technique that combines geosynchronous IR

and MW polar orbit data for hourly rainfall estimates over the Pacific Ocean that

involves twice a day calibration between MW precipitation rate and IR with two

multi-linear regressions. An experimental method has been presented by Turk et al.

(1998) for statistically combining MW-based estimated precipitation from SSM/I

and the TRMM together with geostationary IR satellite data in a near real-time for a

rapid-time update global precipitation analysis. The method has been applied for

quantitative precipitation forecasting (QPF) and numerical weather prediction

models.

TRMM Multi-satellites Precipitation analysis (TMPA) attempt to retrieve rainfall

from space is using a combination of many sensor as well as rain gauge data when

possible in order to improve accuracy, coverage and resolution (0.25° × 0.25° and 3

hourly). TMPA is using various data sets that are collected by the Passive

Microwave (PM) sensors and IR (10.7 μm) sensor as well as several additional data

sources including PM sensor from TMI on TRMM, SSM/I on Defence

Meteorological Satellite Program (DMSP) satellites, Advance Microwave Scanning

Radiometer - Earth Observing System (AMSR-E) on Aqua and Advance Microwave

Sounding Unit - B (AMSU-B) on NOAA as well as TMI and PR from TRMM as a

source of calibration. The GPCP monthly rain gauge analysis was developed by

GPCC and CAMS monthly rain gauge analysis was developed by CPC. TMPA is

available both after and in real time, based on calibration by the TRMM Combined

Instrument and TRMM Microwave Imager precipitation products, respectively.

Figure 2 shows the block diagram for TMPA algorithm. TMPA provides reasonable

performance at monthly scale as well as detecting daily large events. The TMPA,

22

however, has lower skill in correctly specifying moderate and light event amounts

on short time intervals (Huffman, et al. 2007).

Figure 2 Block diagram for both the real time and research product algorithms,

showing input data (left side), processing (center), output data (right side), data

flow (thin arrows), and processing control (thick arrows)(Huffman, et al. 2007)

PERSIANN (Precipitation Estimation from Remotely Sensed Information using

Artificial Neural Networks) system uses neural network function

classification/approximation procedures to compute an estimate of rainfall rate at

each 0.25° x 0.25° pixel of the infrared brightness temperature image provided by

geostationary satellites. An adaptive training feature facilitates updating of the

network parameters whenever independent estimates of rainfall are available. The

23

Precipitation Estimation from Remote Sensing Information using Artificial Neural

Network (PERSIANN) system was based on geostationary infrared imagery and

later extended to include the use of both infrared and daytime visible imagery. The

system uses grid infrared (10.2-11.2 µm) images of global geosynchronous satellites

(GOES-8, GOES-10, GMS-5, Meteosat-6, and Meteosat-7) provided by CPC,

NOAA to generate 30-minute rain rates are aggregated to 6-hour accumulated

rainfall. Model parameters are regularly updated using rainfall estimates from low-

orbital satellites, including TRMM, NOAA-15, -16, -17, DMSP F13, F14, F15

(Hong, et al., 2005; Hong, et al., 2007) . EUMETSAT blending algorithm uses

passive microwave data from the SSM/I instrument on the US-DMSP satellites and

images in the Meteosat IR channel for the estimation of instantaneous rain rates and

daily rainfall averages on the spatial resolution of 3km × 3km and temporal

resolution of 15 min for products from METEOSAT-9 as well as 5 min for products

from METEOSAT-8. Therefore the main goal of the newly developed Multi-sensor

Precipitation Estimate (MPE) was combining the advantages of both retrieval

systems (Heinemann et al., 2002). MPE is based on relating rain rate to the IR

brightness temperature in this way that highest rain rate associated to the coldest

temperature and lowest rain rate associated to the warmest temperature (monotonic

functions) and derived functions have been stored as lookup tables (Fig.3). For

temperature above certain threshold no precipitation is estimated. The form of these

functions depends on the current weather situation. MPE algorithm uses the passive

MW rain rate measurements as calibration values therefore it is adjusted

geographically and temporarily. The adjustments take place based on accumulated

data over a certain time period. Due to low spatial resolution of MW measurements

it cannot be done for each individual image (Heinemann and Kerényi, 2003). In

higher latitudes the repetition rate of the polar orbiting satellites (SSM/I) is higher

than in equatorial regions. Therefore the accumulation time for the co-location

(which is treated as a global parameter) can be shorter. As result 24 hours for the full

earth scanning (MPE from METEOSAT-9) and 18 hours for RSS (MPE from

METEOSAT-8) has been selected (personal communication Dr.Heinemann,

EUMETSAT).

24

Figure 3 Look-up tables (LUT) derived from a 6h period on Aug. 19th, 2001 for

the 5°x5° box number 229, over West Africa (Source: Heinemann and Kerényi,

2003)

2.2. Statistical methods for verifying estimated precipitation by

satelltes

2.2.1. Validation of near real-time vs. climate scale precipitation

The integrated mean rainfall amount over space and time is the quantity of interest in

validating climate scale precipitation estimates against gauge data and the statistics

used to measure the accuracy of the estimates are usually bias, correlation

coefficient and RMSE.

In this case the requirement is that the rainfall estimation algorithm provides an

acceptable estimate on average and therefore errors on shorter time and space scale

are unimportant. For instance, that the single threshold based GOES Precipitation

Index (GPI) incorrectly contacts rain with cirrus clouds and fails to detect rain from

warm clouds because these errors negate over large space and timescales (Levizzani,

et al. 2007).

These types of errors are not acceptable in short-term precipitation estimation. In

hydrological studies accurate estimation of precipitation at the watershed scale is

essential. Identifying the correct rain type and location is much more important than

estimating the exact amount of rain in NWP data assimilation of satellite rainfall.

Detecting the maximum rain rates as well as occurrence of rainfall are crucial for

flash flood warning and tropical rainfall potential (Luque, et al. 2006).

It is evident that for validating near real-time precipitation estimates using more

advanced statistical methods are necessary for achieving complementary information

which is not provided by simpler statistical methods.

25

2.2.2. Validation data

The main sources for validating precipitation estimated by satellite are observations

from rain gauges and radar rainfall estimates. Each source has separate advantages

and disadvantages.

The most important advantage for rain gauges is giving direct measurement of rain

accumulation. However, they are unrepresentative of timescale and areal value

estimated by satellite respectively because gauge accumulation over several minutes

to several hours versus a satellite “snapshot” and they are point measurements. In

addition; gauges are unable to verify oceanic satellites rainfall estimations because

they are limited to land regions and islands (Ebert and McBride, 2000 and Levizzani

et al. 2007).

The main advantages for radar images are high temporal (5-10 min) and spatial (1

km) resolution. Radar observations are representative of timescale and areal value

estimated by satellite in that they give “snapshots”. However, radar images are

themselves indirect measurements of rainfall and are prone to errors of various

kinds. Careful quality control of the data and bias correction using nearby rain-gauge

observations can correct most of the error in radar rainfall estimates. Radars

measurements are limited to land regions and near-coastal regions (Levizzani et al.

2007).

For instantaneous and high temporal and spatial resolution estimates, gauge-

corrected radar estimates or analyses are generally preferable to gauge observations.

At slightly larger space and timescales (6h to daily), rain-gauge analyses or

combined gauge/radar analyses are more accurate and should be used in preference

to raw gauge or radar observations. For pentad (5-day), monthly, and longer

timescales, rain-gauge analyses are usually accurate enough to provide good

validation data (Levizzani et al. 2007).

2.2.3. Standard statistical methods for verifying precipitation

estimated by satellite

A large variety of verification scores are used operationally to verify precipitation

estimated by satellites. Details of these scores can be found at the Centre for

Australian Weather and Climate Research (Forecast Verification – Issues, Methods

and FAQ, 2009) and on the WWRP (2009). Some scores are highly recommended to

evaluate the important aspects of satellite estimates while recognizing that most

users of verification output may not process the large array of scores. Highly

recommended scores are accuracy, bias, probability of detection (POD), false alarm

ration(FAR), critical success index (CSI) and equitable threat score (ETS) for

26

categorical verification as well as mean rainfall amount over space and time, mean

error (ME), root mean square error (RMSE) and mean absolute error (MAE) for

continuous verification methods (WWRP 2009).

2.2.3.1. Categorical verification statistics for assessing errors

in the spatial distribution of precipitation

Most of the categorical statistics are based on a 2*2 contingency table that

summarizes yes/no (hits/misses) events such as rain/no rain, presented in table 1

(Luque et al., 2006 and 2008 , Levizzani et al. 2007, Roebeling, R., and Holleman,

I., 2009).

Table 1 2*2 contingency table

The ACCURACY score shows the overall fraction of correct estimates. The range of

this score is 0 to 1 and perfect score is 1. This score is simple and intuitive.

total

negativecorrecthitsAccuracy

The BIAS score measure the ratio of the frequency of estimated rain area to the

frequency of observed rain area. Indicates whether the estimation system has a

tendency to underestimate (BIAS < 1) or overestimate (BIAS > 1) events. Does not

measure how well the estimation corresponds to the observations, only measures

relative frequencies. The range of this score is between 0 to infinity and the perfect

score is 1.

misseshits

alarmsfalsehitsBIAS

Observed

Yes No

EstimatedYES Hits False alarms Estimated Yes

NO Misses Correct negatives Estimated No

Observed Yes Observed No N=Total

27

The probability of detection (POD) or “hit rate” measures the fraction of observed

“yes” events that were correctly diagnosed. The range of this score is between 0 to 1

and the perfect score is 1.

misseshits

hitsPOD

False Alarm Ratio (FAR) measures the fraction of diagnosed events (yes events) that

were actually did not occur. The range of this score is between 0 to 1 and the perfect

score is 0.

alarmsfalsehits

alarmsfalseFAR

FAR and POD should always be used together. Because good or even perfect value

in either case individually are easily obtained. For example if an algorithm has wet

bias such that it always detects rain everywhere all the time, POD will be perfect

(“1”). However, the FAR will also close to the worse score (“1”) (Levizzani et al.

2007).

Threat Score (TS) or Critical Success Index (CSI) measures the fraction of observed

and/or estimated events that were correctly diagnosed. This index shows that how

well the estimation “yes” events did corresponds to the observed “yes” events. The

range of this score is between 0 to 1 and the perfect score is 1.

alarmsfalsemisseshits

hitsCSITS

Equitable threat score (Gilbert skill score) measures the fraction of observed and/or

estimated events that were correctly diagnosed, adjusted for hits associated with

random chance (for example, it is easier to correctly estimate rain occurrence in a

wet climate than in a dry climate). The ETS is often used in the verification of

satellite estimated rainfall because its "equitability" allows scores to be compared

more fairly across different regimes. It is sensitive to hits. Because it penalises both

misses and false alarms in the same way, it does not distinguish the source of

estimation error. The range of this score is between -1/3 to 1, 0 indicates no skill.

The perfect score is 1.

28

random

random

hitsalarmsfalsemisseshits

hitshitsETS

Where

total

alarmsfalsehitsmisseshitshits random

))((

2.2.3.2. Continuous verification statistics for assessing errors

in magnitude

Continuous verification statistics are used for verifying how the values of the

estimations differ from the values of the observations, i.e. to measure the accuracy of

continuous variable such as precipitation. These methods often include some

exploratory plots such as scatter plots and box plots. In the equations to follow Yi

indicates the estimated value at point or grid box i, Oi indicates the observed value,

and N is the number of samples (Levizzani et al. 2007 and The Centre for

Australian Weather and Climate Research, Forecast Verification – Issues, Methods

and FAQ, 2009).

The mean error (bias) measures the average estimation error. It shows the

differences between estimated and observed values. The range of ME is minus

infinity to infinity and the perfect score is 0.

)(1

1

i

N

i

i OYN

ErrorMean

The mean absolute error (MAE) measures the average of the absolute difference

(i.e., negative differences are changed to positive) between the estimates and

observations. Absolute error retains the differences in magnitude that would

otherwise be reduced because positive and negative differences would cancel each

other to some degree. The range of MAE is 0 to infinity and the perfect score is 0.

N

i

ii OYN

MAE1

1

The root mean square error (RMSE) similar to MAE measures the average error

magnitude but gives greater weight to the larger errors because the differences are

29

squared before summing. The range of RMSE is 0 to infinity and the perfect score is

0.

N

i

ii OYN

RMSE1

21

30

3. Materials and methods

3.1. General description of data

3.1.1. Description of EUMETSAT Multi Sensor Precipitation

Estimation (MPE) products

Meteosat Second Generation (MSG) is a series of four geostationary meteorological

satellites designed and developed by European Space Agency (ESA) and the

European Organisation for the Exploitation of Meteorological Satellites

(EUMETSAT). The MSG-1 satellite (METEOSAT 8) was successfully launched in

August 2002, and the MSG-2 satellite (METEOSAT 9) in December 2005 was

launched and both satellites are operated by EUMETSAT. The planned launch date

for MSG-3 is January 2011 and for MSG-4 is January 2013 (European Space

Agency web page, www.esa.int). All MSG satellites are essentially the same from a

technical standpoint. The Spinning Enhanced Visible and Infrared Imager

(SEVIRI)with eleven 3-km-resolution channels and one 1-km-resolution visible

channel (Table 2), provides more comprehensive and more frequent data to

meteorologists and climate-monitoring scientists(Kidder et al., 2005).

Table 2 MSG SERVI Channel

Band Centre (µm) Spectral range

(µm)

Resolution (km)

HRV 0.75 0.50 – 0.90 1

VIS 0.6 0.635 0.56 – 0.71 3

VIS 0.8 0.81 0.74 – 0.88 3

IR 1.6 1.64 1.50 – 1.78 3

IR 3.9 3.92 3.48 – 4.36 3

WV 6.5 6.25 5.35 – 7.15 3

WV 7.3 7.35 6.85 – 7.85 3

IR 8.7 8.70 8.30 – 9.10 3

IR 9.7 9.66 9.38 – 9.94 3

IR 10.8 10.8 9.80 – 11.80 3

IR 12.1 12.0 11.00 – 13.00 3

IR 13.4 13.40 12.40 – 14.40 3

The MSG satellites normally scan the full Earth disk every 15 min. However, since

13 May 2008 from a position at 9.5° E by decreasing the scanning area to one third

http://www.esa.int/

31

of the earth disk (latitude range 15° N to 70° N), the scan time for METEOSAT-8

(MSG-1) has been reduced to 5 min. This is known as the Rapid Scanning Service

(RSS). MSG RSS generates more frequently images (5-min interval) at the same

time scale currently used for weather radars (http://www.eumetsat.int).

EUMETSAT has been producing instantaneous rain rates from METEOSAT data

based on the blending METEOSAT IR channel with rain rates derived from the

Special Sensor Microwave/Imager (SSM/I) on DMPS. The blending algorithm

assumes that colder clouds produce more precipitation that warmer clouds. In this

method within specified geographical and temporal window a look-up table (LUT)

is created based on the relation between rain rate from SSM/I (as reference data) and

METEOSAT IR channel. By histogram matching techniques the lowest

METEOSAT IR-temperature value matches with highest rain rate from SSM/I. Then

the rain rates in the full spatial and temporal resolution is derived from METEOSAT

images based on the resulting LUT curve by histogram matching (figure 3)

(Heinemann and Kerényi, 2003). The resulting product is known as the

EUMETSAT Multi Sensor Precipitation Estimation (MPE).

3.1.1.1. EUMETSAT Multi Sensor Precipitation Estimation

(MPE) products pre-processing

The pre-processing steps for importing EUMETSAT MPE products from

METEOSAT8 with 5 min temporal resolution and METEOSAT9 with 15 min

temporal resolution are almost the same except the geo-referencing file

specifications. For this reason a set of three batch files have been implemented. The

function of these batch files can summarize as:

- File renaming based on selecting essential characters from the original file

name, an example of resulted file name for MPE from METEOSAT9 is

“MPE_20090501_0000_M9_00” and for MPE_RSS from METEOSAT8 is

“MPE_RSS_20090501_0000”

- Converting original data format (GRIB) to the raw data by WGRIB2.exe

The GRIB-2 data encoding is based on WMO agreements. One of those agreements

includes the requirement to use SI-units in the encoding. Therefore the

(instantaneous) rain-rates are not encoded in mm/hr but kg/m^2/s (which is the SI

unit) or in other words mm/s. Thus, it is needed to multiply the decoded values by

3600 to get mm/hr.

- Implementing some code functions by ILWIS to select the relevant values

as well as multiplying by 3600 to get values in mm/hr.

- Vertical rotating by ILWIS

- Setting geo-reference by ILWIS

32

Related codes and batch files are presented in Appendix A.

Figure 4.a and 4.b illustrates pre-processed EUMETSAT MPE products respectively

from METEOSAT8 (RSS MSG) and METEOSAT9. Visually comparison of these

two images indicates that RSS MSG is only a partial image (latitude range from 15°

N to 70° N).

Figure 4 (a) EUMETSAT MPE product from METEOSAT8 (26th

May 2009,

02:45 UTC). (b) EUMETSAT MPE product from METEOSAT9 (26th

May

2009, 02:45 UTC)

a. b.

(mm/h) (mm/h)

33

3.1.2. Description of ground based radar data

Radio Detection and Ranging (RADAR) is a active remote sensing technique which

is capable of gathering information about objects located at remote distance from the

sensing device (Kerle et al., 2004).

Doppler shift is a frequency shift that occurs in electromagnetic waves due to the

motion of scatterers toward or away from the observer. An example of the Doppler

shift for sound waves is the frequency shift that occurs as race cars approach and

then recede from a stationary observer. Doppler radar is radar that can determine the

frequency shift through measurement of the phase change that occurs in

electromagnetic waves during a series of pulses (Delrieu et al. 2009).

The ground based radar data set has been used in this research was from two

identical C-band (5.62 GHz) Doppler weather radar which are operates by KNMI

(Royal Netherlands Meteorological Institute). These products are bias adjusted and

verified with rain gauge data (Holleman 2007). The De Bilt radar is located at

latitude 52.10° N and longitude 5.18° E. The Den Helder radar is located at latitude

52.96° N and longitude 4.79° E. The weather radar data is usable in the range of 200

km from the radar station (Roebeling and Holleman 2009). Figure 5 illustrates the

radar stations as well as the extent of 200km range circle.

Figure 5 Map shows KNMI weather radars location

The weather radars images have been recorded with 5 min temporal resolution and

1×1 km² spatial resolution. The pseudoCAPPI (pseudo-Constant-Altitude Plan-

Position Indicator) images from both radars have combined into a national

34

composite(Holleman 2006). Table 3 shows the geographical projection parameters

of the KNMI radar images.

Table 3 geographical projection parameters of the KNMI radar images.

Modified from (Holleman 2006)

Parameter Value

Projection Stereographic

Projection origin (lon,lat) 0E, 90N

True scale (lat) 60N

Earth radius (equator, polar) 6378.388 km, 6356.912 km

Pixel size at true scale (x,y) 1.000 km, - 1.000 km

Offset of image corner (i,j) 0.0, 1490.9

Number of rows 256

Number of columns 256

The KNMI radar image corners after applying projection parameters are listed in

table 4.

Table 4 the corners of KNMI radar image (Holleman 2007)

Corner Lon [deg] Lat [deg]

North-west 0.000E 55.296N

North-east 9.743E 54.818N

South-east 8.337E 49.373N

South-west 0.000E 49.769N

The relation between recorded radar reflectivity and precipitation is known as Z-R

relation and expressed in form of power law Z = aRb . Where reflectivity factor Z in

mm6 m

-3 and precipitation rate R in mm h

-1 and a and b are empirical constants. This

relation heavily depends on drop size distribution (DSD) due to the precipitation

type and climatological circumstances. Table 5 shows Z-R relationship for various

precipitation types. The Z-R relation used for converting the KNMI radar reflectivity

to the rainfall intensity is the one developed by Marshal and Palmer (1948)

(Holleman 2007; Roebeling and Holleman 2009) :

Z = 200R1.6

35

Radar reflectivity factors below 7 dBZ (0.1 mm/h) are associated with noise. Radar

reflectivity factors higher than 55 dBZ (100 mm/h) are usually associated with large

hail or residual strong clutter.

Table 5 Derived empirical constants a and b using the typical Z = aRb relation,

for several precipitation types (Henken et al., 2009)

Source Precipitation type a b

Joss and Waldvogel (1990) Stratiform 300 1.6

Rosenfeld et al. (1993) Tropical Rain 250 1.2

Marshal and Palmer (1948) Stratiform 200 1.6

Fujiwara (1969) Thunderstorm 486 1.37

3.1.2.1. Ground based radar data pre-processing

The pre-processing steps for importing ground based radar data to ILWIS and

converting to rainfall intensity using the Z-R relationship has been done by a set of

two batch files. The function of batch file is summarized as below :

- File renaming based on selecting essential characters from the original file

name, an example of resulted file name is

“RAD_NL25_PCP_NA_200905012200”

- Converting original data format (HDF5) to ILWIS data format by

gdal_translate.exe

- Calculating the logarithm (base 10) of rainfall intensity by Z-R relationship

form following formula by ILWIS:

log R = (value-109)/32

1- Calculating rainfall intensity according to the following relation using

ILWIS:

R = 10^LogR

2- Setting the geo-reference by ILWIS

Related codes and batch files are presented in Appendix B.

Figure 6.a and 6.b respectively shows recorded radar reflectivity factor Z map for 26

May 2009, 00:00 UTC and resulted rainfall intensity map by applying Z-R relation.

36

Figure 6 (a) Radar reflectivity factor(dBZ) map and (b) Radar rainfall intensity

(mm/h) map for 26 May 2009, 00:00 UTC

a. b.

(mm/h) (dBZ)

37

3.2. Methodology for comparision the satellite estimates against

independent data from radars

The methodology for this research is including three main parts (figure 7). Data pre-

processing is containing different steps such as converting formats, geo-referencing,

resampling, and reclassifying. Calculating categorical and continuous verification

statistics and finally hypothesis testing based on obtained results in previous part.

Figure 7 the research methodology

3.2.1. Data preparation

The sub map for north-western Europe has been created. Note that because of the

errors of weather radar observations increase with increasing distance from the

ground-based station, the sub-map area restricted to the Netherlands and some part

of Belgium. Water bodies are masked out the weather radar observations to exclude

unrealistically high rain rate values caused by sea clutter (Roebeling and Holleman

2009).

For comparison MPE data sets with radar data it is essential to resample all data sets

to the same grid size as well as same projection system. Therefore all data sets has

been re-sampled to the UTM zone 32_WGS 84 with 3 × 3 km grid size.

Two more steps added to the previously mentioned batch files to manage these data

processing steps (Appendix A and Appendix B)

Preprocessing

Categorical Verification

Statistics

Continuous Verification

Statistics

Hypothesis

testing

38

Figure 8.a, 8.b and 8.c shows an example of processed images as well as statistical

information for MPE_METEOSAT 9, MPE_RSS_METEOSAT 8 and RADAR for

26 May 2009, 04:00 UTC respectively.

a.

(mm/h)

b.

(mm/h)

39

Figure 8 processed images (a) MPE_METEOSAT9, (b)

MPE_RSS_METEOSAT8 and (c) RADAR (26 May 2009, 04:00 UTC)

3.2.2. Categorical verification statistics

After pre-processing and processing steps the next step toward calculating

categorical verification statistics is reclassifying instantaneous maps as well as

aggregated maps to two classes, “rain” and “no rain” then crossing reclassified

maps. For this reason two more command lines have been added to the scripts, one

for reclassification and the second for exporting cross table values as DBF file. Then

creating contingency table and calculating categorical verification statistics for

assessing errors in the spatial distribution of precipitation. The contingency table is

summarized table of hundreds of extracted DBF files from reclassified images.

Therefore applying automated procedure was essential for importing and

summarizing these DBF files. Appendix C shows related codes in Visual Basic

programming language. Figure 9 illustrates flow diagram of methodology for

categorical verification statistics calculation.

3.2.3. Continuous verification statistics

After pre-processing and processing steps the next step toward calculating

continuous verification statistics is exporting image values as DBF file for

instantaneous maps as well as aggregated maps. For this reason two more command

lines have been added to the scripts, one for creating histogram and the second for

c

.

(mm/h)

40

exporting histogram values as DBF file. Then continuous verification statistics for

assessing errors in magnitude of precipitation calculated. The automation procedure

applied for calculating continuous verification statistics from hundreds of DBF files

which have been achieved from previous step. Appendix D shows related codes in

Visual Basic programming language. Figure 10 illustrates the flow diagram of

methodology for continuous verification statistics calculation.

41

METEOSAT8_MPE

5 min temporal

resolution

(mm/h)

METEOSAT9_MPE

15 min temporal

resolution

(mm/h)

Radar rainfall data

5 min temporal

resolution

(mm/h)

Creating sub-map for study area

Data accumulation:

- 1 hour

- 3 hours

Reclassification to “Rain” and “ No Rain”

3Hours

accumulated:

- Radar-Meteosat8

- Radar-Meteosat9

1Hour

accumulated:

- Radar-Meteosat8

- Radar-Meteosat9

Instantaneous:

- Radar-Meteosat8

- Radar-Meteosat9

- Radar-Meteosat8

- Radar-Meteosat9

Cro

ss t

able

Dat

a se

ts

Cat

ego

rica

l

stat

isti

cs

anal

ysi

s

- Radar-Meteosat8

- Radar-Meteosat9

- Radar-Meteosat8

- Radar-Meteosat9

Results

Hypothesis testing

Figure 9 flow diagram of methodology for categorical verification statistics

calculation

42

METEOSAT8_MPE

5 min temporal

resolution

(mm/h)

METEOSAT9_MPE

15 min temporal

resolution

(mm/h)

Radar rainfall data

5 min temporal

resolution

(mm/h)

Creating sub-map for study area

Data accumulation:

- 1 hour

- 3 hours

Creating Histogram

3Hours

accumulated:

- Radar-Meteosat8

- Radar-Meteosat9

Dat

a se

ts

Co

nti

nu

ou

s

stat

isti

cs

anal

ysi

s 1Hour

accumulated:

- Radar-Meteosat8

- Radar-Meteosat9

Instantaneous:

- Radar-Meteosat8

- Radar-Meteosat9

Results

Hypothesis testing

Export to DBF

Figure 10 flow diagram of methodology for continuous verification statistics

calculation

43

4. Results

According to the KNMI report (figure 11), two main rainfall events have been

selected for comparing estimated rainfall from METEOSAT 8 and METEOSAT 9

with ground radar observation. The first event is 14 to 17 of May 2009 and the

second event 25 to 27 of May 2009. The statistical analysis carried out based on

three temporal resolutions level, instantaneous, 1 hour accumulated and 3 hours

accumulated rainfall.

Figure 11 reported rainfall for May 2009 (source: http://www.knmi.nl )

4.1. The first event

DATE and Time: 20090514, 19:00 UTC to 20090517, 23:45 UTC

The Cloud Analysis Image (CLAI) during this event shows high level clouds

(Cirrus) as well as mid level clouds (Nimbostratus) especially over the Netherlands

44

(e.g. figure 12), other colours refer to land cover, water bodies. The KNMI reported

heavy rain with flooding in this period.

Figure 12 the cloud analysis image for the first event

45

4.1.1. Instantaneous comparison

Rainfall intensity for all data sets: mm/h

Number of images (with 15 min interval): 308

Number of pixels in each image: 6576

Number of compared pixels in each part: 2025408 (6576*308).

The grid size for all images 3000(m) and projection: UTM, Zone 32

Categorical statistics for assessing spatial distribution

Radar-METEOSAT8 (RSS)

Based on Number of

pixels:

Ground based RADAR

Yes No

RSS_METEOSAT8 YES 345179 86841 432020

NO 493245 1100143 1593388

838424 1186984 2025408

Based on Percentage:

Ground based RADAR

Yes No

RSS_METEOSAT8 YES 17.04 4.29 21.33

NO 24.35 54.32 78.67

41.40 58.60 100

Table 6 shows the calculated scores based on contingency table. The overall

accuracy is 71%, indicating that 71% of all observations with RSS were correct.

Bias is 52%, indicating underestimating in RSS data. POD is 41%, indicating that

roughly 2/5 of the observed rain events were correctly estimated by RSS. FAR is

20%, indicating that in roughly 1/5 of the estimated rain events, rain was not

observed. CSI is 37%, meaning that 37% of the "rain" events (observed and/or

estimated) were correctly diagnosed. The ETS is 22%, meaning that 22% of the

46

"rain" events (observed and/or estimated) were correctly diagnosed. The ETS

adjusted for hits associated with random chance.

Table 6 calculated categorical statistics scores between METEOSAT8_RSS and

ground based radar (Event1, instantaneous)

Probability of

Detection(POD)

H/(H+M) 0.41

False Alarm

Ratio (FAR)

F/H+F 0.20

Critical Success

Index (CSI)

H/(H+M+F) 0.37

Accuracy (H+R)/Total N 0.71

Bias (H+F)/(H+M) 0.52

ETS (H- Hrand)/(H+M+F-

Hrand) 0.22

Where H are the correct hits, M are the missed precipitation events, F are the false

precipitation alarms, R the correct rejections and N total number of pixels.

Radar-METEOSAT9

Based on Number of

pixels:

Ground based RADAR

Yes No

METEOSAT9 YES 381565 76178 457743

NO 456859 1110806 1567665

838424 1186984 2025408

47


Ground based RADAR

Yes No

METEOSAT9 YES 18.84 3.76 22.60

NO 22.56 54.84 77.48

41.40 58.60 100




almost 1/2 of the observed rain events were correctly estimated by RSS. FAR is








Probability of

Detection(POD)

H/(H+M) 0.46

False Alarm

Ratio (FAR)

F/H+F 0.17

Critical Success

Index (CSI)

H/(H+M+F) 0.42


Bias (H+F)/(H+M) 0.55


Hrand) 0.27

48

Continuous statistics to measure how estimated values by METEOSAT MPE

products differ from the values of ground based radar

Figure 13 shows the comparison between mean rainfall values. These values have

calculated based on weighted rainfall values our whole image. The range of this

value is 0 to 0.7 (mm/h), 0 to 1.48(mm/h), 0 to 2(mm/h) respectively for radar,

METEOSAT 8 and METEOSAT 9.

Figure 13 mean rainfall values (Event1, instantaneous)

The mean error (ME) values shows the differences between estimated and

observed values. The perfect value for ME is zero. Figure 14 illustrates the ME

values for MPE from METEOSAT9 and METEOSAT8. The range of this value is –

0.45 to 0.75(mm/h), - 0.20 to 1.5(mm/h) for METEOSAT8 and METEOSAT9

respectively.

Figure 14 Mean Error (ME) values (Event1, instantaneous)

49

The mean absolute error (MAE) measures the average of the absolute difference

(i.e., negative differences are changed to positive) between the estimates and

observations. Figure 15 shows the MAE values between estimated MPE (from

METEOSAT9 and METEOSAT8) and ground based radar data. The range of this

value is 0 to 0.75(mm/h), 0 to 1.5(mm/h) respectively for METEOSAT 8 and

METEOSAT 9.

Figure 15 Mean Absolute Error values (Event1, instantaneous)

The Root Mean Square Error (RMSE) similar to MAE measures the average error

magnitude but gives greater weight to the larger errors because the differences are

squared before summing. Figure 16 shows the RMSE values between estimated

MPE (from METEOSAT9 and METEOSAT8) and ground based radar data. The

range of this value is 0 to 2.2(mm/h) for METEOSAT 8 and METEOSAT 9.


50

4.1.2. One hour accumulated rainfall comparison

The rainfall intensity in ground based radar data, METEOSAT8 and METEOSAT9

MPE products are based on mm/h with different temporal resolutions. For one hour

accumulated comparison, in METEOSAT8 and ground based radar data cases with 5

min temporal resolution, 12 images added together then divided by 12 to produce 1

hour accumulated rainfall images. In METEOSAT 9 cases, with 15 min temporal

resolution, 4 images added together then divided by 4 to produce 1 hour

accumulated rainfall images.


Number of images (with 1 hour interval): 76






Based on Number of

pixels:

Ground based RADAR

Yes No

RSS_METEOSAT8 YES 123409 23640 147049

NO 120660 232067 352727

244069 255707 499776


Ground based RADAR

Yes No


NO 24.14 46.43 70.58

48.84 51.16 100

51




roughly 1/2 of the observed rain events were correctly estimated by RSS. FAR is






Table 8 calculated categorical statistics scores between METEOSAT8_RSS and

ground based radar (Event1, 1 Hour accumulated)

Probability of

Detection(POD)

H/(H+M) 0.51

False Alarm

Ratio (FAR)

F/H+F 0.16

Critical Success

Index (CSI)

H/(H+M+F) 0.46


Bias (H+F)/(H+M) 0.60


Hrand) 0.26

52

Radar-METEOSAT9

Based on Number of

pixels:

Ground based RADAR

Yes No

METEOSAT9 YES 129439 18994 148433

NO 114630 236713 351343

244069 255707 499776


Ground based RADAR

Yes No

METEOSAT9 YES 25.90 3.80 29.70

NO 22.94 47.36 70.30

48.84 51.16 100




almost 1/2 of the observed rain events were correctly estimated by RSS. FAR is






53



Probability of

Detection(POD)

H/(H+M) 0.53

False Alarm

Ratio (FAR)

F/H+F 0.13

Critical Success

Index (CSI)

H/(H+M+F) 0.49


Bias (H+F)/(H+M) 0.61


Hrand) 0.30



Figure 17shows the comparison between mean rainfall values. These values have


value is 0 to 0.7 (mm/h), 0 to 1.48(mm/h), 0 to 1.9(mm/h) respectively for radar,

METEOSAT8 and METEOSAT9.

Figure 17 mean rainfall values (Event1, 1Hour accumulated)

54

Figure 18 illustrates the ME values for MPE from METEOSAT9 and

METEOSAT8. The range of this value is – 0.30 to 0.8(mm/h), - 0.20 to 1.42(mm/h)

respectively for METEOSAT 8 and METEOSAT 9.

Figure 18 Mean Error (ME) values (Event1, 1Hour accumulated)

Figure 19 shows the MAE values between estimated MPE (from METEOSAT9 and

METEOSAT8) and ground based radar data. The range of this value is 0 to

0.8(mm/h), 0 to 1.42(mm/h) respectively for METEOSAT 8 and METEOSAT 9.

Figure 19 Mean Absolute Error values (Event1, 1Hour accumulated)

55

Figure 20 shows the RMSE values between estimated MPE (from METEOSAT9

and METEOSAT8) and ground based radar data. The range of this value is 0 to

1.4(mm/h), 0 to 1.8(mm/h) respectively for METEOSAT 8 and METEOSAT 9.

Figure 20 Root Mean Square Error (RMSE) values (Event1, 1Hour

accumulated)

4.1.3. Three hours accumulated rainfall comparison

The rainfall intensity in ground based radar data, METEOSAT8 and METEOSAT9

MPE products are based on mm/h with different temporal resolutions. For three

hours accumulated comparison, in METEOSAT8 and ground based radar data cases

with 5 min temporal resolution, 36 images added together then divided by 12 to

produce three hours accumulated rainfall images (mm/3h). In METEOSAT 9 cases,

with 15 min temporal resolution, 12 images added together then divided by 4 to

produce three hours accumulated rainfall images (mm/3h).

Rainfall intensity for all data sets: mm/3h

Number of images (with 3 hours interval): 25




56



Based on Number of

pixels:

Ground based RADAR

Yes No

RSS_METEOSAT8 YES 62118 7002 69120

NO 43469 51811 95280

105587 58813 164400


Ground based RADAR

Yes No


NO 26.44 31.52 57.96

64.23 35.77 100

Table 10 shows the calculated scores based on contingency table. The

overall accuracy is 69%, indicating that 69% of all observations with RSS were

correct. Bias is 65%, indicating underestimating in RSS data. POD is 59%,

indicating that roughly 3/5 of the observed rain events were correctly estimated by

RSS. FAR is 10%, indicating that in roughly 1/10 of the estimated rain events, rain

was not observed. CSI is 55%, meaning that 55% of the "rain" events (observed

and/or estimated) were correctly diagnosed. The ETS is 26%, meaning that 26% of

the "rain" events (observed and/or estimated) were correctly diagnosed. The ETS


57


and ground based radar (Event1, 3 Hours accumulated)

Probability of

Detection(POD)

H/(H+M) 0.59

False Alarm

Ratio (FAR)

F/H+F 0.10

Critical Success

Index (CSI)

H/(H+M+F) 0.55


Bias (H+F)/(H+M) 0.65


Hrand) 0.26

Radar-METEOSAT9

Based on Number of

pixels:

Ground based RADAR

Yes No

METEOSAT9 YES 58462 4678 63140

NO 44708 56552 101260

103170 61230 164400

58


Ground based RADAR

Yes No

METEOSAT9 YES 35.56 2.85 38.41

NO 27.19 34.40 61.59

62.76 37.24 100




indicating that almost 3/5 of the observed rain events were correctly estimated by







ground based radar (Event1, 3Hours accumulated)

Probability of

Detection(POD)

H/(H+M) 0.57

False Alarm

Ratio (FAR)

F/H+F 0.07

Critical Success

Index (CSI)

H/(H+M+F) 0.54


Bias (H+F)/(H+M) 0.61


Hrand) 0.28

59





value is 0 to 2 (mm/3h), 0 to 3.4(mm/3h), 0 to 5.2(mm/3h) respectively for radar,


Figure 21 mean rainfall values (Event1, 3Hours accumulated)


METEOSAT8. The range of this value is -0.80 to 2(mm/3h), -.55 to 3.4(mm/3h)

respectively for radar, METEOSAT 8 and METEOSAT 9.

Figure 22 Mean Error (ME) values (Event1, 3Hours accumulated)

60


METEOSAT8) and ground based radar data. The range of this value is 0 to

2(mm/3h), 0 to 3.4(mm/3h) respectively for radar, METEOSAT 8 and METEOSAT

9.

Figure 23 Mean Absolute Error values (Event1, 3Hours accumulated)


and METEOSAT8) and ground based radar data. The range of this value is 0 to

3.5(mm/3h), 0 to 4.5 (mm/3h) respectively for radar, METEOSAT 8 and

METEOSAT 9.


accumulated)

61

4.2. The second event

DATE and Time: 20090525, 0000 UTC to 20090527, 23:45 UTC

The Cloud Analysis Image (CLAI) during this event shows extensive high level

clouds (Cirrus and Cumulonimbus) as well as mid level clouds (Nimbostratus) and

low level clouds (Stratus) (e.g. figure 25). It is cold frontal event with combination

of very short vertical development of clouds (the sharp change from low level clouds

to high level clouds along the southern portion of the cloud system, across Belgium).

The KNMI reported heavy showers complexes over a large part of the Netherlands

on 25th and 26th of May. The showers were accompanied by exceptionally active

thunderstorms.

Figure 25 the cloud analysis image for the second event

4.2.1. Instantaneous comparison


Number of images (with 15 min interval): 288 (4 * 24 * 3)




62



Based on Number of

pixels:

Ground based RADAR

Yes No

RSS_METEOSAT8 YES 206425 108147 314572

NO 413321 1165995 1579316

619746 1274142 1893888


Ground based RADAR

Yes No


NO 21.82 61.57 83.39

32.72 67.28 100










63


and ground based radar (Event2, instantaneous)

Probability of

Detection(POD)

H/(H+M) 0.33

False Alarm

Ratio (FAR)

F/H+F 0.34

Critical Success

Index (CSI)

H/(H+M+F) 0.28


Bias (H+F)/(H+M) 0.51


Hrand) 0.17

Radar-METEOSAT9

Based on Number of

pixels:

Ground based RADAR

Yes No

METEOSAT9 YES 172457 73772 246229

NO 447245 1200414 1647659

619702 1274186 1893888

64


Ground based RADAR

Yes No

METEOSAT9 YES 9.11 3.90 13

NO 23.62 63.38 87

32.72 67.28 100












Probability of

Detection(POD)

H/(H+M) 0.28

False Alarm

Ratio (FAR)

F/H+F 0.30

Critical Success

Index (CSI)

H/(H+M+F) 0.25


Bias (H+F)/(H+M) 0.40


Hrand) 0.15

65


products differ from the values of ground based radar.



value is 0 to 1.8 (mm/h), 0 to 11(mm/h), 0 to 10.8(mm/h) respectively for radar,


Figure 26 mean rainfall values (Event2, instantaneous)


METEOSAT8. The range of this value is -0.2 to 10 (mm/h), -0.2 to 9.9(mm/h)


Figure 27 Mean Error (ME) values (Event2, instantaneous)

66


METEOSAT8) and ground based radar data. The range of this value is 0 to 10

(mm/h), -0.2 to 9.9(mm/h) respectively for METEOSAT 8 and METEOSAT 9.

Figure 28 Mean Absolute Error (MAE) values (Event2, instantaneous)


and METEOSAT8) and ground based radar data. The range of this value is 0 to 12.5

(mm/h), 0 to 13(mm/h) respectively for METEOSAT 8 and METEOSAT 9.


67

4.2.2. One hour accumulated rainfall comparison


Number of images (with 1 hour interval): 72






Based on Number of

pixels:

Ground based RADAR

Yes No


NO 121417 258962 380379

185350 288122 473472


Ground based RADAR

Yes No


NO 25.64 54.69 80.34

39.15 60.85 100








68




and ground based radar (Event2, 1 Hour accumulated)

Probability of

Detection(POD)

H/(H+M) 0.34

False Alarm

Ratio (FAR)

F/H+F 0.31

Critical Success

Index (CSI)

H/(H+M+F) 0.30


Bias (H+F)/(H+M) 0.50


Hrand) 0.15

Radar-METEOSAT9

Based on Number of

pixels:

Ground based RADAR

Yes No

METEOSAT9 YES 57539 21268 78807

NO 127811 266854 394665

185350 288122 473472

69


Ground based RADAR

Yes No

METEOSAT9 YES 12.15 4.49 16.64

NO 26.99 56.36 83.36

39.15 60.85 100












Probability of

Detection(POD)

H/(H+M) 0.31

False Alarm

Ratio (FAR)

F/H+F 0.27

Critical Success

Index (CSI)

H/(H+M+F) 0.28


Bias (H+F)/(H+M) 0.43


Hrand) 0.15

70





value is 0 to 1.8 (mm/h), 0 to 11(mm/h), 0 to 10 (mm/h) respectively for radar,


Figure 30 mean rainfall values (Event2, 1 Hour accumulated)


METEOSAT8. The range of this value is -0.2 to 10 (mm/h), -0.2 to 9.50(mm/h)


Figure 31 Mean Error (ME) values (Event2, 1 Hour accumulated)

71


METEOSAT8) and ground based radar data. The range of this value is 0 to 10

(mm/h), 0 to 9.50(mm/h) respectively for METEOSAT 8 and METEOSAT 9.

.

Figure 32 Mean Absolute Error (MAE) values (Event2, 1 Hour accumulated)



(mm/h), 0 to 11.7(mm/h) respectively for METEOSAT 8 and METEOSAT 9.

Figure 33 Root Mean Square Error (RMSE) values (Event2, 1 Hour

accumulated)

72

4.2.3. Three hours accumulated rainfall comparison

Rainfall intensity for all data sets: mm/3h

Number of images (with 3 hours interval): 24






Based on Number of

pixels:

Ground based RADAR

Yes No


NO 58749 58785 117534

92266 65558 157824


Ground based RADAR

Yes No


NO 37.22 37.25 74.47

58.46 41.54 100







73





and ground based radar (Event2, 3Hours accumulated)

Probability of

Detection(POD)

H/(H+M) 0.36

False Alarm

Ratio (FAR)

F/H+F 0.17

Critical Success

Index (CSI)

H/(H+M+F) 0.34


Bias (H+F)/(H+M) 0.44


Hrand) 0.13

Radar-METEOSAT9

Based on Number of

pixels:

Ground based RADAR

Yes No

METEOSAT9 YES 32028 5458 37486

NO 60238 60100 120338

92266 65558 157824

74


Ground based RADAR

Yes No

METEOSAT9 YES 20.29 3.49 23.75

NO 38.17 38.08 76.25

58.46 41.54 100











ground based radar (Event2, 3Hours accumulated)

Probability of

Detection(POD)

H/(H+M) 0.35

False Alarm

Ratio (FAR)

F/H+F 0.15

Critical Success

Index (CSI)

H/(H+M+F) 0.33


Bias (H+F)/(H+M) 0.41


Hrand) 0.13

75





value is 0 to 3.5 (mm/3h), 0 to 30(mm/3h), 0 to 27 (mm/3h) respectively for radar,


Figure 34 mean rainfall values (Event2, 3 Hours accumulated)


METEOSAT8. The range of this value is -0.2 to 25.2 (mm/3h), -0.2 to 24(mm/3h)


Figure 35 Mean Error (ME) values (Event2, 3 Hours accumulated)

76


METEOSAT8) and ground based radar data. The range of this value is 0 to 25.2

(mm/3h), 0 to 24(mm/3h) respectively for METEOSAT 8 and METEOSAT 9.

Figure 36 Mean Absolute Error (MAE) values (Event2, 3Hours accumulated)



(mm/3h), 0 to 26(mm/3h) respectively for METEOSAT 8 and METEOSAT 9.


accumulated)

77

5. Discussion

5.1. Spatial distribution assessment

The summery of spatial distribution assessment results presented in table 18, table

19, and table 20, for instantaneous, one hour accumulated and three hours

accumulated time intervals respectively.

The POD is increasing gradually from 0.28 for METEOSAT9, event2, instantaneous

to 0.59 for METEOSAT 8, event1, three hours accumulated. The highest probability

of detection is 0.59, for METEOSAT 8, indicating that roughly 3/5 of the observed

rain events were correctly estimated by METOSAT 8 for event 1 with three hours

accumulated data sets. The POD for METEOSAT9 in event1 with 15 min and one

hour temporal resolution is higher than METEOSAT 8. However, METEOSAT8

yields higher POD in all time intervals for the second event. This shows the number

of detected hits by METEOSAT8 for the second event is more than METEOSAT9.

This score is sensitive to hits and insensitive to false alarms.

The FAR for both satellites is decreasing by increasing time intervals from 15 min to

3 hours accumulated. The minimum false alarm ratio is 0.07, for METEOSAT 9,

event1, three hours accumulated. This value indicates that 7% of diagnosed events

that were actually did not occur. The FAR in all events and time intervals for

METEOSAT9 is lower than METEOSAT8. This shows that METEOSAT8 has wet

bias. FAR is sensitive to false alarms and insensitive to hits. Thus FAR and POD

should always be used together. Because good or even perfect value in either case

individually are easily obtained. For example if an algorithm has wet bias such that

it always detects rain everywhere all the time, POD will be perfect (“1”). However,

the FAR will also be close to the worse score (“1”) (Levizzani et al. 2007).

The CSI is rising gradually by increasing accumulation time from 15min to three

hours accumulated. The maximum resulted CSI is 0.55, for METEOSAT8, first

event, three hours accumulated, which is almost equal to the value, resulted from

METEOSAT9. This value shows 55% of the "rain" events (observed and/or

estimated) were correctly diagnosed. The CSI for METEOSAT9 in event1 with 15

min and one hour temporal resolution is higher than METEOSAT 8. However,

METEOSAT8 yields higher CSI in all time intervals for the second event. The CSI

can be thought as accuracy when correct negatives have been removed from

consideration. The CSI is sensitive to hits and it always increases with rain fraction.

Thus, estimations for wetter regions or seasons might appear to have greater skill

78

than dry seasons even though the ability to estimate rain or not-rain has not changed

(biased to samples with higher precipitation frequency). The CSI penalizes both

misses and false alarms.

The overall accuracy values are decreasing by increasing time intervals. These

values are almost the same for both METEOSAT8 and METEOSAT9. The range of

this score for the first event is 0.69 to 0.74 and the second is 0.58 to 0.72. These

values Indicates 60% to 70% of all observations with METEOSAT9 and

METEOSAT8 were correct. The accuracy value is sensitive to both hits and correct

negative, therefore even if no rain detected it increases with correct negative

fraction.

The bias is increasing gradually by increasing accumulation time from 15min to

three hours accumulated. The range of this score is from 0.40 to 0.65.which shows

underestimation in all cases. For the first event, METEOSAT9 yields better results

for instantaneous and one hour accumulated comparison. However this value for the

METEOSAT8 in the second event (all temporal resolutions) as well as the first event

with three hours accumulated rainfall is higher than METEOSAT9. The best

obtained bias value is 0.65, for METEOSAT 8, second event, three hours

accumulated rainfall. It means the number of estimated pixels with rainfall by

METEOSAT 9 is less than the number of observed pixels by ground based radar.

The ETS value changes trend is almost similar to the CSI. The highest resulted ETS

value is 0.30, for METEOSAT9, first event, one hour accumulated. This value

shows 30% of the "rain" events (observed and/or estimated) were correctly

diagnosed. This score is almost similar to CSI but it is adjusted for hits associated

with random chance. ETS gives a lower score than the CSI.

Table 18 results for categorical statistics analysis (instantaneous)

Statistical

Score

Radar _METEOSAT 9 Radar _METEOSAT 8(RSS)

Event1 Event2 Event1 Event2

POD 0.46 0.28 0.41 0.33

FAR 0.17 0.30 0.20 0.34

CSI 0.42 0.25 0.37 0.28

Accuracy 0.74 0.72 0.71 0.72

Bias 0.55 0.40 0.52 0.51

ETS 0.27 0.15 0.22 0.17

79

Table 19 results for categorical statistical analysis (one hour accumulated)

Statistical

Score



POD 0.53 0.31 0.51 0.34

FAR 0.13 0.27 0.16 0.31

CSI 0.49 0.28 0.46 0.30

Accuracy 0.73 0.69 0.71 0.68

Bias 0.61 0.43 0.60 0.50

ETS 0.30 0.15 0.26 0.15

Table 20 results for categorical statistical analysis (three hours accumulated)

Statistical

Score



POD 0.57 0.35 0.59 0.36

FAR 0.07 0.15 0.10 0.17

CSI 0.54 0.33 0.55 0.34

Accuracy 0.70 0.58 0.69 0.58

Bias 0.61 0.41 0.65 0.44

ETS 0.28 0.13 0.26 0.13

80

5.2. Assessing errors in magnitude

Regression between mean rainfall values for assessing MPE products form

METEOSAT 9 and METEOSA 8 with α=0.05 (95% confidence level). Tables 21,

22 and 23 show the results for regression analysis for instantaneous, one hour

accumulated and three hours accumulated comparison respectively. The results

indicate the MPE product from both satellites for the first event is strongly

correlated with ground based radar data. However, for the second event MPE

product from both satellites moderately correlated with ground based radar data.

The r² is increasing gradually by increasing accumulation time from 15min to three

hours accumulated.

The regressions are all highly significant, with significance of F values well below

the designated α value of 0.05. The significance of F is a measure of relationship

between the x and y variables and tests the null hypothesis that there is no

relationship between the x and y variables (Moore and McCabe 2002). Therefore, if

the significance of F is much less than α, as in the case of three different temporal

resolutions comparison for two different events, it can be assumed that there is linear

relationship between the variables (ground based radar data and satellite MPE

products).

In all cases the slope of the regressions are highly statistically significant at α=0.05,

where the p-values are 1.51E-127 to 7.46E-06. This means that the slopes of the

lines are all significantly different from zero. On this alone it may be fair to say that

the regression could be used as a predictor of the dependent variable (MPE) for any

value of the independent variable (ground based radar data). However, statistical

significance may not necessarily mean practical importance. Just by looking at the

example scatter graph of the second event, instantaneous comparison, in figure 38,

one can see that the MPE algorithm tends to overestimate. The overestimation by

MPE products in all cases has confirmed by resulted graphs for ME, MAE and

RMSE in chapter 4. As it is clearly stated in chapter 4, the amount of overestimation

by MPE products for the second event is more than the first event. Therefore, it is

fair to say the MPE products have more difficulty in estimating severe events.

81

Figure 38 Scatter graph for MPE products, instantaneous comparison, second

event

The diagonal dotted line in the graph shows the ideal 1:1 relationship between

reference values and estimated values by METEOSAT 8 and METEOSAT 9, and

the black line is the regression of the true relationship. The graph shows over

estimation in METEOSAT 8 and METEOSAT 9 MPE values.

Table 21 Regressions for mean rainfall values in MPE products, instantaneous

comparison, α=0.05



Observations (n) 288 308 288 308

Slope coefficient, p-value 7.35, 3.56E-122 1.84, 3.2E-64 7.96, 1.51E-127 1.21, 1.42E-58

Intercept coefficient,

p-value -0.11, 0.047 -0.03, 0.27 -0.16, .004 0.01, 0.50

r² 0.85 0.61 0.87 0.57

Significance F 3.57E-122 3.2E-64 1.51E-127 1.42E-58

82

Table 22 Regressions for mean rainfall values in MPE products, one hour

accumulated, α=0.05




Slope coefficient,

p-value 7.60, 6.13E-35 1.84, 9.72E-17 8.23, 2.59E-36 1.20, 3.89E-15


p-value -0.13, 0.18 -0.03, 0.58 -0.18, .06 0.01, 0.71

r² 0.89 0.60 0.90 0.56


Table 23 Regressions for mean rainfall values in MPE products, three hours

accumulated, α=0.05




Slope coefficient, p-value 7.82, 1.79E-14 1.98, 6.61E-07 8.43, 2.61E-15 1.26, 7.46E-06


p-value -0.50, 0.19 -0.13, 0.59 -0.65, 0.09 0.02, 0.92

r² 0.93 0.67 0.94 0.59


The results indicate by increasing accumulation time (for both events) the

performance of MPE products have been increased as it is shown by Vicente, et al.,

in 1998. This improvement in performance is because of masking time errors

between satellite and radar. However, the MPE products performance for the second

event is relatively poor. It seems these products are no well calibrated for heavy

rainfall events. However, many quality controls are needed to make radar data

usable and even then the rain estimates may not be accurate if fixed Z-R relationship

applied(WWRP 2009). It is fair to say that there is an underestimation in values with

radar for the second event. Therefore it would be better to use different Z-R

relationships for different events.

83

6. Conclusions and Recommendations

6.1. Conclusions

The general objective of this study for evaluating EUMETSAT MPE products in

comparison with rain radar data was achieved with categorical and continuous

statistical analysis of two different rainfall events in three different temporal

resolutions.

What are the differences in spatial distribution of EUMETSAT MPE products

from METEOSAT 8 (5 min temporal resolution) and METEOSAT 9 (15 min

temporal resolution) in comparison with reference data?

The categorical statistical scores for the MPE products from METEOSAT8 and

METEOSAT9 have been improved by decreasing temporal resolution from 15 min

to 3 hours accumulated. These values for the first event are better than the second

event. This indicates these products have some difficulty in estimating severe events.

However, the METEOSAT8 MPE product has relatively better performance in case

of second event. May be this is related to shorter accumulation time for the co-

location with the SSM/I which leads to higher performance in severe events. In case

of the first event the MPE product from METEOSAT9 shows higher scores. It is

easy to infer that in normal rainfall events using MPE product from METEOSAT9 is

more reliable. The null hypothesis of there is no difference in spatial accuracy of

EUMETSAT MPE products from METEOSAT8 and METEOSAT9 rejected.

What are the differences in estimated values by EUMETSAT MPE products from

METEOSAT 8 (5 min temporal resolution) and METEOSAT 9 (15 min temporal

resolution) in comparison with reference data?

Regression analysis for mean rainfall values indicates in case of first event the r² is

roughly 0.90 with 95% confidence level. This shows very strong correlation between

MPE products and ground based radar data. However, this value for the second

event is roughly 60% with 95% confidence level. These results confirm the

weakness of MPE products in estimating severe events. It is worth to mention that

the mean rainfall values from METEOSAT9 MPE product has relatively higher

correlation with ground based radar data in all cases.

84

The time series graphs related to ME, MAE and RMSE shows overestimation in

MPE products. The overestimation is almost the same for both products. The

magnitude of overestimation in the second event is drastically more than the first

event. These graphs also are in the same direction with the previous results, and

verify the limitation of MPE products in estimating severe events. The null

hypothesis of There is no difference in estimated values by EUMETSAT MPE

products from METEOSAT8 and METEOSAT9 rejected.

6.2. Recommendations

For further research, the following suggestions are recommended:

1- The spatial accuracy assessment shows the METEOSAT 9 MPE product

has higher statistical scores for north western Europe. However, it would be

better to do a similar high temporal and spatial comparison for the other

parts of the world.

2- The study was conducted on a short time scale only, so further work on

longer series, once the time become available, can help to improve the

understanding of accuracy of MPE products on a seasonal basis.

3- Applying diagnostic verification methods such as fuzzy verification, which

yield more in depth information about the nature of the errors.

4- Using Cloud Analysis Image (CLAI) in conjunction with MPE products to

study the relationship between cloud type and rainfall estimation accuracy

by METEOSAT 8 and METEOSAT 9.

5- Applying multi categorical analysis for a variety of rain thresholds to see

how the performance depends on the rain intensity.

85

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Hong, Y., Stocker, E. F., and Wolff, D. B., 2007, „The TRMM Multisatellite

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Rainfall Retrieval Algorithm for use by TRMM and GPM‟, Springer Netherlands,

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multi-channel algorithm for Meteosat-7 and radar derived calibration

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89

8. Appendix

Appendix A. Script for pre-processing, importing and processing EUMETSAT

Multi Sensor Precipitation Estimation (MPE) products from METEOSAT 9

and METEOSAT 8

Remark: first script file for starting data importing

@echo off

for %%j in (*.grb) do cmd /c automsgmpe_rename.bat %%j

Remark: second script file “automsgmpe_rename.bat” for renaming data and

selecting essential characters from original file name

set longfilename=%1

set shortfilename2=%longfilename:~0,23%

ren %1 %shortfilename2%.grb

automsgmpe_aggregate1.bat

Remark: third script file “automsgmpe_aggregate1.bat” for importing GRIB

data with wgrib2.exe and implementing some map functions by ILWIS

including map rotate and geo-referencing

@echo off

wgrib2.exe -ieee %shortfilename2%_raw %shortfilename2%.grb

Set ILWISDir= c:\program files\52N\Ilwis

Set WorkingDir= d:\thesis\data\meteosat9

set

hisdir=D:\thesis\FINAL_DATA_PROCESSING\20090525_27\radar\3K\histogram\

" %ILWISDir%\ilwis.exe" -C %WorkingDir%

\%shortfilename2%.mpr:=map('%WorkingDir%

\%shortfilename2%_raw',genras,Convert,3712,0,Real,4,SwapBytes)

"%ILWISDir%\ilwis.exe" -C %WorkingDir%

\%shortfilename2%_1.mpr{dom=value;vr=0:100:0.00001}:=iff('%WorkingDir%

\%shortfilename2%'ge 100000,0,'%WorkingDir% \%shortfilename2%'*3600)

90


\fmsg%shortfilename2%.mpr{dom=value;vr=0:100:0.00001}:=MapMirrorRotate('%

WorkingDir% \%shortfilename2%_1',MirrVert)

"%ILWISDir%\ilwis.exe" -C setgrf %WorkingDir% \fmsg%shortfilename2%.mpr

%WorkingDir% \mpe_georef.grf

Remark: two more steps for data processing, including creating sub map for

study area as well as re-sampling data sets to tha UTM Zone 32_WGS 84 with 3

× 3 km grid size


\%shortfilename2%_R.mpr:=MapResample(%WorkingDir%

\fmsg%shortfilename2%.mpr,new_3K_HOLLAND_00.grf,NearestNeighbour)

"%ILWISDir%\ilwis.exe" -C

%WorkingDir%\%shortfilename2%_3k_LC.mpr:=ifundef(merget_ras,?,

%WorkingDir%\%shortfilename2%_R.mpr)

Remark: the last step for reclassifying resulted maps and exporting related

histograms in DBF format for calculating categorical statistics.


%WorkingDir%\%shortfilename2%_3k_LC.his=TableHistogram(%WorkingDir%\

%shortfilename2%_3k_LC.mpr)

"%ILWISDir%\ilwis.exe" -C export

dBase(%WorkingDir%\%shortfilename2%_3k_LC.his, %shortfilename2%_3k_LC)

copy %WorkingDir%\%shortfilename2%_3k_LC .dbf %hisdir%

del %shortfilename2%_3k_LC .hi#

del %shortfilename2%_3k_LC.his

del %shortfilename2%_3k_LC.dbf

del MPEG%shortfilename2%.grb

del %shortfilename2%.*

del %shortfilename2%_raw.*

del %shortfilename2%_1.*

Remark: Note that the geo-referencing file for METEOSAT 8 is different from

METEOSAT 9. In METEOSAT 8 geo-referencing file the central meridian

must be 9° 30´ instead of 0 in METEOSAT 9.

91

Appendix B. Script for pre-processing, importing and processing ground based

radar data

Remark: first script file for starting data importing

@echo off

for %%j in (*.h5) do convert.bat %%j

Remark: second script file “convert.bat” for renaming data, converting to the

ILWIS format, applying mathematical functions and geo-referencing

@echo off

Set GdalDir= C:\program files\FWTools2.4.2\bin

Set ILWISDir= c:\program files\52N\Ilwis

Set WorkingDir= d:\thesis\data\GRad\05

set

hisdir=D:\thesis\FINAL_DATA_PROCESSING\20090525_27\radar\3K\histogram\

set longfilename=%1

set shortfilename1=%longfilename:~0,28%

" %GdalDir %\gdal_translate.exe" -of ilwis

hdf5:%longfilename%://image1/image_data %longfilename%

ren %1 %shortfilename1%

" %ILWISDir%\ilwis.exe" -C %WorkingDir%

\%shortfilename1%_logR.mpr:=('%WorkingDir% \%shortfilename1%'.mpr-109)/32


\%shortfilename1%_R.mpr:=pow(10,'%WorkingDir%

\%shortfilename1%_logR'.mpr)

"%ILWISDir%\ilwis.exe" -C setgrf %WorkingDir% \fmsg%shortfilename2%.mpr

%WorkingDir% \radar_nl25.grf

Remark: two more steps for data processing, including creating sub map for

study area as well as re-sampling data sets to tha UTM Zone 32_WGS 84 with 3

× 3 km grid size


\%shortfilename2%_R.mpr:=MapResample(%WorkingDir%

\fmsg%shortfilename2%.mpr,new_3K_HOLLAND_00.grf,NearestNeighbour)


%WorkingDir%\%shortfilename2%_3k_LC.mpr:=ifundef(merget_ras,?,

%WorkingDir%\%shortfilename2%_R.mpr)

Remark: the last step for reclassifying and crossing resulted maps, and

exporting related histograms in DBF format for calculating categorical

statistics.

92


%WorkingDir%\%shortfilename2%_3k_LC.his=TableHistogram(%WorkingDir%\

%shortfilename2%_3k_LC.mpr)

"%ILWISDir%\ilwis.exe" -C export

dBase(%WorkingDir%\%shortfilename2%_3k_LC.his, %shortfilename2%_3k_LC)

copy %WorkingDir%\%shortfilename2%_3k_LC .dbf %hisdir%

del %shortfilename2%_3k_LC .hi#

del %shortfilename2%_3k_LC.his

del %shortfilename2%_3k_LC.dbf

93

Appendix C. Excel Macro in Visual Basic programming language for importing

DBF files and creating contingency table for categorical verification statistics

Sub Macro()

Dim fso As New FileSystemObject

Set fso = CreateObject("Scripting.FileSystemObject")

Dim fls As Files

Dim strText As String

Dim i As Integer

Set fls =

fso.GetFolder("D:\thesis\FINAL_DATA_PROCESSING\20090525_27\CROSS_RA

DRA_RSS\3K_DBF").Files

i = 0

For Each f In fls

Workbooks.Open Filename:= f.Name

Workbooks("Macro_v1.xlsm").Sheets(1).Cells(1 + i, 1) = f.Name

Workbooks("Macro_v1.xlsm").Sheets(1).Cells(1 + i, 2) =

Workbooks(f.Name).Sheets(1).Cells(2, 1)


















Workbooks("Macro_v1.xlsm").Save

Workbooks(f.Name).Close

94

i = i + 5

Next

End Sub

95

Appendix D. Excel Macro in Visual Basic programming language for importing

DBF files and calculating continuous statistical verification scores

Sub Macro1()

Dim fso As New FileSystemObject

Set fso = CreateObject("Scripting.FileSystemObject")

Dim fls As Files

Dim strText As String

Dim i As Integer

Set fls =

fso.GetFolder("D:\thesis\FINAL_DATA_PROCESSING\20090525_27\SUBTRAC

T_M9_RADAR\3k\DBF").Files

i = 1

For Each f In fls

Dim Value(5000)

Dim Npix(5000)

Workbooks.Open Filename:=f.Name

RowCount = 2

j = 1

Do

Value(j) = Workbooks(f.Name).Sheets(1).Cells(RowCount, 1)

Npix(j) = Workbooks(f.Name).Sheets(1).Cells(RowCount, 2)

RowCount = RowCount + 1

j = j + 1

Loop While Workbooks(f.Name).Sheets(1).Cells(RowCount, 1) <> ""

Sumtemp1 = 0

Sumtemp2 = 0

Sumtemp3 = 0

Sumtemp4 = 0

For n = 0 To RowCount - 3

Workbooks(f.Name).Sheets(1).Cells(2 + n, 8) = Cells(2 + n, 1) * Cells(2 + n,

2)

Workbooks(f.Name).Sheets(1).Cells(2 + n, 9) = Abs(Cells(2 + n, 1)) * Cells(2

+ n, 2)

Workbooks(f.Name).Sheets(1).Cells(2 + n, 10) = (Cells(2 + n, 1) ^ 2) * Cells(2

+ n, 2)

Sumtemp1 = Workbooks(f.Name).Sheets(1).Cells(2 + n, 8) + Sumtemp1

Workbooks(f.Name).Sheets(1).Cells(RowCount + 3, 8) = Sumtemp1



96





Next

Workbooks("Macro_continious_statistics.xlsm").Sheets(1).Cells(1 + i, 1) =

f.Name


Sumtemp1 / Sumtemp4


Sumtemp2 / Sumtemp4


Sqr(Sumtemp3 / Sumtemp4)

Workbooks("Macro_continious_statistics.xlsm").Save

'SaveChanges:=False

'Workbooks(f.Name).SaveChanges:=False

Workbooks(f.Name).Close SaveChanges:=False

i = i + 1

Next

End Sub

Documents

Comparing satellite derived rainfall with ground based ...€¦ · precipitation estimates are widely used to measure global rainfall on near real-time and monthly timescales for