ii

DETERMINATION OF THE MEAN DYNAMIC TOPOGRAPHY OVER

PENINSULAR MALAYSIAN SEAS USING MULTI-MISSION SATELLITE

ALTIMETRY

ISAAC CHIDI ABAZU

A project report submitted in partial fulfilment of the

requirements for the award of the degree of

Master of Science (Geomatic Engineering)

Faculty of Geoinformation and Real Estate

Universiti Teknologi Malaysia

JUNE 2016

iv

DEDICATION

I dedicate this work to God Almighty and my beloved family.

ACKNOW LEDGEM ENT

To God be the glory, great thing He has done. When I look at your heavens, the work of your fingers, the moon and the stars, which you have set in place, what is man that you are mindful of him, and the son of man that you care for him? Thank you Jesus!

Special thanks goes to my Supervisors, Dr. Ami Hassan Din and Assoc. Prof. Kamaludin Mohd Omar for their tireless advice, constructive comments, seamless support and guidance during this journey. Working with them has added value to my person and skills enormously.

I would like to express my sincerest gratitude to my Father, Mother, Brothers and Sisters for their love, prayers and constant support. I also express my deepest appreciation to my Brothers-in-law for their support both financially, morally and spiritually. With you all I truly understand what having a family means.

I would also like to extend my gratitude to Surv. Yusuf Opaluwa. You have been a mentor, always looking out for my best interest. Only God Almighty can truly repay your honest efforts in investing in people. Also, I am grateful to my colleagues during my coursework and lab work at Geomatics Innovation Research Group, UTM. You have helped me in various ways to actualise this dream. Unfortunately, time and space would constrain me from listing you all. Thanks for the good times.

With utmost appreciation and gratitude, I would like to thank my family in Malaysia - David, Emmanuel, Grace Ormsby-boon, Rhema, Veronica, Marcia Sobers-Charles, Pastors Goke & Lilian Oladokun, Pastors Ronnie & Lyn Tan, Naomi, Elizabeth, Mr. & Mrs. Ezekiel & Seyi Ogunbode, Engr. Dauda Umar, Mrs. Bosede Edwards, Dr. & Dr. Mrs. Philip & Tolani Achimugu, ISS-Nigeria community in UTM, members of RCCG Power Palace Parish, Skudai and also Appointed Church, Bukit Indah. You have been a major driving force, a great source of inspiration for completing my study and have shown me that family isn’t only by blood ties. Thank you for making my stay memorable. Words are not enough to express all you have done, but I remain forever grateful and pray you all continue to excel and break new grounds in your various endeavours. God bless you all.

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a b s t r a c t

Since the launch of the maiden altimetry satellites, altimetric data has enormously been used to better understand worldwide oceanic system and how it evolves at different temporal and spatial scales. The purpose of this study is to determine the mean dynamic topography (MDT) of the Peninsular Malaysian Seas covering the Malacca Strait and South China Sea using multi-missions satellite altimetry. The radar altimeter database system (RADS) was used to capture and process ENVISAT, CRYOSAT-2, SARAL, JASON-1 & JASON-2 satellite altimeter (SALT) data of five years spanning between 2011-2015. In the RADS processing, the 2016 upgraded geophysical and environmental corrections suitable for the Malaysian Seas were applied. The time series of monthly multi-mission SALT data showed estimated sea level trend of 3.6 mm/year, 1.0 mm/year, 2.4 mm/year, 12 mm/year and 2.4 mm/year at Cendering, Gelang, Port Kelang, Keling and Kukup respectively. The correlation analysis for the selected tide gauge stations produced satisfying results of R2 with 0.968, 0.856, 0.911 and 0.89 for Geting, Cendering, Gelang and Sedili respectively. The ITG-Grace2010s geoid model was used to compute the MDT and plotted to a grid of 0.25° for the Malacca Strait and South China Sea of Peninsular Malaysia with Geting, Johor Bahru, Port Kelang, Keling and Sedili tide gauge stations having values determined by interpolation to be 1.2633m, 2.9109m, 1.1863m, 1.1406m and 1.8818m respectively. From the SALT computed MDT with respect to Port Kelang, the north-south sea slope ranges between -0.64 to 0.29 m/50km and between -0.01 to 0.52 m/50km along the east and west coasts of Peninsular Malaysia respectively. The computed SALT and Tidal MDTs were compared, resulting in centimeter differences at the selected tide gauge benchmarks, except at Johor Bahru having a difference of ~ 1.49m. The plotted grid of the derived MDT showed that the separation of the MSS and the ITG- Grace2010s geoid is smoother at the Malacca Straits, while the magnitude is larger and more undulated at the South China Sea. The determined MDT showed the huge potential SALT has in helping to fully map, study and understand the marine topography and environment.

vii

a b s t r a k

Semenjak pelancaran satelit altimetry, data altimetric telah banyak digunakan untuk memahami sistem lautan dunia dan cara ia berkembang pada skala duniawi dan spatial yang berbeza. Tujuan kajian ini dijalankan adalah untuk menentukan min topografi dinamik (MDT) Lautan Semenjung Malaysia yang meliputi Selat Melaka dan Laut China Selatan menggunakan satelit pelbagai misi. Sistem pangkalan data radar altimeter (RADS) telah digunakan untuk mengambil gambar dan memproses data ENVISAT, CRYOSAT-2, Saral, JASON-1 & JASON-2 satelit altimeter (SALT) selama lima tahun merangkumi 2011-2015. Dalam pemprosesan RADS, pembetulan geofizik dan alam sekitar 2016 yang terkini dan sesuai untuk Lautan Malaysia telah diaplikasikan. Siri mata data bulanan bagi pelbagai misi SALT menunjukkan trend anggaran paras laut sebanyak sebanyak 3.6 mm/tahun, 1.0 mm/tahun, 2.4 mm/tahun, 12 mm/tahun dan 2.4 mm/tahun di Cendering, Gelang, Pelabuhan Kelang, Keling dan Kukup masing-masing. Analisis korelasi untuk stesen tolok air pasang surut yang terpilih menghasilkan keputusan R2 yang memuaskan iaitu 0.968, 0.856, 0.911 dan0.89 bagi Genting, Cendering, Gelang dan Sedili. Model geoid ITG-Grace2010s telah digunakan untuk mengira MDT dan diplot untuk grid 0.25° bagi Selat Melaka dan Laut China Selatan yang merangkumi Semenanjung Malaysia dengan stesen tolok air pasang Geting, Johor Bahru, Pelabuhan Kelang, Keling dan Sedili g yang mempunyai nilai yang dihasilkan melalui interpolasi iaitu 1.2633m, 2.9109m, 1.1863m, 1.1406m dan 1.8818m masing-masing. Kiraan MDT Port Kelang melalui SALT menunjukkan bahawa cerun laut utara-selatan berkisar antara -0.64 ke 0.29 m/50km di sepanjang timur Semenajung Malaysia dan antara -0.01 hingga 0.52 m/50km di sepanjang barat pantai Semenanjung Malaysia. Perbandingan kiraan SALT dan MDTs air pasang surut mengakibatkan perbezaan sentimeter di tanda aras tolok air pasang yang terpilih kecuali di Johor Bahru yang mempunyai perbezaan ~1.49m. Plot grid daripada MDT yang diperolehi menunjukkan bahawa pemisahan MSS dan geoid ITG-Grace2010s lebih halus di Selat Melaka, manakala magnitud yang lebih besar dan beralun dihasilkan di Laut China Selatan. MDT yang diperoleh menunjukkan potensi besar SALT dalam membantu pemetaan, kajian, dan pemahaman topografi marin dan alam sekitar.

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t a b l e o f c o n t e n t

c h a p t e r t i t l e p a g e

SUPERVISOR’S DECLARATION i

AUTHOR’S DECLARATION iii

d e d i c a t i o n iv

a c k n o w l e d g e m e n t v

a b s t r a c t vi

ABSTRAK vii

t a b l e o f c o n t e n t viii

l i s t o f t a b l e s xi

l i s t o f f i g u r e s xii

l i s t o f a p p e n d i c e s xiv

1 i n t r o d u c t i o n 1

1.1 Background of Study 1

1.2 Justification of Study 4

1.3 Problem Statement 4

1.4 Aim of Study 5

1.5 Scope 5

1.6 Significance of Study 6

1.7 General Research Methodology 7

1.8 Thesis Outline 8

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2 l i t e r a t u r e r e v i e w 9

2.1 SATELLITE ALTIMETRY 9

2.2 Satellite Altimetry Missions 13

2.2.1 Topex/Poseidon Altrimetric Satellite 14

2.2.1.1 Satellite Description 15

2.2.1.2 Sensors 16

2.2.1.3 Orbit 18

2.2.2 Jason-1 20

2.2.2.1 Instrumentation 21

2.2.2.2 Jason-1 Orbit 21

2.2.3 Cryosat-2 23

2.2.3.1 Instrumentation 23

2.2.4 The Satellite with Argos & Altika (SARAL) 25

2.2.4.1 Instrumentation 26

2.2.5 The Environmental Satellite (ENVISAT) 27

2.3 Radar Altimeter Database System (RADS) 29

2.4 Geophysical Corrections in RADS 30

2.4.1 Correction for Dry Troposphere 31

2.4.2 Correction for Wet Troposphere 32

2.4.3 Correction for Sea-state Bias 32

2.4.4 Ionosphere Correction 33

2.4.5 Dynamic Atmosphere Correction 34

2.4.6 Ocean Tide Correction 35

2.5 Summary 37

3 m e t h o d o l o g y 38

3.1 Altimeter Range Corrections 38

3.2 Multi-Satellite RADS Processing 42

3.3 Inverse Distance Weighting (IDW) Interpolation 46

3.4 Tide Gauge 47

3.5 Summary 49

4 r e s u l t s a n d a n a l y s i s 50

4.1 Sea Level Anomaly Determination from RADS 50

4.2 Sea Surface Height (SSH) 55

4.3 Mean Sea Surface (MSS) 56

4.4 Altimeter Data Verification using Tide Gauge Data 58

4.5 Mean Dynamic Topography 6 8

4.6 Summary 73

5 c o n c l u s i o n a n d r e c o m m e n d a t i o n s 75

5.1 Conclusion 75

5.2 Recommendations 76

r e f e r e n c e s 77

a p p e n d i c e s 80-85

x

l i s t o f t a b l e s

xi

t a b l e n o . t i t l e PAGE

Table 2.1 T/P Orbital Parameters 19

Table 2.2 Jason-1 Orbit Characteristics 22

Table 2.3 Cryosat-2 Parameters 24

Table 2.4 SARAL Orbit Parameters 26

Table 2.5 Characteristics of Envisat Orbital 28

Table 2.6 Altimeter Data Available within RADS 30

Table 2.7 Parameters and Models Applied in RADS Data Processing 36

Table 3.1 Satellite Altimetry Data Processed in RADS for this Study 43

Table 4.1 SALT Sea Level Trend at Selected Tide Gauge Stations 54

Table 4.2 Yearly MSL at Tide Gauge Stations 65

Table 4.3 Computation of Tidal MDT at Tide Gauge Benchmarks 67

Table 4.4 Comparison Between Satellite Altimetry and Tidal MDT 67

Table 4.5 MDT at Tide Gauge Stations from SALT 70

Table 4.6 Difference in SALT MDT Between Tide Gauge Stations 71

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l i s t o f f i g u r e s

FIGURE n o . t i t l e PAGE

Figure 1.1: Coastline of Peninsular Malaysia Showing Tide Gauge Stations 6

Figure 1.2: General Flow of the Methodology 8

Figure 2.1: The Principle of Altimetry 10

Figure 2.2: A Group of Altimetry satellites 14

Figure 2.3: T/P Measurement System 17

Figure 2.4: Jason-1 Satellite Instrumentation 20

Figure 2.5: SARAL Instruments 27

Figure 3.1: Overview of altimeter data processing flows in RADS 42

Figure 3.2: Combination of Altimeter Ground Tracks of Jason-1 and Cryosat-2 44

Figure 4.1: SLA at Cendering from Multi-mission SALT 51

Figure 4.2: Sea Level Anomaly at Tanjung Gelang from Multi-mission SALT 51

Figure 4.3: Sea Level Anomaly at Port Kelang from Multi-mission SALT 52

Figure 4.4: Sea Level Anomaly at Tanjung Keling from Multi-mission SALT 52

Figure 4.5: Sea Level Anomaly at Kukup from Multi-mission SALT 53

Figure 4.6: SLA of Malaysian Seas from 2011-2015 55

Figure 4.7: SSH of Malaysian Seas from 2011-2015 56

Figure 4.8: MSS of Malaysian Seas from 2011-2015 57

Figure 4.9: Comparison of Monthly SALT and Tidal SLA at Port Kelang 59

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Figure 4.10: Comparison of Monthly SALT and Tidal SLA at Johor Bahru 59

Figure 4.11: Correlation Analysis of SALT and Tidal SLA at Kelang 60

Figure 4.12: Correlation Analysis of SALT and Tidal SLA at Johor Bahru 60

Figure 4.13: Comparison of Monthly SALT and Tidal SLA at Geting 61

Figure 4.14: Comparison of Monthly SALT and Tidal SLA at Cendering 61

Figure 4.15: Comparison of Monthly SALT and Tidal SLA at Tanjung Gelang 62

Figure 4.16: Comparison of Monthly SALT and Tidal SLA at Sedili 62

Figure 4.17: Correlation Analysis of SALT and Tidal SLA at Geting 63

Figure 4.18: Correlation Analysis of SALT and Tidal SLA at Cendering 63

Figure 4.19: Correlation Analysis of SALT and Tidal SLA at Tanjung Gelang 64

Figure 4.20: Correlation Analysis of SALT and Tidal SLA at Sedili 64

Figure 4.21: Relationship of the Various Surfaces used to Determine Tidal MDT 6 6

Figure 4.21: GRACE Geoid Profile of Malaysian Seas (GRS80 Ellipsoid) 69

Figure 4.22: Computed SALT MDT Values with Reference to Port Kelang 72

Figure 4.23: MDT of Malaysian Seas Between 2011-2015 (GRS80 Ellipsoid) 73

l i s t o f a p p e n d i c e s

xiv

APPENDIX TITLE

A Getraw Shell Script in RADS

B Cross Over Adjustment Shell scripts in RADS

C Shell script for Gridding Altimetry Data in

RADS

D Shell Script for Gridded Monthly Sea LevelAnomaly Solutions

PAGE

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81

82

85

c h a p t e r 1

i n t r o d u c t i o n

1.1 Background of Study

It is no secret that oceans play a major role in the outcome of the Earth’s

weather due to the fact that they heat, cool, humidify, dry the air, control wind speed

and direction. Consequently, the weather affects various phenomena that ask questions

such as crops getting sufficient rain for maturity, what clothes we should daily wear,

the severity of the hurricane season - fierce or feeble, and many more. The patterns

the weather takes over a long period of time have influence on various things like

property valuation, food and water supply, trade, etc. It can be a double-edged sword

in either being a tool for sporadic economic growth or responsible for its ruins. Though

unpredictable, ability to forecast its occurrence, helps a great deal to manage its impact.

If these predictions and forecasts of its societal impact are to be helpful, a

comprehension of the ocean’s characteristics and behavior is very key.

Since the launch of the maiden altimetry satellites sometime in the 1970s, the

oceanographic community have enormously made use of altimetric data to better

understand worldwide oceanic system and how it evolves at different temporal and

spatial scales (Fu and Cazenave, 2000). With the successful launch of the JASON-3

satellite in January 2016, the quality of altimetric data available can only get better.

The development of satellite altimeters to more adequately sense ocean-surface

topography has been one of the primary objectives of the National Aeronautics and

Space Administration’s (NASA) Ocean Processes Program for over four decades.

Earth-revolving satellites, carrying passive and active remote sensing instruments,

provide a capability for synoptic observation of the global oceans with temporal

sampling that spans from a few days to a month. One of the sensors used by the

satellites for oceanographic applications is the radar altimetry (Shum et al., 1995).

A key instrument on both satellites is the radar altimeter, measuring the height

of the satellite above the ocean surface. From this range measurement, the absolute sea

level or the variations in sea level can be inferred. In order to do so, the range

measurements must be subtracted from the altitude of the satellite, defined in a well-

defined reference frame. The accuracy of the absolute sea level measurements by a

radar altimeter is therefore always limited by the accuracy of the computed satellite

altitude. It is the object of the precise orbit determination to provide the satellite

position at any time during the mission with precision in the order of centimeters

(Andersen and Scharroo, 2011; Scharroo et al., 2013).

In accordance with the laws of physics, it is understood that the ocean surface

is a surface of equal potential gravity field of the earth assuming there are no waves,

winds, tides and currents for the moment. Ocean surface topography can be distinctly

described as the distance between the separation of the mean ocean surface and the

geoid. Ocean surface topography results from ocean currents, atmospheric pressure

loading, ocean waves and tides. The main reason for measuring ocean surface

topography, arguably is for understanding how the ocean circulate at a large-scale. The

differences in height with regards to the ocean surface topography could be larger than

two meters and these differences are affected by the ocean temperature, ocean

circulation and saline content (Fu et al., 1994).

In the context of examining satellite altimetry measurements for oceanographic

studies, a frequently used term is marine geoid. The geoid is an equipotential surface

2

of the Earth’s gravity field, to which a motionless ocean would conform. Often, the

geoid lies far away from the Earth’s reference ellipsoid, which is the smooth

mathematical surface that approximates the entire earth’s shape. Because of Earth’s

gravity variations, the shape of the geoid is often irregular and in some cases

significantly departs from the reference ellipsoid. The geoid surface is obtained

quantitatively from models based on gravity measurements and long-term satellite data

(Joseph, 2013).

The establishment of a consistent archive of worldwide data pertaining to

satellite altimeter took off around the late 1980s. This awakened a conscious effort by

the community of geodesists to develop unified global geoid with estimates of the

MDT, while simultaneously reducing errors due to satellite orbit (Knudsen et al.,

2011). Although the data quality available fell short of what was needed to retrieve the

more specific details regarding the general circulation of the ocean’s, the determination

and comparison could be made between the colossal scales (greater than 5000 km) of

the ocean’s MDT and early estimates of oceanographic MDT solely from

hydrographic data, as in the case with Levitus and Boyer (1994). Through such

comparisons, major issues were identified, chiefly among them happened to be how

consistent are the impacts of permanent tidal corrections on the calculated MDT and

the reference ellipsoids. In similar vein, marine gravity data obtained by mounting

gravimeters on ships was used to regionally refine the spatial distribution of the gravity

field and geoid. Improvements on the accuracy and detailed measurements of the MDT

was achieved by a combination of locally improved geoids with altimeter data

(Knudsen, et al., 2011). Knowledge of the MDT would allow oceanographers to study

the absolute circulation of the ocean and determine the associated geostrophic surface

currents that help to regulate the earth’s climate.

3

4

1.2 Justification of Study

A key tussle in oceanography is to rightly ascertain the MDT of the ocean time.

To achieve this with adequate accuracy, one has to combine with elements of dynamic

topography that are time-reliant, the absolute dynamic topography obtainable would

be from altimetric data with an end product that accurately depicts the surface

geostrophic currents and ocean transports (Bingham et al., 2008). MDT also

contributes to the understanding of heat movements globally via the oceans, an integral

element of Earth’s climate, and for global sea level change monitoring. Considering

the fact that Malaysia is a coastal state, this information is very useful for studying

various environmental concerns that relates to investigations of inundation and climate

change, principally for an area that could do with more interests from the body of

altimeter scientists.

1.3 Problem Statem ent

Sea surface variability and its effects in near coastal areas are an important

aspect of climate change. In surveying, MDT is datum bias, that is, the difference

between the mean sea level (MSL) and geoid. Conventional method of retrieving this

information involves doing GPS ground observation on benchmark. Due to the

sparseness of benchmarks, they cannot give adequate coverage as desired. However,

satellite altimetry has very large ground coverage, hence its use globally for various

oceanography purposes. In order to provide oceanographers and decision makers with

adequate scientific data to improve understanding, prediction and policies that borders

on environmental challenges that mostly relate to inundation investigations and

climate change in a region that requires more attention from the altimeter science

community, the determination of the MDT would be very useful scientific altimetry

data in the hands of all stakeholders in the region.

1.4 Aim of Study

This project aims to determine the Mean Dynamic Topography (MDT) along

the South China Sea and Malacca Strait of Peninsular Malaysia coastline.

To achieve the project’s aim, the following objectives are to be undertaken:

1. Derivation of the instantaneous Height of the Sea Surface (SSH), by

differencing the measured altimeter height above the sea surface from the

satellite altitude above a reference ellipsoid;

2. To estimate the localized Mean Sea Surface (MSS) of the Malaysian Seas;

3. To quantify the localized MDT from the difference of the local MSS and

the marine geoid and verify the results using data from some tide gauge

stations within the region.

1.5 Scope

The project scope is limited to five (5) years (2011-2015) Saral, Jason-1, Jason-

2, CryoSat-2, Envisat-1 satellite altimeter data covering the South China Sea and

Malacca Straits of Peninsular Malaysia coastline. The satellite altimeter processing

will be carried out using TUDelft’s radar altimeter database system (RADS). From the

RADS, sea level anomaly (SLA) and sea surface heights (SSH) are processed,

extracted and used to compute the mean sea surface (MSS). The final product of this

study is the MDT, computed from the MSS and the GRACE geoid model. Also, tidal

5

data obtained from selected tide gauge stations would be used to validate the

determined MDT. Figure 1.1 shows details of the study area.

South GETiNG China Sea

V ‘.CENDERING

0)

« 4*N. GELANG

• PULAU, .̂ Xtioman

BAHRU

100^E 102^ELongitude

KM'^E 106°E

Figure 1.1: Coastline of Peninsular Malaysia Showing Tide Gauge Stations

1.6 Significance of Study

In marine geodesy, it is assumed and proven through various studies that the

local vertical datum (MSS) does not coincide with and may not be parallel to the geoid.

This separation is referred to as the vertical datum bias, sea surface topography or

mean dynamic topography (MDT). The MDT helps to understand certain integral

6

elements of the Earth’s climate through the understanding of heat movements globally

via the oceans, determining the directions of water flow and global sea level change

monitoring. The MDT is one of the components of the marine profile and very useful

in the determination of the marine geoid. Within the Malaysian coastline, the MDT

has been estimated at tide gauge stations using the traditional tide gauge data combined

with GPS observations and local gravimetric data. However, with satellite altimetry

technique, an MDT with denser spatial coverage of the Malaysian seas that is not

affected by land movements can be obtained. Hence, an altimeter determined MDT

will be of immense benefit to all oceanography stakeholders and decision makers.

1.7 General Research M ethodology

The general overview of this research entails the following sequence:

1. Using RADS to process, filter and extract the gridded altimeter SLA and

SSH of the study area from satellite multi-missions from 2011 to 2015.

2. Determination of the MSS representing 2011 to 2015 altimetry data

(referenced to the WGS84 ellipsoid) by computing the difference between

the SLA from the SSH.

3. The computed MSS is transformed to GRS80 reference ellipsoid and used

with the GRACE geoid model to determine the satellite altimetry (SALT)

MDT of the study area.

4. The computed altimeter SLA and MDT are then validated by comparing

with tidal MSL and MDT values at tide gauge benchmarks.

5. Data analysis and conclusions are drawn from the observed data.

The details of the general flow of the methodology is depicted in the Figure

1 .2 .

7

Figure 1.2: General Flow of the Methodology

1.8 Thesis Outline

This thesis report covers five chapters. Chapter 1 covered the general

introduction of the research including the objective, justification, scope and general

method to be used in achieving the aim of the research. Chapter 2 reviewed previous

works related to the subject area, including the definition of various concepts and terms

used in marine geodesy, satellite altimetry, etc. Chapter 3 focused on the various

methods that would be applied in the research and their relationships, for example how

RADS will be used to process altimeter data, etc. Chapter 4 presented and discussed

all the results processed, which includes the SLA, SSH, MSS, MDT and verification

of the satellite altimetry data with tidal data. The research write-up was concluded in

Chapter 5, with conclusions and recommendations drawn from the research findings.

8

REFERENCES

Andersen, O. B. and Scharroo, R. (2011). Range and geophysical corrections in coastal regions: and implications for mean sea surface determination Coastal altimetry (pp. 103-145)Springer.

Aviso (2016). Aviso Satellite Altimetry Data (Vol. 2016).

Azhari, B. M. (2003). An Investigation o f the Vertical Control Network o f Peninsular Malaysia using a Combination o f Levelling, Gravity, GPS and Tidal Data. PhD. Thesis, Universiti Teknologi Malaysia.

Bilitza, D. (2001). International reference ionosphere 2000. Radio Science. 36(2), 261­275.

Bingham, R. J., Haines, K. and Hughes, C. W. (2008). Calculating the ocean's mean dynamic topography from a mean sea surface and a geoid. Journal o f Atmospheric and Oceanic Technology. 25(10), 1808-1822.

Chelton, D. B. (1994). The sea state bias in altimeter estimates of sea level from collinear analysis of TOPEX data. Journal o f Geophysical Research: Oceans. 99(C12), 24995-25008.

Din, A. H. (2014). Sea Level Rise Estimation and Interpretation in Malaysia Using Multi-Sensor Techniques. Ph.D. Thesis, Universiti Teknologi Malaysia, Johor.

Din, A. H. M., Ses, S., Omar, K. M., Naeije, M., Yaakob, O. and Pa’suya, M. F. (2014). Derivation of Sea Level Anomaly Based on the Best Range and Geophysical Corrections for Malaysian Seas using Radar Altimeter Database System (RADS). Jurnal Teknologi. 71(4).

DSMM (2016). Department of Surveying and Mapping Malaysia (Vol. 2012). http://www.geodesi.jupem.gov.my/.

ESA (2016). European Space Agency (Vol. 2016). https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/envisat/.

78

Fu, L.-L. and Cazenave, A. (2000). Satellite altimetry and earth sciences: a handbook o f techniques and applications. (Vol. 69)Academic Press.

Fu, L. L., Christensen, E. J., Yamarone, C. A., Lefebvre, M., Menard, Y., Dorrer, M. and Escudier, P. (1994). TOPEX/POSEIDON mission overview. Journal o f Geophysical Research: Oceans. 99(C12), 24369-24381.

Harvey, B. R. (1994). Practical Least Squares and Statistics for Surveyors. The University o f New South Wales. (Monograph No. 13), pp. 319.

Holgate, S. J., Matthews, A., Woodworth, P. L., Rickards, L. J., Tamisiea, M. E., Bradshaw, E., Foden, P. R., Gordon, K. M., Jevrejeva, S. and Pugh, J. (2012). New data systems and products at the permanent service for mean sea level. Journal o f Coastal Research. 29(3), 493-504.

Johannessen, J. A., Balmino, G., Le Provost, C., Rummel, R., Sabadini, R., Sunkel,H., Tscherning, C., Visser, P., Woodworth, P. and Hughes, C. (2003). The European gravity field and steady-state ocean circulation explorer satellite mission its impact on geophysics. Surveys in Geophysics. 24(4), 339-386.

Joseph, A. (2013). Measuring ocean currents: tools, technologies, and data. Newnes.

Knudsen, P., Bingham, R., Andersen, O. and Rio, M.-H. (2011). A global mean dynamic topography and ocean circulation estimation using a preliminary GOCE gravity model. Journal o f Geodesy. 85(11), 861-879.

Le Traon, P., Schaeffer, P., Guinehut, S., Rio, M., Hernandez, F., Larnicol, G. and Lemoine, J. (2001). Mean ocean dynamic topography from GOCE and altimetry. Proceedings o f the 2001 International GOCE User Workshop, ESTEC, Noordwijk, The Netherlands, ESA, 135-139.

Levitus, S. and Boyer, T. P. (1994). World Ocean Atlas 1994. Volume 4. Temperature. National Environmental Satellite, Data, and Information Service, Washington, DC (United States).

Naeije, M., Schrama, E. and Scharroo, R. (2000). The radar altimeter database system project rads. Proceedings o f the 2000 Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 International: IEEE, 487-490.

NASA (2016). Jet Propulsion Laboratory. California Institute of Technology (Vol. 2016). https://sealevel.jpl.nasa.gov/missions/topex/.

PODAAC (2016). Physical Oceanography Distributed Active Archive Centre (Vol.2016). https://podaac.jpl.nasa.gov/TOPEX-POSEIDON/.

79

PSMSL (2016). Permanent Service for Mean Sea Level Tide Gauge Data (Vol. 2016). http://www.psmsl.org/data/obtaining/.

Ray, R. (2008). Tide corrections for shallow-water altimetry: a quick overview. Proceedings o f the 2008 Second coastal altimetry workshop. Pisa, Italy, 6-7.

Rio, M. H., Pascual, A., Poulain, P.-M., Menna, M., Barcelo-Llull, B. and Tintore, J. (2014). Computation of a new Mean Dynamic Topography for the Mediterranean Sea from model outputs, altimeter measurements and oceanographic in-situ data.

SARAL (2016). Saral Altika (Vol. 2016). http://www.isro.gov.in/Spacecraft/saral/.

Scharroo, R. (2002). A decade o f ERS satellite orbits and altimetry. TU Delft, Delft University of Technology.

Scharroo, R., Leuliette, E., Lillibridge, J., Byrne, D., Naeije, M. and Mitchum, G. (2013). RADS: Consistent multi-mission products. Proceedings o f the 2013 Proceedings o f Symposium on,

SEAMERGES (2016). South East Asia Mastering Environmental Research with Geodetic Space Techniques (Vol. 2016). www.deos.tudelft.nl/seamerges/.

Shum, C., Ries, J. and Tapley, B. (1995). The accuracy and applications of satellite altimetry. Geophysical Journal International. 121(2), 321-336.

Singh, M., Mandal, M. and Basu, A. (2004). Robust KLT tracking with Gaussian and Laplacian of Gaussian weighting functions. Proceedings o f the 2004 null: IEEE, 661-664.

Tang, F. (2012). Coastal Sea Level Change from Satellite Altimetry and Tide Gauges.

Tseng, K., Shum, C., Yi, Y., Fok, H., Kuo, C., Lee, H., Cheng, X. and Wang, X. (2013). Envisat altimetry radar waveform retracking of quasi.

Verron, J., Sengenes, P., Lambin, J., Noubel, J., Steunou, N., Guillot, A., Picot, N., Coutin-Faye, S., Sharma, R. and Gairola, R. (2015). The SARAL/AltiKa altimetry satellite mission. Marine Geodesy. 38(sup1), 2-21.

Wunsch, C. and Stammer, D. (1997). Atmospheric loading and the oceanic “inverted barometer” effect. Reviews o f Geophysics. 35(1), 79-107.