The analysis of total precipitable water from satellite and model data in viet nam from 2008 to 2017

We compared atmospheric total precipitable data from Atmospheric infrared sounder AIRS of Aqua satellite with European Centre for Medium‐Range Weather Forecasts ECMWF: ERA model estimati

THAI NGUYEN UNIVERSITY

UNIVERSITY OF AGRICULTURE AND FORESTRY

NGUYEN THI YEN

THE ANALYSIS OF TOTAL PRECIPITABLE WATER FROM SATELLITE

AND MODEL DATA IN VIET NAM FROM 2008 TO 2017

BACHELOR THESIS

Study Mode: Full-time Major: Environmental Science and Management Faculty: Advanced Education Program Office Batch: 2014-2018

Thai Nguyen, 15/08/2018

Thai Nguyen University of Agriculture and Forestry

Degree Program Bachelor of Environmental Science and Management

Student name Nguyen Thi Yen

Student ID DTN1453110167

Thesis Title The Analysis Of Total Precipitable Water From

Satellite And Model Data In Viet Nam From 2008 To

2017

Supervisor(s) Prof Dr Chian-Yi Liu National Central University,

Taiwan

Assoc.Prof Dr Tran Quoc Hung - Thai Nguyen University

of Agriculture and Forestry, Vietnam

Signature

Abstract: ERA‐Interim is the latest global atmospheric reanalysis produced by the European Centre for Medium‐Range Weather Forecasts (ECMWF) Atmospheric water vapor from reanalysis provides better spatiotemporal resolution for longer period of time compare to satellite Reanalysis data is the product of numerical weather prediction model whereas satellite data is satellite onboard instrumental data In difference subplot in Vietnam have variety of topography and difference amount of annual rainfall, those are two factors that directly affect the flood situation in Vietnam, especially in recently years, this situation is more and more complex To study the climate in smaller spatial resolution over this complex topographic region, the water vapor measurement

is important We compared atmospheric total precipitable data from Atmospheric infrared sounder (AIRS) of Aqua satellite with European Centre for Medium‐Range Weather Forecasts (ECMWF: ERA model estimation-a reanalysis model) over region within latitude interval 5ºN - 25ºN and longitude interval 100ºE - 120ºE and four sub regions in Vietnam from 2008 to 2017 This study has been conducted in order to see which of the dataset captures the topographic and climate impact on water vapor content of the region in better way The thesis indicates that AIRS data maybe consider better than ERA model estimated value for total precipitable water over the considered region Keywords TPW,TCWV, ERA-model estimation, AIRS measurement Number of pages 54

Date of

submission

25th September, 2018

ACKNOWLEAGEMENT Foremost, I would like to say thanks to the cooperation between Thai Nguyen University of Agriculture and Forestry and National Central University for

providing me an amazing opportunity to internship in Taiwan It brings me great pleasure to work and submit my thesis for graduation

I would like to express my sincere gratitude to my advisor Prof Dr Chian-Yi Liu for the continuous support of my thesis, for his patience, motivation, enthusiasm,

and immense knowledge His guidance helped me in all the time of research and writing of this thesis I could not have imagined having a better advisor and mentor for

my bachelor thesis His willingness to give his time so generously to help me finishing

my internship in Taiwan

I sincerely thanks to Assoc.Prof Dr Tran Quoc Hung for his patient

guidance, enthusiastic encouragement and useful critiques of this research work and after I went to Taiwan, helping me to understand and complete proposal and thesis

I would also like to thank the experts who were involved in the validation

survey for this research project: Mr Chi-Hao Chiu and Ms Tran Huyen Trang

Without their passionate participation and input, the validation survey could not have been successfully conducted

I am really fortunate to be in Prof Dr Chian-Yi Liu’s lab Thanks to all the members in Professor Chian-Yi Liu’s laboratory who hearty help me a lot when I

work in there

I also thank to my family for providing me emotional, unceasing encouragement and physical and financial support At last, I would like to thank all

those other persons who helped me in completing this report Because of my lack knowledge, the mistake is inevitable, I am very grateful if I receive the comments and opinions from teachers and others to contribute my report

Sincerely,

Nguyen Thi Yen

TABLE OF CONTENT

PART I: INTRODUCTION 1

1.1 Research rationale 1

1.2 Research objective 2

1.3 The question 2

1.4 The significant of the thesis 2

1.5 Limitations 3

PART II REVIEW OF LITERATURE 4

2.1 Total Precipitable water 4

2.1.1 Water vapor 4

2.1.2 Total precipitable water 6

2.2 Model and Satellite data 7

2.2.1 ECMWF-Interim Daily (model data) 7

2.2.2 AIRS data (satellite data) 9

2.3 MATLAB software 12

PART III METHODOLOGY 15

3.1 Study Frame work 15

3.2 Description of the study area 15

3.2.1 South East Asia 16

3.2.2 Four sub regions 16

3.3 Data collection 18

3.3.1 Model Data collection (ERA-data) 18

3.3.2 Satellite data collection (AIRS data) 19

3.4 Data analysis 21

PART IV RESULT 24

4.1 The difference between AIRS measured and ERA model estimated monthly TPW in four sub regions 24

4.1.1 Northwest region 24

4.1.2 Central Highlands region 28

4.1.3 Hoang Sa archipelagoes 32

4.1.4 Mekong Delta region 35

4.1.5 ERA model estimated and AIRS measured monthly total precipitable water 38

4.2 The difference between AIRS measured and ERA model estimated in study region and four seasons 39

4.2.1 General information 39

4.2.2 The difference between AIRS measured and ERA model estimated in study region 41 4.2.3 The difference between AIRS measured and ERA model estimated in difference season 43

PART 5: DISCUSSION AND CONCLUSION 46

5.1 Discussion……… 46

5.2 Conclusion 48

REFERENCES 50

LIST OF TABLES

Table 4.1 ERA model estimated monthly data of TPW over Northwest 26

region (kgm-2) 26

Table 4.2 AIRS measured monthly data of TPW over Northwest region (kgm-2) 27

Table 4.3 ERA model estimated monthly data of TPW over Central Highlands 30

region (kgm-2) 30

Table 4.4 AIRS measured monthly data of TPW over Central Highlands region 31

(kgm-2) 31

Table 4.5 ERA model estimated monthly data of TPW over Hoang Sa archipelagoes region (kgm-2) 33

Table 4.6 AIRS measured monthly data of TPW over Hoang Sa archipelagoes region (kgm-2) 34

Table 4.7 ERA model estimated monthly data of TPW over Mekong Delta region (kgm-2) 36

Table 4.8 AIRS measured monthly data of TPW over Mekong Delta region 37

Table 4.9 ERA model estimation monthly data of TPW over study region 41

(kgm-2) 41

Table 4.10 AIRS measurement monthly data of TPW over study region (kgm-2) 42

Table 4.11 ERA model estimation and AIRS measurement of TPW in 4 seasons in study region (kgm-2) 43

LIST OF FIGURES

Figure 3.1 Study Frame work 15 Figure 3.2 Study Region within latitude interval 5ºN - 25ºN and longitude interval 100ºE - 120ºE and Four Sub regions (From Google Earth) 16 Figure 3.3 North West of Vietnam within latitude interval 21º15´N-22º30´N and longitude interval 103º30´E- 104º45´E (From Google Earth) 16 Figure 3.4 High Land of Vietnam within latitude interval 12º30´N - 14º15´N and longitude interval 107º45´E - 108º45´E (From Google Earth) 17 Figure 3.5 Hoang Sa Archipelagoes of Vietnam within latitude interval 15º45´N - 17º00´N and longitude interval 111º15´E - 113º00´E (From Google Earth) 17 Figure 3.6 Mekong River Delta of Vietnam within latitude interval 9º30´N - 10º45´N and longitude interval 105º30´E - 106º30´E (From Google Earth) 18

Abbreviations

AIRS : Atmospheric Infrared Sounder

CC : Correlation coefficient

ECMWF : European Center for Medium-range Weather Forecasts

MATLAB : MATrix LABoratory

PWV : Precipitable water vapor

rmse : Root mean square error

TCWV : Total column water vapor

TPW : Total precipitable water

PART I: INTRODUCTION 1.1 Research rationale

Precipitable water is the amount of water potentially available in the atmosphere for precipitation, usually measured in a vertical column that extends from the Earth's surface to the upper edge of the troposphere (Encyclopedia of Soils in the Environment, 2005) Precipitable water in the atmosphere is an important climate parameter, which is expected to increase with global mean sea surface temperature (Liang, J., 2013) Frequent global determination of the distribution of total precipitable water vapor is important to increase the understanding of the hydrological cycle, biosphere-atmosphere interactions, the energy budget, and for monitoring climate change due to the greenhouse gases (Encyclopedia of Soils in the Environment, 2005)

In addition, Precipitable Water Vapor (PWV) plays an important role in weather forecasting It is helpful in evaluating the changes of the weather system via observing the distribution of water vapor (Yeh, T.-K., Shih et al., 2018) Therefore, accurate predictions of total precipitable water are extremely important in weather forecasting Total precipitable water data are generally derived from radiosonde measurement, satellite remote sensing and reanalysis models However, the accuracy of the two methods will vary in each terrain, so it is important to compare satellite measured and model estimated total precipitable water

Vietnam - a country located in Southeast Asia, has a relatively large area covering land and territorial waters in the South China Sea, including many islands and Archipelagoes (Phạm Hoàng Hải Nguyễn Thượng Hùng Nguyễn Ngọc Khánh, 1997). Vietnam, with its long terrain features along the longitude and the coastal, will

have a larger amount of total precipitable water in the air Therefore estimating the amount of precipitable water vapor in Vietnam is extremely important in predict the

weather It was a vital role to conduct research “The Analysis of Total Precipitable Water from Satellite and Model Data in Viet Nam from 2008 to 2017”

1.2 Research objective

- Compare atmospheric Total Precipitable Water content data from Atmospheric infrared sounder (AIRS) and European Centre for Medium-Range Weather Forecasts (ECMWF) in Viet Nam and nearby region

- To assess which of the dataset captures the topographic and climatic impact on water vapor content of the study region in a better way

1.3 Research questions

- How are satellite data and model data different?

- Which dataset is better to use for TPW?

1.4 The significant of the thesis

- For learning and researching purpose:

+ Thesis will be the bridge between knowledge studying and practices, the access to reality to better understand the nature of the problem

+ Through the thesis, I knew how to use Mat lab software to mapping data and analyzing data and practice

-The practical significance:

+ Applying the knowledge on reality combined with collecting and analyzing data make the most accurate predictions about the weather as well as the effect of global warming in the study area

+ It is also important for identifying where in Vietnam has the strongest impact from climate change

1.5 Limitations

Because the time for an internship was too short, this research project cannot perform any other comparison

PART II REVIEW OF LITERATURE 2.1 Total Precipitable water

2.1.1 Water vapor

a) Definition of water vapor

Water vapor, water vapor or aqueous vapor, is the gaseous phase of water It is one state of water within the hydrosphere Water vapor can be produced from the evaporation or boiling of liquid water or from the sublimation of ice Unlike other forms of water, water vapor is invisible (What is water vapor?, 2018) Under typical atmospheric conditions, water vapor is continuously generated by evaporation and removed by condensation It is lighter than air and triggers convection currents that can lead to clouds (Water vapor, 2018, https://en.wikipedia.org/wiki/Water_vapor)

Water vapor is not visible, therefore clouds, fog and most other formations within the atmosphere that can be seen by the naked eye are not water vapor Water vapor, however, can be sensed If enough of it is in the air it is felt as humidity Water vapor is one state of the water cycle within the hydrosphere Water vapor can be produced from the evaporation of liquid water or from the sublimation of ice Under normal atmospheric conditions, water vapor is continuously generated by evaporation and removed by condensation

Being a component of Earth's hydrosphere and hydrologic cycle, it is particularly abundant in Earth's atmosphere where it is also a potent greenhouse gas along with other gases such as carbon dioxide and methane (Water vapor, 2018, https://en.wikipedia.org/wiki/Water_vapor)

Water vapor is a relatively common atmospheric constituent, present even in the solar atmosphere as well as every planet in the Solar System and many astronomical objects including natural satellites, comets and even large asteroids (Water vapor,

2018,https://en.wikipedia.org/wiki/Water_vapor)

b) The important of water vapor

- Water vapor is vital to weather and climate as clouds, rain and snow have their source in water vapor All of the water vapor that evaporates from the surface of the Earth eventually returns as precipitation - rain or snow Water vapor is also the Earth's most important greenhouse gas, giving us over 90% of the Earth's natural greenhouse effect, which helps keep the Earth warm enough to support life When liquid water is evaporated to form water vapor, heat is absorbed This helps to cool the surface of the Earth This "latent heat of condensation" is released again when the water vapor condenses to form cloud water This source of heat helps drive the updrafts in clouds and precipitation systems In order to understand water vapor, some insight must be given into water (Claudette Ojo., Haloe v2.0 upper tropospheric water vapor climatology)

- Water vapor is the dominant greenhouse gas, the most important gaseous source of infrared opacity in the atmosphere (Held, I M., & Soden, B J 2000) For instance, the predicted global warming due to a CO2 doubling with water vapor feedback is approximately twice the warming predicted without feedback (the so-called fixed relative humidity assumption of Manabe and Wetherald 1967) The distribution of water vapor, its transport, and divergence are also essential ingredients

to our understanding of the distribution of solid and liquid water in the atmosphere and

therefore crucial to the significant and perplexing problem of cloud feedback to climate change(Graeme L Stephens, June 1990)

- Water vapor is also important to other physical processes that occur in the atmosphere Water vapor plays a decisive role in the transfer of radiation through the atmosphere and it is important to the transport and release of latent heat (Stephens, G L.,1990)

- The role of water vapor in the atmosphere: water vapor plays a dominant role

in the radiative balance and the hydrological cycle It is a principal element in the thermodynamics of the atmosphere, it transports latent heat, it contributes to absorption and emission in a n umber of bands and it condenses into clouds that reflect and adsorb solar radiation, thus directly affecting the energy balance (Jacob, D., 2001)

2.1.2 Total precipitable water

Precipitable water is the amount of water potentially available in the atmosphere for precipitation, usually measured in a vertical column that extends from the Earth's surface to the upper edge of the troposphere (Encyclopedia of Soils in the Environment, 2005) Precipitable water vapor (PWV) is an important climate parameter indicative of available moisture in the atmosphere; it is also an important greenhouse gas (Falaiye, O A., Abimbola, O J., Pinker, R T., Pérez-Ramírez, D., & Willoughby, A A 2018) The total water vapor contained in a vertical column of atmosphere – from the surface of the earth to the end point of water vapor in the atmosphere which has potential to precipitate, is called precipitable water vapor (PWV) PWV can be measured in different ways, from ground-based to space-borne instruments (Bayat, A., & Mashhadizadeh Maleki, S 2018)

Precipitable water in the atmosphere is an important climate parameter, which is expected to increase with global mean sea surface temperature (Jinyou Liang, in Chemical Modeling for Air Resources, 2013) Precipitable water vapor over oceans represents the main components of the landatmosphere-ocean ecosystem and plays an important role in the exchange of substances and the radiative balance on the global scale (Gong, S., 2018) Precipitable water vapor (PWV) over the oceans represent important components in the atmosphere and they also produce clouds and precipitation, which play a key role in weather and climate changes (Soden, B J., & Lanzante, J R 1996)and( Xu, X., & Wang, J 2015) In addition, as the main components in the land-atmosphere-ocean ecosystem, PWV affect the radiative balance directly and indirectly on a global scale and further have an impact on changes

in the environment and the climate (Ichoku, C 2002)

2.2 Model and Satellite data

2.2.1 ECMWF-Interim Daily (model data)

ERA‐Interim is the latest global atmospheric reanalysis produced by the European Centre for Medium‐Range Weather Forecasts (ECMWF) The ERA‐Interim project was conducted in part to prepare for a new atmospheric reanalysis to replace ERA‐40, which will extend back to the early part of the twentieth century ERA-Interim covers the period from 1 January 1989 onwards, and continues to be extended forward in near-real time (Dee, D P et al, 2011) An extension from 1979 to 1989 is currently in preparation Gridded data products include a large variety of 3 -hourly surface parameters, describing weather as well as ocean-wave and land-surface conditions, and 6-hourly upper-air parameters covering the troposphere and

stratosphere Vertical integrals of atmospheric fluxes, monthly averages for many of the parameters, and other derived fields have also been produced Berrisford et al (2009) provide a detailed description of the ERA-Interim product archive Information about the current status of ERA-Interim production, availability of data online, and near-real-time updates of various climate indicators derived from ERA-Interim data, can be found at http://www.ecmwf.int/research/era

The ERA-Interim reanalysis is produced with a sequential data assimilation scheme, advancing forward in time using 12-hourly analysis cycles In each cycle, available observations are combined with prior information from a forecast model to estimate the evolving state of the global atmosphere and its underlying surface This involves computing a variational analysis of the basic upper-air atmospheric fields (temperature, wind, humidity, ozone, surface pressure), followed by separate analyses

of near-surface parameters (2m temperature and 2m humidity), soil moisture and soil temperature, snow, and ocean waves The analyses are then used to initialise a short-range model forecast, which provides the prior state estimates needed for the next analysis cycle (Dee, D P et al, 2011)

The forecast model has a crucial role in the data assimilation process Use of the model equations makes it possible to extrapolate information from locally observed parameters to unobserved parameters in a physically meaningful way, and also to carry this information forward in time The skill and accuracy of the forecast model determines how well the assimilated information can be retained; better forecasts mean that smaller adjustments are needed to maintain consistency with observations as time evolves

Additionally, while producing a forecast, the model estimates a wide variety of physical parameters such as precipitation, turbulent fluxes, radiation fields, cloud properties, soil moisture, etc Even if not directly observed, these are constrained by the observations used to initialize the forecast The accuracy of these model-generated estimates naturally depends on the quality of the model physics as well as that of the analysis (Dee, D P et al, 2011)

2.2.2 AIRS data (satellite data)

AIRS data is distributed by the NASA Goddard Earth Sciences Data Information and Services Center (GESDISC) Launched aboard the NASA Earth Observing System satellite called "Aqua" in 2002, the Atmospheric Infrared Sounder (AIRS) instrument suite constitutes an innovative space borne atmospheric sounding system comprised of the AIRS hyper spectral infrared instrument and two multichannel microwave instruments the Advanced Microwave Sounding Unit (AMSU-A) and the Humidity Sounder for Brazil (HSB) Together these instruments observe global water and energy cycles, climate variation and trends, and the response

of the climate system to increased greenhouse gases (AIRS atmospheric infrared sounder, https://airs.jpl.nasa.gov/data/overview)

AIRS retrieved data products provide a daily global view of the dimensional physical state of the atmosphere (air temperature, water vapor, clouds) and the distribution of trace gas constituents (ozone, carbon monoxide, carbon dioxide and methane) (AIRS atmospheric infrared sounder)

three-The AIRS instrument suite consisting of the hyperspectral (2378 infrared channels and 4 visible/near-infrared channels) AIRS instrument, the (15 microwave

channel) AMSU-A instrument and the (4 microwave channel) HSB instrument was launched aboard NASA's Aqua Earth Observing System satellite on May 4, 2002 The advantage of the AIRS suite in orbit is the provision of rapid global coverage as radiosonde coverage of much of Earth's land mass and all but a few locations in the oceans is practically nonexistent There are less than 1000 radiosonde launch sites worldwide, most based in Europe and North America and most launch two radiosondes per day AIRS soundings are equivalent to launching 300,000 radiosondes

on a 50 km grid over the globe each day (AIRS atmospheric infrared sounder, https://airs.jpl.nasa.gov/data/overview)

The orbit of the Aqua satellite is polar sun-synchronous with a nominal altitude

of 705 kilometers (438 miles) and an orbital period of 98.8 minutes, completing approximately 14.5 orbits per day The repeat cycle period is 233 orbits (16 days) with

a ground track repeatability of ± 20 kilometers (12 miles) The satellite equatorial crossing local times are 1:30 a.m in a descending orbit and 1:30 p.m in an ascending orbit

The AIRS instrument suite was constructed to obtain atmospheric temperature profiles to an accuracy of 1 kelvin for every 1 kilometer layer in the troposphere and

an accuracy of 1 kelvin for every 4 kilometer layer in the stratosphere up to an altitude

of 40 kilometers The temperature profile accuracy in the troposphere matches that achieved by radiosondes launched from ground stations The advantage of the AIRS suite in orbit is the provision of rapid global coverage as radiosonde coverage of Earth's oceans is practically nonexistent (AIRS atmospheric infrared sounder, https://airs.jpl.nasa.gov/data/overview)

In conjunction with temperature profiles, the AIRS instrument suite obtains humidity profiles to an accuracy of 15% in 2 kilometer layers in the lower troposphere and an accuracy of ~50% in the upper troposphere It also provides integrated column burden for several trace gases The infrared spectrum is rich in information on numerous gases in the atmosphere, and the primary data returned by AIRS is the infrared spectrum in 2378 individual frequencies It forms a "fingerprint" of the state

of the atmosphere for a given time and place that can be used as a climate data record for future generations (AIRS atmospheric infrared sounder)

The AIRS infrared radiance data product is stable to 10 milliKelvin per year and accurate to better than 200 milliKelvin This product is the most accurate and stable set of hyper spectral infrared radiance spectra measurements made in space to date, and it meets the criteria identified by the National Research Council for climate data records

Operational Level 1B, Level 2 and Level 3 data products from the AIRS, AMSU, and HSB instruments are freely available at no charge for use by the general public

- Level 1B data product: geolocated, calibrated observed microwave, infrared and visible/near-infrared radiances, and quality assessment data

- Level 2 data product: geolocated, calibrated cloud-cleared radiances and dimensional and 3-dimensional retrieved geophysical quantities

2 Level 3 data product: retrieved geophysical quantities averaged and binned into 1°x1° grid cells (daily, 8-day, monthly) (AIRS atmospheric infrared sounder, https://airs.jpl.nasa.gov/data/overview)

There are 5 level in AIRS data (Processing level 3, 1, 1B, 2 and 2G), but this thesis only used level 3 data The reason why this study used level 3 data is:

-These L3 files contain geophysical and quality parameters that have been averaged and binned into 1°x1° grid cells For each grid map of mean values there are corresponding maps of standard deviation, counts, minimum, maximum, and in some cases error estimate The counts map provides the user with the number of points per bin that were included in the statistics and can be used to generate custom multi-day maps from the daily gridded products

- The L3 standard products contain retrieved parameters on standard pressure levels roughly matching instrument vertical resolution and are designed for use by the general public in their research Temperature and water vapor profiles are reported on

24 (TempPresLvls) or 12 (H2OPresLvls) pressure levels

- L3 support products contain interim and experimental portions intended for use by the AIRS team and others willing to make a significant investment of time in understanding the product and are reported at higher internal vertical resolution at 100 pressure levels similar to the L2 products (Baijun Tian, et al., 2017)

2.3 MATLAB software

MATLAB is a programming language developed by Math Works It started out

as a matrix programming language where linear algebra programming was simple It can be run both under interactive sessions and as a batch job MATLAB (matrix laboratory) is a fourth-generation high-level programming language and interactive environment for numerical computation, visualization and programming It allows matrix manipulations; plotting of functions and data; implementation of algorithms;

creation of user interfaces; interfacing with programs written in other languages, including C, C++, Java, and FORTRAN; analyze data; develop algorithms; and create models and applications It has numerous built-in commands and math functions that help you in mathematical calculations, generating plots, and performing numerical methods (MATLAB numerical computing book)

Following are the basic features of MATLAB:

- It is a high-level language for numerical computation, visualization and application development

- It also provides an interactive environment for iterative exploration, design and problem solving

- It provides vast library of mathematical functions for linear algebra, statistics, Fourier analysis, filtering, optimization, numerical integration and solving ordinary differential equations

- It provides built-in graphics for visualizing data and tools for creating custom plots MATLAB's programming interface gives development tools for improving code quality, maintainability, and maximizing performance

- It provides tools for building applications with custom graphical interfaces

- It provides functions for integrating MATLAB based algorithms with external applications and languages such as C, Java, NET and Microsoft Excel

MATLAB is widely used as a computational tool in science and engineering encompassing the fields of physics, chemistry, math and all engineering streams It is used in a range of applications including: signal processing and Communications image and video Processing, control systems, test and measurement, computational

finance, computational biology (MATLAB numerical computing book, https://www.tutorialspoint.com/MATLAB/MATLAB_tutorial.pdf)

PART III METHODOLOGY

3.1 Study Frame work

Figure 3.1 Study Frame work

3.2 Description of the study area

This report will be of interest to the TPW data of Asian and the four sub regions

in Viet Nam are the North West, High Land (Tay Nguyen), Hoang Sa Archipelagoes and the Mekong river (Figure 3.2) Four sub regions were chosen based on the topography because the amount of total precipitable water table depend on a lot of factors but the main factor is topography

Collecting data (Model data and Satellite data)

Data analysis ( MATLAB and Excel)

Comparison of satellite and model data base on CC, Average

difference and rmse

3.2.1 South East Asia

Figure 3.2 Study Region within latitude interval 5ºN - 25ºN and longitude interval

100ºE - 120ºE and Four Sub regions (From Google Earth) 3.2.2 Four sub regions

3.2.2.1 North West of Vietnam

Figure 3.3 North West of Vietnam within latitude interval 21º15´N-22º30´N and

3.2.2.2 Central Highlands of Vietnam

Figure 3.4 High Land of Vietnam within latitude interval 12º30´N - 14º15´N and

longitude interval 107º45´E - 108º45´E (From Google Earth)

3.2.2.3 Hoang Sa Archipelagoeses of Vietnam

Figure 3.5 Hoang Sa Archipelagoes of Vietnam within latitude interval 15º45´N - 17º00´N and longitude interval 111º15´E - 113º00´E (From Google Earth)

3.2.2.4 Mekong Delta of Vietnam

Figure 3.6 Mekong River Delta of Vietnam within latitude interval 9º30´N - 10º45´N and longitude interval 105º30´E - 106º30´E (From Google Earth)

3.3 Data collection

The region selected for the study is within latitude interval 5°N-25°N and longitude interval 100°E-120°E (Figure 3.2), the area averaged total precipitable water from AIRS instrument (of Aqua satellite) and ECMWF interim daily model have been collected on monthly scale for the period of 2008 to 2017

3.3.1 Model Data collection (ERA-data)

The Model data have been collected from ECMWF-interim daily on monthly

dataset: ERA5, ERA-Interim, ERA-Interim/Land, ERA-20C, ERA-20CM, CERA-20C and CERA-SAT In this thesis used ERA-Interim data, Total Precipitable Water is presented by Total column water vapour Type of level in this dataset is Surface and ERA Interim Fields is Synoptic Monthly Means This study chose Synoptic Monthly

Means because Synoptic Monthly Means (stream=mnth) are the monthly averages27:

- For each analysis time (at the four main synoptic hours – 00, 06, 12, and 18 UTC)

- For each forecast start time (00 and 12 UTC) and step (3, 6, 9, 12 etc)

The period collected from 2008 to 2017 and the parameter is Total column water vapour The study area has latitude interval 5°N-25°N and longitude interval 100°E-120°E and data has spatial resolution 1° x 1°

3.3.2 Satellite data collection (AIRS data)

Satellite data has been collected from “AIRS ATMOSPHERIC INFRARED SOUNDER” on monthly scale and the dataset is AIRS Version 6 L3

Refined by:

- Subject: atmospheric Water Vapor

- Measurement: Total Precipitable Water

- Source: Aqua AIRS

- Refine date range: From 01/01/2008 to 12/31/2017

- Refine spatial region: 100°E, 5°N, 120°E, 25°N

- Variable: Moisture Variables:

- Dimension: H2OPressureLay and H2OPressureLev

- File format: NetCDF

Note:

- TotH2OVap : Total integrated column water vapor burden (kg/m2)

- Ascending: tag: ‘A’: Information collected while the spacecraft is in the ascending part of its orbit (Daytime data except near the poles.) Each field and level is individually quality controlled

- Descending: tag ‘D’: Information collected while the spacecraft is in the descending part of its orbit (Nighttime data except near the poles.) Each field and n level is individually quality controlled

The Total Precipitable Water is presented by Total Precipitable Water The data scale is monthly mean and study region has latitude interval 5°N-25°N and longitude interval 100°E-120°E and data has spatial resolution 1° x 1° with the period from 2008

- Data downloaded from ECMWF and AIRS will be saved as "dataname.nc"

- The MATLAB software was used to open and read the data using the following commands:

1 filename=‘source where the file is stored’ to open NetCDF file

2 ncdisp(filename) : to read and display the file data in Command Window

of MATLAB

3 variabledata=ncread(‘filename’, ‘variable’) to read and display the

variable ECMWF model is tcwv and AIRS is TPW Command Window of MATLAB (in this command ‘value’ mean the variable had to read ex: ncdisp(‘filename’, ‘tcwv’))

 Exxtract data to Excel

To extract data from MATLAB to Excel use command:

xlswrite(‘where to save the data file/filename.xls’,variable name)

Then once the data has been read, the monthly TPW data has been extract to Excel file and saved as ‘’dataname.xls’’ The data of each month recorded corresponds

to the selected data area

Step 2 Aggregation of data

Each year will have 12 months so there was 12 table data of TPW in each year for ERA model data and 24 table for AIRS data (daytime average and nighttime average)

So in total 10 years will have:

+ 120 table of ERA model data in Excel

+ 120 table of daytime average AIRS data in Excel

+ 120 table of nighttime average AIRS data in Excel

The average monthly of TPW was calculated for both the study area and four subplot regions areas in both ERA model estimation and AIRS measurement

Step 3 Compare data

The data have been compared in terms of correlation coefficient (CC), average difference and root mean square error (rmse) using the following relation For finding the difference between the two parameters, ERA model data is subtracted from AIRS data

Correlation coefficient, cc=

Average difference=

Root Mean Square Error (rmse)=

Where:

Si: the total precipitable water estimated by AIRS Gi: the total precipitable water estimated by ERA are their arithmetic means

n: number of months considered

PART IV RESULT 4.1 The difference between AIRS measured and ERA model estimated monthly TPW in four sub regions

4.1.1 Northwest region

4.1.1.1 General information

Northwest Vietnam is one of important sub-regions of Vietnam This region plays a crucial role in Vietnam defense and security Northwest Vietnam is always characterized by untouched beauty of landscapes and local people The area of this region is: 5.64 million ha and population: 3.5 million people (Northwest Vietnam introduction, Website: alotrip.com.) North West (Hoang Lien Son mountains area) is within latitude interval 21º15´N-22º30´N and longitude interval 103º30´E- 104º45´E (Figure 3.3) Northwest is the mountainous area in the northwest of Vietnam The region has borders with Laos and China Northwest is also commonly known as North northwestern Vietnam, and one of 3 natural geographical sub-regions of North Vietnam Northwestern geography in Vietnam is characterized by rugged terrain with high mountains running from northwest to southeast Hoang Lien Son Mountain Range, the roof of Vietnam has a length of 180 km, width of 30 km, with a number of mountains at the height from 2800m to 3000m Typical rivers in this region are Da River and Ma River which contribute greatly to generating hydraulic power in North Vietnam in particular and all Vietnam in general Major topography in this region is limestone plateau stretching from the Phong Tho to Thanh Hoa The plateau can be also subdivided into plateaus of Ta Phin, Moc Chau, and Na San; and basins of Nghia

Lo and Muong Thanh

Northwest mainly consists of medium and alpine mountains This area has the most fragmented, dangerous and highest terrain in Vietnam Popular northern midland and mountainous terrain types are high mountain ranges, deep valleys or gorges area, and limestone plateaus with an average altitude Belonging to this area, Hoang Lien Son range is worth the highest and most voluminous mountain with many peaks over 2500m height, of which Fansipan is the tallest one (with 3143m) Northwestern nature

is quite diverse with many sub-region with characteristic topography, soil, climate, and hydrology Vietnam weather in the northwest is clearly characterized by continental climate with extreme weather phenomenon Daytime temperature range is quite large Many places have all 4 seasons in a day such as Moc Chau plateau According to geographers, the Northwest Vietnam is not only rich in natural unearthed resources such as land, forests, vegetation, flora and fauna system, but also wealthy in unexplored fossil resources, especially in rugged remote areas (Northwest Vietnam introduction, Website: alotrip.com) The area of Hoang Lien Son Mountain has a cold climate throughout the year, with a dry season lasting from November to February In the western part of the Hoang Lien Son mountain range, the dry season lasts until March The average temperature of the North West is 18°C to 23°C (Thanh Van,Nguyen, 2015)

4.1.1.2 The difference between AIRS measured and ERA model estimated monthly TPW in Northwest region

Table 4.1 ERA model estimated monthly data of TPW over Northwest

Table 4.2 AIRS measured monthly data of TPW over Northwest region (kgm -2 )

1 (in Appendix) So, it can be seen that the total precipitable water recorded by AIRS