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

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 Signature of Agriculture and Forestry, Vietnam. 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 i 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 25th September, 2018 submission ii 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 ii 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 iii 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 iv 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 v 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 vi 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 vii 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 viii 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 1 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. 2 + 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. 3 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). 4 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 socalled 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 5 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). 6 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. ERAInterim 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 7 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 shortrange 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. 8 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 threedimensional 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) . The AIRS instrument suite consisting of the hyperspectral (2378 infrared channels and 4 visible/near-infrared channels) AIRS instrument, the (15 microwave 9 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). 10