HUBUNGAN TIME-LAG ANTARA SUHU PERMUKAAN LAUT LOKAL DENGAN CURAH HUJAN DIATAS KAWASAN JAWA-BALI ABSTRAK

Transcription

1 HUBUNGAN TIME-LAG ANTARA SUHU PERMUKAAN LAUT LOKAL DENGAN CURAH HUJAN DIATAS KAWASAN JAWA-BALI ABSTRAK Indonesia merupakan negara kepulauan yang dikelilingi oleh lautan dan termasuk dalam wilayah maritime tropis dimana curah hujan tidak dapat diprediksi. Anomali suhu permukaan laut (SPL) di perairan Indonesia, Pasifik dan Hindia juga berkontribusi untuk maju mundurnya awal musim hujan dan panjang musim hujan pendek yang mempengaruhi keragaman curah hujan di Indonesia. Teknik penginderaan jauh dapat digunakan untuk mengidentifikasi dan menganalisis hubungan time-lag antara SPL lokal dengan curah hujan di atas wilayah Jawa-Bali. TRMM 3B42 V7 mewakili nilai dari data curah hujan dan TMI mewakili nilai data SST. Tujuan dari penelitian ini adalah untuk menentukan puncak time-lag antara SPL lokal dan curah hujan, menentukan pengaruh dari SPL lokal untuk curah hujan, dan untuk mengidentifikasi pengaruh distribusi spasial SPL lokal dengan curah hujan di atas kawasan Jawa-Bali. Data harian time series dari tahun dari TMI dan TRMM 3B42 V7 digunakan dalam penelitian ini. Metode korelasi silang digunakan dalam melakukan analisis time-lag antara SPL dan curah hujan yang terdapat di perairan Jawa Bali. Untuk SPL dibedakan menjadi dua area yang berbeda yakni Laut Jawa dan Samudera Hindia. Sementara untuk data curah hujan melingkupi seluruh area Jawa Bali termasuk daratan dan lautan. Analisa time-lag dilakukan baik secara temporal maupun secara spasial. Analisa spasial untuk melihat pengaruh dari SPL dari masing-masing lautan (Laut Jawa dan Samudera Hindia) terhadap curah hujan di seluruh area Jawa-Bali. Hasil dari penelitian menunjukkan bahwa SPL Samudera Hindia dan Laut Jawa memiliki jeda dalam memberikan pengaruh terhadap curah hujan di kawasan Jawa Bali. Analisa time-lag antara SPL dan curah hujan secara harian dari tahun menunjukkan di Samudera Hindia memiliki lag sebesar 5 hari (r=0.51), Laut Jawa memiliki lag 7 hari (r=0.32), dan dari kedua lautan tersebut adalah 5 hari (r=0.49). Sementara untuk tim- lag secara musiman menunjukkan bahwa musim kemarau memiliki pengaruh lebih besar daripada musim hujan. Analisa tim-lag pada saat musim hujan menunjukkan di Samudera Hindia memiliki lag sebesar 5 hari (r=0.13), Laut Jawa dengan lag -11 (r=-0.29), dan dari kedua lautan tersebut memiliki lag -9 (r=-0.15). Pada saat musim kering (kemarau) menunjukkan di Samudera Hindia memiliki lag -6 (r=0.48), di Laut Jawa memiliki lag 4 hari (r=0.52), dan dari kedua lautan tersebut adalah -6 (r=0.5). Analisa time-lag secara spasial menunjukkan bahwa pada area daratan memiliki lag secara negatif (-2 sampai -4 hari), dan lag positif pada bagian lautan (0-14 hari). Pada analisa musiman, hasil pada saat musim hujan memiliki pola yang sama dengan analisa harian ( ), sedangkan pada saat musim kemarau memiliki pola lag negatif pada area daratan dan Laut Jawa dan pada Samudera Hindia memiliki lag positif. Kata kunci: TMI, TRMM, time-lag, curahhujan, SPL viii

2 TIME-LAG RELATIONSHIP BETWEEN THE LOCAL SEA SURFACE TEMPERATURE WITH THE RAINFALL OVER OF JAVA-BALI REGION ABSTRACT Indonesia is an archipelago surrounded by oceans and included in the tropical maritime areas where rainfall is unpredictable. Anomalous sea surface temperature (SST) in Indonesian waters, the Pacific and Indian Oceans also contribute for the rainfall outset of the rainy season and the length of the short rainy season that affects the diversity of rainfall in Indonesia. The remote sensing technique was used to identify and analyzed time-lag relationship between the local SST and rainfall over the Java-Bali region. The TRMM 3B42 V7 represents the value of the rainfall s data and TMI represents the SST s data value. The aim of this research was to determine the peak of time-lag between local SST and rainfall, to determine the effect of local SST to rainfall, and to identify the effect of the spatial distribution of local SST and rainfall over Java-Bali region.the daily time series data from 1998 to 2014 by TMI and TRMM 3B42 V7 used in this study. The cross correlation method is used in analyzing the time lag between SST and rainfall of Java and Bali. SST can be divided into two different areas; The Java Sea and the Indian Ocean. Meanwhile, the rainfall data covered all areas of Java and Bali including land and sea. The spatial analysis of time-lag is to see the effect of STT from both areas (Indian Ocean and Java Sea) to the rainfall in the area of Java- Bali. The result of this study indicates the SST of Indian Ocean and Java Sea are lagged in giving the effect to the rainfall in the area of Java and Bali. The Analysis of time-lag between SST and rainfall on a daily basis of the year shows the Indian Ocean has of 5 days lag (r = 0.51), Java Sea has of 7 days lag (r = 0:32), and of both seas are 5 days lag ( r = 0.49). Meanwhile the seasonally time lag shows that drought has a greater effect than the wet season. The analysis of time lag during the wet season shows the Indian Ocean has a lag of 5 days (r = 0:13), Java Sea has a lag of -11 (r = -0.29), and the lag from both seas are -9 days lag (r = -0.15). The dry season shows the lag of Indian Ocean is -6 days (r = 0:48), The Java Sea has a lag of 4 days (r = 0.52), and from both seas are -6 days lag (r = 0.5). The Analysis of spatial time-lag indicates the area of land has a negative lag (-2 to -4 days), and a positive lag in the ocean (0 to14 days). In the seasonal analysis, the result in the wet season has the same pattern with the daily analysis from1998 to 2014, whereas during the dry season there is a negative lag pattern in the land and sea areas of Java and the positive lag pattern in the Indian Ocean. Keywords: TMI, TRMM, time-lag, rainfall, SST ix

3 TIME-LAG RELATIONSHIP BETWEEN THE SEA SURFACE TEMPERATURE WITH THE RAINFALL OVER OF JAVA-BALI REGION SUMMARY Indonesia is an archipelago surrounded by oceans and included in the tropical maritime areas where rainfall is unpredictable. These tropical regions have an important effect on the atmosphere; it is like a heat source in the Earth's climate system (Ramage, 1971). Anomalous sea surface temperature (SST) in Indonesian waters, the Pacific and Indian Oceans also contribute for the precipitation outset of the rainy season and the length of the short rainy season that affects the diversity of rainfall in Indonesia. Rainfall is very influential in various sectors. The rainfall would affect agricultural planning system for monitoring crops and cropping patterns. Rain is also a major source of water in the availability of natural resources (lakes, reservoirs, rivers, and ground water), so the accurate rainfall data is important for hydrological modeling. Rainfall is also one of the important climate parameter which is important in anticipating natural disasters. Therefore, the accuracy of rainfall prediction and the analysis of rainfall in Indonesia has become an interesting yet important study. The remote sensing technique is used to identify and analyzed time-lag relationship between the local SST and rainfall over the Java-Bali region. The TRMM 3B42 V7 represents the value of the rainfall data and TMI represents the data values of SST. From both analyses, the value of satellite data is analyzed and correlated spatially to obtain the location information, temporal and spatial distribution of the local SST affecting the rainfall in the area. The aims of this research are: (1) to determine the peak of time-lag relationship between sea surface temparture (SST) and the rainfall over Java-Bali region by using TRMM 3B42 V7 and TMI, (2) to determine the local SST most affected by rainfall over of Java-Bali region based on the analysis of time-lag by using TRMM 3B42 V7 and TMI, and (3) to determine the effect of the spatial distribution of the local SST on the rainfall over of Java-Bali region. The research used two time series of satellite data; TRMM 3B42 V7 represented daily rainfall data while the TMI daily SST data was used to determine time-lag relationship between the local SST with the rainfall over of Java-Bali region located at S and E. The daily SST data by TMI and daily rainfall data by TRMM 3B42 V7 in the periode from 1998 to 2014 were used to find time-lag relationship between the local SST with the rainfall over of Java-Bali region. The spatial data in this research covers S and E. In practice, the SST northern of Java-Bali is stated by SST- JS on location S to E and the SST southern of Java-Bali is stated by x

4 SST-IO on location S to E. Each spatial average value SST-JS and SST-IO Java-Bali are analyzed to get the peak of time-lag to rainfall in the area. Then, analyze the spatial correlation with the local SST against average spatial of the rainfall over of Java-Bali region. The statistical value was used to analyze time-lag relationship between the SST-JS and SST-IO and the rainfall over of Java-Bali region. The approach to the estimated index value of the satellite consists of cross correlation coefficient (r), which is difined as follows (Lyon, 2010). Where X is the index value SST (SST-JS and SST-IO) from TMI, Y is the rainfall from TRMM 3B42 V7 values, and r is number of coefficient correlation. In the statistics, the coefficient r is a measurement of the size and direction of linier relationship between variables x and y (Lyon, 2010). If these varibles x and y move togather, where they both rise at an identical rate, then r=+1. If the variables does not bugde, then r=0. If the other variable falls at identical rate, then r=-1. In other sense, if r is greater then zero, it has a positive correlation. Whereas if r is less then zero, it has a negatif correlation. A positive correlation of the SST indicates increased of the rainfall. Meanwhile, a negative correlation of the SST indicates decreased of the rainfall. The main analysis in this research is for the diurnal and seasonal relationship. The analysis was conducted in each pixel and it used the coordinates to identify the identity. The data were extracted from TRMM 3B42 V7 for each pixel and coordinates then generated point by point. Each point has the informations about the coordinates, also the daily, weekly, monthly, seasonally and anually value of rainfall. Then, the data was sorted by the purpose of the analysis. The same process was also perfomed on value of TMI (SST JS-IO). Furthermore, the cross correlation coefficient r (eq.1) was calculated. After the correlation value was obtained, the data points were converted into a raster data format with the same spatial resolution of 0.25 x 0.25 and daily temporal resolution. These processes were conducted by using Ms. Ecxel and ArcGIS 10.1 sofware packages. Daily analysis was used by correlating the daily data regarding the same day in different days and seasonal. Seasonal analysis was conducted based on the monsoon activity. The year was divided into the following two seasons; i.e dry season on May to October and the wet season on November to April. DJF represents the peak of the wet season, and JJA represents the peak of the dry season. The daily SST Data by TMI and the daily rainfall of data by TRMM 3B42 V7 for the period from 1998 to 2014 used to find time-lag relationship between the local SST and the rainfall over of Java-Bali region. The corelation cross methods used in the analysis of time lag between the local SST and the rainfall of Java and Bali. The SST can be divided into two different areas; the Java Sea and the Indian Ocean. For the rainfall data, it covers all areas of Java and Bali including land and sea. The analysis time lag is carried out both temporal and spatial. Spatial analysis was used to xi

5 see the effect of SST from Indian Ocean and Java Sea to the rainfall over of Java- Bali region. This research concluded that; (1) the peak of time-lag relationship between local SST to rainfall over Java-Bali region using TRMM 3B42 V7 and TMI based on daily analysis is 7 days. While the seasonal analysis is 11 days in wet season, (2) SST in the Indian Ocean has strongest effect on the rainfall over of Java-Bali region based on daily analysis with correlation coefficient of 0.51, meanwhile based on the seasonal analysis the SST in Java Sea has the most powerfull effect with correlation coefficients of 0.53, and (3)The effect of local SST to rainfall over Java-Bali region was greater during the dry season, while during the wet season the rainfall is more influenced by the region. xii

6 TABLE OF CONTENTS Page INSIDE COVER... AGREEMENT SHEET... EXAMINEER COMITEES SHEET... STATEMENT FREE FROM PLAGIARISM... ACKNOWLEDGEMENT... i ii iii iv v ABSTRAK... viii ABSTRACT... SUMMARY... ix xi TABLE OF CONTENTS... xiv LIST OF FIGURES... xvii LIST OF TABLES... xviii LIST OF ABBREVIATIONS... xix CHAPTER I. INTRODUCTION Background Problem Formula Aims of Research Research Benefits... 6 CHAPTER II. LITERATURE REVIEW Rainfall Patterns in Indonesia Monsoon Patterns Equatorial Patterns Local Patterns xiii

7 2.2 Sea Surface Temperature (SST) Remote Sensing Tropical Rainfall Measuring Mission (TRMM) TRMM Microwave Imager (TMI) TRMM 3B42 V Time Series Time-lag Correlation Cross Correlation CHAPTER III. FRAMEWORK OF RESEARCH CHAPTER IV. RESEARCH METHOD Research Location Research Data and Method Research Instrument Research Procedure Data collecting Image processing Data analysis methods CHAPTER V. RESULTS Long Term Variability Long term SST variability Long term rainfall variability Temporal Average of Time-lag between SST with Rainfall Time series analysis xiv

8 5.2.2 Seasonal analysis Spatial Average of Time-lag between SST with rainfall Daily average of SST and rainfall Time series analysis Seasonal analysis Wet season Dry season CHAPTER VI. DISCUSSION Long Term Variability Temporal Average of Time-lag between SST with Rainfall Spatial Average of Time-lag between SST with Rainfall CHAPTER VII. CONCLUSION AND SUGGESTION Conclusion Suggestion BIBLIOGRAPHY APPENDIX xv

9 LIST OF FIGURES Page 2.1 Rainfall patterns of Indonesia Remote sensing illustration TRRM Measurements Framework of Research Research location Research scheme Long term SST from Long term rainfall from Time series cross correlation Cross correlation between of SST with rainfall on wet season Cross correlation between of SST with rainfall on dry season Daily average of rainfall and SST from 1998 to Spatial time-lag SST-IO with rainfall Spatial time-lag of SST with rainfall on wet season Spatial time-lag SST with rainfall on dry season xvi

10 LIST OF TABLES Page Table 1. Characteristics of the TRMM satellite Table 2. Time series data xvii

11 LIST OF ABBREVIATIONS ASCII ACF CPC CERES DJF ENSO HQ IOD ITF ITCZ IR JAXA JJA LIS MW MJJ MAM NASA NCDC ND NDJ NOAA NASDA PR RMS SST SON : American Standard Code for Information Interchange : Autocorrealtion Function : Climate Prediction Center : Clouds and the Earth's Radiant Energy System : December-January-February : El Niño Southern Osciltion : High Quality : Indian Ocean Dipole : Indonesian Trough Flow : Intertropical Convergence Zone : Infrared : Japan Aerospace Epxlortion Agency : June - July-August : Lightning Imaging Sensor : Microwave : May June July : March-April-May : National Aeronautics and Space Administration : National Climatic Data Center : November-December : November December January : National Oceanic Atmospheric Administration : National Space Development Agency : Precipitation Radar : Root Mean Square : Sea Surface Temperature : September-October-November xviii

12 SSM/I SST-JS SST-IO TMI TRMM UTC VIRS : Special Sensor Microwave/Imager : Sea Surface Temperature Java Sea : Sea Surface Temperature Indian Ocean : TRMM Microwave Imager : Tropical Rainfall Measuring Mission : Coordinated Universal Time : Visible and Infrared Scanner xix

13 CHAPTER I INTRODUCTION 1.1. Background Indonesia is an archipelago surrounded by oceans and included in the tropical maritime areas where rainfall is unpredictable (Aldrian, 2003). These tropical regions have an important effect on the atmosphere; it is like a heat source in the Earth's climate system (Ramage, 1971). Anomalous sea surface temperature (SST) in Indonesia s sea water, the Pacific and Indian Oceans also contribute for the rainfall outset of the rainy season and the length of the short rainy season that affects the diversity of the rainfall in Indonesia. Rainfall is very influential in various sectors (Estiningtyas, 2007). Rainfall would affect agricultural planning system for monitoring crops and cropping patterns. Rain is also a major source of water in the availability of natural resources (lakes, reservoirs, rivers, ground water), so accurate rainfall data to be important for hydrological modeling. Rainfall is also one climate parameter very important in anticipating natural disasters (Maarten, 2006). Therefore, the accuracy of rainfall prediction and the analysis of rainfall in Indonesia has become an interesting yet important study. The rainfall pattern distribution in Indonesia is correlated with the global climatic indices, such as sea surface temperature (Saji et al., 1999). The change in SST known to have a huge influence on the variability of rainfall and it is related to the changes in the pattern of the SST anomalies both spatially and temporally. 1

14 2 The hydrological cycle is the circulation of water from the atmosphere to the earth that recurred to the atmosphere through condensation, precipitation, evaporation and transpiration. The warming of ocean water by sunlight is a key of hydrological cycle process. The water evaporated, and falls as precipitation in the form of rain, snow, sleet and snow (sleet), drizzle or fog. Thus, SST becomes one of the parameters that are important in the formation of precipitation on earth. The evaporation process requires time-lag until the occurrence of rain. Therefore, the analysis of time-lag is important as a reference in rainfall prediction for the system development planning and the disaster mitigation. Previous research showed that SST phenomena related to rainfall events. Hendon (2003), Prabowo & Nicholls in Faqih (2004), Boer et al. (1999), stated that the SST anomalies in the Niño 3.4 has a stronger relationship with the monthly rainfall anomalies compared with SST anomalies in the other zones. Furthermore, Aldrian and Susanto (2003) connected the two parameters in more detail, by linking rainfall Indonesia in three regions rainfall (monsoon, equatorial and local) with local and regional SST. The close relationship between the two parameters provides a good indication thus providing opportunities for using their information more applicable. One of such information is an estimate precipitation particular location. According to Nicholls (1981, 1984) showed that the surface pressure in the area of Darwin, northern Australia and SST around Indonesia can be used to predict future climate and rainfall in Indonesia. Rainfall from June to November was positively correlated with SST , the relationship was strongest in the southern part of Central

15 3 Java, where 70 percent of the area is a plateau area (Kirono and Tapper, 1999). The SST fluctuations are hot issues in Indonesia and throughout the tropical regions of the Pacific (Trenberth and Shea, 1987; Trenberth and Hoar, 1996). Thiaw and Mo (2005), stated that one of the main sources of error rainfall prediction comes from one estimate of SST. Therefore, the study is important to identify and analyze of the time lag relationship between the local SST variability and rainfall. Such relationship measure SST on an increase and decrease in rainfall with a time-lag difference. One of interesting areas for this study is the area of Java and Bali, which is part of the Indonesian archipelago. It is because the geographical location of Java-Bali region flanked by two water areas. The north side is the Java Sea and the south is the Indian Ocean which has an important role for the occurrence of rainfall in the local, region, and also affect the rainfall in Indonesia in general. This is corroborated by several earlier studies. Tangang and Juneng (2004) showed that during the boreal summer rainfall, Malaysia was significantly correlated with SST Java Sea, but not with the South China Sea SST. According to monsoon, the flows during this period in the Java Sea southeasterlies, southeasterlies anomalies or southerlies in the region can increase evaporative cooling and hence lead to lower SST. According to Wang et al. (2003), found the atmosphere-ocean interactions play an important role in keeping the SST dipole anomaly and cold water in the Java Sea and the surrounding areas. Hendon (2003) also expressed during the La Niña, rainfall variation in Indonesia during the dry season has been accompanied by the increase in the gradation of local SST anomalies equatorial Indian Ocean, which is strongly

16 4 associated with El Niño Southern Osciltion (ENSO) during the dry season. After the wet season begins, the Indian Ocean tends to have the same marked SST anomalies (positive during the El Niño and negative for La Niña). The research on time-lag relationship between SST local and rainfall over of Java-Bali region has not been found, so further research is needed to study about the relationship. The remote sensing techniques be used to identify and analyzed the time lag relationship between the local SST with rainfall over of Java-Bali region. The TRMM 3B42 V7 represents the value of the rainfall data and TMI representing data values SST. From both the value of satellite data will then be analyzed and correlated spatially to obtain location information, temporal and spatial distributions of the local SST affecting rainfall in the area. Remote sensing is the study to obtain the informations about an object through the analysis of data acquired by a device (sensor) that is not in contact with the object (remote). Remote sensing technology develops analogous with the availability of satellite imagery data widely (worldwide) on various subordinate space such as LANDSAT, SPOT, Quick Bird, IRS, RADARSAT, etc. Precipitation is a difficult parameter to evaluate, mostly because of its high spatial and temporal variability. Although most locations on the Earth's surface experience precipitation in some time, in any year, and some locations on more days, the instantaneous precipitation is a rare phenomenon (Barret, 2000).

17 5 The Tropical Rainfall Measuring Mission (TRMM) is a joint mission between National Aeronautics and Space Administration (NASA) of United State and Japan Aerospace Epxlortion Agency (JAXA) to study for weather and climate research. The main objective of satellite was to provide a better understanding of precipitation structure and heating in the tropical regions of the earth (Simpson et al,.1996). The TRMM satellite is operation on a non-sun synchronous orbit that enables it to observe tropical rainfall. Observation of spatial patterns on the rainfall over Indonesia using TRMM was done. As-syakur et al. (2014), shows that remote sensing data can provide good spatial-temporal clustering interactions information about the relationship between rainfall, El Niño Southern Osciltion (ENSO) and Indian Oscilation Dipole (IOD) in land and ocean area. The existence of spatial-temporal clustering zone gives the probability information on global climate which influences the difference in the ENSO and IOD strength, such as the SST and Intertropical Convergence Zone (ITCZ) effect. One part of TRMM is TRMM Microwave Imager (TMI) which is a multichannel/dual-polarized microwave radiometer which will provide data related to rainfall rates over the oceans. The TMI data together with Precipitation Radar (PR) data will be the primary data set of precipitation measurement. The TMI data was combined with the data from the PR and Visible Infrared Scaner (VIRS) will also be utilized for deriving precipitation profile (JAXA, 2007).

18 Problems Formula 1. How to determine the peak of time-lag relationship between the local SST with the rainfall over of Java-Bali region using TRMM 3B42 V7 and TMI?. 2. How to determine local SST that most affect of rainfall over of Java-Bali region based on analysis of time lag using TRMM 3B42 V7 and TMI? 3. How to determine the spatial distribution effects the local SST with the rainfall over of Java-Bali region? Aims of Research 1. To determine the peak of time-lag relationship between the local SST with the rainfall over Java-Bali region using TRMM 3B42 V7 and TMI. 2. To determine the local SST most affected by the rainfall over of Java-Bali region based on analysis of time lag by using TRMM 3B42 V7 and TMI. 3. To determine of the spatial distribution effect a local SST with rainfall over of Java-Bali region. 1.4 Research Benafits 1. To provide the information about the shortest time-lag relationship between the local SST with the rainfall over of Java-Bali region based on TRMM 3B42 V7 and TMI.

19 7 2. To provide new information and understanding about the shortest time lag relationship between the local SST with the rainfall over of Java-Bali region based on TRMM 3B42 V7 and TMI. 3. For the next researcher, this study can be used as reffrence for analyzing phenommenas about the local SST and the rainfall characteristics over of Java-Bali region.