International Journal of Remote Sensing, 2013 Vol. 34, No. 21, 7723–7738, http:dx.doi.org10.108001431161.2013.826837
Indonesian rainfall variability observation using TRMM multi-satellite data
Abd. Rahman As-syakur
a,b
, Tasuku Tanaka
a,c
, Takahiro Osawa
a
, and Made Sudiana Mahendra
d
a
Centre for Remote Sensing and Ocean Science CReSOS, Udayana University, Denpasar, Bali 80232, Indonesia;
b
Environmental Research Centre PPLH, Udayana University, Denpasar, Bali 80232, Indonesia;
c
Graduate School of Science and Engineering, Yamaguchi University, Ube Shi Tokiwadai 2-16-1, Ube 7550092, Japan;
d
Graduate Study of Environmental Sciences, Udayana University, Denpasar, Bali 80232, Indonesia
Received 3 January 2012; accepted 24 April 2013 It is important to understand the characteristics of Indonesian rainfall within the world’s
climate system. The large rainfall in the Indonesian archipelago plays an essential role as a central atmospheric heat source of the Earth’s climate system throughout the year.
Monthly rainfall satellite data, measured by the Tropical Rainfall Measuring Mission TRMM 3B43 over the course of 13 years, were employed to analyse monthly means,
total means, maximum and minimum variability, standard deviation, and the trends anal- ysis of Indonesian rainfall variability. The rainfall estimated from satellite data was then
compared to the rain gauge data over the Indonesian region to determine the accuracy level. The results show that oceans, islands, monsoons, and topography clearly affect the
spatial patterns of Indonesian rainfall. Most high-rainfall events in Indonesia peak dur- ing the December–January–February DJF season and the lowest rainfall events occur
during the June–July–August JJA season. Those conditions are associated and gen- erated with the northwest and southeast monsoon patterns. High fluctuations between
maximum and minimum monthly rainfall data of over 400 mm month
−1
occur over Jawa Java Island, the Jawa Sea, and southern Sulawesi Island. A high annual and
monthly rainfall typically occurs throughout Indonesia over island areas. The trend analysis shows an increasing trend in rainfall from 1998 to 2010 in Kalimantan, Jawa,
Sumatra, and Papua. Decreasing rainfall trends occur along the west and south coast of Sumatra, eastern Jawa, southern Sulawesi, Maluku Islands, western Papua, and Bali
Island.
1. Introduction
The Indonesian archipelago is a central atmospheric heat source characterized by huge quantities of rainfall which represent important contributions to understanding the world’s
climate system. Owing to Indonesia’s geographical location, rainfall is strongly influ- enced by the Asian–Australian monsoon system. Wyrtki 1961 described the peaks of
the southeast monsoon as June–July–August JJA, while the northwest monsoon peaks in December–January–February DJF. The transition between monsoons occurs during
the months of March–April–May MAM and September–October–November SON. However, Susanto, Moore Ii, and Marra 2006 described April and October as transition
Corresponding author. Email: ar.assyakurpplh.unud.ac.id
© 2013 Taylor Francis
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7724 A.R. As-syakur et al.
months between the northwest monsoon from November to March and the southeast monsoon from May to September.
Moreover, Indonesian rainfall is also influenced by year-to-year fluctuations in El NiñoSouthern Oscillation ENSO, the Indian Ocean Dipole IOD phenomenon Nicholls
1988; Saji et al. 1999; Vimont, Battisti, and Naylor 2010, local air–sea interactions Hendon 2003, and local topography Chang et al. 2005; Qian 2008. The ENSO and
IOD events create extreme high and low rainfall values in some parts of Indonesia, result- ing in the incidence of floods and droughts Hamada et al. 2002; Hendon 2003; Saji and
Yamagata 2003. On the other hand, complex distribution of land, sea, and terrain results in significant local variations in the annual rainfall cycle Chang et al. 2005. The com-
plex topography of the Indonesia islands affects rainfall quantities Sobel, Burleyson, and Yuter 2011. The differential solar heating between different surface types such as between
land and sea, or highland and lowland, causes strong local pressure gradients Qian 2008. These conditions result in sea-breeze convergence over islands and orographic precipitation
Qian, Robertson, and Moron 2010.
Indonesia, covered mostly by ocean, is the world’s largest archipelago. Therefore, there are several problems in studying and simulating rainfall of the region for an appropriate
land–sea representation Aldrian, Gates, and Widodo 2007 and a complex topographical distribution Qian 2008. In Indonesia, rain gauge data represent precipitation throughout
the country. However, rain gauge measurement networks in the Indonesian archipelago are not as concentrated or regular as in other major continents. Thus, satellite observations
of rainfall may be the best solution for adequate temporal and spatial coverage of rain- fall. Remote-sensing data provide spatial–temporal resolution covering large rainfall study
areas As-syakur 2011 and have become a viable tool to capture the variability of precip- itation systems Villarini et al. 2008. The availability and global coverage of satellite data
offer effective and economical means for calculating areal rainfall estimates in sparsely gauged regions Artan et al. 2007. Rainfall data with better spatial and temporal resolu-
tion allow for a more quantitative understanding of causal links between Indonesian rainfall and larger-scale climate features Aldrian and Susanto 2003.
The Tropical Rainfall Measuring Mission TRMM, jointly co-sponsored by NASA and JAXA, has been collecting data since November 1997 Kummerow et al. 2000. The
main objective of TRMM data collection is to provide a better understanding of precipita- tion structure and heating in the tropical regions of the Earth Simpson et al. 1996. The
TRMM standard products are classified into three levels. Level 3 products, referred to as climate rainfall products, are time-averaged parameters mapped on to a uniform space–
time grid Feidas 2010. Level 3 TRMM 3B43 data are often called TRMM Multi-satellite Precipitation Analysis TMPA products. The data products of 3B43 are the first rain prod-
ucts, combining TRMM precipitation radar PR and TRMM microwave imager TMI rain rates to calibrate rain estimates from other microwave and infrared measurements Huffman
et al. 2007.
Over the years, several groups have studied Southeast Asia and its surrounding areas to validate TRMM data. TRMM-derived product research included the following.
Chokngamwong and Chiu 2008 used rain gauge data from Thailand; As-syakur et al. 2011 compared the TRMM 3B43-3B42 with rain gauge data in Bali, Indonesia; Fleming
et al. 2011 evaluated the TRMM 3B43 using gridded rain-gauge data over Australia; and Semire et al. 2012 validated TRMM 3B43 rainfall in Malaysia. Vernimmen et al.
2011 and Prasetia, As-syakur, and Osawa 2013 validated for other types of TRMM in Indonesia. Vernimmen et al. 2011 compared and used real-time TRMM 3B42 in moni-
toring drought in Indonesia, and Prasetia, As-syakur, and Osawa 2013 validated TRMM precipitation radar over Indonesia and found the accuracy ranging from low 0.07 to high
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International Journal of Remote Sensing 7725
0.73. The results underscore the superiority of the TRMM products, especially for TRMM 3B43, and suggest that the goal of the algorithm was largely achieved.
Rainfall distribution information and the structure of precipitation systems from large areas of Indonesia are important for TRMM data validation. This study observes the
Indonesian rainfall variability determined by TRMM 3B43 products, showing the capa- bility of these products to contribute to the analysis of climatic-scale rainfall in Indonesia.
To validate the results, the rainfall estimated from satellite data was compared with gauge observations over Indonesia, and we sought to determine how well the 3B43 product is an
adequate representation of monthly rainfall in Indonesia.
2. Study area
