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International Journal of Remote Sensing

An official journal of the Remote Sensing and Photogrammetry SocietyISSN0143-1161 (Print), 1366-5901 (Online)Publication Frequency24 online issues, 12 print issues per year

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Editorial board

Editor-in-ChiefTimothy A. Warner: West Virginia University, USA

Co-Editor-in-ChiefArthur P. Cracknell: University of Dundee, UK

Editor-in-Chief EmeritusGiles Foody: University of Nottingham, UK

EditorsMichael J. Collins: University of Calgary, CanadaGutemberg B. França: Universidade Federal do Rio de Janeiro, BrazilMarco Gianinetto: Politecnico di Milano, ItalyPeng Gong: Tsinghua University, ChinaYang Hong: University of Oklahoma, USAWeigen Huang: Second Institute of Oceanography, PR ChinaE. Raymond Hunt Jr: USDA-ARS, USAClaudia Kuenzer: Deutschen Zentrums für Luft- und Raumfahrt (DLR), GermanyXiaofeng Li: National Oceanic and Atmospheric Administration, USAJian Guo Liu: Imperial College London, UKFarid Melgani: University of Trento, ItalyMaurizio Migliaccio: Università di Napoli, ItalySoe Myint: Arizona State University, USASimonetta Paloscia: Instituto di Fisica Applicata, ItalyChristine Pohl: Universiti Teknologi MalaysiaArthur C. B. Roberts: Simon Fraser University, CanadaArun K. Saraf: Indian Institute of Technology, Roorkee, IndiaRamesh P. Singh: Chapman University, USADouglas A. Stow: San Diego State University, USASotaro Tanaka: Remote Sensing Technology Centre of JapanKevin Tansey: University of Leicester, UKJakob Van Zyl: Jet Propulsion Laboratory, NASA, USACostas A. Varotsos: University of Athens, GreeceL. Wang: University at Buffalo, USAMichael J. C. Weir: University of Twente, The NetherlandsYong Xue: London Metropolitan University, UK

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Volume 34, Issue 21, 2013

Taylor & FrancisPublication HistorySample this titleAlert me

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International Journal of Remote Sensing

An official journal of the Remote Sensing and Photogrammetry Society

0143-1161 (Print), 1366-5901 (Online)Publication Frequency24 online issues, 12 print issues per year

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Articles

Classification of radar echoes with a textural–fuzzy approach: an application for the removal of ground clutter observed in Sétif (Algeria) and Bordeaux(France) sites

Leila Sadouki & Boualem Haddadpages 7447-7463

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DOI:10.1080/01431161.2013.823522Published online: 08 Aug 2013Citing articles: 0Article Views: 222

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International Journal of Remote Sensing - Volume 34, Issue 21 http://www.tandfonline.com/toc/tres20/34/21

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International Journal of Remote Sensing, 2013Vol. 34, No. 21, 7723–7738, http://dx.doi.org/10.1080/01431161.2013.826837

Indonesian rainfall variability observation using TRMMmulti-satellite data

Abd. Rahman As-syakura,b*, Tasuku Tanakaa,c, Takahiro Osawaa,and Made Sudiana Mahendrad

aCentre for Remote Sensing and Ocean Science (CReSOS), Udayana University, Denpasar, Bali80232, Indonesia; bEnvironmental Research Centre (PPLH), Udayana University, Denpasar, Bali80232, Indonesia; cGraduate School of Science and Engineering, Yamaguchi University, Ube ShiTokiwadai 2-16-1, Ube 7550092, Japan; dGraduate 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’sclimate system. The large rainfall in the Indonesian archipelago plays an essential roleas 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 thencompared to the rain gauge data over the Indonesian region to determine the accuracylevel. The results show that oceans, islands, monsoons, and topography clearly affect thespatial patterns of Indonesian rainfall. Most high-rainfall events in Indonesia peak dur-ing the December–January–February (DJF) season and the lowest rainfall events occurduring the June–July–August (JJA) season. Those conditions are associated and gen-erated with the northwest and southeast monsoon patterns. High fluctuations betweenmaximum and minimum monthly rainfall data of over 400 mm month−1 occur overJawa (Java) Island, the Jawa Sea, and southern Sulawesi Island. A high annual andmonthly rainfall typically occurs throughout Indonesia over island areas. The trendanalysis 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 coastof Sumatra, eastern Jawa, southern Sulawesi, Maluku Islands, western Papua, and BaliIsland.

1. Introduction

The Indonesian archipelago is a central atmospheric heat source characterized by hugequantities of rainfall which represent important contributions to understanding the world’sclimate system. Owing to Indonesia’s geographical location, rainfall is strongly influ-enced by the Asian–Australian monsoon system. Wyrtki (1961) described the peaks ofthe southeast monsoon as June–July–August (JJA), while the northwest monsoon peaksin December–January–February (DJF). The transition between monsoons occurs duringthe 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: [email protected]

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months between the northwest monsoon from November to March and the southeastmonsoon from May to September.

Moreover, Indonesian rainfall is also influenced by year-to-year fluctuations in ElNiño/Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD) phenomenon (Nicholls1988; 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 andIOD 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 andYamagata 2003). On the other hand, complex distribution of land, sea, and terrain resultsin 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, andYuter 2011). The differential solar heating between different surface types such as betweenland 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, thereare several problems in studying and simulating rainfall of the region for an appropriateland–sea representation (Aldrian, Gates, and Widodo 2007) and a complex topographicaldistribution (Qian 2008). In Indonesia, rain gauge data represent precipitation throughoutthe country. However, rain gauge measurement networks in the Indonesian archipelago arenot as concentrated or regular as in other major continents. Thus, satellite observationsof 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 studyareas (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 dataoffer effective and economical means for calculating areal rainfall estimates in sparselygauged 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 rainfalland larger-scale climate features (Aldrian and Susanto 2003).

The Tropical Rainfall Measuring Mission (TRMM), jointly co-sponsored by NASAand JAXA, has been collecting data since November 1997 (Kummerow et al. 2000). Themain 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). TheTRMM standard products are classified into three levels. Level 3 products, referred to asclimate 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-satellitePrecipitation Analysis (TMPA) products. The data products of 3B43 are the first rain prod-ucts, combining TRMM precipitation radar (PR) and TRMM microwave imager (TMI) rainrates to calibrate rain estimates from other microwave and infrared measurements (Huffmanet al. 2007).

Over the years, several groups have studied Southeast Asia and its surroundingareas 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; Fleminget 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 inIndonesia. 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 TRMMprecipitation radar over Indonesia and found the accuracy ranging from low (0.07) to high

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(0.73). The results underscore the superiority of the TRMM products, especially for TRMM3B43, and suggest that the goal of the algorithm was largely achieved.

Rainfall distribution information and the structure of precipitation systems from largeareas of Indonesia are important for TRMM data validation. This study observes theIndonesian 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 gaugeobservations over Indonesia, and we sought to determine how well the 3B43 product is anadequate representation of monthly rainfall in Indonesia.

2. Study area

Research was conducted in the archipelago of Indonesia, which is composed of17,508 islands of various sizes. Spatial data covered 8◦ 00′ N to 13◦ 45′ S and 92◦ 00′ Eto 141◦ 30′ E. Figure 1 indicates the distribution of Indonesian topography, and six north–south cross-section lines were used to compare values of rainfall and elevation. Indonesiais located between two continents and oceans, with a population of 237,641,326 in2010. Sumatra, Kalimantan, Jawa (Java), Sulawesi, and Papua are the five major islands,with diverse topographical distributions. Several important mountains in Indonesia areJayawijaya in Papua, Bukitbarisan in Sumatra, Kendeng in Jawa, Fenema and Gorontaloin Sulawesi, and Muller in Kalimantan. Jawa Island is the most populated island andthe most important industrial and agricultural region in Indonesia. Meanwhile, Sumatra,Kalimantan, Sulawesi, and Papua are important islands that have tropical rainforests. Smallislands in Indonesia are also unique and important, such as Bali, Lombok, and Halmahera.

3. Data and methods

Monthly rainfall data from 1998 to 2010, measured and collected by TRMM 3B43 satel-lite data, were employed to observe Indonesian rainfall variability. The TMPA is acalibration-based sequential scheme for combining precipitation estimates from multiplesatellites and gauge analyses (where feasible), providing global coverage of precipitationspatially over the 50◦ S–50◦ N latitude belt at 0.25◦ × 0.25◦ at three-hourly temporal

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Figure 1. The study area and Indonesian topography. Lines A-B to K-L indicate the south–northcross sections used to compare values of rainfall and elevation. Black dots indicate the rain gaugelocations.

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resolution for 3B42, and monthly temporal resolution for 3B43 (Huffman et al. 2007).The TMPA estimates are produced in four stages: (1) microwave estimates of precipita-tion are calibrated and combined; (2) infrared precipitation estimates are created usingthe calibrated microwave precipitation; (3) microwave and infrared estimates are com-bined; and (4) monthly data is rescaled and applied (Huffman et al. 2007, 2010). TheTMPA retrieval algorithm used for this product is based on the technique by Huffmanet al. (1995, 1997) and Huffman (1997). The TMPA data sets consist of 45% precipitationfrom passive microwave radiometers (TRMM-TMI, Aqua-Advanced Microwave ScanningRadiometer (Aqua-AMSR) and the Defense Meteorological Satellite Program SpecialSensor Microwave Imagers (DMSP-SSMIs)), 40% from operational microwave sound-ing frequencies (National Oceanic and Atmospheric Administration Advanced MicrowaveSounding Units (NOAA-AMSUs)), and 15% infrared measurements from geostationarysatellites (Geostationary Operational Environmental Satellite (GOES)) Meteosat/MeteosatSecond Generation (Meteosat/MSG) (Mehta and Yang 2008). According to Huffman andBolvin (2007), the TMPA is designed to maximize data quality, so TMPA is stronglyrecommended for any research work not specifically focused on real-time applications.

Several statistical scores were used to determine Indonesian rainfall variability. Thetypes of analysis were monthly means, total means, maximum and minimum variability,standard deviation, and trends. To investigate the effect of the Asian–Australian monsoonon Indonesia rainfall, peak-to-peak amplitude phase analysis extracted the annual signal ineach grid point of rainfall. After calculating the mean of the annual signal, the mean wasremoved to reveal the monthly peak amplitude phase. Furthermore, the analyses were alsocarried out for the effect of land area and topography on rainfall quantities. The distribu-tion of island, sea, and topography obtained from the Shuttle Radar Topography Mission(SRTM) mission was used to compare values of rainfall with regard to island distributionand elevation.

Monthly, seasonal, and long-term time-series analyses were conducted. Monthly analy-sis compared data from the same months of annual observation. Seasonal analysis is basedon the monsoon activity, described by Wyrtki (1961), over the entire observation period.Likewise, long-term analysis observed all time-series data over the entire observationperiod.

Monthly accumulated rainfall data from five rain gauges (derived from daily mea-surements) over Indonesia (see Figure 1), observed by the Indonesian Meteorology,Climatology, and Geophysics Agency (BMKG) standard manual (observatory) and auto-matic (Hellmann) rain gauges, were used as references to compared satellite estimations.The rain-gauge data cover the period from 1998 to 2010. These data were checked forconsistency where unreasonable values from a climatological viewpoint, for instance zero-rainfall months during the wet season, were deleted. The locations of rain gauges areMedan, Pontianak, Denpasar, Ternate, and Jayapura. The monthly average rainfall esti-mated from satellite data is compared with the rain-gauge data, particularly to determine theaccuracy level between the rainfall estimated from satellite and the rain-gauge data. Point-by-point analysis was conducted on the monthly data. Point-by-point analysis consisted ofa comparison between gauge data and satellite data.

4. Results and discussion

4.1. Variability in Indonesian rainfall

The distribution of annual averaged rainfall over Indonesia during 1998–2010 is shown inFigure 2. In general, the highest total annual rainfall extends across the equatorial belt, with

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Figure 2. Distribution of annual averaged rainfall over Indonesia,1998–2010.

the lowest occurring in southeastern Indonesia around the Nusa Tenggara Islands. The fourmain islands of Indonesia, except Jawa Island, show high annual rainfall. Complex topogra-phy affects rainfall quantities in this area (Sobel, Burleyson, and Yuter 2011), such as in theJayawijaya and Bukitbarisan Mountains of Papua and Sumatra Islands, respectively. Thisfigure also shows that rainfall was distributed more heavily over land than sea, except forthe west coast of Sumatra. These patterns were different from those modelled by Aldrian(2003) and Gunawan (2006), which showed higher rainfall over sea than over land. Higherrainfall over land seems to be caused by the magnitude of convergence determined by thedifferent heating qualities of land and sea. The differential heating controls the direction ofland–sea breezes that distribute the precipitation centrally over islands, where sea-breezefronts converge between coasts, lifting moist air and triggering convection (Qian 2008).Figure 2 also shows the area close to the coast; the heavier rainfall was distributed overthe sea, especially on the western coast of Sumatra. Heavy rainfall found along the coastalplain is possibly due to ascent over near-coast hills or seaward-propagating orogenic con-vective systems. Nocturnal rainfall activity along the coast in the western Sumatra Islandsis created by land breezes (from the Bukitbarisan Mountains) converging with the westernWalker cell (from the Indian Ocean) to generate offshore convection and produce heavyrainfall. As noted by Wu et al. (2009), heavy rainfall offshore, west of Sumatra Island,is controlled by the mountainous topography of the island and by the thermally and con-vectively induced local circulation and diurnal changes in thermodynamic stability in theatmosphere offshore.

Figure 3 shows the mean annual rainfall over land and sea in Indonesia during1998–2010. The figure shows annual rainfall over land to be higher than over the sea. Overland, the total mean rainfall was 2779.82 mm year−1, while over the sea it was 1998.21 mmyear−1. This finding confirms that Indonesian rainfall is greater over land than sea.

The monthly climatological mean of rainfall from January 1998 to December 2010 isshown in Figure 4. Throughout the year, the highest rainfall concentrations are observedcentred over the major Indonesian islands such as in Sumatra, Kalimantan, Papua, andSulawesi. During the northwest monsoon (November–February), high rainfall patterns areobserved in all parts of Indonesia except the Nusa Tenggara region of islands in southeast-ern Indonesia. Around Nusa Tenggara, high rainfall only occurs from December to March,consistent with the phase of the southwest monsoon.

Low rainfall in Indonesia started around the island of Timor in April and continuedextending towards the southeast coast of Sumatra, the south coast of Kalimantan, southern

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Figure 3. Total mean of annual rainfall values over land and sea in Indonesia, 1998–2010.

Sulawesi, and reached the southern area of the Maluku Islands in August. This phenomenonis consistent with northwestward movement of the Asian–Australian monsoon system, inwhich the peak of the southeast monsoon occurs in August. During September–November,the high rainfall moves northwest to southeast. In these months, the southeast monsoonweakened while the southwest monsoon strengthened (Susanto, Moore Ii, and Marra 2006).The seasonal shift of heavy rainfall in January and poor rainfall in July is associated withseasonal migration of the intertropical convergence zone (ITCZ). In January, the centralmass of the heavy rainfall is south of the equator while in July it is north of 10◦ N (Changet al. 2005).

The monthly climatological mean of rainfall is also shown throughout the year; thehighest rainfall concentrations fall over land rather than over sea (see Figure 5). The largestand smallest differences occurred in November (95.54 mm month−1) and June (21.73 mmmonth−1), respectively. The annual cycles of rainfall differences between land and sea aresimilar to the annual cycles of the monsoonal climate regions, which have one peak andone trough and experience the strong influence of two monsoons (see Aldrian and Susanto2003).

As described previously, complex topography affects quantities of rainfall over this area(see Figures 2 and 4). To illustrate the impact of topography on the annual and seasonalclimatological average rainfall distribution, Figure 6 shows six north–south cross-sectionlines of rainfall and elevation (see Figure 1). It will be clearly seen in this figure that areasaround the region with high elevation also have high rainfall compared with low elevationsand the sea, which manifests more during wet seasons than during dry and transition sea-sons. However, in one part, topographical effects lead to increasing rainfall amount not onlyin the high elevation region, but also in the vicinity of low elevations. This is partly respon-sible for a very poor correlation (r = 0.01) between total rainfall and elevation in this area(see Figure 7). Additionally, the low spatial resolution of the satellite data (0.25◦ × 0.25◦)may also affect this very poor correlation result. The influence of topography on seasonalrainfall is generally similar to its annual pattern, except during the JJA season in southernIndonesia when a high elevation does not markedly affect rainfall.

In the surrounding islands, heavy rainfall also occurred, such as between the islandsof Jawa and Kalimantan, the northwestern coast of Kalimantan, and the southern coast ofSumatra. This condition is caused by several factors, varies by location, and can clearly

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be seen by remote-sensing data. As explained by Qian (2008), heavy rainfall over the JawaSea (between the islands of Jawa and Kalimantan) (see Figure 6(b) at –5◦ to –7◦) is affectedby the diurnal cycle of land breezes from both Kalimantan and Jawa. Heavy rainfall on thenorthwestern coast of Kalimantan (see Figure 6(b) at 3◦–2◦) was also influenced by landbreezes. In this area, the locally large-scale convergence concentrated over the sea wasdue to diurnal land breeze interaction with the prevailing winter monsoon flow, producingoffshore convection (Houze et al. 1981; Geotis and Houze 1985). On the other hand, theinfluence of the western Walker cell over the Indian Ocean (with an anomalous low-level

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equatorial zonal wind) affects the rainfall on the sea region southwest of the island ofSumatra (see Figure 6(a) at –4◦ to –6◦; Chang et al. 2004).

Figure 8 shows the spatial rainfall distribution of peak maximum, peak minimum,amplitude variability (difference between peak maximum and minimum), and standarddeviation. Indonesian peak maximum rainfall ranged from 103.9 to 632.21 mm month−1

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and mostly occurred in DJF (see Figure 9(a)). Peak minimum rainfalls of 0.06–362.07 mmmonth−1 mostly occurred in JJA (see Figure 9(b)). Peak maximum rainfalls were unevenlydistributed, but in general occurred over land. Meanwhile, minimum peak rainfalls spreadacross the equator belt. The amplitude variability shows that high rainfall variabilityoccurred over Jawa Island and the Jawa Sea, indicated by high variability (see Figure 8(c))and standard deviation values in the region (see Figure 8(d)). The variability of rainfallis less in the north than in the south of Indonesia, indicating that southern Indonesia isinfluenced by movement of the Asian–Australian monsoon system.

The spatial distributions of peak maximum and minimum rainfall phase amplitude(months) derived from TRMM 3B43 are shown in Figure 9. As described previously, themaximum and minimum peak phases of rainfall mostly occur in DJF and JJA, respectively.In DJF, especially in January, the northwest monsoon is fully developed, while during JJA,this region is influenced by the southeast monsoon (Wyrtki 1961). In western Sumatra andareas of Kalimantan, the maximum peak phase of rainfall occurs during SON. This phase isassociated with the southward and northward movement of the ITCZ. The ITCZ, character-ized by active convection, covers the equatorial region during SON. Furthermore, becausethe ITCZ passed the same region during MAM (Sakurai et al. 2005), this region usually

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Figure 9. Spatial distribution of peak amplitude phases (months) derived from TRMM 3B43:(a) maximum and (b) minimum phases.

has two peaks of heavy rainfall (Aldrian and Susanto 2003). These phenomena were notsupported in this study.

In Papua Island, the peak phase of maximum rainfall occurred in MAM and the min-imum during JJA (see Figure 9(a)). In addition to the ITCZ, these conditions are alsoinfluenced by the effects of the northwest monsoon and a large-scale zonal (east–west)circulation over the equatorial Pacific (Hall 1984). In Maluku Islands, the peak rainfallphase maximum differs from that in other areas during JJA, in line with the western tropicalPacific region. The climate in this region is affected by the conditions of the western tropicalPacific region (Aldrian and Susanto 2003; Kubota et al. 2011). The reason for this influ-ence is unclear, although Aldrian and Susanto (2003) explain that during JJA the Indonesianthroughflow (ITF) brings warm water from a warm pool area in the western tropical Pacificregion, resulting in heavy rainfall during this season. During the dry season in these areas,cooler water moving from the warm pool to the Maluku Sea inhibits the formation of aconvective zone and results in a minimum rainfall peak phase (see Figure 9(b)).

Figure 10 shows an increasing trend of spatial distribution for monthly rainfall time-series data from January 1998 to December 2010 collected by TRMM 3B43 over Indonesia.In general, rainfall trend was more positive in the north than the south of Indonesia. In addi-tion, rainfall tended to increase over land areas, especially the four major islands, exceptSulawesi Island. On the other hand, downward trends in rainfall occurred along the westernand southern coast of Sumatra, eastern Jawa, southern Sulawesi, Maluku Islands, west-ern Papua, and Bali Island. The extreme climate of ENSO and IOD was associated withinterannual months of Indonesian rainfall variability. For 13 years, several extreme rainfallevents were influenced by ENSO and IOD, causing variations in rainfall trends.

4.2. Comparison with rain gauges

The monthly TRMM 3B43 satellite data products were compared with gauge observa-tions from the five stations over Indonesia (see Figure 1), and we sought to determine the3B43 estimated values and the magnitude of rainfall on the ground. The average results

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from point-by-point analysis (across all stations) at five rain gauges show that annual rain-fall from 3B43 was lower than that from the gauge data: 2414.38 and 2470.81 mm year−1,respectively. However, in regard to individual rain gauges, the amount of rainfall varied.Figure 11 shows histograms of annual rainfall from 3B43 and rain gauge estimates in fivelocations during 1998–2010. The histograms of annual rainfall indicate that there are tworain gauges (Medan and Pontianak) in the western part of Indonesia with values lowerthan satellite estimates (overestimated), whereas three rain gauges (Denpasar, Ternate, andJayapura) in the eastern part of Indonesia have rainfall values higher than satellite esti-mates (underestimated). Overestimated values were also found in Malaysia (western partof Indonesia) by Semire et al. (2012), and underestimated in Bali (eastern part of Indonesia)by As-syakur et al. (2011).

Figure 12 shows the intra-annual variation of the long-term mean monthly rainfall mea-sured by 3B43 and the rain gauge data from five stations. This figure indicates that thesimilarity of monthly rainfall patterns from five locations confirmed the close relationshipbetween 3B43 and rain gauges. The satellite data and ground reference data yielded highto very high correlations for these products for each rain gauge. The correlations between3B43 and rain gauges in Medan, Pontianak, Denpasar, Ternate, and Jayapura are 0.98, 0.90,0.98, 0.95, and 0.85, respectively.

The comparison histograms of peak maximum, peak minimum, and amplitude variabil-ity derived from TRMM 3B43 and rain gauges from five locations are shown in Figure 13.

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Figure 13. Comparison histograms of maximum, minimum, and amplitude variability averagedrainfall measured by 3B43 and rain gauges from January 1998 to December 2010.

The histograms between peak maximum/minimum rainfall phase amplitude and annualrainfall show a similar pattern; two rain gauges have values lower than satellite estimatesand three rain gauges have rainfall values higher than satellite estimates. Peak maximumand amplitude variability display great differences between satellite and rain gauge datacompared with the peak minimum rainfall phase. Overall, a comparison between monthly3B43 products and gauge observations show that differences still exist.

We suggest that the reasons for these differences between 3B43 and rain gauge dataare due to temporal and spatial sampling uncertainties. First, since TRMM is a majorcomponent of the 3B43 product, one would expect it not to record some rainfall owingto unfavourable timing of its overflight of Indonesia. Fleming et al. (2011) found this tobe the case in a study in Australia. Because of the non-Sun-synchronous satellite orbit,the TRMM records locations approximately once every 3.6 days in the tropical region

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(As-syakur 2011). Furthermore, limitations of the TRMM data suffer from both a narrowswath and insufficient sampling time intervals, resulting in loss of information about rain-fall values and rainfall types. The Indonesian region is characterized by a high variabilityin rainfall and strong convective activity. Precipitation events outside these satellite obser-vation windows directly resulted in monthly and seasonal statistical errors. In addition, therainfall data used in this study have a limited sampling representation, where 0.25◦ × 0.25◦3B43 data are represented by only one rain gauge.

Previous studies show that convective rainfall predominantly controls tropical rainfallpeaks in Indonesia (Kubota, Numaguti, and Emori 2004; Mori et al. 2004; Tabata et al.2011; Prasetia, As-syakur, and Osawa 2013). Liao and Meneghini (2009), using ground-based radar, found (particularly in heavy rain) underestimation of TRMM PR attenuationfor convective rain, while stratiform rain was more accurately corrected. In addition, whenconvective rain is about 50–70%, this is possibly due to the overestimate of rainfall by TMI(Nakazawa and Rajendran 2004). Therefore, differences in rainfall in this region may becaused by a succession of convective and stratiform rain types throughout the year. Bothconvective and stratiform rain types dominate in the Indonesian archipelago (Schumacherand Houze 2003; Yulihastin and Kodama 2010; Prasetia, As-syakur, and Osawa 2013).

5. Summary and conclusions

An investigation of Indonesian rainfall variability using satellite data from TRMM3B43 over 13 years (January 1998–December 2010) is presented here. Monthly, seasonal,and long-term time-series analyses were conducted by applying several statistical methods.Rainfall over different elevations was compared to determine the effect of land and topog-raphy on rainfall events. Monthly 3B43 rainfall estimates were compared with monthlyrain gauge measurements from BMKG to evaluate rainfall variability. Data from five raingauges across Indonesia, covering a 13 year period (1998–2010), were used here.

The results clearly show the effect of oceans, islands, monsoons, and topography onspatial patterns of rainfall. Monthly climatological rainfall means show that rainfall overthe land area is greater than rainfall over the sea throughout the year. The analysis showedthat 58.18% (2779.82 mm year−1) of the total rainfall in Indonesia falls over land whileonly 41.82% (1998.21 mm year−1) falls over sea. In general, the largest difference betweenrainfall occurring over land and sea is clearly seen in the monsoon transition periods ofSON and MAM, with a maximum difference in November and a minimum in June.

Looking at a north–south cross section, the topography clearly affects rainfall variabil-ity in southern Indonesia. In the southern part of Indonesia, the highest elevation showedhigher levels of rainfall. The effect of the topography is also evident in other parts ofIndonesia. However, topographical effects increase rainfall amounts not only in the high-elevation regions, but also in surrounding low elevations, leading to a very poor correlationbetween rainfall and elevation.

Most parts of Indonesia have a peak of high-rainfall events during DJF and a troughof low-rainfall events during JJA. These conditions are associated with the northwest andsoutheast monsoon. Generally, the maximum peak and trough of rainfall events occur overland and at higher altitudes. High maximum and minimum monthly rainfall fluctuationoccurs over Jawa Island and the Jawa Sea. In that area, the fluctuation of rainfall reachesover 400 mm month−1. However, rainfall is relatively stable in the equatorial region. Highannual rainfall typically occurs in these island areas, except southeast Indonesia. The trendanalysis shows that rainfall has a tendency to increase in northern Indonesia, but to decreasein southern Indonesia.

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Comparing 3B43 with rain gauges shows strong agreement, with a high to very highcorrelation coefficient (r = 0.85–0.98). However, comparison results still showed differ-ences especially when heavy rain occurs. Temporal and spatial sampling uncertaintiespossibly cause this instability.

Utilization of TRMM 3B43 remote-sensing data helps monitor Indonesian rainfall inoceanic and unpopulated land areas for which rain gauge data are unavailable. Rainfallcharacteristics over the Maritime continent of Indonesia provide information explaining aregional and global climate system strongly influenced by local conditions. However, toobtain better results, the quality of remote-sensing satellite data needs to be improved forbetter spatial resolution in order to compare the small islands and complex topography ofIndonesia.

AcknowledgementsThis work was supported by CReSOS and JAXA mini-ocean projects in Indonesia. We grate-fully acknowledge data received from the following organizations: TRMM 3B43 V6 data fromthe National Aeronautics and Space Administration (NASA) and the Japan Aerospace ExplorationAgency (JAXA); the Shuttle Radar Topography Mission (SRTM) 30 Plus from Scripps Institutionof Oceanography, University of California San Diego; and rain gauge data from the IndonesianMeteorology, Climatology, and Geophysics Agency (BMKG).

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