Indonesian rainfall variability observation using TRMM multi-satellite data.
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International Journal of Remote Sensing, 2013
Vol. 34, No. 21, 7723–7738, http://dx.doi.org/10.1080/01431161.2013.826837
Indonesian rainfall variability observation using TRMM
multi-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, Bali 80232, Indonesia;bEnvironmental Research Centre (PPLH), Udayana University, Denpasar, Bali
80232, Indonesia;cGraduate School of Science and Engineering, Yamaguchi University, Ube Shi Tokiwadai 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’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.assyakur@pplh.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
Niño/Southern 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
Research was conducted in the archipelago of Indonesia, which is composed of
17,508 islands of various sizes. Spatial data covered 8◦ 00′ N to 13◦ 45′ S and 92◦ 00′ E
to 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. Indonesia is located between two continents and oceans, with a population of 237,641,326 in 2010. Sumatra, Kalimantan, Jawa (Java), Sulawesi, and Papua are the five major islands, with diverse topographical distributions. Several important mountains in Indonesia are Jayawijaya in Papua, Bukitbarisan in Sumatra, Kendeng in Jawa, Fenema and Gorontalo in Sulawesi, and Muller in Kalimantan. Jawa Island is the most populated island and the most important industrial and agricultural region in Indonesia. Meanwhile, Sumatra, Kalimantan, Sulawesi, and Papua are important islands that have tropical rainforests. Small islands 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 a calibration-based sequential scheme for combining precipitation estimates from multiple satellites and gauge analyses (where feasible), providing global coverage of precipitation
spatially over the 50◦ S–50◦ N latitude belt at 0.25◦ × 0.25◦ at three-hourly temporal
96° 00′
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Figure 1. The study area and Indonesian topography. Lines A-B to K-L indicate the south–north
cross sections used to compare values of rainfall and elevation. Black dots indicate the rain gauge locations.
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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 precipitaprecipita-tion estimates are created using the calibrated microwave precipitation; (3) microwave and infrared estimates are com-bined; and (4) monthly data is rescaled and applied (Huffman et al. 2007, 2010). The TMPA retrieval algorithm used for this product is based on the technique by Huffman et al. (1995, 1997) and Huffman (1997). The TMPA data sets consist of 45% precipitation from passive microwave radiometers (TRMM-TMI, Aqua-Advanced Microwave Scanning Radiometer (Aqua-AMSR) and the Defense Meteorological Satellite Program Special Sensor Microwave Imagers (DMSP-SSMIs)), 40% from operational microwave sound-ing frequencies (National Oceanic and Atmospheric Administration Advanced Microwave Sounding Units (NOAA-AMSUs)), and 15% infrared measurements from geostationary
satellites (Geostationary Operational Environmental Satellite (GOES)) Meteosat/Meteosat
Second Generation (Meteosat/MSG) (Mehta and Yang 2008). According to Huffman and
Bolvin (2007), the TMPA is designed to maximize data quality, so TMPA is strongly recommended for any research work not specifically focused on real-time applications.
Several statistical scores were used to determine Indonesian rainfall variability. The types of analysis were monthly means, total means, maximum and minimum variability, standard deviation, and trends. To investigate the effect of the Asian–Australian monsoon on Indonesia rainfall, peak-to-peak amplitude phase analysis extracted the annual signal in each grid point of rainfall. After calculating the mean of the annual signal, the mean was removed to reveal the monthly peak amplitude phase. Furthermore, the analyses were also carried 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 distribution and elevation.
Monthly, seasonal, and long-term time-series analyses were conducted. Monthly analy-sis compared data from the same months of annual observation. Seasonal analyanaly-sis is based on the monsoon activity, described by Wyrtki (1961), over the entire observation period. Likewise, long-term analysis observed all time-series data over the entire observation period.
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 for consistency where unreasonable values from a climatological viewpoint, for instance zero-rainfall months during the wet season, were deleted. The locations of rain gauges are Medan, Pontianak, Denpasar, Ternate, and Jayapura. The monthly average rainfall esti-mated from satellite data is compared with the rain-gauge data, particularly to determine the accuracy level between the rainfall estimated from satellite and the rain-gauge data. Point-by-point analysis was conducted on the monthly data. Point-Point-by-point analysis consisted of a 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 in Figure 2. In general, the highest total annual rainfall extends across the equatorial belt, with
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International Journal of Remote Sensing 7727
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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 four main 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 the Jayawijaya and Bukitbarisan Mountains of Papua and Sumatra Islands, respectively. This figure also shows that rainfall was distributed more heavily over land than sea, except for the 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. Higher rainfall over land seems to be caused by the magnitude of convergence determined by the different heating qualities of land and sea. The differential heating controls the direction of land–sea breezes that distribute the precipitation centrally over islands, where sea-breeze fronts 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 over the sea, especially on the western coast of Sumatra. Heavy rainfall found along the coastal plain 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 Islands is created by land breezes (from the Bukitbarisan Mountains) converging with the western Walker cell (from the Indian Ocean) to generate offshore convection and produce heavy rainfall. 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 the atmosphere offshore.
Figure 3 shows the mean annual rainfall over land and sea in Indonesia during 1998–2010. The figure shows annual rainfall over land to be higher than over the sea. Over
land, the total mean rainfall was 2779.82 mm year−1, while over the sea it was 1998.21 mm
year−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 is shown in Figure 4. Throughout the year, the highest rainfall concentrations are observed centred over the major Indonesian islands such as in Sumatra, Kalimantan, Papua, and Sulawesi. During the northwest monsoon (November–February), high rainfall patterns are observed 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 continued extending towards the southeast coast of Sumatra, the south coast of Kalimantan, southern
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7728 A.R. As-syakur et al.
1500 1700 1900 2100 2300 2500 2700 2900
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Location of rainfall falls
Rainfall (mm year
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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 phenomenon is consistent with northwestward movement of the Asian–Australian monsoon system, in which the peak of the southeast monsoon occurs in August. During September–November, the high rainfall moves northwest to southeast. In these months, the southeast monsoon weakened 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 with seasonal migration of the intertropical convergence zone (ITCZ). In January, the central
mass of the heavy rainfall is south of the equator while in July it is north of 10◦ N (Chang
et al. 2005).
The monthly climatological mean of rainfall is also shown throughout the year; the highest rainfall concentrations fall over land rather than over sea (see Figure 5). The largest
and smallest differences occurred in November (95.54 mm month−1) and June (21.73 mm
month−1), respectively. The annual cycles of rainfall differences between land and sea are
similar to the annual cycles of the monsoonal climate regions, which have one peak and one trough and experience the strong influence of two monsoons (see Aldrian and Susanto 2003).
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 seasonal climatological average rainfall distribution, Figure 6 shows six north–south cross-section lines of rainfall and elevation (see Figure 1). It will be clearly seen in this figure that areas around the region with high elevation also have high rainfall compared with low elevations and 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 only in 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 seasonal rainfall is generally similar to its annual pattern, except during the JJA season in southern Indonesia when a high elevation does not markedly affect rainfall.
In the surrounding islands, heavy rainfall also occurred, such as between the islands of Jawa and Kalimantan, the northwestern coast of Kalimantan, and the southern coast of Sumatra. This condition is caused by several factors, varies by location, and can clearly
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International Journal of Remote Sensing 7729
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Figure 4. Monthly climatological mean of rainfall derived from the TRMM 3B43 based on monthly
composites from January 1998 to December 2010.
be seen by remote-sensing data. As explained by Qian (2008), heavy rainfall over the Jawa
Sea (between the islands of Jawa and Kalimantan) (see Figure 6(b) at –5◦to –7◦) is affected
by the diurnal cycle of land breezes from both Kalimantan and Jawa. Heavy rainfall on the
northwestern coast of Kalimantan (see Figure 6(b) at 3◦–2◦) was also influenced by land
breezes. In this area, the locally large-scale convergence concentrated over the sea was due to diurnal land breeze interaction with the prevailing winter monsoon flow, producing offshore convection (Houze et al. 1981; Geotis and Houze 1985). On the other hand, the influence of the western Walker cell over the Indian Ocean (with an anomalous low-level
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7730 A.R. As-syakur et al.
0 50 100 150 200 250 300 350
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month
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Figure 5. Average mean of monthly rainfall values over islands and sea in Indonesia derived from
TRMM 3B43 data based on 1 month composites from January 1998 to December 2010.
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Figure 6. North–south distribution of Indonesian rainfall with elevation at six different
cross-sections (see Figure 1). (a) A–B, (b) C–D, (c) E–F, (d) G–H, (e) I–J, and (f) K–L.
equatorial zonal wind) affects the rainfall on the sea region southwest of the island of
Sumatra (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 standard
deviation. Indonesian peak maximum rainfall ranged from 103.9 to 632.21 mm month−1
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International Journal of Remote Sensing 7731 0 1000 2000 3000 4000 5000 6000
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Rainfall (mm year
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Figure 7. Scatter plots of annual rainfallversuselevation for all research regions over land.
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peak maximum and minimum), and (d) standard deviation of monthly averaged rainfall from TRMM
3B43 from January 1998 to December 2010.
and mostly occurred in DJF (see Figure 9(a)). Peak minimum rainfalls of 0.06–362.07 mm
month−1mostly occurred in JJA (see Figure 9(b)). Peak maximum rainfalls were unevenly
distributed, but in general occurred over land. Meanwhile, minimum peak rainfalls spread across the equator belt. The amplitude variability shows that high rainfall variability
occurred 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 rainfall
is less in the north than in the south of Indonesia, indicating that southern Indonesia is influenced 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, the maximum 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 and areas of Kalimantan, the maximum peak phase of rainfall occurs during SON. This phase is associated with the southward and northward movement of the ITCZ. The ITCZ, character-ized by active convection, covers the equatorial region during SON. Furthermore, because the ITCZ passed the same region during MAM (Sakurai et al. 2005), this region usually
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7732 A.R. As-syakur et al.
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 not supported 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 also
influenced 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 rainfall phase maximum differs from that in other areas during JJA, in line with the western tropical Pacific region. The climate in this region is affected by the conditions of the western tropical Pacific 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 Indonesian throughflow (ITF) brings warm water from a warm pool area in the western tropical Pacific region, 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 a
convective 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, except Sulawesi Island. On the other hand, downward trends in rainfall occurred along the western and 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 with interannual months of Indonesian rainfall variability. For 13 years, several extreme rainfall events 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 staobserva-tions over Indonesia (see Figure 1), and we sought to determine the 3B43 estimated values and the magnitude of rainfall on the ground. The average results
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International Journal of Remote Sensing 7733
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Figure 10. Spatial-distribution trend of time-series monthly rainfall from TRMM 3B43 from
January 1998 to December 2010.
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Medan Pontianak Denpasar Ternate Jayapura
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Figure 11. Histograms of annual rainfall between 3B43, with rain gauge estimates from five
locations, over the period from 1998 to 2010.
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 five locations during 1998–2010. The histograms of annual rainfall indicate that there are two rain gauges (Medan and Pontianak) in the western part of Indonesia with values lower than satellite estimates (overestimated), whereas three rain gauges (Denpasar, Ternate, and Jayapura) in the eastern part of Indonesia have rainfall values higher than satellite esti-mates (underestimated). Overestimated values were also found in Malaysia (western part of 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 the similarity of monthly rainfall patterns from five locations confirmed the close relationship between 3B43 and rain gauges. The satellite data and ground reference data yielded high to very high correlations for these products for each rain gauge. The correlations between 3B43 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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7734 A.R. As-syakur et al.
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Figure 12. Monthly average rainfall pattern measured by 3B43 and rain gauges. (a) Medan,
(b) Pontianak, (c) Denpasar, (d) Ternate, and (e) Jayapura, over the period from 1998 to 2010.
0 100 200 300 400 500 M ax imu m Mi n im u m V ar ia b ility M ax imu m M in imu m V ar ia b ility M ax imu m Mi n im u m V ar ia b ility M ax imu m M in imu m V ar ia b ility M ax imu m M in imu m V ar ia b ility
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Figure 13. Comparison histograms of maximum, minimum, and amplitude variability averaged
rainfall measured by 3B43 and rain gauges from January 1998 to December 2010.
The histograms between peak maximum/minimum rainfall phase amplitude and annual
rainfall show a similar pattern; two rain gauges have values lower than satellite estimates and three rain gauges have rainfall values higher than satellite estimates. Peak maximum and amplitude variability display great differences between satellite and rain gauge data compared with the peak minimum rainfall phase. Overall, a comparison between monthly 3B43 products and gauge observations show that differences still exist.
We suggest that the reasons for these differences between 3B43 and rain gauge data are due to temporal and spatial sampling uncertainties. First, since TRMM is a major component of the 3B43 product, one would expect it not to record some rainfall owing to unfavourable timing of its overflight of Indonesia. Fleming et al. (2011) found this to be 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
(22)
International Journal of Remote Sensing 7735 (As-syakur 2011). Furthermore, limitations of the TRMM data suffer from both a narrow swath and insufficient sampling time intervals, resulting in loss of information about rain-fall values and rainrain-fall types. The Indonesian region is characterized by a high variability in rainfall and strong convective activity. Precipitation events outside these satellite obser-vation windows directly resulted in monthly and seasonal statistical errors. In addition, the
rainfall 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 rainfall peaks 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 attenuation for convective rain, while stratiform rain was more accurately corrected. In addition, when convective 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 be caused by a succession of convective and stratiform rain types throughout the year. Both convective and stratiform rain types dominate in the Indonesian archipelago (Schumacher and 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 TRMM 3B43 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 monthly rain gauge measurements from BMKG to evaluate rainfall variability. Data from five rain gauges across Indonesia, covering a 13 year period (1998–2010), were used here.
The results clearly show the effect of oceans, islands, monsoons, and topography on spatial patterns of rainfall. Monthly climatological rainfall means show that rainfall over the land area is greater than rainfall over the sea throughout the year. The analysis showed
that 58.18% (2779.82 mm year−1) of the total rainfall in Indonesia falls over land while
only 41.82% (1998.21 mm year−1) falls over sea. In general, the largest difference between
rainfall occurring over land and sea is clearly seen in the monsoon transition periods of SON 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 showed higher levels of rainfall. The effect of the topography is also evident in other parts of Indonesia. However, topographical effects increase rainfall amounts not only in the high-elevation regions, but also in surrounding low high-elevations, leading to a very poor correlation between rainfall and elevation.
Most parts of Indonesia have a peak of high-rainfall events during DJF and a trough of low-rainfall events during JJA. These conditions are associated with the northwest and southeast monsoon. Generally, the maximum peak and trough of rainfall events occur over land and at higher altitudes. High maximum and minimum monthly rainfall fluctuation occurs over Jawa Island and the Jawa Sea. In that area, the fluctuation of rainfall reaches
over 400 mm month−1. However, rainfall is relatively stable in the equatorial region. High
annual rainfall typically occurs in these island areas, except southeast Indonesia. The trend analysis shows that rainfall has a tendency to increase in northern Indonesia, but to decrease in southern Indonesia.
(23)
7736 A.R. As-syakur et al.
Comparing 3B43 with rain gauges shows strong agreement, with a high to very high
correlation coefficient (r = 0.85–0.98). However, comparison results still showed
differ-ences especially when heavy rain occurs. Temporal and spatial sampling uncertainties possibly cause this instability.
Utilization of TRMM 3B43 remote-sensing data helps monitor Indonesian rainfall in oceanic and unpopulated land areas for which rain gauge data are unavailable. Rainfall characteristics over the Maritime continent of Indonesia provide information explaining a regional and global climate system strongly influenced by local conditions. However, to obtain better results, the quality of remote-sensing satellite data needs to be improved for better spatial resolution in order to compare the small islands and complex topography of Indonesia.
Acknowledgements
This 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 from the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA); the Shuttle Radar Topography Mission (SRTM) 30 Plus from Scripps Institution of Oceanography, University of California San Diego; and rain gauge data from the Indonesian Meteorology, Climatology, and Geophysics Agency (BMKG).
References
Aldrian, E. 2003. “Simulations of Indonesian Rainfall with a Hierarchy of Climate Models.” Examensarbeit Nr. 92, 159 pp. Hamburg: Max-Planck–Institut für Meteorologie.
Aldrian, E., L. D. Gates, and F. H. Widodo. 2007. “Seasonal Variability of Indonesian Rainfall in
ECHAM4 Simulations and in the Reanalyses: The Role of ENSO.” Theoretical and Applied
Climatology87: 41–59.
Aldrian, E., and R. D. Susanto. 2003. “Identification of Three Dominant Rainfall Regions Within
Indonesia and Their Relationship to Sea Surface Temperature.” International Journal of
Climatology23: 1435–1452.
Artan, G., H. Gadain, J. L. Smith, K. Asante, C. J. Bandaragoda, and J. P. Verdin. 2007. “Adequacy
of Satellite Derived Rainfall Data for Stream Flow Modeling.”Natural Hazards43: 167–185.
As-syakur, A. R. 2011. “Status of the TRMM Level 3 in Indonesia.” In Proceeding of the 2nd
CReSOS International Symposium on South East Asia Environmental Problems and Satellite Remote Sensing, Denpasar, Bali, February 21–22, 140–142. Denpasar: Udayana University. As-syakur, A. R., T. Tanaka, R. Prasetia, I. K. Swardika, and I. W. Kasa. 2011. “Comparison of
TRMM Multisatellite Precipitation Analysis (TMPA) Products and Daily-Monthly Gauge Data
over Bali.”International Journal of Remote Sensing32: 8969–8982.
Chang, C.-P., Z. Wang, J. Ju, and T. Li. 2004. “On the Relationship Between Western Maritime
Continent Monsoon Rainfall and ENSO During Northern Winter.” Journal of Climate 17:
665–672.
Chang, C.-P., Z. Wang, J. McBride, and C.-H. Liu. 2005. “Annual Cycle of Southeast Asia – Maritime
Continent Rainfall and the Asymmetric Monsoon Transition.”Journal of Climate18: 287–301.
Chokngamwong, R., and L. S. Chiu. 2008. “Thailand Daily Rainfall and Comparison with TRMM
Products.”Journal of Hydrometeorology9: 256–266.
Feidas, H. 2010. “Validation of Satellite Rainfall Products over Greece.” Theoretical and Applied
Climatology99: 193–216.
Fleming, K., J. L. Awange, M. Kuhn, and W. Featherstone. 2011. “Evaluating the TRMM 3B43
Monthly Precipitation Product Using Gridded Raingauge Data over Australia.” Australian
Meteorological and Oceanographic Journal61: 171–184.
Geotis, S. G., and R. A. Houze. 1985. “Rain Amounts Near and over North Borneo During Winter
MONEX.”Monthly Weather Review113: 1824–1828.
Gunawan, D. 2006. “Atmospheric Variability in Sulawesi, Indonesia – Regional Atmospheric Model Results and Observations.” PhD Thesis, Georg-August-Universität zu Göttingen, Germany, 148 pp.
(1)
90° 100° 110° 120° 130° 140° 150° 90° 100° 110° 120° 130° 140° 150°
−0.045 to −0.035
Trend
(mm month−1)
0.035−0.046 0.025−0.035 0.015−0.025 0.005−0.015 −0.005−0.005 −0.015 to −0.005 −0.025 to −0.015 −0.035 to −0.025
10
°
10
°
0
°
10
°
10
°
0
°
Figure 10.
Spatial-distribution trend of time-series monthly rainfall from TRMM 3B43 from
January 1998 to December 2010.
0 1000 2000 3000 4000
Medan Pontianak Denpasar Ternate Jayapura Rain gauges location
Rainfall (mm year
−
1)
Rain gauge 3B43
Figure 11.
Histograms of annual rainfall between 3B43, with rain gauge estimates from five
locations, over the period from 1998 to 2010.
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 five
locations during 1998–2010. The histograms of annual rainfall indicate that there are two
rain gauges (Medan and Pontianak) in the western part of Indonesia with values lower
than satellite estimates (overestimated), whereas three rain gauges (Denpasar, Ternate, and
Jayapura) in the eastern part of Indonesia have rainfall values higher than satellite
esti-mates (underestimated). Overestimated values were also found in Malaysia (western part
of 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 the
similarity of monthly rainfall patterns from five locations confirmed the close relationship
between 3B43 and rain gauges. The satellite data and ground reference data yielded high
to very high correlations for these products for each rain gauge. The correlations between
3B43 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.
(2)
0 100
(a)
(d) (e)
(c) (b)
200 300 400 500 Ja n Fe b Ma r Ap r Ma y Ju n Ju l Au g Se p Oc t No v De c Month R ain fa
ll (mm mo
n th − 1 ) Ja n Fe b Ma r Ap r Ma y Ju n Ju l Au g Se p Oc t No v De c Month Ja n Fe b Ma r Ap r Ma y Ju n Ju l Au g Se p Oc t No v De c Month 0 100 200 300 400 500 Ja n Fe b Ma r Ap r Ma y Ju n Ju l Au g Se p Oc t No v De c Month Ra in fa ll ( m m m o n th − 1 ) Ja n Fe b Ma r Ap r Ma y Ju n Ju l Au g Se p Oc t No v De c Month
Rain gauge 3B43
Figure 12.
Monthly average rainfall pattern measured by 3B43 and rain gauges. (
a
) Medan,
(
b
) Pontianak, (
c
) Denpasar, (
d
) Ternate, and (
e
) Jayapura, over the period from 1998 to 2010.
0 100 200 300 400 500 M ax imu m Mi n im u m V ar ia b ility M ax imu m M in imu m V ar ia b ility M ax imu m Mi n im u m V ar ia b ility M ax imu m M in imu m V ar ia b ility M ax imu m M in imu m V ar ia b ility
Medan Pontianak Denpasar Ternate Jayapura
Rain gauges location
R ai n fa ll (mm mo n th − 1 )
Rain gauge 3B43
Figure 13.
Comparison histograms of maximum, minimum, and amplitude variability averaged
rainfall measured by 3B43 and rain gauges from January 1998 to December 2010.
The histograms between peak maximum
/
minimum rainfall phase amplitude and annual
rainfall show a similar pattern; two rain gauges have values lower than satellite estimates
and three rain gauges have rainfall values higher than satellite estimates. Peak maximum
and amplitude variability display great differences between satellite and rain gauge data
compared with the peak minimum rainfall phase. Overall, a comparison between monthly
3B43 products and gauge observations show that differences still exist.
We suggest that the reasons for these differences between 3B43 and rain gauge data
are due to temporal and spatial sampling uncertainties. First, since TRMM is a major
component of the 3B43 product, one would expect it not to record some rainfall owing
to unfavourable timing of its overflight of Indonesia. Fleming et al. (2011) found this to
be 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
(3)
(As-syakur 2011). Furthermore, limitations of the TRMM data suffer from both a narrow
swath and insufficient sampling time intervals, resulting in loss of information about
rain-fall values and rainrain-fall types. The Indonesian region is characterized by a high variability
in rainfall and strong convective activity. Precipitation events outside these satellite
obser-vation windows directly resulted in monthly and seasonal statistical errors. In addition, the
rainfall 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 rainfall
peaks 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 attenuation
for convective rain, while stratiform rain was more accurately corrected. In addition, when
convective 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 be
caused by a succession of convective and stratiform rain types throughout the year. Both
convective and stratiform rain types dominate in the Indonesian archipelago (Schumacher
and 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 TRMM
3B43 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 monthly
rain gauge measurements from BMKG to evaluate rainfall variability. Data from five rain
gauges across Indonesia, covering a 13 year period (1998–2010), were used here.
The results clearly show the effect of oceans, islands, monsoons, and topography on
spatial patterns of rainfall. Monthly climatological rainfall means show that rainfall over
the land area is greater than rainfall over the sea throughout the year. The analysis showed
that 58.18% (2779.82 mm year
−1) of the total rainfall in Indonesia falls over land while
only 41.82% (1998.21 mm year
−1) falls over sea. In general, the largest difference between
rainfall occurring over land and sea is clearly seen in the monsoon transition periods of
SON 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 showed
higher levels of rainfall. The effect of the topography is also evident in other parts of
Indonesia. However, topographical effects increase rainfall amounts not only in the
high-elevation regions, but also in surrounding low high-elevations, leading to a very poor correlation
between rainfall and elevation.
Most parts of Indonesia have a peak of high-rainfall events during DJF and a trough
of low-rainfall events during JJA. These conditions are associated with the northwest and
southeast monsoon. Generally, the maximum peak and trough of rainfall events occur over
land and at higher altitudes. High maximum and minimum monthly rainfall fluctuation
occurs over Jawa Island and the Jawa Sea. In that area, the fluctuation of rainfall reaches
over 400 mm month
−1. However, rainfall is relatively stable in the equatorial region. High
annual rainfall typically occurs in these island areas, except southeast Indonesia. The trend
analysis shows that rainfall has a tendency to increase in northern Indonesia, but to decrease
in southern Indonesia.
(4)
Comparing 3B43 with rain gauges shows strong agreement, with a high to very high
correlation coefficient (
r
=
0.85–0.98). However, comparison results still showed
differ-ences especially when heavy rain occurs. Temporal and spatial sampling uncertainties
possibly cause this instability.
Utilization of TRMM 3B43 remote-sensing data helps monitor Indonesian rainfall in
oceanic and unpopulated land areas for which rain gauge data are unavailable. Rainfall
characteristics over the Maritime continent of Indonesia provide information explaining a
regional and global climate system strongly influenced by local conditions. However, to
obtain better results, the quality of remote-sensing satellite data needs to be improved for
better spatial resolution in order to compare the small islands and complex topography of
Indonesia.
Acknowledgements
This 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 from
the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration
Agency (JAXA); the Shuttle Radar Topography Mission (SRTM) 30 Plus from Scripps Institution
of Oceanography, University of California San Diego; and rain gauge data from the Indonesian
Meteorology, Climatology, and Geophysics Agency (BMKG).
References
Aldrian, E. 2003. “Simulations of Indonesian Rainfall with a Hierarchy of Climate Models.”
Examensarbeit Nr. 92, 159 pp. Hamburg: Max-Planck–Institut für Meteorologie.
Aldrian, E., L. D. Gates, and F. H. Widodo. 2007. “Seasonal Variability of Indonesian Rainfall in
ECHAM4 Simulations and in the Reanalyses: The Role of ENSO.”
Theoretical and Applied
Climatology
87: 41–59.
Aldrian, E., and R. D. Susanto. 2003. “Identification of Three Dominant Rainfall Regions Within
Indonesia and Their Relationship to Sea Surface Temperature.”
International Journal of
Climatology
23: 1435–1452.
Artan, G., H. Gadain, J. L. Smith, K. Asante, C. J. Bandaragoda, and J. P. Verdin. 2007. “Adequacy
of Satellite Derived Rainfall Data for Stream Flow Modeling.”
Natural Hazards
43: 167–185.
As-syakur, A. R. 2011. “Status of the TRMM Level 3 in Indonesia.” In
Proceeding of the 2nd
CReSOS International Symposium on South East Asia Environmental Problems and Satellite
Remote Sensing
, Denpasar, Bali, February 21–22, 140–142. Denpasar: Udayana University.
As-syakur, A. R., T. Tanaka, R. Prasetia, I. K. Swardika, and I. W. Kasa. 2011. “Comparison of
TRMM Multisatellite Precipitation Analysis (TMPA) Products and Daily-Monthly Gauge Data
over Bali.”
International Journal of Remote Sensing
32: 8969–8982.
Chang, C.-P., Z. Wang, J. Ju, and T. Li. 2004. “On the Relationship Between Western Maritime
Continent Monsoon Rainfall and ENSO During Northern Winter.”
Journal of Climate
17:
665–672.
Chang, C.-P., Z. Wang, J. McBride, and C.-H. Liu. 2005. “Annual Cycle of Southeast Asia – Maritime
Continent Rainfall and the Asymmetric Monsoon Transition.”
Journal of Climate
18: 287–301.
Chokngamwong, R., and L. S. Chiu. 2008. “Thailand Daily Rainfall and Comparison with TRMM
Products.”
Journal of Hydrometeorology
9: 256–266.
Feidas, H. 2010. “Validation of Satellite Rainfall Products over Greece.”
Theoretical and Applied
Climatology
99: 193–216.
Fleming, K., J. L. Awange, M. Kuhn, and W. Featherstone. 2011. “Evaluating the TRMM 3B43
Monthly Precipitation Product Using Gridded Raingauge Data over Australia.”
Australian
Meteorological and Oceanographic Journal
61: 171–184.
Geotis, S. G., and R. A. Houze. 1985. “Rain Amounts Near and over North Borneo During Winter
MONEX.”
Monthly Weather Review
113: 1824–1828.
Gunawan, D. 2006. “Atmospheric Variability in Sulawesi, Indonesia – Regional Atmospheric Model
Results and Observations.” PhD Thesis, Georg-August-Universität zu Göttingen, Germany,
148 pp.
(5)
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