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Simulation of Daily Rainfall Data using Articulated Weather Generator Model for Seasonal Prediction of ENSO-Affected Zones in Indonesia

Indonesian Journal of Geography · Vol. 52 No. 2 (2020) · pp. 143–153

DOI
10.22146/ijg.50862
ISSN
2354-9114, 0024-9521
Published
2020-09-02
Publisher
Faculty of Geography, Universitas Gadjah Mada
Language
eng
License
creativecommons.org/licenses/by-nc/4.0

Abstract

Synthetic rainfall simulation using weather generator models is commonly used as a substitute at locations with incomplete or short rainfall data. It incorporates a method that can be developed into forecasts of future rainfall. This study was designed to modify a rainfall prediction system based on the principles of weather generator models and to test the validity of the modelling results. It processed the data collected from eight rain stations in zones affected by El-Nino Southern Oscillation (ENSO). A large-scale predictor, that is, SST prediction data in the Nino 3.4 region over the Pacific Ocean was used as the influencing variable in projecting rainfall for the following six months after the predefined dates. Rainfall data from weather stations and SST in 1960-2000 were analyzed to identify the effects of ENSO and build a statistical model based on the regression function. Meanwhile, the model was validated using the data from 2001 to 2007 by backtesting six months in a row. The analysis results showed that the model could simulate both low rainfall in the dry season and high one in the rainy season. Validation by the student's t-test confirmed that the six-month synthetic rain data at nearly all observed stations was homogenous. For this reason, the developed model can be potentially used as one of the season prediction systems.

Keywords

Collaboration network of the authors

40 authors · 84 co-authorship links · showing the 40 most connected

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  • Universitas Gadjah Mada
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Irwansyah SukriAffiliation unknown3
Rina Dwi ArianiAffiliation unknown3
Bowo SusiloAffiliation unknown3
Sudrajat SudrajatAffiliation unknown2
Setyawan PurnamaAffiliation unknown2
Eko HaryonoUniversitas Gadjah Mada2
I Kade Alfian Kusuma WirayudaUniversitas Gadjah Mada1
Prima WidayaniUniversitas Gadjah Mada1
Iga MaligaAffiliation unknown1
Suryo PurwonoAffiliation unknown1
Herni HasifahAffiliation unknown1
Ana LestariAffiliation unknown1
Elya Pratiwi Isti FarohAffiliation unknown1
Hayyun NadiaAffiliation unknown1
Utia KafafaAffiliation unknown1
Bachtiar W. MutaqinAffiliation unknown1
Ariko V. MunandarAffiliation unknown1
Jatmiko JatmikoAffiliation unknown1
Ig.L. Setyawan PurnamaAffiliation unknown1
Evita Hanie PangaribowoAffiliation unknown1
Hadi Sabari YunusAffiliation unknown1
Slamet HartonoAffiliation unknown1
Hadi Sabari YunusAffiliation unknown1
Kasto .Affiliation unknown1
Harini RikaAffiliation unknown1
Sudibyakto SudibyaktoAffiliation unknown1
Dyah Rahmawati HizbaronAffiliation unknown1
Utia SuarmaAffiliation unknown1
Sukamdi SukamdiUniversitas Gadjah Mada1
Ratih Fitria PutriAffiliation unknown1
Josaphat Tetuko SriSumantyoAffiliation unknown1
Iswari Nur HidayatiAffiliation unknown1
R SuharyadiAffiliation unknown1
Iklila RahmatikaAffiliation unknown1
Mulia NasutionAffiliation unknown1
Dian Hudawan SantosoAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://jurnal.ugm.ac.id/ijg/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Simulation of Daily Rainfall Data using Articulated Weather Generator  Model for Seasonal Prediction of  ENSO-Affected Zones in Indonesia</dc:title>
	<dc:creator>Sekaranom, Andung Bayu</dc:creator>
	<dc:creator>Nurjani, Emilya</dc:creator>
	<dc:creator>Harini, Rika</dc:creator>
	<dc:creator>Muttaqin, Andi Syahid</dc:creator>
	<dc:subject xml:lang="en-US">hydrology;</dc:subject>
	<dc:subject xml:lang="en-US">El-Nino Southern Oscillation;  synthetic rainfall data; weather generator model  Surakarta.</dc:subject>
	<dc:subject xml:lang="en-US">hydrometeorology</dc:subject>
	<dc:description xml:lang="en-US">Synthetic rainfall simulation using weather generator models is commonly used as a substitute at locations with incomplete or short rainfall data. It incorporates a method that can be developed into forecasts of future rainfall. This study was designed to modify a rainfall prediction system based on the principles of weather generator models and to test the validity of the modelling results. It processed the data collected from eight rain stations in zones affected by El-Nino Southern Oscillation (ENSO). A large-scale predictor, that is, SST prediction data in the Nino 3.4 region over the Pacific Ocean was used as the influencing variable in projecting rainfall for the following six months after the predefined dates. Rainfall data from weather stations and SST in 1960-2000 were analyzed to identify the effects of ENSO and build a statistical model based on the regression function. Meanwhile, the model was validated using the data from 2001 to 2007 by backtesting six months in a row. The analysis results showed that the model could simulate both low rainfall in the dry season and high one in the rainy season. Validation by the student's t-test confirmed that the six-month synthetic rain data at nearly all observed stations was homogenous. For this reason, the developed model can be potentially used as one of the season prediction systems.  </dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Geography, Universitas Gadjah Mada</dc:publisher>
	<dc:contributor xml:lang="en-US">Ministry of Research, Technology and Higher Education of the Republic of Indonesia</dc:contributor>
	<dc:date>2020-09-02</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:type xml:lang="en-US">statistics</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijg/article/view/50862</dc:identifier>
	<dc:identifier>10.22146/ijg.50862</dc:identifier>
	<dc:source xml:lang="en-US">Indonesian Journal of Geography; Vol 52, No 2 (2020): Indonesian Journal of Geography; 143-153</dc:source>
	<dc:source>2354-9114</dc:source>
	<dc:source>0024-9521</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijg/article/view/50862/pdf</dc:relation>
	<dc:relation>https://journal.ugm.ac.id/ijg/article/downloadSuppFile/50862/9787</dc:relation>
	<dc:relation>https://journal.ugm.ac.id/ijg/article/downloadSuppFile/50862/9788</dc:relation>
	<dc:coverage xml:lang="en-US">Indonesia</dc:coverage>
	<dc:coverage xml:lang="en-US">1960-2007</dc:coverage>
	<dc:coverage xml:lang="en-US">8 weather stations</dc:coverage>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 Authors</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-nc/4.0</dc:rights>
</oai_dc:dc>