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

https://doi.org/10.22146/ijg.50862

Andung Bayu Sekaranom(1*), Emilya Nurjani(2), Rika Harini(3), Andi Syahid Muttaqin(4)

(1) Faculty of Geography, Universitas Gadjah Mada, Bulaksumur 55281 Indonesia. Disaster Research Center, Universitas Gadjah Mada, Bulaksumur 55281 Indonesia
(2) Faculty of Geography, Universitas Gadjah Mada, Bulaksumur 55281 Indonesia
(3) Faculty of Geography, Universitas Gadjah Mada, Bulaksumur 55281 Indonesia
(4) Faculty of Agriculture, Universitas Gadjah Mada, Bulaksumur 55281 Indonesia
(*) Corresponding Author

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


El-Nino Southern Oscillation; synthetic rainfall data; weather generator model Surakarta.

Full Text:

PDF


References

Chowdhury, R. K., & Beecham, S. (2013). Influence of SOI, DMI and Niño3.4 on South Australian rainfall. Stochastic Environmental Research and Risk Assessment, 27(8), 1909–1920. https://doi.org/10.1007/s00477-013-0726-x

Chung, C. T. Y., & Power, S. B. (2014). Precipitation response to La Niña and global warming in the Indo-Pacific. Climate Dynamics, 43(12), 3293–3307. https://doi.org/10.1007/s00382-014-2105-9

Grimm, A. M., & Tedeschi, R. G. (2009). ENSO and extreme rainfall events in South America. Journal of Climate, 22(7), 1589–1609. https://doi.org/10.1175/2008JCLI2429.1

Iglesias, A., & Garrote, L. (2015). Adaptation strategies for agricultural water management under climate change in Europe. Agricultural Water Management, 155, 113–124.

Ivanov, V. Y., Bras, R. L., & Curtis, D. C. (2007). A weather generator for hydrological, ecological, and agricultural applications. Water Resources Research, 43(10). https://doi.org/10.1029/2006WR005364

Izumo, T., Lengaigne, M., Vialard, J., Luo, J. J., Yamagata, T., & Madec, G. (2014). Influence of Indian Ocean Dipole and Pacific recharge on following year's El Niño: Interdecadal robustness. Climate Dynamics, 42(1–2), 291–310. https://doi.org/10.1007/s00382-012-1628-1

Jourdain, N. C., Gupta, A. Sen, Taschetto, A. S., Ummenhofer, C. C., Moise, A. F., & Ashok, K. (2013). The Indo-Australian monsoon and its relationship to ENSO and IOD in reanalysis data and the CMIP3/CMIP5 simulations. Climate Dynamics, 41(11–12), 3073–3102. https://doi.org/10.1007/s00382-013-1676-1

Marfai, M. A., Sekaranom, A. B., & Cahyadi, A. (2015). Profiles of marine notches in the Baron coastal area—Indonesia. Arabian Journal of Geosciences, 8(1), 307–314. https://doi.org/10.1007/s12517-013-1232-7

Marfai, M. A., Sekaranom, A. B., & Ward, P. (2015). Community responses and adaptation strategies toward flood hazard in Jakarta, Indonesia. Natural Hazards, 75(2), 1127–1144. https://doi.org/10.1007/s11069-014-1365-3

Meinke, H., & Stone, R. C. (2005). Seasonal and inter-annual climate forecasting: The new tool for increasing preparedness to climate variability and change in agricultural planning and operations. In Increasing Climate Variability and Change: Reducing the Vulnerability of Agriculture and Forestry (pp. 221–253). https://doi.org/10.1007/1-4020-4166-7_11

Nurjani, E., Harini, R., Sekaranom, A.B., & Mutaqqin, A.S. (2020). Tobacco farmers Perspective towards increasing climate change risk on agriculture sector: a case study of Temanggung-Indonesia. IOP Conference Series: Earth and Environmental Science, 451(1). https://doi.org/10.1088/1755-1315/451/1/012101

Qian, J. H. (2008). Why precipitation is mostly concentrated over islands in the maritime continent. Journal of the

Atmospheric Sciences, 65(4), 1428–1441. https://doi.org/10.1175/2007JAS2422.1

Richardson, C. W., & Wright, D. A. (1984). WGEN: A Model for Generating Daily Weather Variables. In United States Department of Agriculture, Agriculture Research Service ARS-8. Retrieved from ftp://ftp.biosfera.dea.ufv.br/users/francisca/Franciz/papers/Richardson & Wright.pdf

Sekaranom, A. B., & Masunaga, H. (2017). Comparison of TRMM-derived rainfall products for general and extreme rains over the maritime continent. Journal of Applied Meteorology and Climatology, 56(7), 1867–1881. https://doi.org/10.1175/JAMC-D-16-0272.1

Sekaranom, A. B., & Masunaga, H. (2019). Origins of heavy precipitation biases in the TRMM PR and TMI products assessed with cloudsat and reanalysis data. Journal of Applied Meteorology and Climatology, 58(1), 37–54. https://doi.org/10.1175/JAMC-D-18-0011.1

Sekaranom, A. B., & Nurjani, E. (2019). The development of Articulated Weather Generator model and its application in simulating future climate variability. IOP Conference Series: Earth and Environmental Science, 256(1). https://doi.org/10.1088/1755-1315/256/1/012044

Sekaranom, A. B., Nurjani, E., Hadi, M. P., & Marfai, M. A. (2018). Comparsion of TRMM Precipitation Satellite Data over Central Java Region - Indonesia. Quaestiones Geographicae, 37(3), 97–114. https://doi.org/10.2478/quageo-2018-0028

Sekaranom, A. B., Nurjani, E., & Pujiastuti, I. (2018). Cloud structure evolution of heavy rain events from the East-West Pacific Ocean: A combined global observation analysis. IOP Conference Series: Earth and Environmental Science, 148(1). https://doi.org/10.1088/1755-1315/148/1/012006

Sekaranom, A.B., Suarma, U., & Nurjani, E. (2020). Climate extremes over the maritime continent and their associations with Madden-Jullian Oscillation. IOP Conference Series: Earth and Environmental Science, 451(1).https://doi.org/10.1088/1755-1315/451/1/012006

Ummenhofer, C. C., D'Arrigo, R. D., Anchukaitis, K. J., Buckley, B. M., & Cook, E. R. (2013). Links between Indo-Pacific climate variability and drought in the Monsoon Asia Drought Atlas. Climate Dynamics, 40(5–6), 1319–1334. https://doi.org/10.1007/s00382-012-1458-1

Wilby, R. L. (1999). The weather generation game: A review of stochastic weather models. Progress in Physical Geography, Vol. 23, pp. 329–357. https://doi.org/10.1191/030913399666525256

Wilks, D. S. (1999). Multisite downscaling of daily precipitation with a stochastic weather generator. Climate Research, 11(2), 125–136. https://doi.org/10.3354/cr011125



DOI: https://doi.org/10.22146/ijg.50862

Article Metrics

Abstract views : 3599 | views : 2529

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Copyright (c) 2020 Andung Bayu Sekaranom, Emilya Nurjani, Rika Harini, Andi Syahid Muttaqin

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Accredited Journal, Based on Decree of the Minister of Research, Technology and Higher Education, Republic of Indonesia Number 225/E/KPT/2022, Vol 54 No 1 the Year 2022 - Vol 58 No 2 the Year 2026 (accreditation certificate download)

ISSN 2354-9114 (online), ISSN 0024-9521 (print)

Web Analytics IJG STATISTIC