Comparison and Analysis of Rainfall Spatial Patterns IMERG (Integrated Multi-Satellite Retrievals for GPM) Data and Observation Data on Bali Province
Abstract
Limitations of observational data such as insufficient data length, incomplete, and uneven station distribution make it difficult to analyze and predict rain, so it requires supporting instruments such as satellites to provide a better and broader picture of rainfall distribution. However, it is necessary to test the accuracy of satellite data because the resolution and conditions of each region are different. This research aims to validate IMERG rain data against observation data in the 2015 El Nino period using observation rainfall data from BMKG Negara and IMERG data from GPM satellite at 12 rain points in Bali Province. The analytical method used is quantitative statistics, the calculation of errors and correlations and the comparison of the spatial pattern of the two data. The results of the analysis of the spatial pattern of the IMERG data show that, there was a decrease in rainfall from May to July, but the rainfall increased into August, and again experienced a decline entering the months of September to December where the same pattern was also shown from the results of the spatial pattern analysis on the Observation data. The decrease in rainfall in the May-December 2015 period was a strong El Nino effect as evidenced by the results of the correlation analysis of the SOI index on rainfall which showed a fairly strong correlation value, namely 0.55.The validation of IMERG data on monthly observation data showed that the average correlation was sufficient strong is 0.42 and analysis per rain post shows a weak correlation namely 0.31, which means that data IMERG is not yet accurate as an alternative to the observation rainfall data in Bali Province.
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References
[2] S. Philander, El Niño, La Niña, and The Southern Oscillation, vol. 46, 1st ed. New York: Academic Press, 2003.
[3] O. Setiawan, Analisis Variabilitas Curah Hujan dan Suhu di Bali, Jurnal Analisis Kebijakan Kehutanan, vol. 9, no. 1, 2012, pp. 66-79.
[4] H. Feidas, Validation of Satellite Rainfall Products Over Greece, Theoretical and Applied Climatology Journal, vol. 99, 2010, pp. 193–216.
[5] P. Xie, A. Yatagai, et al., A Gauge-Based Analysis of Daily Precipitation over East Asia, Hydrometeor Journal, vol. 8, 2007, pp. 607–626.
[6] G. J. Huffman, R.F. Adler, D.T. Bolvin, et al., The TRMM Multi-satellite Precipitation Analysis: Quasi-Global, Multi-Year, Combined-Sensor Precipitation Estimates at Fine Scale, Hydrometeor Journal, vol. 8 (1), 2019, pp. 38-55.
[7] ppm.nasa.gov [home page in internet] NASA: National Aeronautics and Space Administration Available from https://disc.sci.gsfc.nasa.gov/datasets/GPM_3IMERGM_06/summary?keywords= IMERG [Cited 2020 February 26].
[8] F.F. Rey, S. H. J. Tongkukut, dkk, Analisis Spasial Pengaruh Dinamika Suhu Muka Laut Terhadap Distribusi Curah Hujan di Sulawesi Utara, Jurnal MIPA Unsrat, vol. 3, no. 1, 2014, pp. 25-29.
[9] S. Maulidani, N. Ihsan, dkk, Analisis pola dan Intensitas Curah Hujan Berdasarkan Data Obsevasi dan Satelite Tropical Rainfall Measuring Missions (TRMM) 3B42 V7 di Makassar, Jurnal Fisika UNM, vol. 11, no. 1, 2014, pp. 98-103.
[10] E. Aldrian, Simulations of Indonesia rainfall with Hierarchy of Climate Models, Ph.D. dissertation, Max-Planck-Institut Fur Meteorologie, Hamburg, 2003.
[11] A. Sayakur, A. Rahman, Identifikasi Hubungan Fluktuasi Nilai SOI Terhadap Curah Hujan Bulanan di Kawasan Batukaru-Bedugul, Bali, Jurnal Bumi Lestari, vol. 7, no. 2, 2007, pp. 123-129.
[12] J. Tongkukut, S. Herlina, El-Nino dan Pengaruhnya Terhadap Curah Hujan Di Manado Sulawesi Utara, Jurnal Ilmiah Sains, Vol. 11, no. 1, 2011, pp. 102-108.
[13] A. Fadholi, Studi Dampak El Nino dan Indian Ocean Dipole (IOD) terhadap Curah Hujan di Pangkal Pinang, Jurnal Ilmu Lingkungan, vol. 11, no. 1, 2013, pp. 43-50.
[14] Akdon, Riduwan, Rumus dan Data Dalam Analisis Statistika, Penerbit Alfabeta, 2005.
