ANALISIS SEA LEVEL VARIABILITY MENGGUNAKAN SATELIT SARAL ALTIKA DAN JASON
Abstract
Altimetry satellite technology is used to regulate sea level. Observations are carried out every year to study the dynamics of sea level in the world. Sea level anomalies (SLA) in each region have different values and vary greatly. The main cause that increases from sea level is the thermal increase that increases from the mass of water from melting ice and glaciers on the surface of the earth. Therefore, this research aims to calculate the SLA from the SARAL / AltiKa satellite data and Jason's satellite series to study the variability of sea level in Indonesia's western sea, namely: the Java Sea, Karimata Strait and the South China Sea. From the research conducted, sea level rise obtained using SARAL / AltiKa satellite data in the range of -10 mm to 8 mm at a rate of decline of 0,459 mm / year. Meanwhile, Jason's series satellite data produces sea surface variations of around -2 mm to 11 mm at a rate of decline of 0,817 mm / year. From these two satellite observations, sea level decreases occur in the Java Sea, while in the Karimata Strait and parts of the South China Sea increasing sea level rise. In addition, this study uses research analysis to study the association of SLA data from SARAL / AltiKa and Jason satellite observations. The results of comparative analysis are very strong and in line with the estimated coefficient value of 0,9332.
Keywords: Altimetri, Jason, Perairan Indonesia, SARAL/AltiKa, Sea Level Anomaly, Trend
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Ablain, M., Legeais, J. F., Prandi, P., Marcos, M., Fenoglio-Marc, L., Dieng, H. B., ... & Cazenave, A. (2017). Satellite altimetry-based sea level at global and regional scales. In Integrative study of the mean sea level and its components (pp. 9-33). Cham: Springer International Publishing.
Andersen, O. B., & Scharroo, R. (2011). Range and geophysical corrections in coastal regions: and implications for mean sea surface determination. Coastal altimetry, 103-145.
An, J. Y., Unsdorfer, K. M., & Weinreb, J. C. (2019). BI-RADS, C-RADS, CAD-RADS, LI-RADS, Lung-RADS, NI-RADS, O-RADS, PI-RADS, TI-RADS: Reporting and data systems. Radiographics, 39(5), 1435-1436.
Birol, F., & Niño, F. (2015). Ku–and Ka-band altimeter data in the Northwestern Mediterranean Sea: impact on the observation of the coastal ocean variability. Marine Geodesy, 38(sup1), 313-327.
Cazenave, A., Henry, O., Munier, S., Delcroix, T., Gordon, A. L., Meyssignac, B., ... & Becker, M. (2012). Estimating ENSO influence on the global mean sea level, 1993–2010. Marine Geodesy, 35(sup1), 82-97.
Cazenave, A., & Nerem, R. S. (2004). Present‐day sea level change: Observations and causes. Reviews of Geophysics, 42(3).
Chelton, D. B., Ries, J. C., Haines, B. J., Fu, L. L., & Callahan, P. S. (2001). Satellite altimetry. In International geophysics (Vol. 69, pp. 1-ii).
Church, J. A., White, N. J., & Hunter, J. R. (2006). Sea-level rise at tropical Pacific and Indian Ocean islands. Global and Planetary Change, 53(3), 155-168.
Church, J., Wilson, S., Woodworth, P., & Aarup, T. (2007). Understanding sea level rise and variability. EOS. 88(4), 37-46
Faridatunnisa, M. (2018). Utilization of Tide Observation and Satellite Altimetry Data for Detecting Land Vertical Movement. JGISE: Journal of Geospatial Information Science and Engineering, 1(2).104-112
Fenoglio-Marc, L., Schöne, T., Illigner, J., Becker, M., Manurung, P., & Khafid. (2012). Sea level change and vertical motion from satellite altimetry, tide gauges and GPS in the Indonesian region. Marine Geodesy, 35 (sup1), 137-150.
Handoko, E. Y., Fernandes, M. J., & Lázaro, C. (2017). Assessment of altimetric range and geophysical corrections and mean sea surface models—impacts on sea level variability around the Indonesian seas. Remote Sensing, 9(2), 102.
Handoko, E. Y., Yuwono, Y., Ariani, R., & Filaili, R. B. (2018). Korelasi multivariate El Niño Southern Oscillator Index dan variasi permukaan laut di Perairan Indonesia. Geoid, 14(1), 1-5.
Mahmood, A., Tariq M.A.K., dan Nadeem F. (2004). Correlation between multivariate ENSO Index (MEI) and Pakistan’s Summer Rainfall. Pakistan Journal of Meteorology, 53 – 64.
Nicholls, R. J., & Cazenave, A. (2010). Sea-level rise and its impact on coastal zones. science, 328(5985), 1517-1520.
Nisa, T. C., Siregar, R. R. A., & Suliyanti, W. N. (2019). Estimasi daya beban listrik pada gardu induk cengkareng dengan menggunakan metode time series model dekomposisi. Jurnal Teknologia, 1(2).114-133
Rafi, M., Wahyuni, W. T., Arif, Z., & Heryanto, R. (2021). Autentikasi kumis kucing (Orthosiphon aristatus) menggunakan kombinasi spektrum ultraviolet-tampak dan partial least square regression. Indonesian Journal of Chemometrics and Pharmaceutical Analysis, 1(2), 93-101.
Yuni, S., Talakua, M. W., & Lesnussa, Y. A. (2015). Peramalan jumlah pengunjung perpustakaan Universitas Pattimura Ambon menggunakan metode dekomposisi. BAREKENG: Jurnal Ilmu Matematika dan Terapan, 9(1), 41-50.
Verron, J., Sengenes, P., Lambin, J., Noubel, J., Steunou, N., Guillot, A., ... & Gupta, P. K. (2015). The SARAL/AltiKa altimetry satellite mission. Marine Geodesy, 38(sup1), 2-21.
Wijaya, Y. D. (2020). Penerapan metode rapid application development (RAD) dalam pengembangan sistem informasi data toko. Jurnal SITECH: Sistem Informasi dan Teknologi, 3(2), 95-102.
DOI: https://doi.org/10.17509/k.v19i2.44969
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