1
[1] Sucitra, A.W., and Oemar, M.A.F. (2023). Korelasi antara Tren Curah Hujan dan ENSO di Makassar. J. Minfo Polgan, 12 (2), 2618–2623.
2
[2] Turner, D., Hedrick, D., Starnes, S.L., and Van Haren, R.M. (2025). Looking through the particles: a narrative review of air pollution and lung cancer. J. Thorac. Dis., 17 (12), 11369–11376.
3
[3] Li, Z., Zhang, X., Liu, X., and Yu, B. (2022). PM2. 5 pollution in six major Chinese urban agglomerations: spatiotemporal variations, health impacts, and the relationships with meteorological conditions. Atmosphere (Basel)., 13 (10), 1696.
4
[4] Lim, E.Y., and Kim, G.-D. (2024). Particulate matter-induced emerging health effects associated with oxidative stress and inflammation. Antioxidants, 13 (10), 1256.
5
[5] Okaem, T.T. (2022). Pengembangan instrumen pengukuran kualitas udara menggunakan sensor PMS7003. Megasains, 13 (1), 31–38.
6
[6] Zulistyawan, K.A. (2023). Identifikasi Konsentrasi CO, CO2, NO2, SO2, dan PM10 yang Terukur di Stasiun GAW Bukit Kototabang Selama Mudik Lebaran Tahun 2019-2023. Megasains, 14 (2), 39–47.
7
[7] Ariska, M., Suhadi, Supari, Irfan, M., and Iskandar, I. (2023). Spatio-Temporal Variations of Indonesian Rainfall and Their Links to Indo-Pacific Modes. Atmosphere (Basel)., 14 (2), 300.
8
[8] Plocoste, T., Carmona-Cabezas, R., Gutiérrez de Ravé, E., and Jiménez-Hornero, F.J. (2022). Wet scavenging process of particulate matter (PM10): A multivariate complex network approach. Atmos. Environ., 268, 118801.
9
[9] Pancariniwati, S., Fadila, R., and Melinda, S. (2024). Pengaruh Madden Julian Oscillation (MJO) terhadap Sifat Hujan di 2x11 Kayu Tanam, Sumatera Barat. Bul. GAW Bariri, 5 (2), 50–59.
10
[10] Hanifa, R., and Wiratmo, J. (2024). ENSO and IOD Influence on Extreme Rainfall in Indonesia: Historical and Future Analysis using CMIP6. Agromet, 38 (2), 88–101.
11
[11] Muharsyah, R., Ratri, D.N., and Kussatiti, D.F. (2021). Improving prediction quality of sea surface temperature (SST) in Niño3. 4 region using Bayesian Model Averaging. IOP Conf. Ser. Earth Environ. Sci., 893 (1), 12028.
12
[12] Santoso, A.B., Supriana, T., and Girsang, M.A. (2022). Precipitation impact on cassava yield in Indonesia.
13
[13] Carslaw, D.C., and Ropkins, K. (2023). openair — An R package for air quality data analysis. Environ. Model. Softw.
14
[14] (NOAA), N.O. and A.A., and Center, C.P. (2024). Cold & Warm Episodes by Season (Oceanic Niño Index, ONI).
15
[15] Institute, C.C. (2026). Climate Reanalyzer.
16
[16] Maruyama, F. (2024). Analysis of the annual number of tropical cyclones over Japan using the extreme value theory. Cont. Shelf Res., 282, 105341.
17
[17] Berendrecht, W., Van Vliet, M., and Griffioen, J. (2023). Combining statistical methods for detecting potential outliers in groundwater quality time series. Environ. Monit. Assess., 195 (1), 85.
18
[18] Wilks, D.S. (2020). Statistical Methods in the Atmospheric Sciences, Academic Press.
19
[19] Arat, M.M. (2025). Detection of anomalous Nitrogen Dioxide concentration of Ankara: a Reconstruction-based approach. Politek. Derg., 28 (1), 101–114.
20
[20] Sun, C., Huang, G., and Fan, Y. (2020). Multi-indicator evaluation for extreme precipitation events in the past 60 years over the Loess Plateau. Water, 12 (1), 193.
21
[21] Yusof, K.A., Abdullah, M., Hamid, N.S.A., Ahadi, S., and Ghamry, E. (2021). Statistical global investigation of pre-earthquake anomalous geomagnetic diurnal variation using superposed epoch analysis. IEEE Trans. Geosci. Remote Sens., 60, 1–13.
22
[22] Walton, S.D., and Murphy, K.R. (2022). Superposed epoch analysis using time-normalization: A Python tool for statistical event analysis. Front. Astron. Sp. Sci., 9, 1000145.
23
[23] Gong, S., Liu, Y., He, J., Zhang, L., Lu, S., and Zhang, X. (2022). Multi-scale analysis of the impacts of meteorology and emissions on PM2. 5 and O3 trends at various regions in China from 2013 to 2020 1: Synoptic circulation patterns and pollution. Sci. Total Environ., 815, 152770.
24
[24] Jia, Z., Doherty, R.M., Ordóñez, C., Li, C., Wild, O., Jain, S., and Tang, X. (2022). The impact of large-scale circulation on daily fine particulate matter (PM 2.5) over major populated regions of China in winter. Atmos. Chem. Phys., 22 (10), 6471–6487.
25
[25] Pujadini, N.A., Zaki, S., Effendi, F., Aini, R.T., Vonnisa, M., Ramadhan, R., Yusnaini, H., Hashiguchi, H., Shimomai, T., and Marzuki, M. (2023). Seasonal variation of cloud layer over Sumatra from long-term ceilometer observation. Int. Conf. Geosci. Remote Sens. Technol., 135–143.
26
[26] Moron, V., and Robertson, A.W. (2020). Tropical rainfall subseasonal-to-seasonal predictability types. npj Clim. Atmos. Sci., 3 (1), 4.
27
[27] Ding, D., Gandy, A., and Hahn, G. (2020). A simple method for implementing Monte Carlo tests. Comput. Stat., 35 (3), 1373–1392.
28
[28] Rodrigues, J.C., Facao, J., and Carvalho, M.J. (2024). Parameter identification and uncertainty evaluation in quasi-dynamic test of solar thermal collectors with Monte Carlo method. Renew. Energy, 236, 121403.
29
[29] Parchami, A., Iranmanesh, H., and Gildeh, B.S. (2022). Monte Carlo statistical test for fuzzy quality. Iran. J. Fuzzy Syst., 19 (1), 115.
30
[30] Sengottuvel, S., Devi, S.S., Sasikala, M., Satheesh, S., and Selvaraj, R.J. (2021). An epoch based methodology to denoise magnetocardiogram (MCG) signals and its application to measurements on subjects with implanted devices. Biomed. Phys. Eng. Express, 7 (3), 35006.
31
[31] Zhang, T., and Ebihara, Y. (2022). Superposed epoch analyses of geoelectric field disturbances in Japan in response to different geomagnetic activities. Sp. Weather, 20 (5), e2021SW002893.