Analyze Surface Ocean Currents (SOCs) with one year of HF Radar data (2018-2019) for each season to determine the characteristics of the SOC direction and speed of the crossing route and its control factors carried out in the Bali Strait and the Flores Sea. Method of data analysis by computing the SOC speed and direction of the zonal and meridional components. The results showed that the SOC pattern in the Bali Strait affects the season where its speed in the DJF season is lower than the JJA season. Moreover, the SOC direction in the Bali Strait is dominant towards the south due to the influence of bathymetry. Meanwhile, the SOC pattern in the Flores Sea has a random pattern every season for the influence of topography in the form of small islands that influence the SOC dominant pattern. Furthermore, the SOC characteristics on the Bali Strait crossing route throughout the month are divided into two patterns: random on the eastern side of East Java Island and dominant towards the south on the west side of Bali Island with a maximum speed of 83 cm/s. Meanwhile, the crossing route in the Flores Sea is random, with a maximum speed of 32 cm/s. Whereas, based on the normal cross-correlation method, the SOC control factors in the Bali Strait tend to be influenced by tides, while the factors in the Flores Sea are less influential based on the distribution of zonal and meridional currents of HF Radar.

This paper was done by using the HF Radar data from 2018-2019 to study the characteristics of Sea Surface Current (SSC) in the Bali Strait. The data processing method was done by calculating the speed and SSC direction of the zonal and meridional components. Furthermore, SSC analysis was performed every hour and month by calculating the average of all data at the same hour and month. It was found that the unique SSC pattern in the Bali Strait occurred on the western side of Bali Island and the eastern side of Java Island. On the west side of the Bali Island, there was a decrease in SSC speed at 0.00-7.00 and 13.00-18.00, as well as a two-fold increase at 8.00-12.00 and 19.00-2.00, both of which were in a fluctuating speed range from 0-140 cm s-1 in the direction of dominant towards the south. On the eastern side of Java Island, SSC speed ranges from 0 to 40 cm s-1 all the time with the dominant direction heading from east to southeast. The monthly SSC pattern was also seen more clearly in this study, meanwhile during December-March the SSC rate was lower than during June-September, ranging from 0 to 20 cm s-1 and from 40 to 140 cm s-1, respectively. Furthermore, the two SSC patterns above can be simplified into two periods, namely periods of relaxation and agitation. This study also applies the device to ship accidents that occurred in the Bali Strait as case studies.

The Western Waters of Indonesian (WWI) present a diverse interaction of ocean-atmosphere dynamics. One of them represents the event of Indian Ocean Dipole (IOD), El Niño–Southern Oscillation (ENSO), and upwelling. The objective of this study is to determine the dynamics of chlorophyll-a concentration (Chl–a), especially during IOD and ENSO. Also, this study is aimed to examine the temporal and spatial distribution of the upwelling area from 2000 to 2017. The data utilized consisted of Chl–a, wind stress, Sea Level Anomaly (SLA), and Sea Surface Temperature (SST). The technique used to determine the upwelling area was by examining the maximum conditions of Chl–a, the low temperature of SST, and SLA. The results showed the sea surface temperature had a relationship with the concentration of Chl–a. It was obtained if the Directional Movement Index (DMI) and N3.4 (Niño 3.4 Index) moved stably (not too fluctuation) resulting in high concentrations of Chl–a. High standard deviations of SST are recognized around the Sunda Strait (June – October). When the standard deviation of SST is high, there is also a tendency for high Chl–a concentrations, while the results of empirical calculations show that large areas of upwelling occurred in January and September respectively at 12,447.72 km2 and 8,146.20 km2. Based on the results of the analysis, it can be concluded that the upwelling does not only occur at the coastal area of Western Sumatra (coastal upwelling), but it also occurs in the eastern territorial waters of the Indian Ocean. In addition, the upwelling area has the same pattern as the Chl–a concentration in January - October.

A limited number of marine meteorological instruments for making observations in Indonesian waters are problems in verifying the BMKG-OFS model. The satellite altimetry was selected as a verification tool due to its wide measurement range. The verification was carried out by adjusting the coordinates, time, and grid of SWH obtained and orbit of the satellite path from the satellite altimetry to the model and overlaying the models' results as a pattern analysis in July 2018 -June 2019. The next step was a statistical analysis to determine the performance of the model. The analysis obtained 43% maximum SWH formed due to the low-pressure centers in the Pacific Ocean. The remaining spreads across the South China Sea, Indian Ocean, Andaman Sea and the Gulf of Australia. This study revealed that the SWH values from satellites were higher than the model. On every three hourly and monthly bases, the SWH of the bias, RMSE, and correlation coefficient were equivalent. The lowest bias of 0.26 occurred at 9.00 UTC, the lowest RMSE of 0.48 occurred at 21:00 UTC, and the maximum correlation coefficient of 0.82 occurred at 18:00 UTC. Whereas on a monthly scale, the lowest bias and RMSE, and the maximum correlation coefficient occurred in November. Based on these results, the BMKG-OFS model can be used to predict SWH in Indonesian waters. Besides, this verification technique can be an alternative as a new tool to verify maritime weather in the operational of BMKG.