The Asian–Australian Monsoon (AAM), the El Nino-Southern Oscillation (ENSO), and the Indian Ocean Dipole (IOD) have been known to induce variability in ocean surface characteristics along the southern coast of Lesser Sunda Island (LSI). However, previous studies used low spatial resolution data and little Ekman dynamics analysis. This study aims to investigate the direct influence of AAM winds on ocean surface conditions and to determine how ENSO and IOD affect the ocean surface and depth with higher spatial resolution data. In addition, the variability in Ekman dynamics is also described along with the inconsistent relationship between wind and sea surface temperature (SST) in four different areas. The results indicate that persistent southeasterly winds are likely to induce low SST and chlorophyll-a blooms. Based on the interannual variability, the positive chlorophyll-a (up to 0.5 mg m−3) and negative SST (reaching −1.5 °C) anomalies observed in the El Nino of 2015 coincide with +IOD, which also corroborates positive wind stress and Ekman Mass Transport (EMT) anomalies. In contrast, the La Nina of 2010 coincides with -IOD, and positive SST and negative chlorophyll-a anomalies (more than 1.5 °C and −0.5 mg m−3 respectively) were observed. Furthermore, we found that southern coast of Java and Bali Island have a different generated mechanism that controls SST variability.
Increasing the accuracy of rainfall forecasts is crucial as an effort to prevent hydrometeorological disasters. Weather changes that can occur suddenly and in a local scope make fast and precise weather forecasts increasingly difficult to inform. Additionally, the results of the numerical weather model used by the Indonesia Agency for Meteorology, Climatology, and Geophysics are only able to predict the rainfall with a temporal resolution of 1–3 h and cannot yet address the need for rainfall information with high spatial and temporal resolution. Therefore, this study aims to provide the rainfall forecast in high spatiotemporal resolution using Himawari-8 and GPM IMERG (Global Precipitation Measurement: The Integrated Multi-satellite Retrievals) data. The multivariate LSTM (long short-term memory) forecasting is employed to predict the cloud brightness temperature by using the selected Himawari-8 bands as the input and training data. For the rain rate regression, we used the random forest technique to identify the rainfall and non-rainfall pixels from GPM IMERG data as the input in advance. The results of the rainfall forecast showed low values of mean error and root mean square error of 0.71 and 1.54 mm/3 h, respectively, compared to the observation data, indicating that the proposed study may help meteorological stations provide the weather information for aviation purposes.
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