Research on financial ratios and its effect to the profitability of banks in

On March 2, 2020, the first Coronavirus Disease (COVID-19) case was reported in Jakarta, Indonesia. One and half month later (15/05/2020), the cumulative number of infection cases was 16496 with a total of 1076 mortalities. This study is aimed to investigate the possible role of weather in the early cases of COVID-19 incidence in six selected cities in Indonesia. Daily data of temperature and relative humidity from weather stations nearby each city were collected during the period 3 March - 30 April 2020, together with data of COVID-19 cases. Correlation tests and regression analysis were performed to examine the association of those two data series. In addition, we analysed the distribution of COVID-19 with respect to weather data to estimate the effective range of weather data supporting COVID-19 incidence. Our results reveal that weather data is generally associated with COVID-19 incidence. The daily average temperature (T-ave) and relative humidity (RH) presents significant positive and negative correlation with COVID-19 data, respectively. However, the correlation coefficients are weak with the strongest correlations found at 5 day lag time i.e. 0.37 (-0.41) for T-ave (RH). The regression analysis consistently confirmed this relation. The distribution analysis reveals that the majority of COVID-19 cases in Indonesia occurred in the daily temperature range of 25-31oC and relative humidity of 74-92%. Our findings suggest that COVID-19 incidence in Indonesia has a weak association with weather conditions. Therefore, non-meteorological factors seem to play a larger role and should be given greater consideration in preventing the spread of COVID-19.

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This study was focused on cumulonimbus (Cb) cloud prediction based on Integrated Forecast System (IFS) European Medium-Range Weather Forecast (ECMWF) model in the Flight Information Region (FIRs) Jakarta and Ujung Pandang. The Cb cloud prediction was calculated using convective cloud cover (CC) of the precipitation product. The model predictability was examined through categorical verification. The Cb cloud observation was based on brightness temperature (BT) IR1 and brightness temperature difference (BTD) IR1-IR2. The results showed that CC 50%' predictor was the best predictor to estimate the Cb cloud. The study in the period other than 2019 is suggested for the next research because Indian Ocean Dipole (IOD) is extreme that may affect the Cb cloud growth in the study area.