Indonesian Maritime Continent has the second longest coastline in the world, but the characteristics of offshore rainfall and its relation to coastline type are not clearly understood. As a region with eighty percent being an ocean, knowledge of offshore rainfall is important to support activity over oceans. This study investigates the climatology of offshore rainfall based on TRMM 3B42 composite during 1998-2015 and its dynamical atmosphere which induces high rainfall intensity using WRF-ARW. The result shows that concave coastline drives the increasing rainfall over ocean with Cenderawasih Bay (widest concave coastline) having the highest rainfall offshore intensity (16.5 mm per day) over Indonesian Maritime Continent. Monthly peak offshore rainfall over concave coastline is related to direction of concave coastline and peak of diurnal cycle influenced by the shifting of low level convergence. Concave coastline facing the north has peak during northwesterly monsoonal flow (March), while concave coastline facing the east has peak during easterly monsoonal flow (July). Low level convergence zone shifts from inland during daytime to ocean during nighttime. Due to shape of concave coastline, land breeze strengthens low level convergence and supports merging rainfall over ocean during nighttime. Rainfall propagating from the area around inland to ocean is approximately 5.4 m/s over Cenderawasih Bay and 4.1 m/s over Tolo Bay. Merger rainfall and low level convergence are playing role in increasing offshore rainfall over concave coastline.
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.
A fixed climatological year is generally used to determine rainy season onset and cessation. However, due to changes in climate, the fixed climatological year might not be the right basis for the onset and cessation dates estimation. This study proposes the usage of the driest period in the year to establish a flexible climatological year to determine rainy season onset and cessation dates. The driest period of a climatological year is defined as the period of 14 consecutive days, which has the lowest accumulated precipitation. The flexible climatological year begins on the first day of the driest period and ends before the driest period of next year. The onset and cessation dates resulting using this new flexible climatological year are compared against those resulting from the traditional approach. Three onset estimation methods were selected for demonstration of the method: agronomy, anomalous accumulation, and a modified local method. The results showed that overall, the three methods produced similar onsets for both types of climatological years. However, the use of a flexible year showed clear advantages in the application of anomalous accumulation for large and heterogeneous climatic zones because it helped to set a start date and an average daily precipitation, which improved the onset and cessation date calculations.
East Java BPBD data recorded 18 marine accidents in 2018, which increased by 1 event compared to the previous year. It is interesting to study the waters around East Java which are divided into 9 regions. The wind is a major factor in the high wave generation, but the contribution of weather phenomena triggered by the marine environment is important to identify. Phenomenon such as Madden-Julian Oscillation (MJO) has a cycle through the Indonesia territory, becomes a factor that should be suspected. MJO identification uses the Real-Time Multivariate MJO (RMM)-1 and RMM-2 index, which can be combined with the wind speed data using data mining classification techniques to get the thresholds value of wave height data obtained from the analysis of Windwave-05 model. The classification is helped by WEKA’s machine learning algorithm, by determining 4 selected classification algorithms including Naïve Bayes, J48, JRip, and Multi-Class Classifier. The data validation using the K-fold cross-validation method with a number of folds is 10 units. The accuracy value of the best algorithm obtained in each waters region ranges from 63.02% to 84.50%. The overall accuracy value increases by 0.24% to 4.41% compared to only using wind factors, except for the Waters of Bawean Island and Masalembu Islands.
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