Nias is the largest island in the Indian Ocean west of Sumatra. The geographical condition of Nias Island which is a small island surrounded by waters causes strong weather dynamics and high intensity of rainfall. This study was conducted to determine changes in weather dynamics conditions during high rainfall on Nias Island, including the parameters of the vertical profile of air humidity, vorticity, divergence, vertical velocity, reflectivity; and cloud top temperatures. The simulation was carried out on 4 days of heavy rain in 2020, each representing each season period, namely 7 February 2020 for December-January-February (DJF), 30 April 2020 for March-April-May (MAM), 3 August 2020 for June-July-August (JJA), and 8 October 2020 for September-October-November (SON). The data used are Final Analysis Data (FNL) with a spatial resolution of 1°x1° as input data for the Weather Research and Forecast (WRF) model, IR1 data (band#13 – 10.4µm) Himawari-8 satellite, and observational rainfall data from the Binaka and Global Meteorological Stations. Precipitation Measurement – The Integrated Multi-Satellite Retrievals for GPM (GPM IMERG) with a spatial resolution of 0.1°x0.1°. The results showed that the presence of Cumulonimbus convective clouds caused heavy rain, with cloud top temperatures reaching -60°C to -80°C. The relatively humid atmosphere, accompanied by the convection mechanism that occurs, causes the convective activity on Nias Island to be quite intense.
Located adjacent to the Indian Ocean and the Malacca Strait as a source of water vapour, and traversed by the Barisan Mountains which raise the air orographically causing high diurnal convective activity over the North Sumatra region. The convective system that was formed can cause heavy rainfall over a large area. Weather Research and Forecasting (WRF) was a numerical weather model used to make objective weather forecasts. To improve the weather forecasts accuracy, especially for predict heavy rain events, needed to improve the output of the WRF model by the assimilation technique to correct the initial data. This research was conducted to compare the output of the WRF model with- and without assimilation on 17 June 2020 and 14 September 2020. Assimilation was carried out using the 3D-Var technique and warm starts mode on three assimilation schemes, i.e. DA-AMSU which used AMSU-A satellite data, DA-MHS which used MHS satellite data, and DA-BOTH which used both AMSU-A and MHS satellite data. Model output verification was carried out using the observational data (AWS, AAWS, and ARG) and GPM-IMERG data. The results showed that the satellite data assimilation corrects the WRF model initial data, so as increasing the accuracy of rainfall predictions. The DA-BOTH scheme provided the best improvement with a final weighted performance score of 0.64.
Indonesia has diverse topographical conditions that result in Indonesia having a unique climate. One of the unique climate elements to be studied is rainfall, because rainfall has a different pattern in each region, this different rainfall pattern is caused by several climate phenomena factors that affect the rainfall pattern, including El-Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Madden Julian Oscillation (MJO). Medan City is the capital of North Sumatra province which is one of the areas in the flood-prone category in North Sumatra, where the factor of flooding is due to rainfall events in a long period of time, so the author wants to know which climatic phenomena factors can affect rainfall events in Medan city by using Machine Learning technology through the Matlab application, where in this study has a method by forming four combination models, namely the combination of the influence of IOD, SOI and MJO; second combination of IOD and SOI; third combination of SOI and MJO; and fourth combination of MJO and IOD, these four combinations will be the rainfall value of the four models. Furthermore, the rainfall value of the model is compared with the observed rainfall value and verification test using Mean Absolute Error (MAE) and correlation. Then the calculation of the comparison between the four rainfall models with the observed rainfall obtained the lowest MAE value during the SOI and MJO phenomenon of 15.0 mm and the highest correlation value during the IOD and SOI and SOI and MJO phenomena. So it is concluded that the combination of SOI and MJO has the best verification value. This shows that based on Machine Learning modeling, the model shown as the best predictor in Medan city is when the model combination consists of SOI and MJO.
Located between the Indian Ocean and the Malacca Strait, also the presence of the Bukit Barisan Mountains cause high convective activity in the North Sumatra region. The Himawari-8 satellite has 16 atmospheric observation channels that allow for observations of the convective system growth phase. The Red-Green-Blue (RGB) composite method is used to display a variety of satellite image composite information. The nocturnal convective system that often forms in the coastal areas of Sumatra causes heavy rains. A nocturnal convective system observation method is needed to publish early warning information on extreme weather. This research was conducted to observe the nocturnal convective system during heavy rain events in the North Sumatra region using a modification of RGB composite. This research used the Himawari-8 satellite data, Coloumn Max (CMAX) products of Medan weather radar data, and Global Satellite Mapping of Precipitation (GSMaP) rainfall estimation data. Comparison of RGB modified products with Night Microphysics RGB products and CMAX weather radar products, as well as time-series rainfall analysis. The results showed that the RGB modification product could capture the beginning of the convective system's growth, development, and spatial movement. The convective cloud distribution pattern corresponds to the area of heavy rain. There is a slight difference in cloud growth area between the satellite and radar products indicated the parallax error from the satellite image.
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