bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prediction of soil moisture and rainfall induced landslides: A comparison of several PBL parameters in the WRF model
Authors: Nuryanto D E , Satyaningsih R , Nuraini T A , Fajariana Y , Anggraeni R , Sopaheluwakan A , Yuliyanti M S 
Published: December 2021

Abstract
Rainfall intensity thresholds only do not take advantage of the awareness of the slope's hydrological processes, so they appear to produce large false and missed alert rates, decreasing the credibility of early warning systems for landslides. This study analyzes this dilemma by modeling the behavior of slopes to precipitation, including the potential effect of soil moisture uncertainty given by numerical modeling. For the simulation of soil moisture during the study period and event rainfall thresholds of an extreme event used to describe the intensity of a rainfall event, the Weather Research and Forecasting (WRF) model is used. The three days simulation conducted during a landslide event in Samigaluh, Kulon Progo on 28 November 2018. The four Planetary Boundary Layer (PBL) parameters in the WRF model are compared to understand each character, i.e., Yonsei University (YSU), Mellor-Yamada-Janjic (MYJ), Shin-Hong (SH), and Bougeault-Lacarrère (BL). To evaluate the precipitation as simulated by WRF, we use observation data from rain gauge and the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). In general, all parameters have an underestimation of precipitation. Each PBL parameter's response to rainfall is different. Both MYJ and SH schemes are closer to observation than others for day 1 and day 2 of simulation, daily precipitation. For all PBL schemes, increased soil moisture is seen, suggesting that the soil is wetter and more vulnerable to landslide events. As an early warning predictor of landslides in terms of rainfall parameters, the SH method is very useful in this analysis. For early warning of landslides, a short period (<6 hours) of precipitation with a high accumulation of precipitation would be very beneficial.
Rainfall prediction over Ambon Meteorological Station using Multi-Physics Ensemble WRF-ARW
Authors: Alfahmi F , Charolydya R , Khaerima A 
Published: December 2021

Abstract
One of the methods to create good forecast using WRF-ARW modelling is tuning the parameterization. However, this method cannot provide rainfall event probability. Current research result revealed that it was able to simulate and forecast some weather parameters. However, based on the verification results, there were some weather parameters which still had low accuracy. Due to such low accuracy on some weather parameters, the authors were interested in performing post-processing methods in forecasting the weather during extreme weather at Pattimura Ambon Meteorological Station. In this study, we employed multi-physics ensemble prediction system (MEPS) by combining 20 WRF-ARW parameterization schemas, which were processed to obtain the ensemble mean, ensemble spread, and basic probability to get the uncertainty from each weather parameters. Verification process was done by using spreads, skill method and ROC curves. It was discovered that MEPS products have a better skill compared to the forecast control, the correlation value of MEPS products is larger and has the lowest error value. In addition, the result of ROC curves shows that the MEPS has an ability to predict weather condition during cloudy and extreme rain.
Early warning of heavy rainfall event using time-lagged ensemble prediction system (case study February 15th 2019)
Authors: Rahmadini H N , Efendi U , Rifani A , Kristianto A 
Published: December 2021

Abstract
Convective clouds can be related to the development of intense storms that produce various extreme weather. The development of extreme weather could involve strong nonlinear interactions of many factors in the atmosphere, hence the ability to forecast extreme weather especially heavy rainfall and issued an early warning, becomes very important. BMKG has developed a time-lagged ensemble prediction system by utilizing the initial time difference, which is considered capable of providing data updates more closely to the forecasts final results. This study examines the percentile classification in the ensemble prediction system, to look for an extreme values distribution, then used it as extreme threshold. The extreme threshold was tested in a heavy rain case on February 15th 2019, on D-7, D-3, and D-1 of early warning dissemination. Based on this research, it was found that the use of the 90th and 95th percentile classification method was able to show a signal of extreme events on D-7 and D-3 events with a consistent probability pattern. In the D-1 prediction period, the probability value increases and the average precipitation value exceeds the extreme threshold.
WRF-model sensitivity test and assimilation studies of Cempaka tropical cyclone
Authors: Sadarang Fazrul Rafsanjani , Sari Fitria Puspita 
Published: December 2021

Abstract
The WRF model was used to forecast the most intensive stage of Cempaka Tropical Cyclone (TC) on 27 - 29 November 2017. This study evaluates the combination of cumulus and microphysics parameterization and the efficiency of assimilation method to predict pressure values at the center of the cyclone, maximum wind speed, and cyclone track. This study tested 18 combinations of cumulus and microphysics parameterization schemes to obtain the best combination of both parameterization schemes which later on called as control model (CTL). Afterward, assimilation schemes using 3DVAR cycles of 1, 3, 6 hours, and 4DVAR, namely RUC01, RUC03, RUC06, and 4DV, were evaluated for two domains with grid size of each 30 and 10 km. GFS data of 0.25-degree and the Yogyakarta Doppler Radar data were used as the initial data and assimilation data input, respectively. The result of the parameterization test shows that there is no combination of parameterization schemes that constantly outperform all variables. However, the combination of Kain-Fritsch and Thompson can produce the best prediction of tropical cyclone track compared to other combinations. While, the RUC03 assimilation scheme was noted as the most efficient method based on the accuracy of track prediction and duration of model time integration.
Sea-level prediction for early warning information of coastal inundation in Belawan coastal area using Delft3D model
Authors: Jaya A M N , Sari F P , Saragih I J A , Dafitra I 
Published: December 2021

AbstractCoastal inundation has a great impact on the environment, such as damage to infrastructure and pollution of land and water. One of the efforts to prevent coastal inundation is to predict the water level. Delft3D is a hydrodynamic model that's able to simulate the water level. Coastal inundation research using the Delft3D model is still rarely done in Indonesia, especially on the east coast of Sumatra. This research is conducted in Belawan coastal area by simulating the water level that caused the coastal inundation using the Delft3D model. The best bathymetry for the prediction of water level and the magnitude of the wind effect was obtained from the simulation. The final step is to predict the water level in Belawan coastal area. The result of this research shows that the Delft3D model can simulate the water level which causes the coastal inundation in the Belawan coastal area. The correlation of the Delft3D model is 0.9, and the RMSE of GEBCO bathymetry is 0.39 meters and the RMSE of NOAA bathymetry is 0.46 meters. The GEBCO bathymetry is better than NOAA bathymetry in describing the water level in the Belawan coastal area. The wind effect on the water level simulations is not significant because the coefficient of determination is 0.47%. Besides, the Delft3D model with GEBCO bathymetry input can predict the water level which causes the coastal inundation with correlation reaches 0.92 and RMSE is 0.39 meters.
Initiative use of climate change hotspots for targeting adaptation sites in Indonesia
Authors: Mustofa Ikrom , Perdinan  , Pratiwi Syafararisa Dian , Aprilia Suvany , Tjahjono Raden Eliasar Prabowo , Janna Sabila Cahyaning , Wibowo Arif , Kardono  , Wijanarka Koko 
Published: December 2021

Abstract
Designing climate change adaptation actions are considerably a challenge, as the actions should be targeted uniquely addressing climate change impacts. One of the challenges is to determine climate change adaptation sites. The complexity raises considering climate change impact a wide range of economic sectors, which require a lot of resources to conduct a comprehensive climate change assessments. This study proposes the use of climate change hotspots as an initiative to firstly consider the potential targeted sites. The target of global efforts to maintain air temperature under 2°C was employed as a clue to prioritize areas that air temperature is increasing beyond the thresholds to which can affect human activities. This study employed spatial and threshold analysis to develop climate change hotspots of projected temperature change for 2021-2050 over Indonesia. The thresholds were defined by considering the effects of base temperature of 32 °C, 35 °C, and 38 °C on agriculture, environment, and human health in combination with elevated temperature from 0.75 to 2 °C. The initiative method was applied to the baseline and projected air temperature obtained from higher resolution of climate model outputs simulated under representative carbon pathway scenario of 4.5 (RCP 4.5 and 8.5) as a case study. The maps of climate change hotspots provide the potential targeted areas for climate change adaptation actions. Referring to the target of suppressing global temperatures below 2°C, we identified the distribution of climate change hotspots in Indonesia with a scenario of increasing temperature of 2°C from baseline conditions so that future air temperatures will be more than 35°C. The maps can also be combined with the other maps related to climate change analyses, which are available in Indonesia such as SIDIK to refine the priority areas and/or more general geographic information such as city location. As an example, the overlay of climate change hotspots and city location can provide early anticipation on which city will experience urban heat island. The development of climate change hotspots nationally is also expected to initiate climate change services that can be provided to the end users to ease them in defining suitable actions to adapt to the impacts of climate change.
Performance of probabilistic forecast of the onset of the rainy season over Java Island based on the application of Constructed Analogue (CA) method on Climate Forecast System Version 2 (CFSV2) model output
Authors: Lubis F , Saragih I J A 
Published: December 2021

AbstractThe onset of the rainy season is one of the forecast products that is issued regularly by the Indonesian Agency of Meteorology, Climatology, and Geophysics (BMKG), with deterministic information about the month of which the initial 10-days (dasarian) of the rainy season will occur in each a designated area. On the other hand, state-of-the-art of seasonal forecasting methods suggests that probabilistic forecast products are potentially better for decision making. The probabilistic forecast is also more suitable for Indonesia because of the large rainfall variability that adds up to uncertainty in climate model simulations, besides complex geographical factors. The research aims to determine the onset of rainy season and monsoon over Java Island based on rainfall prediction by Constructed Analogue statistical downscaling of CFSv2 (Climate Forecast System version 2) model output. This research attempted to develop a method to produce a probabilistic forecast of the onset of the rainy season, as well as monsoon onset, by utilizing the freely available seasonal model output of CFSv2 operated by the US National Oceanic and Atmospheric Administration (NOAA). In this case, the output of the global model is dynamically downscaled using the modified Constructed Analogue (CA) method with an observational rainfall database from 26 BMKG stations and TRMM 3B43 gridded dataset. This method was then applied to perform hindcast using CFS-R (re-forecast) for the 2011-2014 period. The results show that downscaled CFS predictions with initial data in September (lead-1) give sufficient accuracy, while that initialized in August (lead-2) have large errors for both onsets of the rainy season and monsoon. Further analysis of forecast skill using the Brier score indicates that the CA scheme used in this study showed good performance in predicting the onset of the rainy season with a skill score in the range of 0.2. The probabilistic skill scores indicate that the prediction for East Java is better than the West- and Central-Java regions. It is also found that the results of CA downscaling can capture year-to-year variations, including delays in the onset of the rainy season.
Analysis of climate indicator association with hotspots in Indonesia using heterogeneous correlation map
Authors: Dafri M , Nurdiati S , Sopaheluwakan A , Septiawan P 
Published: December 2021

Abstract
In several regions, land and forest fires of Indonesia occurred almost annually during the drought season. The severity of Indonesia's drought season is mainly influenced by the Australian Monsoon, local cloud formation controlled by Sea Surface Temperature (SST) around Indonesia. Moreover, it affects the severity of land and forest fires itself indirectly. This research aims to examine the association of the Australian Monsoon and local SST with land and forest fires in Indonesia. This research uses the Australian Monsoon Index (AUSMI) as an indicator for the Australian Monsoon and SST in the Karimata Strait and the Java Sea as indicators of local SST. An indicator of land and forest fires that will be used is the number of hotspots. A heterogeneous Correlation Map (HCM) is used to describe hotspots associated with AUSMI and local SST. The analysis shows that the east wind pattern of AUSMI associated with hotspots in Indonesia, especially in years when zonal winds enter an upward phase more slowly. Karimata Strait’s SST is associate with hotspots in the coastal part of Riau. Meanwhile, Java Sea’s SST is associate with hotspots in Lampung, South Sumatra, Jambi, and Kalimantan.
Estimation of the spatial distribution of maximum PM10 and PM2.5 concentration in Bandung City and surrounding countries using WRF-Chem Model (case study in July and October 2018)
Authors: Salsabila H , Turyanti A , Nuryanto DE 
Published: December 2021

Abstract
Bandung is one of big cities in Indonesia with high activities on industrial and transportation that will increase the air pollutant emission and causes adversely affect the public health. Based on that matter, monitoring of air pollutant concentration is urgently needed to predict the direction of pollutant dispersion and to analyze which locations are vulnerable to maximum exposure of the pollutant. Field monitoring of air pollutant concentration needs much time and high cost, but modeling could help for this. One of the models that can be used to predict the direction of pollutant distribution is the Weather Research Forecasting/Chemistry (WRF-Chem) model, which is a model that combines meteorological models with air quality models. The output of the WRF-Chem running model on July and October 2018, which has been analyzed visually, showed the dispersion pattern of PM10 and PM2.5 is spread mostly to the west, northwest, and north following the wind direction. According to the output of the WRF-Chem model, Bandung Kulon is the most polluted subdistrict by PM10 and PM2.5 with an exposure frequency of 22 hours (PM10), 24 hours (PM2.5) on July 2018 and 19 Hours (PM10), 14 hours (PM2.5) on October 2018. The correlation value for meteorological parameters is quite high in July 2018 (R = 0.9 for wind speed and R = 0.82 for air temperature). So based on the meteorological factor, WRF-Chem model can be used to predict the direction of pollutant distribution.
Preliminary assessment for sub-seasonal to seasonal precipitation model on four specific conditions over western Indonesia
Authors: Vivi Agita , Hidayat Rahmat , Faqih Akhmad , Alfahmi Furqon 
Published: December 2021

Abstract
Preliminary assessment of sub-seasonal to seasonal reforecast precipitation model (S2S) was conducted to analyze the model's performance over western Indonesia on four conditions. The ECMWF S2S model was compared to quality controlled daily precipitation data from 645 observation points over the region. The control and perturbed model for the first three time steps and the last three were utilized to obtain the best performance comparison. The analysis was conducted in monthly period, MJO events, NCS events, and when both of them were active during period of November-December-January-February (NDJF) from 1998 to 2017. The results show that the first three time steps perform much better than the last one with a slightly higher correlation coefficient from the control model with relatively similar RMSE in Natuna Islands. Spatial analysis indicates that both of the control and perturbed models can catch the variation brought by the wet season in the NDJF period, by the MJO, show a hint of NCS effect, and the combination when MJO and NCS were active at the same time. The models can depict the precipitation pattern pretty well with the tendency to overestimate low rainfall intensity and underestimate the high one. The models relatively overestimate the intensity in Sumatra for the whole period. Meanwhile, consistently good spatial performance is shown by the models over Java, both in NDJF periods or MJO events.
An Assessment of Convective Initiation Nowcasting Algorithm within 0-60 Minutes using Himawari-8 Satellite
Authors: Perdana I F P , Septiadi D 
Published: December 2021

Abstract
Convective cloud monitoring since its growth stage primarily related to location and time of the first convective cloud initiated, called convective initiation (CI), could be the primary key in providing an earlier heavy rainfall event prediction. This study aimed to assess the accuracy and lead time of CI nowcasting using Satellite Convection Analysis and Tracking (SATCAST) algorithm in predicting the CI event within 0-60 minutes over Surabaya and surrounding area using Himawari-8 satellite during June-July-August (JJA) period in 2018. Three main processes used in this study were cloud masking, cloud object tracking, and CI nowcasting. Twelve interest fields were utilized as predictors based on six bands of Himawari-8 satellite, which represented cloud physics attributes such as cloud-top height, glaciation, or cooling rate. The verification was conducted by comparing CI prediction to CI location and time based on Surabaya weather radar within the next 0-60 minutes. The algorithm resulted that the prediction could achieve 87.3% of accuracy from the 3449 cloud objects. The prediction had POD, FAR, and CSI scores of 57.1%, 52.2%, and 35.2%, respectively. The 32.3 minutes of averaged lead time prediction indicated that CI nowcasting could detect growing cumulus about 30 minutes prior to the CI event.
Spatial temporal analysis for potential wave energy resources in Indonesia
Authors: Habibie M N , Marfai M A , Harsa H , Linarka U A 
Published: December 2021

Abstract
Future energy becomes a concern all over the country. The fossil energy resources are decreasing now, and the exploitation these resources leave behind environmental problems. It was increasing the gas emission of CO2 and affected global warming. Renewable and environmentally friendly energy resource is the right choice to solve the problem. Wave power is one of the marine resources that have an advantage in hight density and continuity. This research aims to investigate the spatial-temporal distribution of wave power potency. This study location between 90°E – 150°E; 15°N – 15°S. We used a hindcast data simulation of WAVEWATCH-III with 0.125° (~14 km) spatial resolution and six-hourly data for 25 years (1991-2015). We determine the potential wave power resources by considering the wave flux, Presence of Exceedance (PE), Coefficient of Variation (Cv), Monthly Variability Index (MV), and Seasonal Variability Index (SV). The result shows that in the open sea, such as the Indian Ocean and Pacific Ocean, contains higher wave power density. The level of stability shows that this area is more stable than the inner sea. The power density changes periodically conducted with the monsoonal cycle. The highest energy flux in the Indian Ocean achieved when Australian monsoon and lowest when Asian monsoon, whereas in the Pacific Ocean, the peak of power density reaches when Asian monsoon onset and the lowest in June-July-August. The most stable level coherent with the highest power density, and the lowest level is in the transition period. Based on this analysis, the most potential areas for wave power development are in Enggano, Lampung, Banten, West Java, Central Java, DIY, East Java until Bali.
Evaluation of CCTV Data For Estimating Rainfall Condition
Authors: Sipayung Sinta Berliana , Slamet Lilik , Maryadi Edy , Susanti Indah , Nurlatifah Amalia 
Published: December 2021

Abstract
Rainfall characteristics of Indonesia's tropical climate have high variability according to space and time, so to determine the rainfall pattern of a location, an in situ rainfall measuring instrument (AWS = automatic weather station) is needed with high density. The existence of AWS also requires relatively high maintenance costs and a standard placement location (according to the rules of WMO = World Meteorological Organization) which is relatively broad and is not obstructed by other objects that can make the result of rainfall data is not representative. With the concept of computer vision, research will be carried out to estimate the rainfall condition from the CCTV cameras. The CCTV camera data which have qualitative characteristic into rainfall data which have quantitative characteristics. This research is also motivated by the large number of CCTVs that are placed in a lot of locations by local governments along with the Smart City program in districts and cities throughout Indonesia. The preliminary research was conducted in Center for Atmospheric Science and Technology office in Bandung. Rainfall data from AWS was used to validate CCTV data which placed in same location. The process of converting CCTV data into rainfall data goes through 6 stages. The first is reading the image mapping data and AWS (in rainfall accumulation data form). Second, read the image data in grayscale. Third, extract the features. Fourth, split the reference and sample data. Fifth, conducts the K-NN Mapping Reference Image and rainfall accumulation data. Sixth is to praise K-NN Testing. The accuracy is calculate with comparing the estimated number of CCTV cameras that are correct with the total sample size. The evaluation result states that the highest accuracy is obtained with K = 1. When K=1, the accuracy percentage reaching 94.8%. Accuracy decreases with increasing value of K and drastically decreases with K> 2. In the 1-10 days reference data, the highest accuracy is obtained by the number of reference data for 10 days, which is around 97%, stable until the value of K = 8. While the lowest accuracy is obtained when the reference data is 1 day with an accuracy value of about 43%. Based on the results of this study, it can be concluded that rain data from CCTV can be used to estimate the rainfall data. The best result happened when K-value is equal to 1.