bluuebunny/crossref_metadata_2025 huggingface dataset:
Inactivation of SARS-CoV-2 in clinical exhaled breath condensate samples for metabolomic analysis
Authors: Hu Shuang , McCartney Mitchell M , Arredondo Juan , Sankaran-Walters Sumathi , Borras Eva , Harper Richart W , Schivo Michael , Davis Cristina E , Kenyon Nicholas J , Dandekar Satya
Published: December 2021

Exhaled breath condensate (EBC) is routinely collected and analyzed in breath research. Because it contains aerosol droplets, EBC samples from SARS-CoV-2 infected individuals harbor the virus and pose the threat of infectious exposure. We report for the first time a safe and consistent method to fully inactivate SARS-CoV-2 in EBC samples and make EBC samples safe for processing and analysis. EBC samples containing infectious SARS-CoV-2 were treated with several concentrations of acetonitrile. The most commonly used 10% acetonitrile treatment for EBC processing failed to completely inactivate the virus in samples and viable virus was detected by the assay of SARS-CoV-2 infection of Vero E6 cells in a biosafety level 3 laboratory. Treatment with either 50% or 90% acetonitrile was effective to completely inactivate the virus, resulting in safe, non-infectious EBC samples that can be used for metabolomic analysis. Our study provides SARS-CoV-2 inactivation protocol for the collection and processing of EBC samples in the clinical setting and for advancing to metabolic assessments in health and disease.
Chemical compaction of deep buried mudstone and its influence on pressure prediction
Authors: Chao Li , Xiaorong Luo , Likuan Zhang , Yuhong Lei , Ming Chen , Lan Yu
Published: December 2021

The chemical compaction of mudstones which is dominated by the transformation of clay minerals leads to significant changes in the mineral composition and microstructure of mudstone during process of deep burial. In particular, the transformation of smectite to illite in mudstones results in noticeable impact on the pore pressure formation and the overpressure logging responses. At present, the study about the pressurization mechanism of chemical compaction and the impact on overpressure logging responses is really weak, which made it hard to pore pressure identification and pressure prediction for deep buried formations. Taking the Paleogene Shahejie Formation in the Dongying depression of the Bohai Bay Basin in eastern China as typical case, this paper analyses the characteristics of clay mineral transformation of the Shahejie Formation in the Dongying depression, the logging responses of overpressures, and the influence of chemical compaction on the prediction of pore pressure. The results showed that the chemical compaction of mudstones changes the relationship between the petrophysical properties of mudstone and vertical effective stress and the logging responses of overpressure. The typical characteristic of chemical compaction manifested as density increase continuous with the depth. The normal compaction trends of the different compaction stages are the basis for overpressure mechanisms identification and pore pressure prediction. The depth of the rapid transformation of clay minerals has a good consistency with the top of overpressure zone (2000–2800 m) in Dongying depression, which indicates that the overpressure and its logging responses may be related to the chemical compaction of mudstones. The measured pressure in intervals deeper than 3000 m is closer to the predicted pressure based on the normal compaction trend of chemical compaction.
Application of potassium nitrate to increase flower and seeds formation on different shallots varieties
Authors: Bakri A , Syam’un E , Musa Y , Ulfa F
Published: December 2021

The use of True Shallot Seeds (TSS) as an alternative planting material to produce quality shallots (Allium cepa L.), is constrained by the low formed of flowers and seeds. This study investigates the effect of potassium nitrate (KNO3) concentration on the flower and seeds formation of three shallots cultivars, and was conducted from September 2019 to January 2020 in District of West Sinjai, Sinjai Regency, South Sulawesi, at 1250 m above sea level. The experiments was set up as split plots design and provided with 3 replications. The main plot was shallots varieties (Sanren, Superpilip, and Tajuk) and the subplot was PN dosage (0, 7, 9, and 11 g per plant). Experimental results revealed that interaction of PN and shallots varieties gave a significant effect onthe average number of flower buds, number of filled flower capsules, and seeds weight. Application of 11 g PN on Superpilip varieties was able to produce the highest average number of flower buds (63), number of filled flower capsules (66.62), and seed weight (4.4 grams). This finding concludes that application of 11 g potassium nitrate per plantinduce flowering and increase seeds production of shallots, however the results will vary depend on the varieties.
TOXICITY TEST (LD-50) of HERBAL PREPARATIONS Nepenthes ampularia AS The STANDARD of SECURITY of JAMU HYPERTENSION
Authors: Mardhiana  , Rand Abdul , Harlinda K
Published: December 2021

The excavation of the potential utilization of herbs as a plant biopharma or as a material to manufacture. There is empirical evidence that Nepenthes has been used as a lowering of hypertension by taking a decoction of its pitchers. For the use and utilization as a medicinal plant need to be done research that refers to the scientifically certified Jamu. The research aims to detect acute toxic effects so that can be obtained the water safety overview Nepenthes pitchers. The toxicity test was carried out by the Brine Shrimp Lethality Test (BSLT), Artemia salina larva with four extract concentrations (0, 10, 500, 1000 ppm). Based on the results of toxic tests showed that the death rate of A. salina at all levels of concentration is 0 deaths. It is evidence that Nepenthes ampularia is not toxic to A. salina..
Analysis of Vortex Development over Eastern Indian Ocean using Potential Vorticity
Authors: Damayanti R P , Trilaksono N J , Abdillah M R
Published: December 2021

A vortex phenomenon may have a significant influence, especially on wind circulation patterns and extreme weather in Indonesia. The formation of the vortex, initially located over the eastern part of the Indian Ocean has drawn attention due to the highest frequency of its occurrence and as the source of the vortex over the Indonesian region. Vortices generated in this region is also suspected as one of contributing factor for flooding events at Jakarta in 2002 and 2007, studying both formation and development mechanism of these vortices is essential. The evolution of vortex development is investigated to characterize the vortex motion and development pattern in the Eastern Indian Ocean region. The study was conducted for 17 years starting from 1998 to 2016 on every December-January-February (DJF) period using ECMWF (European Center for Medium-Range Weather Forecast) ERA-Interim Reanalysis data. The analysis of vortex evolution was conducted for each event using a composite evolution of potential vorticity anomalies in the isentropic layer. The result shows 84 vortex systems identified with three characteristic patterns of vortex movement that occurred during 295 days of the observation period. Composite analysis of potential vorticity anomalies shows that the initial formation of vortices in the Eastern Indian Ocean is related to the emergence of negative potential vorticity anomalies from the west, which subsequently forming the vortices.
Influence of QBO-MJO connection on the turbulence variation in the TTL observed with equatorial atmosphere radar
Authors: Rosyidah A N , Trilaksono N J , Noersomadi
Published: December 2021

Atmospheric turbulence triggers vertical transport of heat and material exchange within troposphere and the stratosphere known as Stratosphere-Troposphere Exchange (STE). This phenomenon occurs in Tropical Tropopause Layer (TTL), a transition layer within 14-18.5 km. The state of TTL is the key to understand how phenomena in the troposphere and stratosphere interact. The interaction between Quasi-Biennial Oscillation (QBO), a zonal wind oscillation in stratosphere, and Madden-Julian Oscillation (MJO) defined as an eastward moving disturbance of convective system in the previous study is closely linked in boreal winter. This study presents the variation of turbulence intensity toward QBO-MJO interaction in TTL calculated from spectral width observed with Equatorial Atmosphere Radar (EAR) located in Agam, West Sumatra (0.2°S, 100.32°E). Analysis is focused on the extended boreal winter period. Turbulence intensity (σturb) in TTL tends to have an inversely proportional value toward zonal wind in 50 hPa. Generally, enhancement of turbulence intensity is observed on active phase MJO. Zonal wind and vertical wind are stronger in active phase MJO occurred in QBOE than QBOW that can lead to increase turbulence intensity in TTL. These parameters intensification is associated with stronger convective and precipitation system during QBOE-MJO that is caused by more unstable atmosphere.
The Use of Laser Precipitation Monitor (LPM) of Disdrometer and Weather Radar to Determine the Microphysics Characteristics of Extreme Rainfall in Jakarta. (Jakarta Flood Case Study February 25, 2020)
Authors: Putra I D G A , Sopaheluwakan A , Adi B P , Sudama K A , Rizal J , Heriyanto E , Hidayanto N , Permana D S , Riama N F , Soleh L
Published: December 2021

Heavy rains on February 24, 2020, caused flooding in most parts of Jakarta and its surroundings. The one-day observation of accumulated rainfall from the Laser Precipitation Monitor (LPM) was recorded at 358.6 mm/day at the Kemayoran station on February 25, 2020, at 00.00 UTC (07.00 Jakarta Time). In this study, analysis of the microphysical characteristics of extreme rainfall using LPM installed at Kemayoran meteorology station and weather radar at Cengkareng meteorology station with a spatial radius of 250 km. LPM is used to measure the diameter of the raindrops, the velocity of falling raindrops, LPM reflectivity, and the amount of accumulated rainfall with time resolution per minute and stored in excel data format. While the weather radar is used to measure the reflectivity spatially and temporally in the data volume format (.vol). The method used is, first, to find the relationship between LPM reflectivity and the amount of LPM rainfall with regression analysis. Second, the radar reflectivity is converted into estimated rainfall intensity for the Jakarta area and its surroundings. The results of this study found a relationship between LPM reflectivity (X) and rainfall accumulation LPM (Y) to form a regression relationship with the formula Y = 0.013X with R2 = 0.3777. Based on the record of the LPM time series, the peak of rainfall occurred at 18.17 UTC with 1000 raindrops, the maximum fall speed was 10 m/s, and the maximum diameter is 8.5 millimeters. Based on the results of microphysical measurements of LPM, spatial plots, and vertical cross-section radar, it can be concluded that flooding in Jakarta is due to heavy rain from convective clouds.
High-Resolution North Sulawesi Drought Hazzard Mapping Based on Consecutive Dry Days (CDD)
Authors: Setiawan A M , Syafrianno A A , Rahmat R , Supari
Published: December 2021

North Sulawesi is one of the Province in northern Indonesia with high spatial annual rainfall variations and influenced by global climate anomaly that can lead to extreme events and disaster occurrence, such as flood, landslide, drought, etc. The purpose of this study is to generate high-resolution meteorological hazard map based on long-term historical consecutive dry days (CDD) over the North Sulawesi region. CDD was calculated based on observed daily precipitation data from Indonesia Agency for Meteorology, Climatology, and Geophysics (BMKG) surface observation station network (CDDobs) and the daily-improved Climate Hazards group Infrared Precipitation with Stations (CHIRPS) version 2.0 (CDDCHIRPS) during 1981 – 2010 period. The Japanese 55-year Reanalysis (JRA-55) data obtained from iTacs (Interactive Tool for Analysis of the Climate System) with the same time scale period also used to explain physical – dynamical atmospheric properties related to drought hazard over this region. The Geostatistical approach using regression kriging method was applied as spatial interpolation technique to generate high resolution gridded (0.05° × 0.05°) drought hazard map. This method combines a regression of CDDobs as dependent variable (target variable) on CDDCHIRPS as predictors with kriging of the prediction residuals. The results show that most of the areas were categorized as medium drought hazard level with CDD values ranging from 80-100 days. Meanwhile, small islands around main Sulawesi island such as Sangihe and Karakelong island are dominated by low drought hazard levels with CDD values ranging from 50-60 days. The highest levels of drought hazard area are located in South Bolaang Mongondow Regency.
Spatiotemporal Variation of Drought Characteristics Based on Standardized Precipitation Index in Central Java over 1990-2010
Authors: Misnawati  , Boer R , Ramdhani F
Published: December 2021

Drought is a natural hazard that results from a deficiency of precipitation, leading to low soil moisture and river flows, reduced storage in reservoirs, and less groundwater recharge. This study investigates the spatial variations of drought characteristics (drought event frequency, duration, severity, and intensity). This study using the Standardized Precipitation Index (SPI) to analyse the drought characteristics in Central Java during 1990-2010. The rain gauge station data and CHIRPS rainfall data over Central Java is used to calculate the SPI index. The SPI was calculated at multiple timescales (1-, 3-, 6-, 12-, 24- and 48-month), the run theory was used for identification and characterization of drought events. Analysis of drought characteristics by SPI from 1990 to 2010 shows the longest drought event is four months, the maximum drought severity is 6.06, and the maximum drought intensity is 2.02. El Nino year probability drought occurrence reached 100% in August for moderate drought, severe drought, and extreme drought category, whereas the probability drought occurrences in the Normal and La Nina year range 0-70% for moderate drought, 0-50% for severe drought category and 0-40% for extreme drought category. The results of this study may help inform researchers and local policymakers to develop strategies for managing drought.
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

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

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

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

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

Coastal 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

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

The 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

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

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

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

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

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

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.
Utilization of weather-radar data to observe the Sea Breeze Front (SBF) over the North Coast of Banten-Jakarta (case study in 2018)
Authors: Purba D L A , Saragih I J A , Sosaidi D S
Published: December 2021

One of the important factors in weather and climate dynamics that can trigger precipitation on the coast and the surrounding area is a sea breeze. Sea breeze occurs because of differences in the surface temperature between land and sea due to solar heating which then forms a pressure gradient that leads to a land called the sea breeze circulation. An important part of sea breeze circulation is the Sea Breeze Front (SBF). SBF is a boundary area where wind from the sea direction meets the wind from the land direction, which is marked by significant changes in temperature, humidity, wind and can trigger convective activity. This study aims to determine the characteristics of the SBF on the north coast of Banten-Jakarta in 2018 which were identified using a Doppler weather radar Plan Position Indicator (PPI) product and convective activity using the Coloumn Maximum (CMAX) product. Qualitative and quantitative methods are used to determine the SBF parameters such as frequency of occurrence, onset time, duration, length, column depth, and SBF penetration, and convective activity during the occurrence of SBF. The results showed that SBF was detected more in the rainy season January, February, and December 2018, and occur between 08:08 LT and 15:20 LT. SBF can trigger the occurrence of convective clouds and affect the temperature and humidity conditions around the SBF.
Evaluation gridded precipitation datasets in Indonesia
Authors: Wati T , Hadi T W , Sopaheluwakan A , Hutasoit L M
Published: December 2021

This preliminary study evaluates ten gridded precipitation datasets in Indonesia, namely APHRODITE, CMORPH, CHIRPS, GFD, SA-OBS, TMPA 3B42 v7, PERSIAN-CDR at 0.25°, moreover GSMaP_NRT V06, GPM-IMERG (Early-Run) V06, and MSWEP V2 at 0.1» in the period of 2003 to 2015. The evaluation focuses on time series bias using metrics such as Mean Error, Coefficient of Variation, Relative Change (Variability), and Kolmogorov-Smirnov test (KS-test) at daily, monthly, seasonal, and annual time scales. The statistical relationship between the precipitation datasets with reference observational data use Taylor diagrams for evaluating the relative skill of the precipitation dataset. The study aims to evaluate the uncertainty of the precipitation datasets compared to rain gauge datasets. Time series bias of SA-OBS and MSWEP have the nearest value to zero as the best score. The relative skill of monthly rainfall based on rainfall typical shows that MSWEP outperformed in regions A and B, GPM-IMERG in C region. GPM-IMERG's relative skill is outperformed than other datasets at annual time scale in Region A and B, while TMPA 3B42 in Region C. The application of existing precipitation datasets is essential to cope with the limitation of rain gauge observations. This study implicates the development of precipitation products in the Indonesia region.
Improved Performance of The CHIRPS Monthly Rainfall Estimation Extraction from Google Earth Engine (GEE) platform in South Sulawesi Region
Authors: Bangsawan L , Satriagasa M C , Bahri S
Published: December 2021

The integration of the availability and processing of The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data by the Google Earth Engine (GEE) platform is used in this study to extract the estimated monthly rainfall in South Sulawesi. Several areas are selected based on the characteristics of the rainy period cycle representing South Sulawesi, namely Makassar, Masamba, Wajo, and Bone. Monthly rainfall estimation data of CHIRPS in the year 2019 were validated by monthly observed rainfall at the same period showing the CHIRPS rainfall estimation has not been maximized with correlation coefficient values are 0.94, 0.63, 0.65, 0.75, and RMSE percentage 54%, 52%, 95%, 64% for each of the study areas. Then the increase in rainfall estimation performance is carried out by applying multiple linear regression method and considering both monthly observed and estimated rainfall during 30 years from 1989 to 2018, latitude and longitude point as well as elevation in every location. The results show an increase of correlation coefficient to 0.95, 0.74, 0.74, and 0.87 and a general decrease of RMSE percentage to 53%, 39%, 80%, and 67%. Thus, monthly rainfall estimation performance improvement is successfully achieved in various rainy period cycles of the study area.
Evaluating skill of BMKG wave model forecast (Wavewatch-3) with observation data in Indian Ocean (5 – 31 December 2017).
Authors: Kurniawan R , Harsa H , Ramdhani A , Fitria W , Rahmawati D , Habibie M N , Hutapea T D
Published: December 2021

Providing Maritime meteorological forecasts (including ocean wave information) is one of BMKG duties. Currently, BMKG employs Wavewatch-3 (WW3) model to forecast ocean waves in Indonesia. Evaluating the wave forecasts is very important to improve the forecasts skill. This paper presents the evaluation of 7-days ahead BMKG’s wave forecast. The evaluation was performed by comparing wave data observation and BMKG wave forecast. The observation data were obtained from RV Mirai 1708 cruise on December 5th to 31st 2017 at the Indian Ocean around 04°14'S and 101°31'E. Some statistical properties and Relative Operating Characteristics (ROC) curve were utilized to assess the model performance. The evaluation processes were carried out on model’s parameters: Significant Wave Height (Hs) and Wind surface for each 7-days forecast started from 00 UTC. The comparation results show that, in average, WW3 forecasts are over-estimate the wave height than that of the observation. The forecast skills determined from the correlation and ROC curves are good for the first- and second-day forecast, while the third until seventh day decrease to fair. This phenomenon is suspected to be caused by the wind data characteristics provided by the Global Forecasts System (GFS) as the input of the model. Nevertheless, although statistical correlation is good for up to 2 days forecast, the average value of Root Mean Square Error (RMSE), absolute bias, and relative error are high. In general, this verifies the overestimate results of the model output and should be taken into consideration to improve BMKG’s wave model performance and forecast accuracy.
Background Optical Depth Correction over Urban Areas to Improve Land Aerosol Retrieval from Himawari-8
Authors: Risyanto
Published: December 2021

A conventional clear-sky minimum reflectance method, which has been widely used for AOD retrieval from geostationary satellites, usually has less accuracy over urban than other land areas. Urban areas usually have more complex surface properties and various aerosol types from different emission sources. When the surface reflectance is calculated from the clear-sky minimum reflectance, background aerosol optical depth (BOD) is assumed to be closed to zero. This assumption generates larger surface reflectance which leads to underestimation of AOD retrieved. This study proposed a correction for BOD value to be applied for AOD retrieval primary over urban areas where the pollution or natural source aerosol is persistent for long term. The study area covers Indonesia’s land region, while for evaluating the impact of specific treatment over urban areas we used the AERONET data from Bandung, Pontianak, and Makassar sites. The comparison of AOD retrieved from modified BOD and the AERONET ground-based data showed that the corrected surface reflectance improved the accuracy of AOD in the three sites, with a correlation coefficient increased from 0.23 to 0.37 and the fraction of ‘good retrieval’ changed from 35% to 51%.
Determination of Step Check Quality Control Thresholds on Air Temperature Data at South Tangerang Climatological Station
Authors: Halida M , Pramono SA
Published: December 2021

All data, including air temperature data, must be verified by conducting quality control using the step check method. Step check quality control is carried out by looking at the difference of a parameter in a certain period compared to the threshold value that was already determined. Therefore before carrying out step check quality control, it is necessary to determine the ceiling and floor boundaries of the difference in air temperature data every hour. The data used in this study are hourly air temperature data and hourly present weather data from weather observations at the South Tangerang Climatological Station during 2016 - 2020. In determining the threshold for air temperature step check quality control, the air temperature data is paired with weather condition data to obtain a threshold value according to rain and no rain conditions. The threshold conducted in this study is based on a check for unusual climatological values, where the limits for an unusual and impossible jump in hourly air temperature changes are determined based on a certain percentage of the data distribution. This study uses percentile analysis to determine the threshold, where 5% in the lower and upper part of the data distribution are used as the threshold. The results show various thresholds every hour. The increase in temperature dominates the changes of hourly air temperature in no-rain conditions. The highest threshold for temperature increase occurs at 00.00 – 01.00 UTC at 3.2°C and continues to decrease over time. The highest threshold for temperature decrease occurs at 09.00 UTC - 10.00 UTC at 2.2°C. In rain conditions, the increase in temperature can still occur. However, the decrease in temperature mainly occurs. The highest threshold for temperature increase during rainy conditions is 1.8°C at 01.00 - 02.00 UTC, while the highest threshold for the temperature decrease is 5.8°C at 06.00 UTC – 07.00 UTC. With these results, observers can first carry out quality control with the Step Check method before filling in the data into the system database. Thus, any suspect data either from reading errors or tool errors can be minimized and finally produce a valid dataset.