bluuebunny/crossref_metadata_2025_split huggingface dataset:
Four-dimensional variational (4DVAR) performance test with assimilation satellite and radar data (case study of a heavy rainfall Bengkulu March 4, 2019)
Authors: Syafitri J D I , Sari F P 
Published: December 2021

Abstract
Four-dimensional variational (4DVAR) is one of the assimilation techniques considering time integration to distribute observational data at time window intervals. In this study, we aim to evaluate the 4DVAR assimilation technique using satellite and radar data to simulate a heavy rainfall case in Bengkulu on March 4, 2019. The result shows that radar data assimilation (DA-RAD) can improve rainfall pattern over Bengkulu mainland areas, while the satellite data assimilation (DA-SAT) enhances rainfall over the ocean. In addition, for temporal analysis, the DA-RAD successfully correct the initial time of the event, producing the smallest error and the best correlation in statistical verification, also a small bias and higher accuracy for discrete verification. However, DA-SAT is more capable to improve rainfall accumulation with the lowest FAR value. In conclusion, compared to others, both satellite and radar can be used as assimilation data for 4DVAR methods as they have different roles in increasing the quality of model performance.
The impact of land cover changes on temperature parameters in new capital of Indonesia (IKN)
Authors: Denryanto R A F , Virgianto R H 
Published: December 2021

Abstract
North Penajam Paser Regency and Kutai Kartanegara Regency which are located in East Kalimantan Province are two locations that are planned as the New Capital of Indonesia (IKN). This has become one of the factors changing land cover from vegetation land to urban land, so that it can contribute to temperature changes. In this work, we analyze impacts of land use change on temperature in the new capital city. The change will simulate land cover changes using the Weather Research and Forecasting (WRF) model with two scenarios of land cover change from vegetation land to 547 % and 1,222 % urban land. Scenarios I and II will increase the temperature to 1.17 and 1.77 °C, respectively. This could means that the addition of more urban areas results in an increase in temperature. The quantitative values of this connection will be beneficial for urban planners to manage the development of new capitals without having a significant impact on climate change.
Spatial fraction verification of high resolution model
Authors: Made Kembar Tirtanegara I , Sari Fitria Puspita 
Published: December 2021

Abstract
Fraction Skill Score (FSS) is one of spatial verification method to evaluate model performance on spatial scale variations. The method was applied to assess the Weather Research and Forecasting (WRF) model using 2 km (MODEL2KM) and 6 km (MODEL6KM) grid size. Cloud Top Temperature (CTT) data from Himawari-8 satellite was utilised as a ground truth data. This study aims to evaluate the model performance by FSS with absolute and percentile treshold on convective cloud simulation for three heavy rain events. The threshold considers the evaluation of absolute and percentile aspect. The result shows that there is no significant change in the FSS value for resolution increase of MODEL2KM compared to MODEL6KM. Also, the events of heavy rain having a lower CTT generate a higher FSS value for absolute threshold. Whilst, the percentile threshold for three cases have a greater FSS value, though it cannot provide the information of CTT absolute temperature value.
Utilization of ECMWF Seasonal Rainfall Forecast System (SEAS5) for forest fire prediction over Sumatera Island, Indonesia
Authors: Alfahmi F , Khaerima A , Byantoro A W 
Published: December 2021

Abstract
As part of the lungs of the world, the forest which covers Sumatra Island has a significant impact on the world oxygen production and the absorption of carbon dioxide. Drought over Sumatra Island often causes forest fires that can damage the function of forests as the world's lungs. Prediction of the seasonality of forest fires is needed to prevent and overcome forest fires that will occur next month. This study utilized seasonal rainfall predictions to predict the incidence of forest fires based on the drought index obtained. The result showed that ECMWF SEAS5 had good performance to predict rainfall over Sumatera Island for the first until the fourth months (lead time of 0 - 3). The Negative Standardized Precipitation Index (SPI) coincided with the increasing number of the hotspots. Furthermore, a linear equation has been applied to the calculated number of hotspots based on SPI from ECMWF.
The skill assessment of ENSO prediction issued by JMA ensemble prediction system and CFSv2
Authors: Rahmat R , Setiawan A M , Supari  
Published: December 2021

Abstract
Indonesian climate is strongly affected by El Niño-Southern Oscillation (ENSO) as one of climate-driven factor. ENSO prediction during the upcoming months or year is crucial for the government in order to design the further strategic policy. Besides producing its own ENSO prediction, BMKG also regularly releases the status and ENSO prediction collected from other climate centers, such as Japan Meteorological Agency (JMA) and National Oceanic and Atmospheric Administration (NOAA). However, the skill of these products is not well known yet. The aim of this study is to conduct a simple assessment on the skill of JMA Ensemble Prediction System (EPS) and NOAA Climate Forecast System version 2 (CFSv2) ENSO prediction using World Meteorological Organization (WMO) Standard Verification System for Long Range Forecast (SVS-LRF) method. Both ENSO prediction results also compared each other using Student's t-test. The ENSO predictions data were obtained from the ENSO JMA and ENSO NCEP forecast archive files, while observed Nino 3.4 were calculated from Centennial in situ Observation-Based Estimates (COBE) Sea Surface Temperature Anomaly (SSTA). Both ENSO prediction issued by JMA and NCEP has a good skill on 1 to 3 months lead time, indicated by high correlation coefficient and positive value of Mean Square Skill Score (MSSS). However, the skill of both skills significantly reduced for May-August target month. Further careful interpretation is needed for ENSO prediction issued on this mentioned period.
Automatic Rain Detection System Based on Digital Images of CCTV Cameras Using Convolutional Neural Network Method
Authors: Hakim Arif Luqman , Dewi Ristiana 
Published: December 2021

Abstract
The Meteorology, Climatology and Geophysics Agency (BMKG) has a duty to provide weather information including rainfall. BMKG has several types of rainfall gauges, but these are not evenly distributed across regions. The solution to increase the density of rainfall observations is to use existing sources to obtain weather information. This research uses Closed Circuit Television (CCTV) that is spread across the Jakarta area to produce information on rainy conditions. The method used is the Convolutional Neural Network (CNN). The image from CCTV will be used for the training and testing process, so as to get the best accuracy model. The results of this model will be used for rain detection on CCTV digital images. The rain detection process is carried out automatically and in real time. The results of the rain detection process will be displayed on the map according to the location where the CCTV was installed. This research has succeeded in making a CNN model for rain detection with a training accuracy of 98.8% and a testing accuracy of 96.4%, as well as evaluating the BMKG observation data, so it has an evaluation accuracy of 96.7%.
Why was the sky red in Jambi during the forest fire?
Authors: Kusumaningtyas S D A , Aldrian E , Suradi  , Ahmad M , Krisnawan G D 
Published: December 2021

Abstract
Extreme biomass burning occurred in Jambi, Indonesia, in 2019 and coincided exacerbated with El Nino. Peak burning season was in September, with a total hotspot of 7034. Red sky has been reported on September 21 during the day. Sun photometer measurements in Jambi as one of the Aerosol Robotic Network (AERONET) stations in Indonesia from 1 to September 26, 2019, were used to investigate the red sky phenomenon. Assessment of aerosol optical properties and spectral variation analysis is conducted. The study reveals that the red sky occurred due to, firstly, very high aerosol loading with fine size particles were present. The aerosol optical depth (AOD) was 0.34 at 500 nm on a non-hazy day (early September) and increased sharply to 5.74 during a hazy day. A high level of fine-mode particle was indicated with Angstrom Exponent>1. Secondly, during September 23, only longer wavelengths of AOD were measured at 675, 870, 1020, and 1640 nm, while AOD in shorter wavelengths cannot be retrieved. Highest AOD on September 23 was 6.19 at 675 nm, which is associated with the red sky in the previous day. Thirdly, SSA was near 1, indicating purely aerosol scattering due to coagulated fine-mode particles due to high humidity.
Measurement and Analysis of Ocean Current using High- Frequency (HF) Radar Observation in the Bali Strait
Authors: Firdaus R , Siadari E L , Alfahmi F 
Published: December 2021

Abstract
High-Frequency (HF) Radar is an instrument using radio waves to measure ocean currents and waves remotely. This technology has many advantages, including has unprecedented spatial and temporal resolution, can operate in any weather condition, and is not dangerous for the environment. However, HF Radar's research is still limited in Indonesia. This research aimed to analyze the tidal and residual current in the Bali Strait in July 2020. Radial velocity from two HF Radar sites is combined to obtain the total currents. Current data from HF Radar were compared with Acoustic Doppler Current Profiler (ADCP) data to investigate its accuracy. Surface current data were analyzed using harmonic analysis to separate tidal and residual currents. Comparison between HF Radar and ADCP data are in good agreement for meridional current with a very high correlation of 0.813 and a small RMSE value of 0.22 m/s. Harmonic analysis shows that the dominant currents are tidal currents. The current direction was northward (southward) at flood (ebb), with maximum northward (southward) velocities are 2.17 m/s (2.97 m/s), respectively. The residual current has a random pattern, slightly faster northward than southward, and has similar spectral with the wind.
Application of Attenuation Correction to Quantitative Precipitation Estimation on C-Band Weather Radar in Bengkulu
Authors: Handoyo M F , Hadi M P , Suprayogi S 
Published: December 2021

Abstract
A weather radar is an active system remote sensing tool that observes precipitation indirectly. Weather radar has an advantage in estimating precipitation because it has a high spatial resolution (up to 0.5 km). Reflectivity generated by weather radar still has signal interference caused by attenuation factors. Attenuation causes the Quantitative Precipitation Estimation (QPE) by the C-band weather radar to underestimate. Therefore attenuation correction on C-band weather radar is needed to eliminate precipitation estimation errors. This study aims to apply attenuation correction to determine Quantitative Precipitation Estimation (QPE) on the c-band weather radar in Bengkulu in December 2018. Gate-by-gate method attenuation correction with Kraemer approach has applied to c-band weather radar data from the Indonesian Agency for Meteorology and Geophysics (BMKG) weather radar network Bengkulu. This method uses reflectivity as the only input. Quantitative Precipitation Estimation (QPE) has obtained by comparing weather radar-based rain estimates to 10 observation rain gauges over a month with the Z-R relation equation. Root Mean Square Error (RMSE) is used to calculate the estimation error. Weather radar data are processed using Python-based libraries Wradlib and ArcGIS 10.5. As a result, the calculation between the weather radar estimate precipitation and the observed rainfall obtained equation Z=2,65R1,3. The attenuation correction process with Kreamer's approach on the c-band weather radar has reduced error in the Qualitative Precipitation Estimation (QPE). Corrected precipitation has a smaller error value (r = 0.88; RMSE = 8.38) than the uncorrected precipitation (r = 0.83; RMSE = 11.70).
Investigating consequences of choosing IDW interpolation parameters in East Java using raster analyses
Authors: Kurniawan A , Ryan M , Rafi A M , Haq B E A , Sudirman  , Suprayitno A 
Published: December 2021

Abstract
An observation network will never be enough for creating good information about monthly rainfall. An interpolation method is always needed. For operational purposes, inverse distance weighting (IDW) method is used. In East Java, 197 observation points are involved, then IDW's parameters used are neighbor=12 and power=2. The consequences of this framework are investigated in this study. By reversing IDW's formula, two kinds of raster analyses are developed, distance to neighbor used (DNU) and coefficient from point (CFP). DNU shows how far points are used for doing interpolation in some area by kilometer (km) meanwhile CFP describes an area impacted by a point and value sent to another cell from an observation point. Data used in this study are longitude and latitude of 197 observation points. The scripts are written in R Language. Analysis based on local governmental region shows that Sumenep has very far DNU. In average, the first point used located more than 25 km, and its twelfth is 112 km (average other regions DNU1=7 km and DNU12=35 km). It means there should be a unique interpolation mechanism for Sumenep. CFP confirms that some points give impact in unnatural ways (impacted area=5741 km2). We propose DNU and CFP as alternative quality control parameters for investigating consequences in interpolating rainfall.
Characteristics of Sea Surface Current in the Bali Strait, Indonesia using HF Radar and Its Utilization in Safety Navigation
Authors: Supriyadi E , Hidayat R , Santikayasa IP , Ramdhani A 
Published: December 2021

Abstract
This paper was done by using the HF Radar data from 2018-2019 to study the characteristics of Sea Surface Current (SSC) in the Bali Strait. The data processing method was done by calculating the speed and SSC direction of the zonal and meridional components. Furthermore, SSC analysis was performed every hour and month by calculating the average of all data at the same hour and month. It was found that the unique SSC pattern in the Bali Strait occurred on the western side of Bali Island and the eastern side of Java Island. On the west side of the Bali Island, there was a decrease in SSC speed at 0.00-7.00 and 13.00-18.00, as well as a two-fold increase at 8.00-12.00 and 19.00-2.00, both of which were in a fluctuating speed range from 0-140 cm s-1 in the direction of dominant towards the south. On the eastern side of Java Island, SSC speed ranges from 0 to 40 cm s-1 all the time with the dominant direction heading from east to southeast. The monthly SSC pattern was also seen more clearly in this study, meanwhile during December-March the SSC rate was lower than during June-September, ranging from 0 to 20 cm s-1 and from 40 to 140 cm s-1, respectively. Furthermore, the two SSC patterns above can be simplified into two periods, namely periods of relaxation and agitation. This study also applies the device to ship accidents that occurred in the Bali Strait as case studies.
Utilization of remote sensing data for mapping the effect of Indian Ocean Dipole (IOD) and El Nino Southern Oscillation (ENSO) in Sumatra Island
Authors: Agustina L , Virgianto R H , Fitrianto A N 
Published: December 2021

Abstract
IOD and ENSO are two global phenomena that are quite influencing the territory of Indonesia. Sumatra is one of the islands in Indonesia with unique local characteristics and is located in the westernmost. This study attempts to analyze how much the IOD and ENSO have affected rainfall in the Sumatra Island and which is more dominant, and the distribution that can be used as a reference in forecasting seasons and rainfall. In a period of 37 years (1981-2017), the influence of IOD and ENSO was analyzed using a correlation method which was then mapped. The rainfall data used was obtained from CHIRPS with a scale of 0.05. Data is processed using Climate Data Operator (CDO) and R-statistics to obtain correlation maps. Correlation calculations are carried out on the whole data and in the season period. The effect of IOD was seen to be significant in the southern part of Sumatra, with a maximum correlation of 0.4, while in the north and center, the correlation only -0.25. The effect of ENSO looks more evenly compared to the impact of IOD, with a correlation reaching -0.4 in the northern and southern parts of Sumatra. In contrast, the middle part shows a smaller correlation value with ranges of -0.1 to -0.3. In the DJF and MAM season periods, a positive correlation between rainfall and IOD occurred throughout Sumatra, and in the JJA and SON, a dominant-negative correlation occurred. In comparison, the ENSO shows dominant negative correlation in each period. Based on the correlation, ENSO is more influential on Sumatra compared to IOD in overall data. However, on seasonal data, IOD is more significant than ENSO. The southern part of Sumatra Island is the most affected by ENSO and IOD compared to other parts.
Forest and land fire smoke detection using GCOM-C data (case study: Pulang Pisau, Central Kalimantan)
Authors: Rahmi K I N , Febrianti N , Prasasti I 
Published: December 2021

Abstract
Forest/land fire give bad impact of heavy smoke on peatland area in Indonesia. Forest/land fire smoke need to be identified the distribution periodically. New satellite of GCOM-C has been launched to monitor climate condition and have visible, near infrared and thermal infrared. This study has objective to identify fire smoke from GCOM-C data. GCOM-C data has wavelength range from 0.38 to 12 μm it covers visible, near infrared, short-wave infrared and thermal infrared. It is relatively similar to MODIS or Himawari-8 images which could identify forest/land fire smoke. The methodology is visual interpretation to detect forest/land fire smoke using near infrared band (VN08), shortwave infrared band (SW03), and thermal bands (T01 and T02). Hotspot data is overlaid with GCOM-C image to represent the location of fire events. Combination of composite RGB image has been applied to detect forest/land fire smoke. GCOM-C image of VN8 bands and combination of thermal band in composite image could be used to detect fire smoke in Pulang Pisau, Central Kalimantan.
Evaluation of ECMWF model to predict daily and monthly solar radiation over Indonesia region
Authors: Sianturi Y , Sopaheluwakan A , Sartika K A 
Published: December 2021

Abstract
Solar radiation forecast is a pivotal information needed in the operational activity of large-scale solar energy production. In this study, the reliability of SSRD (surface solar radiation downward) forecast from the 51 ensemble members in the ECMWF (European Centre for Medium Range Forecast) long-range forecast to predict daily and monthly radiation in 5 climatological stations in Indonesia is evaluated. The global horizontal irradiance (GHI) data from the solar radiation observation network from January 2018 – December 2020 are used in the quantitative evaluation of the SSRD forecast. Post-processing methods are applied to the model output, namely the bilinear interpolation method and the empirical quantile mapping to reduce consistent biases in the model output. The evaluation was carried out for different cloud covers based on the calculation of clearness index (k_t). The cloud condition affects the performance of the model, where the highest correlation value is achieved during sunny days (0.18 – 0.65) and the lowest correlation happens in overcast days (0.05 – 0.35). Models also tend to underestimate radiation when the sky is clear and overestimate it in cloudy days, based on negative MBE values during clear days (-0.47 kWh/m2 – -1.29 kWh/m2). The spatial averaging method did not necessarily improve the accuracy of the forecast, but the empirical quantile mapping method provides better accuracy, which is indicated by a values (mean error ratio) lower than 1 in most stations. Information about the influence of cloud cover on model performance can be used in future application of the model output and the bias correction process carried out in this study can be applied to reduce bias in the model.
Impact Based Forecast analysis uses multi-model ensemble data and National Digital Forecast data in ArcMap 10.8.1
Authors: Salman R S , Ayufitriya  
Published: December 2021

Abstract
The number of people seeking public weather service information is growing, making it a challenge for all bureaus of meteorology around the world. Although, in the last decade, routine public weather service information has brought excellent weather forecast information for people and services to people with rapid, accurate, widely available, and easy to grasp information, which they may get in a variety of places, such as a website or an application. However, in this decade and in the future, it will not be enough. People want information such as what the impact should be and how people react to that impact, which should be displayed on a static Geographic Information System (GIS) map in a standard format. We will investigate and create an IBF map based on multi-model ensemble data and National Digital Forecast (NDF) data in this work. Then, using the GIS software ArcMap 10.8.1, we rank and score the geographic disaster data to determine the impact area. To create the effect area, we will employ primary and advanced methods of ArcMap 10.8.1. The information on the IBF map will be immediately understood by stakeholders and users.
Potential spesies from the Dipterocarp family at Mandailing Natal Forest Production Management Unit, North Sumatra, Indonesia
Authors: Latifah S , Zahrah M 
Published: December 2021

Abstract
Sumatra has extensive forests, rich in tree species, especially the Dipterocarpaceae species, which have high economic and ecological value in the development sector. The enormous number of Dipterocarpaceae species and the paucity of herbarium collections make identifying species quite easy. The study’s goal was to learn more about the Dipterocarpaceae family tree’s species, diameter, and height in the Mandailing Natal Forest Production Management Unit (KPHP ) in North Sumatra. Diameter and height data of Shorea sp was based on secondary data processing. The inventory revealed that the Dipterocarpaceae family has 12 different species. Trees in the Dipterocarpaceae family have an average diameter of 44.5 cm and a height of 20 cm. This inventory is expected to offer information on the several Dipterocarpacea families that can be found in this area, particularly those with high economic value and those that are protected.