bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.
The Regionalization of Indonesian Maritime Continent Rainfall based on Integrated Multi-satellite Retrievals for GPM (IMERG)
Authors: Yuda IWA , Osawa T , Nagai M , Prasetia R 
Published: December 2021

Abstract
The need for adequate rainfall data in all regions of Indonesia cannot be achieved only by relying on ground observation tools. This work aims to evaluate the application of spatial satellite rainfall data in characterizing rainfall associated with climatic condition over Indonesia. This study applied an Integrated Multi-satellite Retrievals for GPM (IMERG) data using a double correlation method (DCM). The analysis was carried out in the period April 2014 to March 2019. Before regionalization, IMERG V06 data were validated using observed rainfall data from the Agency for Meteorology, Climatology, and Geophysics of the Republic of Indonesia (BMKG). The results showed that 96% of 154 total validation locations have a high correlation score between IMERG and rain gauges (r = 0.5 – 0.97). IMERG was also able to identify monthly and annual rainfall patterns in Indonesia. Based on DCM, we obtained four rainfall regions in Indonesia. Region A has the monsoonal characteristic, covers central and south Indonesia from south Sumatra to Nusa Tenggara, south parts of Kalimantan, some areas of Sulawesi, and parts of Papua. Region B has an equatorial pattern (semi-monsoonal), located in the equatorial area of Indonesia and covers the west and east part of Sumatra and the north-central part of Kalimantan. Region C, with an anti-monsoonal pattern, covers Maluku, western-central Papua, and parts of Sulawesi. Region D is influenced by monsoon and cold surge characteristics, located in the north part of Sumatera and a small portion of northern Kalimantan to the South China Sea region. Besides the new region D, this research also showed five other differences between IMERG-based map and gridded rain gauges’ data-based map (2003). The regionalization results based on IMERG reveal that there is a possibility of updating areas with certain rainfall characters in Indonesia related to resolution, density, and updates data sources.
Impacts of the MJO on Rainfall at Different Seasons in Indonesia
Authors: Permana D S , Supari  
Published: December 2021

Abstract
The Madden-Julian Oscillation (MJO) is the dominant mode of intraseasonal variability of rainfall in Indonesia, but its signal is often obscured in individual station data, where effects are most directly felt at the local level. This study aims to investigate the general impacts of MJO on rainfall at different seasons in Indonesia, particularly during boreal summer. Impacts of the MJO on daily rainfall anomaly during the four climatic seasons: DJF, MAM, JJA, and SON in Indonesia have been evaluated using in-situ data from 86 stations during 1981 - 2012 and remote sensing data using GPM IMERGV06 from 2001 - 2019. The greatest impact of the MJO on rainfall over Indonesia occurs during the DJF and MAM seasons (austral summer), with the magnitude varying across regions. Enhanced rainfall generally occurs over the western parts of Indonesia on phases 2 to 4, central parts of Indonesia on phase 4, and eastern parts of Indonesia on phases 4 to 5. Conversely, suppressed rainfall generally occurs over the western parts of Indonesia on phases 5 to 8, central parts of Indonesia on phases 6 to 8, and eastern parts of Indonesia on phases 1 to 2 and 6 to 8. In addition, the MJO influence during the JJA and SON seasons are slightly less, in terms of intensity, than during the DJF and MAM seasons, which is likely due to the northward shift of ITCZ and, hence, the intraseasonal oscillation convective envelope during boreal summer. Generally, enhanced rainfall occurs over the western and northern parts of Indonesia on phases 2 to 3, and suppressed rainfall on phases 6 to 7. The results indicate that convectively active MJO may increase the possibility of daily extreme rainfall in particular regions in Indonesia at different seasons.
Development of air quality mobile tools for observation
Authors: Hakim I T , Budianto B , Immanuel GS , Rakhman A , Kinasih S A K W , Boer R 
Published: December 2021

Abstract
Mobile weather stations are needed because of their better coverage balance than stationary stations. Center for Climate Risk and Opportunity Management in Southeast Asia Pacific (CCROM-SEAP) of Bogor Agricultural University (Institut Pertanian Bogor or IPB University) developed a low-cost mini observation system using Espressif ESP32 DOIT Development Kit V1 module, which based on the internet of things (IoT) to monitor real-time meteorological elements (such as temperature, humidity, and pressure), CO2, PM2.5, and PM10 concentration for Bogor (Center of Bogor City). With Firebase (database service by Google) integration, the system records data every 2 minutes and sent automatically to Firebase. We also create an unpublished android application called ServMo for exporting JSON to CSV format. The results show this system has a good performance for real-time monitoring purposes for a better balance of measurements coverage.
Coverage Sensitivity of High-Rise Tower NIES Monitoring System
Authors: Effendi A , Budianto B , Immanuel G S , Rakhman A , Kinasih S A K W , Boer R 
Published: December 2021

Abstract
National Institute for Environmental Studies (NIES) and Centre for Climate Risk and Opportunity Management in Southeast Asia Pacific (CCROM-SEAP) working together to develop a beneficial monitoring system for measuring air pollutants. The monitoring system consists of system control units and instrument for measuring pollutants and located in a high-rise tower. This system can count how much anthropogenic and nature emission in this region. The primary sources of pollutants from the anthropogenic activity are traffic from vehicular transportation. This research focuses on air pollutants in the form of particulate matter (PM10 and PM2.5), Ozone (O3), methane (CH4), nitrogen oxide (NOx), sulphur dioxide (SO2), and carbon monoxide (CO). This research aims to measure the spatial coverage sensitivity of this NIES monitoring system in capturing the effect of the nearest traffic in Bogor. The method used is a sampling method using one day of data with the criteria of no rain and wind speed below two m/s to avoid washing out pollutants and turbulence from these pollutants. The data used are pollutants data on Saturday, June 6th 2020, as sampling data and data traffic in Bogor City. The result from this research is NIES monitoring system can catch air pollutants in Bogor and can measure as far a radius of 0,01 degrees from the NIES monitoring system.
Effect of ozone precursors on surface ozone variations in GAW Kototabang and Cibeureum
Authors: Utami A I , Nasution R I , Asnia M 
Published: December 2021

Abstract
Ozone composition is widely distributed in the troposphere. Surface ozone, known as a secondary pollutant, is a by-product of burning fossil fuels. Increasing the concentration of GHG (CO2, CH4, and CO) as precursors can affect the surface ozone concentration. This study aims to determine the type of precursor that affects the concentration of surface ozone and also to determine the impact of surface ozone and its precursors in rural and remote areas. In general, surface ozone concentrations in both Kototabang and Cibereum begin to increase at 08 – 09 WIB, following the increase in solar radiation intensity, and decrease at 18 – 19 WIB. This pattern is because surface ozone is a secondary pollutant formed by photochemical reactions, in which the photochemical reactions are triggered by energy from solar radiation. The correlation of the surface ozone concentration with CO2, CH4, and CO in the Cibeureum was -0.17, 0.31, and 0.40. The correlation values of surface ozone concentration with CO2, CH4, and CO in the Cibeureum area are 0.09, 0.45, and 0.48. The highest correlation is shown by a CO correlation of 0.40. moreover the highest correlation is shown by a CO correlation of 0.48. The results in this study indicate the effect of each precursor on surface ozone concentration and the accompanying processes.
Evaporation Climate Zonation Based on Observation Data in Indonesia Using Cluster Analysis
Authors: Putra I D G A , Perdana R B , Sopaheluwakan A , Suryadi  
Published: December 2021

Abstract
Identification of regional evaporation zones is very important because information on the classification of evaporation patterns in an area can be used for various purposes, one of which is knowing the amount of water in the reservoir that is lost due to evaporation. In this study, the evaporation zone in Indonesia was created using cluster analysis. The evaporation data from 127 BMKG observation stations for 6 years from 2014 to 2019 were used after the normal monthly calculation was then standardized with mean = 0 and standard deviation = 1 to ensure that all variables were given the same weight so that it became the 127 x 12 matrix used in the analysis. Hierarchical cluster analysis was chosen to regionalize evaporation. Five different techniques are applied to initially determine the most suitable method for the area. Cluster stability is also tested. It was decided that the Ward method is the most likely to produce acceptable results in this evaporation variable. 3 different classifications of evaporation zones are found in Indonesia. Evaporation zone 1 forms a seasonal cycle with one peak evaporation that occurs with the highest peak occurring in August and the minimum evaporation occurring in February in areas in the northern and central regions of Sumatra island, eastern Kalimantan, parts of Java island, North Sulawesi and West Papua. Evaporation zone 2 forms a seasonal cycle with two peaks that occur in December and March and minimum evaporation occurs in June which is in the southern region of the island of Sumatra, almost the entire island of Kalimantan, a small part of the island of Java, central Sulawesi, the archipelago. Maluku as well as the northern island of Papua. Evaporation zone 3 forms a seasonal cycle with one peak evaporation that occurs in October and the minimum evaporation occurs in June which is in a small area of the island of Central Sumatra, South Kalimantan, the west coast of Sulawesi, and all parts of the island of Nusa Tenggara.
Application of Consecutive Dry Days (CDD) Multi-Model Ensemble (MME) Prediction to Support Agricultural Sector in South Sulawesi Rice Production Centers
Authors: Setiawan A M , Koesmaryono Y , Faqih A , Gunawan D 
Published: December 2021

Abstract
Sufficient water availability during the vegetative, reproductive, and early ripening phases of the rice plants is essential. Information on drought, such as Consecutive Dry Days (CDD) predictions in this period, became very crucial and had an important role in maintaining rice production stability. The aim of this study is to investigate the performance of CDD Multi-Model Ensemble prediction, which is applied to South Sulawesi rice production centers. CDD observation was calculated using high resolution gridded precipitation blending data, obtained from BMKG precipitation network stations and the daily-improved Climate Hazards group InfraRed Precipitation with Stations (CHIRPS) version 2.0. The North American Multi-Model Ensemble (NMME) monthly precipitation hindcast data during 1982 – 2010 periods from each nine individual global climate models were used to develop seasonal CDD predictions. World Meteorological Organization (WMO) Standard Verification for Long Range Forecast (SVS-LRF) method applied to describe this CDD prediction performance on four different seasons. Investigation of model performance during strong El Niño event in 1997 also conducted in order to get general skill overview regarding extreme climate event. Best performance of CDD prediction generally occurred during JJA and DJF period. MME CDD prediction shows better performance compared to individual model performance for almost all season. Spatial coherence between prediction and observation over rice production centers during 1997 El Niño confirms the skill of CDD predictions. The application of this prediction on agricultural sector will be very useful in order to support rice production sustainability and food security. Further analysis result can be found on full paper.