Geophysical Agency Research Articles

Design of Portable Automatic Weather Station Based on Raspberry PI

Observation of the weather elements is very necessary for the welfare of mankind, the weather elements that are observed will be used as material to predict the weather in the future. Weather data can also be used to reduce the risk of adverse effects caused by the weather itself. To measure the weather with an automated monitoring system that is cheaper and of good quality, AWS was created. The AWS design using Raspberry Pi was made as a use of Raspberry Pi mini computer technology for the purposes of the Meteorology, Climatology and Geophysics Agency (BMKG). humidity, Rain Counter, wind direction, wind speed, air pressure, altitude, and GPS. Display data can be monitored via the web in the form of an Analog Gauge.

Implementation of Data Mining using Naïve Bayes Classifier Method in Food Crop Prediction

Lampung province has development activity orienting on source potential in the agricultural sector mainly food crops. Yield estimation of food crops is one of the things crucial problems in the agricultural sector, because of the farmers' lack of knowledge about the bountiful harvest, and climate change big impact on the yield of food crops. Then it was needed to be developed modeling to prediction system of food crops by data mining, with Naïve Bayes Classifier (NBC) which expected will give information and can use by the farmer and industrial food crops. On classification, progress attributes that use there is the temperature (°C), humidity (%), rainfall (mm), photoperiodicity (hour), and production result (ton) as a class attribute. The data of research that getting there are climate data and yield of food crops by data from the Central Bureau of Statistics (BPS) and the Meteorology, Climatology and Geophysics Agency (BMKG) from 2010 to 2017 at Lampung Province. Data of food crops used in this research there are paddy, maize, and soybean. The research results about the average accuracy of modeling that development using the 10-fold cross-validation method, that had an accuracy value of 72.78% and Root Mean Square Error (RMSE) there is 0.438.

Variability and time series trend analysis of rainfall and temperature in Dramaga Sub-District, Bogor, Indonesia

Human-induced climate change is considered a threat to communities and the natural system, in particular, agriculture, forestry, economy, health, and well-being. Explicitly, extreme weather events namely flood and long dry season, are great concerns for Indonesia and the region. The purpose of this study is to detect rainfall and temperature trends in the Sub-district of Dramaga for the past thirty years. Dramaga has a huge number of populations relying on rain-fed agriculture, to feed themselves and support food security. Monthly rainfall and temperature data were obtained from the Dramaga Climatology Station of Meteorology, Climatology, and Geophysics Agency from the period 1990-2019. A Mann-Kendall statistical test has been run to detect the statistical significance of time series in comparison with the linear model. The findings indicated no significant trends in the seasonal and annual rainfall in the past thirty years, whereas, annual mean and maximum temperature are rising significantly. Further, the annual minimum temperature does not show a statistically significant increase. Climate events owing to the rainfall variation and Temperature rise threaten smallholder farmers. Results of the study might help decision-makers for climate-smart agricultural projects development at the community level. As to be able, to access environmentally sustainable self-sufficient agriculture.

METEOROLOGICAL GNSS APPLICATION FOR HEAVY RAIN ON DECEMBER 31, 2019 IN THE JAKARTA AND SURROUNDING AREAS

This study aims to utilize GNSS for meteorology in Indonesia. With the "goGPS" software, the zenith troposphere delay (ZTD) value is estimated. Calculations in rainy conditions, the ZTD value is converted into a water vapor value (PWV). The research area for the phenomenon of heavy rain occurred at the end of 2019 in Jakarta and its surroundings, which caused flooding on January 1, 2020. According to the Geophysical Meteorology and Climatology Agency (BMKG), the flood's primary cause was high rainfall. Meanwhile, the rainfall at Taman Mini and Jatiasih stations was 335 mm/day and 260 mm/day, respectively. We get an interesting pattern of PWV values for this rain phenomenon. GNSS data processing, the PWV value at five GNSS stations around Jakarta, shows the same pattern even though the average distance between GNSS stations is ~ 30 km. The PWV value appears to increase at noon on December 30, 2019, and the peak occurs in the early hours of December 31, 2019. The PWV value suddenly decreases at noon on January 1, 2020. Next, the PWV value increases again but not as high as at the previous peak. Since January 2, 2020, the PWV value has decreased and remained almost constant until January 4, 2020. In that period, there were two events that the PWV value increased. The PWV value at the first peak is ~ 70 mm, and at the second peak ~ 65 mm. The most significant increase in PWV value was recorded at CJKT stations.

Climate variability (temperature, humidity, rain intensity and wind speed) and dengue hemorrhagic fever case correlations in Depok City in 2009-2018

Climate issues caused by industrial pollutant has affected the spread of vector-borne diseases such as Dengue Hemorrhagic Fever (DHF). DHF cases are suspected to have links with the everchanging environmental climate conditions. Using an ecological design-based study, this research aims to determine the correlations between climate variability and the DHF cases at Depok City in a time period from 2009 to 2018. This study utilizes secondary data in which DHF cases were collected through the records of Department of Health of Depok City, and temperature, humidity, rain intensity, and wind speed from Depok City Meteorological, Climatological, and Geophysical Agency. Analytical method consists of univariate analysis and bivariate analysis which is shown by a correlation value and linear regression between the variables. Results revealed that there was a significant relationship between temperature and the number of DHF cases (p-value = 0.023, r = - 0.645) and also between humidity and the number of DHF cases (p-value = 0.016, r = 0.673). No significant correlations were found within rain intensity and wind speed variables. In conclusion, this study showed that the effects of climate temperature and humidity changes also affect the rise and declining number of DHF cases in this study location.

Developing an Early Warning System Based on Correlation Analysis For Dengue Haemorrhagic Fever

Several cities in Indonesia have been declared as Dengue Haemorrhagic Fever (DHF)-endemic areas, including East Jakarta, an administrative city of DKI Jakarta Province. Surveillance is the most important activity in controlling and monitoring the development of DHF. This ecological study was conducted to assess the correlation between DHF cases, vector density (Larva Free Index [LFI]) and climate (rainfall and humidity). While the DHF cases were collected from hospitals report, vector density data were obtained from public health centres and climate data were obtained from the Meteorological, Climatological, and Geophysical Agency (BMKG). The correlations between DHF cases, vector density, and the climate were analysed based on weekly data, from week 1 to week 33 of 2019. This study showed a consistent trend of increasing and decreasing DHF cases with rainfall and LFI. DHF cases and LFI had a strong negative correlation (r = –0.72) at a time lag of six weeks. LFI and rainfall also showed a strong negative correlation (r = –0.86) at a time lag of five weeks. The strongest correlation between DHF cases and rainfall was found at week 8 (r = 0.87). Humidity, also an indicator of climate, had a strong positive correlation (r = 0.80) with DHF cases at the 11th-week time lag. However, contrarily, humidity had a strong negative correlation with LFI at the 5th-week time lag. These findings can be used for developing an early warning system that is reinforced by utilizing the application of HDF–climate information.
 Keywords: dengue fever, vector, climate, early warning system

Combination of Passive Seismic (HVSR) and Active Seismic (MASW) Methods to Obtain Shear Wave Velocity Model of Subsurface in Majalengka

Majalengka is a district in West Java which is located close to local faults and subduction zone, so that made this area has a high enough risk of earthquake hazards. Engineering Seismology Team of Meteorology Climatology and Geophysics Agency has been carried out microtremor and MASW survey in Majalengka. The combination of HVSR and MASW inversion methods is applied to obtain the value of the shear wave velocity (Vs), a combination of the two methods is expected to overcome the deficiency of each method. Results of the distribution of the value of the shear wave velocity can explain the subsurface conditions related to a vulnerability in the study area to seismic hazards. The results of HVSR and MASW inversion are 1-D (Vs) profiles at each measurement point, after that interpolated to obtain a 3-D Vs30 model. The results showed that Vs30 values in Majalengka District varied around 80 - 1500 m/s. Layers with low (Vs) values were relatively thick dominant in the north and northwest regions of the study area, soil types in this area were mostly composed of medium soils, a small part was composed of soft soil layers and rock layers. Therefore, in theory, the disaster in the north and northwest regions has a relatively higher vulnerability compared to other regions in Majalengka District.

Effects of climate change on dryland agriculture vegetation index in Nangapanda, East Nusa Tenggara

Dryland agriculture produces agricultural commodities in the food and plantation sectors. However, the potential for dryland agriculture in Indonesia is one of the agricultural bases, which is also threatened by climate anomalies. This research aims to examine one of the climatic factors, namely Land Surface Temperature (LST), which is influenced by environmental carrying capacity factors, namely the vegetation index on the productivity of dryland agriculture. The vegetation indexes used are NDVI, SAVI, and EVI, using Landsat 5 TM and Landsat 8 OLI imagery for 1999, 2004, 2009, 2014, and 2019 then analyzed by statistical regression tests. Another data used are temperature comparison data from the Meteorological Climatological and Geophysical Agency of Indonesia as known as Badan Meteorologi Klimatologi dan Geofisika (BMKG), agricultural productivity data from Statistics Indonesia as known as Badan Pusat Statistik (BPS), and Agricultural Counseling Agency of Nangapanda District as known as Badan Penyuluhan Pertanian (BPP). The result obtained from this research is that there is a significant inverse relationship between the vegetation index and LST. Later, the increase in LST can cause a decrease in the productivity of dryland agriculture.

APLIKASI MODEL RANTAI MARKOV UNTUK MENGANALISIS TINGKAT KENYAMANAN DI KOTA MAJENE BERDASARKAN TEMPERATURE HUMIDITY INDEX (THI)

This study applied the Markov chain model on the daily temperature and relative humidity data that was collected from the Meteorology and Geophysics Agency station in Majene district for the period 1983 to 2011. This study aims to analyze the comfortable level category in the Majene city based on the Temperature Humidity Index by calculating the probability of steady-state, the mean residence time and the mean first passage time. Categorizing the level of comfortable which is based on the Temperature Humidity Index consists of three categories, namely the comfortable, quite comfortable and uncomfortable. The trend of comfortable levels in the Majene city from 1983 to 2011 was fluctuated in the categories of quite comfortable and uncomfortable. Uncomfortable category occurs in October and November each year. The steady-state probability values indicates that the quite comfortable category has the highest chance of appearance, which is around 70%, and the comfortable category has the smallest chance of appearance, which is only about 5%. Meanwhile, the mean residence time and the mean first passage time indicate that the quite comfortable category have the longest duration of occurrence, which is around 5 days, and has the shortest duration to recur after occurring in the previous event, which is around 1.43 days.

Design and manufacture of air quality measurements based on arduino ATmega 2560 using dust ZH03A laser sensor

This study aims to design an air quality measuring instrument that is more efficient, relatively cheaper and can be carried because it has a smaller shape and size compared to the existing tools in the Meteorology, Climatology and Geophysics Agency (BMKG). The main processor uses the Arduino ATmega 2560 microcontroller. This microcontroller functions as a processor for PM2.5 and PM10 concentrations which are the output of the ZH03A sensor. The design results are calibrated with a standard tool, namely BAM1020 which is owned by the BMKG air quality sector to measure air quality. The calibration results obtained that the average value issued by the reference tool which is used as a standard compared to the ZH03A sensor in the design results for PM 2.5 is 6.5 µm / m3, and for the average value of PM1.0 is 2.8 µm / m3 the calculated Air Pollution Standard Index (ISPU) value is 126 µm / m3 including the medium category according to the Air Pollution Standard Index.

Shear failure of building floor beams due to December 7, 2016 Pidie Jaya earthquake

The 6.5 Mw magnitude earthquake experienced in Pidie Jaya Regency on December 7, 2016 caused the failure of several structures including the District Attorney’s Building with almost all the beams on the first floor observed to have shear cracks. Besides that, some flexural cracks were also found, although only in a few places. This study aimed to determine the cause of the shear failure in the building and the amount of deformation observed in the structures due to the earthquake. Therefore, an assessment was conducted in the field to determine the quality of concrete and the adequacy of shear reinforcement installed. A time history analysis was also performed using the Pidie Jaya earthquake history recorded by the Meteorology, Climatology and Geophysics Agency (BMKG). The results showed the required shear strength of the floor beams due to earthquake load was lower than its shear capacity due to a low compressive strength of the concrete in the beam and installation of insufficient shear reinforcement. Furthermore, the flexural strength required in the beams in some places was also greater than the available flexural capacity. The lateral deformations due to the earthquake were also observed to be very large thereby causing the inter-story drift of the building structures to exceed the value allowed by earthquake-resistant building standards (SNI 1726:2012). This subsequently led to the failure of the building floor beams, however, since the strength of the column was greater than the required standard, no crack was observed in the columns.

Sediment and Crustal Structure Beneath East Kalimantan and East Java, Indonesia From Receiver Function

We investigate the sediment and Earth’s crustal structure beneath the East Kalimantan and East Java by modelling the stacked receiver function from six seismic broadband stations belong to the Meteorology, Climatology and Geophysics Agency of Indonesia (BMKG). The calculation of receiver functions from 60 teleseismic earthquake events using iterative time-domain deconvolution method. Inversion result shows a significant variation in the sediment layer thickness from 1 km to 4 km beneath East Java. It is correlated with the South East Java basinal area (station KRK) and the North East Java Basin (station GRJI and BWJI). The sediment layer of about 5 to 10 km beneath East Kalimantan is associated with the Kutai Basin (station SGKI, SMKI, and BKB). Average Sediment layer shear velocity (Vs) beneath all station is observed to be less than 2.31 km/s. Crustal thickness beneath East Java varies between 20 to 40 km and 34 to 50 km beneath East Kalimantan, with average crustal shear wave velocity, Vs 4.60 km/s.

Temperature, pressure, relative humidity and rainfall sensors early error detection system for automatic weather station (AWS) with artificial neural network (ANN) backpropagation

To improve the quality and quantity of meteorological data over Indonesia, Meteorology Climatology and Geophysics Agency of Indonesia (BMKG) is continuously developing automatic weather observations. BMKG has 63 units Automatic Weather Station (AWS) and 165 units Automatic Weather Observation System (AWOS) both inside and outside the BMKG Station environment. To make the control of sensor conditions easier, especially for temperature, pressure, relative humidity, and rainfall sensors, an additional system is needed to monitor and warn when problems occur with these sensors. The correlation among weather parameters data is the key to monitoring the sensor condition, these data are going to be trained and tested with the Artificial neural network (ANN) method. Then, the sensor condition (normal or error indicated) can be well detected based on AWS’s data. The quality improvement of automatic weather station data is expected to increase the utilization of the data.

Analisis Distribusi Frekuensi dan Periode Ulang Hujan

Increasing water demand for various needs can be a complex problem so it is necessary to manage water resources. Analysis of hydrological data is very necessary to get information about water resources where the information can be used as a benchmark for planning a water resources builder. One of hydrological analysis is the analysis of rainfall data where this analysis uses frequency distribution analysis and rain return periods. There are four types of distribution used, namely normal distribution, normal log distribution, Gumbel distribution and type III log Pearson distribution. The goodness of fit test uses the Kolmogorov-Smirnov method, Chi-Square and Anderson-Darling. Rainfall return calculation is calculated when it is known the type of distribution of the data studied. This research uses rainfall data of Long Iram Sub-Distric, West Kutai Distric in 2013 to 2017 obtained from the Meteorology, Climatology and Geophysics Agency (BMKG) of Samarinda City. The results from research showed that the Gumbel distribution was the right distribution or distribution that was the best with the results of the return period of rain for the return period of 2 years obtained by rainfall of 519 mm, 5-year return period of 796 mm, 10-year return period of 980 mm, return period 20 years of 1.154 mm, a 50 year return period of 1.348 mm and in a 100 year return period of 1.752 mm.

Analisis Sebaran Spasial Kerawanan Penyakit Demam Berdarah Dengue Tahun 2010 – 2019 di Kota Banda Aceh

Banda Aceh City is one of the dengue-endemic area. During a 10 year period (2010 - 2019), the total number of cases of Dengue Hemorrhagic Fever (DHF) in Banda Aceh City was 3,168 cases with a total of 16 deaths. Thus, it is necessary to map the vulnerability of dengue in Banda Aceh City. The purpose of this study was to map the distribution of dengue-prone areas in the city of Banda Aceh. The parameters used in the study were rainfall, air temperature, humidity, and population density. This parameter is used because it is considered to have a close relationship to habitat for the development of the Aedes aegypti mosquito. Secondary data were collected from the Central Statistics Agency (BPS), the Meteorology, Climatology and Geophysics Agency (BMKG), the Regional Development Planning Agency (Bappeda), the Marine and Fisheries Agricultural Food Service (DPPKP), and the Banda Aceh City Health Office from 2010 - 2019 shows that dengue cases correlate with rainfall of 25.07%. DHF cases correlate to humidity, which is 42.06%. Meanwhile, the correlation between temperature and DHF cases was -47.26%. The results of the spatial analysis of DHF vulnerability show that dengue is highly vulnerable in Jaya Baru District, Baiturrahman District, Kuta Alam District, and Syiah Kuala District

Penerapan Principal Component Analysis dalam Penentuan Faktor Dominan Cuaca Terhadap Penyebaran Covid-19 di Surabaya

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the transmission can mediate human-to human by enviroment. According to Indonesian Meterological, Climatological, and Geophysical Agency found that weather and climate were supporting factors of COVID-19 outbreak so, research and analysis is carried out regarding the most factor were supporting the spread of COVID-19. In this study, using secondary data obtained from data reported by Indonesian Meterological, Climatological, and Geophysical Agency. According the aims of this study by using Principal Component Analysis (PCA) there are three principal components which represents the most factor were supporting the spread of COVID-19 they are temperature, humidity, and length of sunshine.

The impacts of Seroja Tropical Cyclone towards extreme weather in East Nusa Tenggara

This paper aims to discuss the Seroja Tropical Cyclone and its impact on extreme weather. Seroja tropical cyclones occur from April 4 to 5th 2021, in the East Nusa Tenggara (NTT) region. Based on data from the Meteorology, Climatology and Geophysics Agency (BMKG), the initial position of the Seroja tropical cyclone was in the Savu Sea, southwest of Timor Island. Since April 1, 2021, the NTT region has become the center of low pressure that triggers the formation of this cyclone. When a tropical storm occurs, the intensity of rainfall which initially reaches less than 60 mm/day, increases rapidly to more than 100 mm/day on April 4 to 5, 2021. This is the impact of the low-pressure center that triggers the formation of tropical cyclones in the region.

Rainfall modeling based on early predicted and season zone characteristic in the BMKG season zone over Lombok river basin

Planning water resources for long-term (annual, monthly) and operational (daily) requires a more accurate analysis of water availability calculations. The Meteorology, Climatology and Geophysics Agency (BMKG) publishes bulletin of forecasting every year in the form of the early wet season and the characteristics of the wet and dry season in each Season Zone (ZOM). BMKG conducts analysis and forecasting with complex climate parameters. Thomas Fiering analysis with the help of Excel Visual Basic Application (VBA) software is used to forecast rainfall at each rainfall station in BMKG Season Zone. The average error of forecasting rainfall during the wet season on the BMKG season zone in 2015/2016, 2016/2017 and 2017/2018 were 20%, 30% and 26%. While the average error for the dry season on the BMKG season zone is 44%, 34% and 52%. The existence of this deviation is caused by several factors including the uncertain season changes every year, as well as the influence of “El Nino.”.

Climate factors and dengue fever occurrence in Makassar during period of 2011–2017

ObjectiveDengue fever is a global burden because of high cases number. Climate factors became determinant of the mosquito's growth. This study aimed to analyze the relationship between climate factors (humidity, temperature, wind speed, rainfall) and dengue cases in Makassar during 2011–2017. MethodsIt was quantitative study located in Makassar. Data were analyzed by General Estimating Equation (GEE). Gee was used to showing the model of variables. This study used secondary data from Health District Office of Makassar to get Dengue Cases Data and Meteorological, Climatological, and Geophysical Agency of Makassar for monthly climate data. ResultsThe result showed significant correlation between climate variables that have been researched which were temperature, humidity, rainfall, and wind speed to dengue fever cases. ConclusionsAs conclusion, the humidity had strongest correlation to dengue fever cases. It also showed positive correlation, while others showed negative correlation.

Management on biotic stress to improve the nutmeg quality in North Moluccas, Indonesia

Indonesia dominates about 76% of nutmeg production and export in the world, where around 28.26% is produced in North Moluccas and 24.25% of its from North Halmahera. The objectives of this study were to reduce the biotic stress, increase the yield and improve the quality of the nutmeg produced by Tarakani Farmer Group in Galela District. (1) the Morphology of dry fruit rot disease in each stratum and (2) nutmeg cultivation system collected through interviews; and (3) climatic data, including temperature, and rainfall obtained from the Meteorology, Climatology, and Geophysics Agency of Galela District. This study was conducted at west Galela District and Tarakani Farmer Group. The farmers implemented the sanitation techniques of the crops; i.e.: pruning, collection and pile up/bury of the dropped fruits and fumigation by burning some leaves of local trees and biotic stress be controlled. During one year a grade A of nutmeg increased from 5.7 tons to 37.6 tons or increased from 17.6% to 38.2%. Nutmeg grade C reduced from 40% to 25%%. This effort will still be forwarded by utilizing biological agents to reduce biotic stress, such as Trichoderma spp. to control fungal pathogens and Bacillus thurigiensis to pest control.