Heavy rains that cause floods and landslides in the Ketapang Regency can be predicted by utilizing the weather research and forecast (WRF) model. The WRF model used, of course, needs to be configured to represent the conditions that exist in Ketapang Regency. This study evaluates the combination of cumulus and microphysics parameterization, producing the best prediction of 24-hour accumulated rainfall. The combination of cumulus and microphysics parameterization tested as many as 24 schemes which later will be obtained which combination can produce the best prediction of rainfall accumulation with the comparison of rainfall measured at the Observation Station of the Meteorology, Climatology, and Geophysics Agency (BMKG) in Ketapang Regency. The results show that combining the KF-Scheme cumulus parameterization scheme and the Kessler-Scheme microphysics can better predict 24-hour accumulated rainfall than other tested parameterization schemes. This result is based on the root mean square error (RMSE), which shows that this combination scheme produces the smallest value and large correlation coefficient (CORR). From this research, it can also be seen that cumulus parameterization has a more dominant role than microphysics parameterization.
{"title":"WRF-MODEL PARAMETERIZATION TEST FOR PREDICTING EXTREME HEAVY RAINFALL EVENT OVER KETAPANG REGENCY","authors":"Fazrul Rafsanjani Sadarang","doi":"10.31172/jmg.v24i1.924","DOIUrl":"https://doi.org/10.31172/jmg.v24i1.924","url":null,"abstract":"Heavy rains that cause floods and landslides in the Ketapang Regency can be predicted by utilizing the weather research and forecast (WRF) model. The WRF model used, of course, needs to be configured to represent the conditions that exist in Ketapang Regency. This study evaluates the combination of cumulus and microphysics parameterization, producing the best prediction of 24-hour accumulated rainfall. The combination of cumulus and microphysics parameterization tested as many as 24 schemes which later will be obtained which combination can produce the best prediction of rainfall accumulation with the comparison of rainfall measured at the Observation Station of the Meteorology, Climatology, and Geophysics Agency (BMKG) in Ketapang Regency. The results show that combining the KF-Scheme cumulus parameterization scheme and the Kessler-Scheme microphysics can better predict 24-hour accumulated rainfall than other tested parameterization schemes. This result is based on the root mean square error (RMSE), which shows that this combination scheme produces the smallest value and large correlation coefficient (CORR). From this research, it can also be seen that cumulus parameterization has a more dominant role than microphysics parameterization.","PeriodicalId":32347,"journal":{"name":"Jurnal Meteorologi dan Geofisika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135135250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Overshooting top (OT) in convective clouds is an essential feature in extreme weather nowcasting performed by weather forecasters to represent the core location of the severe region of the convective cloud. In addition, we can estimate the location of extreme weather events by utilising OT climatology. Unfortunately, it cannot be realised in tropical Indonesia, especially on Java Island at present, because there still needs to be more research on the presence of OT in extreme weather events. This research aims to study the presence of OT in extreme weather events on Java Island using extreme weather reports and the Himawari 8 satellite data. We detect the presence or absence of OT patterns at the location of the extreme weather event with Visual identification by using a visible channel (0.64 μm) with a spatial resolution of 500 m and sandwich products. We found that about 87% of extreme weather occurred accompanied by the appearance of OT patterns from convective clouds. A parallax effect of Himawari 8 caused the detected OT location in the southwest direction of the actual location. Extreme weather events accompanied by the OT feature of convective clouds most often occur in the transitional period of the rainy to dry season (MAM) and the rainy season (DJF). Meanwhile, extreme weather events rarely occur during the dry season (JJA). Extreme weather events accompanied by OT often occur from midday to late afternoon. OT in this study has a diameter between 2-15 km during extreme weather events. A time lag between the appearance of OT and extreme weather events in Java Island gives us opportunities for approximating and nowcasting the extreme weather events.
{"title":"OVERSHOOTING TOP OF CONVECTIVE CLOUD IN EXTREME WEATHER EVENTS OVER JAVA REGION BASED ON VISUAL IDENTIFICATION OF HIMAWARI 8 IMAGERY","authors":"Bony Septian Pandjaitan, Akhmad Faqih, Furqon Alfahmi, Perdinan .","doi":"10.31172/jmg.v24i1.967","DOIUrl":"https://doi.org/10.31172/jmg.v24i1.967","url":null,"abstract":"Overshooting top (OT) in convective clouds is an essential feature in extreme weather nowcasting performed by weather forecasters to represent the core location of the severe region of the convective cloud. In addition, we can estimate the location of extreme weather events by utilising OT climatology. Unfortunately, it cannot be realised in tropical Indonesia, especially on Java Island at present, because there still needs to be more research on the presence of OT in extreme weather events. This research aims to study the presence of OT in extreme weather events on Java Island using extreme weather reports and the Himawari 8 satellite data. We detect the presence or absence of OT patterns at the location of the extreme weather event with Visual identification by using a visible channel (0.64 μm) with a spatial resolution of 500 m and sandwich products. We found that about 87% of extreme weather occurred accompanied by the appearance of OT patterns from convective clouds. A parallax effect of Himawari 8 caused the detected OT location in the southwest direction of the actual location. Extreme weather events accompanied by the OT feature of convective clouds most often occur in the transitional period of the rainy to dry season (MAM) and the rainy season (DJF). Meanwhile, extreme weather events rarely occur during the dry season (JJA). Extreme weather events accompanied by OT often occur from midday to late afternoon. OT in this study has a diameter between 2-15 km during extreme weather events. A time lag between the appearance of OT and extreme weather events in Java Island gives us opportunities for approximating and nowcasting the extreme weather events.","PeriodicalId":32347,"journal":{"name":"Jurnal Meteorologi dan Geofisika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135135085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Akhmad Faqih, Alif Akbar Syafrianno, Supari Supari, Rini Hidayati
Indonesia's climate is known to be challenging to adequately simulate by climate models because of the complexity of the weather system and sea-land distribution. Model evaluation is essential to measure confidence in the model results. This study evaluates the performance of the CORDEX-SEA model in simulating monthly rainfall patterns and the characteristics of seasonal rainfall, i.e., pattern, timing, length, and intensity, in Indonesia during 1986-2005. The performance of weighted (WMME) and unweighted ensemble methods are also calculated. Corrected CHIRPS data with similar seasonal patterns with point observation data is used as reference data to evaluate models. Percentage of the agreement of seasonal patterns between models and observation, FAR, and POD values were used to assess the model's ability to simulate seasonal patterns. WMME has the best seasonal patterns agreement with observation, 67% of all grids. The best model performance is shown by monsoonal patterns, with a POD value of 83% by WMME. Otherwise, all models could not describe an anti-monsoonal pattern, with a small POD (0-33%) and a high FAR (60-100%). In simulating the wet season on climatological, annual, and annual mean scales, both MMEs have similar performance and are better than individual models, with WMME performing best. However, on an annual scale, the yearly wet season produced by all models tends to approach its climatology value, making it less reliable in extreme years. Most models have higher daily and monthly rainfall than observation. In conclusion, the weighted ensemble method describes Indonesia's rainy season well, thus providing a reasonable basis for further research in climate projection analysis.
{"title":"EVALUATION OF THE CORDEX-SEA MODELS PERFORMANCE IN SIMULATING CHARACTERISTICS OF WET SEASON IN INDONESIA","authors":"Akhmad Faqih, Alif Akbar Syafrianno, Supari Supari, Rini Hidayati","doi":"10.31172/jmg.v24i1.965","DOIUrl":"https://doi.org/10.31172/jmg.v24i1.965","url":null,"abstract":"Indonesia's climate is known to be challenging to adequately simulate by climate models because of the complexity of the weather system and sea-land distribution. Model evaluation is essential to measure confidence in the model results. This study evaluates the performance of the CORDEX-SEA model in simulating monthly rainfall patterns and the characteristics of seasonal rainfall, i.e., pattern, timing, length, and intensity, in Indonesia during 1986-2005. The performance of weighted (WMME) and unweighted ensemble methods are also calculated. Corrected CHIRPS data with similar seasonal patterns with point observation data is used as reference data to evaluate models. Percentage of the agreement of seasonal patterns between models and observation, FAR, and POD values were used to assess the model's ability to simulate seasonal patterns. WMME has the best seasonal patterns agreement with observation, 67% of all grids. The best model performance is shown by monsoonal patterns, with a POD value of 83% by WMME. Otherwise, all models could not describe an anti-monsoonal pattern, with a small POD (0-33%) and a high FAR (60-100%). In simulating the wet season on climatological, annual, and annual mean scales, both MMEs have similar performance and are better than individual models, with WMME performing best. However, on an annual scale, the yearly wet season produced by all models tends to approach its climatology value, making it less reliable in extreme years. Most models have higher daily and monthly rainfall than observation. In conclusion, the weighted ensemble method describes Indonesia's rainy season well, thus providing a reasonable basis for further research in climate projection analysis.","PeriodicalId":32347,"journal":{"name":"Jurnal Meteorologi dan Geofisika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135135254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rido Dwi Ismanto, Indah Prasasti, Hana Listi Fitriana
The need for rainfall data, especially for areas where the number of observation stations is not very close, is very important for local climate analysis activities. This data need can be met, one of which is from remote sensing data, such as Himawari 8. The Himawari 8 rainfall data are data derived using the INSAT Multi-Spectral Rainfall Algorithm (IMSRA) method based on the infrared channel on the Himawari 8 satellite. However, research on the IMSRA method was carried out using a case study of a region in India. Thus, validation is needed to determine the ability of Himawari 8 rainfall data to detect rain in Indonesia. The data used for comparison are CHIRPS and GSMaP rainfall data. In addition, BMKG rainfall data are used as benchmark data. The technique used for validation is using the Contingency Table method. The results of the validation show that the rain detection ability for Himawari 8 rainfall data is relatively good, namely 66% for 2019 and 85% for 2020. In addition, the ability to detect rain using Himawari 8 rainfall data is quite good compared to the ability to detect rain using CHIRPS rainfall data and GSMaP rainfall data.
{"title":"COMPARISON ANALYSIS OF HIMAWARI 8, CHIRPS AND GSMaP DATA TO DETECT RAIN IN INDONESIA","authors":"Rido Dwi Ismanto, Indah Prasasti, Hana Listi Fitriana","doi":"10.31172/jmg.v24i1.863","DOIUrl":"https://doi.org/10.31172/jmg.v24i1.863","url":null,"abstract":"The need for rainfall data, especially for areas where the number of observation stations is not very close, is very important for local climate analysis activities. This data need can be met, one of which is from remote sensing data, such as Himawari 8. The Himawari 8 rainfall data are data derived using the INSAT Multi-Spectral Rainfall Algorithm (IMSRA) method based on the infrared channel on the Himawari 8 satellite. However, research on the IMSRA method was carried out using a case study of a region in India. Thus, validation is needed to determine the ability of Himawari 8 rainfall data to detect rain in Indonesia. The data used for comparison are CHIRPS and GSMaP rainfall data. In addition, BMKG rainfall data are used as benchmark data. The technique used for validation is using the Contingency Table method. The results of the validation show that the rain detection ability for Himawari 8 rainfall data is relatively good, namely 66% for 2019 and 85% for 2020. In addition, the ability to detect rain using Himawari 8 rainfall data is quite good compared to the ability to detect rain using CHIRPS rainfall data and GSMaP rainfall data.","PeriodicalId":32347,"journal":{"name":"Jurnal Meteorologi dan Geofisika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135135257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The April 10, 2021, earthquake in the south of East Java was classified as destructive. The secondary impact of this earthquake was quite significant. Many houses collapsed, and not a few casualties. This earthquake is unique because usually, destructive earthquakes occur at shallow depths, but earthquakes with a magnitude of 6.1 are classified as medium-depth earthquakes at sea. The earthquake in the south of East Java is classified as an intraplate earthquake because it is located on the continental plate, not in the plate contact area. The question is whether the damage that occurred to the building was purely due to the magnitude of the stress released by the earthquake or whether there were other factors. This study uses seismogram data for the earthquake south of East Java on April 10, 2021, with a radius (∆) of 300-1000 recorded at MEEK, MORW, and ARMA stations in Australia. It calculates the amount of stress based on the stress drop, while the stress column determines the stress mechanism. Calculation of stress drop from the source spectrum is obtained by the deconvolution method, namely the seismogram component separation technique in the form of Source (f), Path (f), Site (f), and Instrument (f). The analysis of the observed displacement spectrum used the Nelder Mead Simplex nonlinear inversion method. Meanwhile, the Stress Columb calculation was obtained using the Columb 3.3 program from the United States Geological Survey (USGS). The result of this research is that the stress drop value is 1.69 MPa, with the type of focus mechanism being a thrust fault in the sea. The earthquake in the south of East Java was caused by rock activity in the intraplate. The value of the stress drop is more significant when compared to the subduction contact area. This area is of intraplate rock with various variations, and earthquakes are rare. This study aims to analyze the stress, both the magnitude of the stress drop and the mechanism of the column stress results, so that the stress caused by the earthquake can be known and why the earthquake in the south of East Java is destructive. The quake in Southeast Java is classified as dangerous, not because of the magnitude of the stress generated or its mechanism. The damage was due to the amplification of earthquake waves in the building. The injury occurred because most of the buildings were built on soft soil, especially in several areas in East Java, such as Lumajang, Pasuruan, Trenggalek, Probolinggo, Ponorogo, Jember, Tulunggagung, Nganjuk, Pacitan, and several urban areas, namely Blitar, Kediri, Malang, and Stone. So, there is a need for earthquake disaster mitigation, especially in densely populated areas that live on soft soil. This mitigation effort is to minimize the occurrence of casualties by building buildings according to earthquake-resistant standards and avoiding development in the regions that have the potential for amplification of earthquake waves.
{"title":"STRESS ANALYSIS AND CHARACTERISTICS DUE TO THE SOUTH JAVA EARTHQUAKE, APRIL 10, 2021","authors":"Rahmat Setyo Yuliatmoko, Sulastri Sulastri","doi":"10.31172/jmg.v24i1.770","DOIUrl":"https://doi.org/10.31172/jmg.v24i1.770","url":null,"abstract":"The April 10, 2021, earthquake in the south of East Java was classified as destructive. The secondary impact of this earthquake was quite significant. Many houses collapsed, and not a few casualties. This earthquake is unique because usually, destructive earthquakes occur at shallow depths, but earthquakes with a magnitude of 6.1 are classified as medium-depth earthquakes at sea. The earthquake in the south of East Java is classified as an intraplate earthquake because it is located on the continental plate, not in the plate contact area. The question is whether the damage that occurred to the building was purely due to the magnitude of the stress released by the earthquake or whether there were other factors. This study uses seismogram data for the earthquake south of East Java on April 10, 2021, with a radius (∆) of 300-1000 recorded at MEEK, MORW, and ARMA stations in Australia. It calculates the amount of stress based on the stress drop, while the stress column determines the stress mechanism. Calculation of stress drop from the source spectrum is obtained by the deconvolution method, namely the seismogram component separation technique in the form of Source (f), Path (f), Site (f), and Instrument (f). The analysis of the observed displacement spectrum used the Nelder Mead Simplex nonlinear inversion method. Meanwhile, the Stress Columb calculation was obtained using the Columb 3.3 program from the United States Geological Survey (USGS). The result of this research is that the stress drop value is 1.69 MPa, with the type of focus mechanism being a thrust fault in the sea. The earthquake in the south of East Java was caused by rock activity in the intraplate. The value of the stress drop is more significant when compared to the subduction contact area. This area is of intraplate rock with various variations, and earthquakes are rare. This study aims to analyze the stress, both the magnitude of the stress drop and the mechanism of the column stress results, so that the stress caused by the earthquake can be known and why the earthquake in the south of East Java is destructive. The quake in Southeast Java is classified as dangerous, not because of the magnitude of the stress generated or its mechanism. The damage was due to the amplification of earthquake waves in the building. The injury occurred because most of the buildings were built on soft soil, especially in several areas in East Java, such as Lumajang, Pasuruan, Trenggalek, Probolinggo, Ponorogo, Jember, Tulunggagung, Nganjuk, Pacitan, and several urban areas, namely Blitar, Kediri, Malang, and Stone. So, there is a need for earthquake disaster mitigation, especially in densely populated areas that live on soft soil. This mitigation effort is to minimize the occurrence of casualties by building buildings according to earthquake-resistant standards and avoiding development in the regions that have the potential for amplification of earthquake waves.","PeriodicalId":32347,"journal":{"name":"Jurnal Meteorologi dan Geofisika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134932306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Try Al Tanto, I. W. Nurjaya, I. Jaya, Tri Hartanto, Amir Yarkhasy, Akmala Dwi Nugraha, Somantri Somantri
{"title":"KARAKTERISTIK DAN PELAPISAN MASSA AIR DI PERAIRAN TELUK BUNGUS DAN BEBERAPA PULAU-PULAU KECIL DI KOTA PADANG","authors":"Try Al Tanto, I. W. Nurjaya, I. Jaya, Tri Hartanto, Amir Yarkhasy, Akmala Dwi Nugraha, Somantri Somantri","doi":"10.31172/jmg.v23i2.882","DOIUrl":"https://doi.org/10.31172/jmg.v23i2.882","url":null,"abstract":"","PeriodicalId":32347,"journal":{"name":"Jurnal Meteorologi dan Geofisika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80860330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bayu Retna Tri Andari, N. J. Trilaksono, M. Munandar
Accurate weather forecasts should support the increase in safety of aviation operations in Indonesia. This weather forecast is needed, especially in detecting turbulence, considering that geographically Indonesia has effective solar radiation resulting in convective cloud formation. Convective clouds can trigger turbulence then produce disruption and even accidents on flights. This research uses a case study on the Etihad Airways flight on Bangka Island on May 4, 2016. At the time of the incident, there was turbulence at 39,000 feet altitude, and the aircraft did not enter the cloudy area. The Weather Research and Forecasting (WRF) model is used to simulate the turbulence in this study, which is downscaled up to 3 km with a microphysics parameterization of WRF Single Moment 6 Class (WSM6). The results were then verified using correlation and linear regression for temperature, wind direction, wind speed, and pattern resemblance between cloud fraction and the convective nuclei distribution. The turbulence is analyzed from the south-north and west-east vertical airflow. The turbulence spotted at 06.40 UTC when there is a quite strong updraft which can cause turbulence. The turbulence parameters used, such as the eddy dissipation rate (EDR) parameter, which has a value of 0.05 , Richardson number with a value of less than 0.25, and turbulence index (TI 1) with a maximum value of 4 x 10-7 s-2 found that turbulence was in a strong category. The turbulence that occurs in this study is identified as near cloud turbulence (NCT) event due to cloud formation observed in the west of the turbulence and intense updraft activity at the location of turbulence.
{"title":"Turbulence analysis on the flight of Etihad airways in Bangka Island using the WRF case study May 4, 2016","authors":"Bayu Retna Tri Andari, N. J. Trilaksono, M. Munandar","doi":"10.31172/jmg.v23i3.912","DOIUrl":"https://doi.org/10.31172/jmg.v23i3.912","url":null,"abstract":"Accurate weather forecasts should support the increase in safety of aviation operations in Indonesia. This weather forecast is needed, especially in detecting turbulence, considering that geographically Indonesia has effective solar radiation resulting in convective cloud formation. Convective clouds can trigger turbulence then produce disruption and even accidents on flights. This research uses a case study on the Etihad Airways flight on Bangka Island on May 4, 2016. At the time of the incident, there was turbulence at 39,000 feet altitude, and the aircraft did not enter the cloudy area. The Weather Research and Forecasting (WRF) model is used to simulate the turbulence in this study, which is downscaled up to 3 km with a microphysics parameterization of WRF Single Moment 6 Class (WSM6). The results were then verified using correlation and linear regression for temperature, wind direction, wind speed, and pattern resemblance between cloud fraction and the convective nuclei distribution. The turbulence is analyzed from the south-north and west-east vertical airflow. The turbulence spotted at 06.40 UTC when there is a quite strong updraft which can cause turbulence. The turbulence parameters used, such as the eddy dissipation rate (EDR) parameter, which has a value of 0.05 , Richardson number with a value of less than 0.25, and turbulence index (TI 1) with a maximum value of 4 x 10-7 s-2 found that turbulence was in a strong category. The turbulence that occurs in this study is identified as near cloud turbulence (NCT) event due to cloud formation observed in the west of the turbulence and intense updraft activity at the location of turbulence.","PeriodicalId":32347,"journal":{"name":"Jurnal Meteorologi dan Geofisika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82309684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Meutia Farida, Asri Jaya, Ilham Alimuddin, Safruddim Safruddim, Asmita Ahmad
{"title":"IKLIM PURBA PADA LINGKUNGAN KARBONAT FORMASI TONASA BERDASARKAN FORAMINIFERA PLANKTONIK, SULAWESI SELATAN","authors":"Meutia Farida, Asri Jaya, Ilham Alimuddin, Safruddim Safruddim, Asmita Ahmad","doi":"10.31172/jmg.v23i2.839","DOIUrl":"https://doi.org/10.31172/jmg.v23i2.839","url":null,"abstract":"","PeriodicalId":32347,"journal":{"name":"Jurnal Meteorologi dan Geofisika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78202067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"ANALISIS SPASIAL FENOMENA URBAN HEAT ISLAND MENGGUNAKAN ALGORITMA LAND SURFACE TEMPERATURE KOTA KENDARI","authors":"La Gandri","doi":"10.31172/jmg.v23i2.852","DOIUrl":"https://doi.org/10.31172/jmg.v23i2.852","url":null,"abstract":"","PeriodicalId":32347,"journal":{"name":"Jurnal Meteorologi dan Geofisika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83899111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dyah Makutaning Dewi, A. Romadhon, Istu Indah Setyaningsih, I. Wulansari
Jakarta is a region with a high number of COVID-19 cases in Indonesia. This study investigates the impact of the COVID-19 pandemic and the resulting large scale social restriction on air pollution levels in Jakarta, Indonesia, by studying particulate matter (PM10) levels. This study employs ARIMA intervention using daily COVID-19 case data from January 1, 2020 to September 30, 2020 (the period before and after the first case of COVID-19 in Indonesia on March 2, 2020). The analysis shows COVID-19 started to impact PM10 in Jakarta on the 11th day after confirming the first case in Indonesia, which is indicated by an unordinary increase in PM10 level. However, on the 12th day after intervention, the PM10 level decreases. This occurred at the beginning of the period when large-scale social restrictions are imposed. However, one month after intervention, PM10 increases again and continues to increase until the end of the study. This is allegedly because people are accustomed to being ignorant and bored with the pandemic situation. Social restrictions and movements are no longer effective, which results in the rise of PM10 levels again. Hence, it can be concluded that COVID-19 impacts air quality in Jakarta even though the impact is minimal and in the short term.
{"title":"THE IMPACT OF COVID-19 OUTBREAK ON AIR POLLUTION LEVELS USING ARIMA INTERVENTION MODELLING: A CASE STUDY OF JAKARTA, INDONESIA","authors":"Dyah Makutaning Dewi, A. Romadhon, Istu Indah Setyaningsih, I. Wulansari","doi":"10.31172/jmg.v23i3.791","DOIUrl":"https://doi.org/10.31172/jmg.v23i3.791","url":null,"abstract":"Jakarta is a region with a high number of COVID-19 cases in Indonesia. This study investigates the impact of the COVID-19 pandemic and the resulting large scale social restriction on air pollution levels in Jakarta, Indonesia, by studying particulate matter (PM10) levels. This study employs ARIMA intervention using daily COVID-19 case data from January 1, 2020 to September 30, 2020 (the period before and after the first case of COVID-19 in Indonesia on March 2, 2020). The analysis shows COVID-19 started to impact PM10 in Jakarta on the 11th day after confirming the first case in Indonesia, which is indicated by an unordinary increase in PM10 level. However, on the 12th day after intervention, the PM10 level decreases. This occurred at the beginning of the period when large-scale social restrictions are imposed. However, one month after intervention, PM10 increases again and continues to increase until the end of the study. This is allegedly because people are accustomed to being ignorant and bored with the pandemic situation. Social restrictions and movements are no longer effective, which results in the rise of PM10 levels again. Hence, it can be concluded that COVID-19 impacts air quality in Jakarta even though the impact is minimal and in the short term.","PeriodicalId":32347,"journal":{"name":"Jurnal Meteorologi dan Geofisika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89979333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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