There is uniqueness in climate services in East Java. Rainfall information is delivered as N-1 analysis for ongoing months N+1, N+2, and N+3 for monthly prediction. This study aims to investigate whether updating monthly predictions improves prediction accuracy. The verification method for this study is based on the percentage accuracy of the rain class category according to SNI 8196: 2015. The data used for this study is ECMWF's monthly rainfall prediction that has three lags system (1, 2, and 3). Rasters of monthly rainfall interpolation from the main rainfall observation (197 locations) in East Java from April 2015 to May 2020 (62 months) are used for the verification process. The temporal and spatial analysis then conducted using R (+ package raster). Studies based on the local governmental zone are also used. In general, the result shows that almost all months need updating, except April-September-October. Verification of ECMWFs4 shows a better verification result (0,56) in the past five years (2016-2020) for March. The regions that need monthly updating are Bawean island, the coast of Gresik, Pasuruan, and Banyuwangi
{"title":"IMPORTANCE OF UPDATING FOR MONTHLY RAINFALL PREDICTION BASED ON ECMWFs4","authors":"Achmad Maulana Rafi","doi":"10.31172/jmg.v23i3.803","DOIUrl":"https://doi.org/10.31172/jmg.v23i3.803","url":null,"abstract":"There is uniqueness in climate services in East Java. Rainfall information is delivered as N-1 analysis for ongoing months N+1, N+2, and N+3 for monthly prediction. This study aims to investigate whether updating monthly predictions improves prediction accuracy. The verification method for this study is based on the percentage accuracy of the rain class category according to SNI 8196: 2015. The data used for this study is ECMWF's monthly rainfall prediction that has three lags system (1, 2, and 3). Rasters of monthly rainfall interpolation from the main rainfall observation (197 locations) in East Java from April 2015 to May 2020 (62 months) are used for the verification process. The temporal and spatial analysis then conducted using R (+ package raster). Studies based on the local governmental zone are also used. In general, the result shows that almost all months need updating, except April-September-October. Verification of ECMWFs4 shows a better verification result (0,56) in the past five years (2016-2020) for March. The regions that need monthly updating are Bawean island, the coast of Gresik, Pasuruan, and Banyuwangi","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":"86818004","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 development of tourism is quite rapid in several parts of Indonesia. No exception in East Nusa Tenggara which offers a beautiful part of the beach. Most tourists will use this information in the form of the climate comfort index to determine the right travel time. The Climate Comfort Index can be searched using the Tourism Climate Index (TCI) method including weather parameters such as maximum air temperature and minimum air humidity (Daytime Comfort Index), average air temperature and average air humidity (Daily Comfort Index), rainfall, length of sunshine and average wind speed). Monthly data for the years 1991 - 2015 were provided from eight BMKG meteorological stations. The results showed that the Rote and Maumere regions were in the 'Very Good' category in the summer (June and July). Meanwhile, Sabu and Rote had the most comfortable seven months. In general, during the summer (June - August) the TCI value (≥ 70) has increased so as to provide comfort for beach tourism destinations. The best time for traveling is best visited during the peak of the dry season (June to August) while in the rainy season (November - February) is the worst time to travel.
{"title":"ANALYSIS AND DETERMINATION OF TOURISM CLIMATE INDEX (TCI) IN EAST NUSA TENGGARA","authors":"Nizar Manarul Hidayat","doi":"10.31172/jmg.v23i3.821","DOIUrl":"https://doi.org/10.31172/jmg.v23i3.821","url":null,"abstract":"The development of tourism is quite rapid in several parts of Indonesia. No exception in East Nusa Tenggara which offers a beautiful part of the beach. Most tourists will use this information in the form of the climate comfort index to determine the right travel time. The Climate Comfort Index can be searched using the Tourism Climate Index (TCI) method including weather parameters such as maximum air temperature and minimum air humidity (Daytime Comfort Index), average air temperature and average air humidity (Daily Comfort Index), rainfall, length of sunshine and average wind speed). Monthly data for the years 1991 - 2015 were provided from eight BMKG meteorological stations. The results showed that the Rote and Maumere regions were in the 'Very Good' category in the summer (June and July). Meanwhile, Sabu and Rote had the most comfortable seven months. In general, during the summer (June - August) the TCI value (≥ 70) has increased so as to provide comfort for beach tourism destinations. The best time for traveling is best visited during the peak of the dry season (June to August) while in the rainy season (November - February) is the worst time to travel.","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":"84594026","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 sea breeze is a meteorological phenomenon that occurs due to the contrast temperature between land and oceans. The propagation velocity of sea breeze are influenced strongly by e.g., synoptic wind and geographical conditions. Therefore, it is important to understand the relationship between the spatial distribution of sea breeze velocity and the surface characteristic, for instance over urbanized and less-urbanized coastal areas. When the sea breeze propagates inland, a cumulus cloudline will form in the vicinity of the sea breeze front (SBF). Previous studies have successfully detected the cloudline automatically using the morphological-snake algorithm. In this paper, we estimate the SBF velocity using Himawari-8 satellite images. The proposed method segmented the cloudline data points using a clustering approach, named machine learning-based k-means++, on the level-set obtained from snake algorithm. We then estimate the SBF velocity by calculating the haversine distance of the segmented cloudline points that propagate over time. The comparison of estimated cloudline speed with SBF speed measured at two observation sites, namely KKP and BPL, reveals the root mean square errors 1.39 m/s and 1.41 m/s, respectively. And the propagation direction was mainly southward.
{"title":"The estimation of sea-breeze front velocity over coastal urban using Himawari-8 images: A case study in Jakarta","authors":"Muhammad Rezza Ferdiansyah, A. Wijayanto","doi":"10.31172/jmg.v23i3.810","DOIUrl":"https://doi.org/10.31172/jmg.v23i3.810","url":null,"abstract":"The sea breeze is a meteorological phenomenon that occurs due to the contrast temperature between land and oceans. The propagation velocity of sea breeze are influenced strongly by e.g., synoptic wind and geographical conditions. Therefore, it is important to understand the relationship between the spatial distribution of sea breeze velocity and the surface characteristic, for instance over urbanized and less-urbanized coastal areas. When the sea breeze propagates inland, a cumulus cloudline will form in the vicinity of the sea breeze front (SBF). Previous studies have successfully detected the cloudline automatically using the morphological-snake algorithm. In this paper, we estimate the SBF velocity using Himawari-8 satellite images. The proposed method segmented the cloudline data points using a clustering approach, named machine learning-based k-means++, on the level-set obtained from snake algorithm. We then estimate the SBF velocity by calculating the haversine distance of the segmented cloudline points that propagate over time. The comparison of estimated cloudline speed with SBF speed measured at two observation sites, namely KKP and BPL, reveals the root mean square errors 1.39 m/s and 1.41 m/s, respectively. And the propagation direction was mainly southward.","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":"87047518","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}
In this study, prediction of tropical cyclones using the Weather Research and Forecasting (WRF) model was used to test the double-moment (DM) and single-moment (SM) microphysical parameterization schemes in event of Lili and Mangga Tropical Cyclones. Models with microphysical parameterization schemes WDM5, WDM6, WSM5, WSM6, and without microphysical parameterization schemes (CTL) were each tested against track predictions, the pressure value, and maximum wind speed. The results of track prediction show that the best schemes in the tropical cyclone case of Lili and Mangga is WSM6 and WDM6, respectively, with an average error value of 78.1 and 80.1 km. Based on the Taylor diagram, the prediction results of the pressure value and the maximum wind speed in case of Lili Tropical Cyclones get the WDM6 scheme as the best scheme. Meanwhile, the results of the pressure prediction at the cyclone center in the case of Mangga Tropical Cyclones show that the WDM6 scheme is the best. However, the prediction of maximum wind speed in Mangga tropical cyclones produces the CTL scheme as the best scheme. This study shows that DM dan SM microphysical parameterization schemes have a big impact on track prediction compare to pressure value and maximum wind speed variable.
{"title":"STUDY OF SINGLE- AND DOUBLE-MOMENT MICROPHYSICS SCHEME IMPACT ON LILI AND MANGGA TROPICAL CYCLONE","authors":"Fazrul Rafsanjani Sadarang","doi":"10.31172/jmg.v23i3.804","DOIUrl":"https://doi.org/10.31172/jmg.v23i3.804","url":null,"abstract":"In this study, prediction of tropical cyclones using the Weather Research and Forecasting (WRF) model was used to test the double-moment (DM) and single-moment (SM) microphysical parameterization schemes in event of Lili and Mangga Tropical Cyclones. Models with microphysical parameterization schemes WDM5, WDM6, WSM5, WSM6, and without microphysical parameterization schemes (CTL) were each tested against track predictions, the pressure value, and maximum wind speed. The results of track prediction show that the best schemes in the tropical cyclone case of Lili and Mangga is WSM6 and WDM6, respectively, with an average error value of 78.1 and 80.1 km. Based on the Taylor diagram, the prediction results of the pressure value and the maximum wind speed in case of Lili Tropical Cyclones get the WDM6 scheme as the best scheme. Meanwhile, the results of the pressure prediction at the cyclone center in the case of Mangga Tropical Cyclones show that the WDM6 scheme is the best. However, the prediction of maximum wind speed in Mangga tropical cyclones produces the CTL scheme as the best scheme. This study shows that DM dan SM microphysical parameterization schemes have a big impact on track prediction compare to pressure value and maximum wind speed variable.","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":"82907935","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}
Tropical squall line is a linear type of Mesoscale Convective Systems (MCS) phenomenon. On December 6-7, 2020, the Infrared (IR1) Himawari-8 satellite image in the Indian Ocean of Indonesian region, shows a cloud line identified as the tropical squall line. This study aims to identify the characteristics of the tropical squall line phenomenon that occurs in the Indian Ocean south of West Java using Himawari-8 Infrared (IR1) satellite imagery. Satellite image data is processed using an algorithm adapted to the MCC Maddox 1980 criteria. Furthermore, an objective analysis is carried out on the data based on the criteria from previous studies. The result shows that the tropical squall occurred for 19 hours with the initial type of tropical squall formation as intersecting convective band. In the mature stage, the trailing stratiform region and convective line develops an asymmetric pattern and shows a vortex (Mesoscale Convective Vortices) that forms inside the stratiform region. The result of rainfall distribution using the GSMaP model shows a category of heavy rain with rainfall in tropical squall areas exceeding 10 mm per hour.
{"title":"IDENTIFICATION OF TROPICAL SQUALL LINE USING INFRARED CHANNEL HIMAWARI-8 SATELLITE IMAGERY (CASE STUDY OF 6-7 DECEMBER 2020 IN THE INDIAN OCEAN)","authors":"Nurul Izzah Fitria, Novvria Sagita, Arnelia Indah Cahyani","doi":"10.31172/jmg.v23i3.808","DOIUrl":"https://doi.org/10.31172/jmg.v23i3.808","url":null,"abstract":"Tropical squall line is a linear type of Mesoscale Convective Systems (MCS) phenomenon. On December 6-7, 2020, the Infrared (IR1) Himawari-8 satellite image in the Indian Ocean of Indonesian region, shows a cloud line identified as the tropical squall line. This study aims to identify the characteristics of the tropical squall line phenomenon that occurs in the Indian Ocean south of West Java using Himawari-8 Infrared (IR1) satellite imagery. Satellite image data is processed using an algorithm adapted to the MCC Maddox 1980 criteria. Furthermore, an objective analysis is carried out on the data based on the criteria from previous studies. The result shows that the tropical squall occurred for 19 hours with the initial type of tropical squall formation as intersecting convective band. In the mature stage, the trailing stratiform region and convective line develops an asymmetric pattern and shows a vortex (Mesoscale Convective Vortices) that forms inside the stratiform region. The result of rainfall distribution using the GSMaP model shows a category of heavy rain with rainfall in tropical squall areas exceeding 10 mm per hour.","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":"84644549","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}
On April 27, 2018 heavy rain was occurred in Palangkaraya. Based on surface data observations at Tjilik Riwut Meteorological Station, the peak of rain occurred between 18-21 UTC, which 54 mm within 3 hours. As a result, the flood inundated on the following day. This research purposed to discover the cause of heavy rain used the WRF model of 3DVar technique that assimilated with AMSU-A satellite which used the tropical physic suite parameterization scheme and Himawari-8 Satellite (IR-1 data), processed by Python Programming. Based on the results, the WRF of the 3DVar model is not representative enough in total rainfall results. However, several weather disturbances show the potency for severe weather occurrence from WRF 3DVar modeling. These are indicated by the shear line and eddy circulation at 18 and 21 UTC, and the time series of air pressure decreases with a 0.5 Mb tendency between 15 to 18 UTC. Moreover, the cloud top temperature graph from Himawari-8 Satellite data shows a drastic reduction in temperature to -61.4323 at 18.20 UTC, which supports the heavy rain process. The weather analysis above show that WRF 3DVar is not representative enough for total rainfall result, but appropriate for other weather aspects (shear line, eddy, and air pressure). Therefore, the heavy rain is caused by shear line and eddy condition, air pressure and low temperature of the cloud top.
{"title":"The Utilization of Weather Research Forecasting (WRF) Model of 3DVar (Three Dimensional Variational) and Himawari-8 Satellite Imagery to the Heavy Rain in Palangkaraya (Case Study : April 27, 2018)","authors":"N. Ayasha, Leny Octaviana Bota","doi":"10.31172/jmg.v23i3.790","DOIUrl":"https://doi.org/10.31172/jmg.v23i3.790","url":null,"abstract":"On April 27, 2018 heavy rain was occurred in Palangkaraya. Based on surface data observations at Tjilik Riwut Meteorological Station, the peak of rain occurred between 18-21 UTC, which 54 mm within 3 hours. As a result, the flood inundated on the following day. This research purposed to discover the cause of heavy rain used the WRF model of 3DVar technique that assimilated with AMSU-A satellite which used the tropical physic suite parameterization scheme and Himawari-8 Satellite (IR-1 data), processed by Python Programming. Based on the results, the WRF of the 3DVar model is not representative enough in total rainfall results. However, several weather disturbances show the potency for severe weather occurrence from WRF 3DVar modeling. These are indicated by the shear line and eddy circulation at 18 and 21 UTC, and the time series of air pressure decreases with a 0.5 Mb tendency between 15 to 18 UTC. Moreover, the cloud top temperature graph from Himawari-8 Satellite data shows a drastic reduction in temperature to -61.4323 at 18.20 UTC, which supports the heavy rain process. The weather analysis above show that WRF 3DVar is not representative enough for total rainfall result, but appropriate for other weather aspects (shear line, eddy, and air pressure). Therefore, the heavy rain is caused by shear line and eddy condition, air pressure and low temperature of the cloud top.","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":"72959815","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}
Indonesia was shocked again by a Puting beliung or tornado-like incident on November 22, 2018, in the Jakarta area. This incident caused many losses. In this regard, a study was conducted to observe the reflectivity products to identifiy the hook echo or bow echo patterns, and the velocity products for mesocyclone patterns as the characteristic of the Puting Beliung used radar product. The study required the Cengkareng C-Band raw data radar that was processed to produce CMAX, VCUT, and CAPPI (V) at 0.5 km, 1.0 km, and 1.5 km elevations overlay by HWIND. The CMAX and VCUT radar products are used to identify the cloud structure that caused Puting Beliung, by observed the highest reflectivity of the Puting Beliung-producing clouds. Then the CAPPI product overlay by HWIND is used to identify the movement of the wind which is suspected to be the beginning of the formation of a Puting Beliung at that location which is characterized by the presence of a mesocyclone pattern in the form of wind components and radial velocity. It was suspected that there was a wind rotating in the Central Jakarta area which indicated a Puting Beliung in the area. The analysis of this radar interpretation was then validated using satellite imagery to detect the cumulonimbus clouds forming the Puting Beliung. From this research, it is known that the Puting Beliung occurred around 08.12 UTC. The growth of cyclone-producing clouds occurred rapidly with a reflectivity value between 35 - 45 dBZ and wind speed up to 35 knots. Analysis of satellite imagery showed a significant decrease in cloud peak temperature so that the formation of convective clouds in the form of Cumulonimbus clouds indicates the phenomenon of the Puting Beliung. However, in this case study the Puting Beliung phenomenon is indicated as seen from the mesocyclone pattern of the rotating radial velocity component, and not from the hook echo or bow echo pattern.
{"title":"Identification of the Puting Beliung Event by Utilizing the Interpretations of Radar Products and Himawari-8 Weather Satellite (Case Study: Puting Beliung Incident, November 22, 2018 in Jakarta)","authors":"I. Rusmala","doi":"10.31172/jmg.v23i3.799","DOIUrl":"https://doi.org/10.31172/jmg.v23i3.799","url":null,"abstract":"Indonesia was shocked again by a Puting beliung or tornado-like incident on November 22, 2018, in the Jakarta area. This incident caused many losses. In this regard, a study was conducted to observe the reflectivity products to identifiy the hook echo or bow echo patterns, and the velocity products for mesocyclone patterns as the characteristic of the Puting Beliung used radar product. The study required the Cengkareng C-Band raw data radar that was processed to produce CMAX, VCUT, and CAPPI (V) at 0.5 km, 1.0 km, and 1.5 km elevations overlay by HWIND. The CMAX and VCUT radar products are used to identify the cloud structure that caused Puting Beliung, by observed the highest reflectivity of the Puting Beliung-producing clouds. Then the CAPPI product overlay by HWIND is used to identify the movement of the wind which is suspected to be the beginning of the formation of a Puting Beliung at that location which is characterized by the presence of a mesocyclone pattern in the form of wind components and radial velocity. It was suspected that there was a wind rotating in the Central Jakarta area which indicated a Puting Beliung in the area. The analysis of this radar interpretation was then validated using satellite imagery to detect the cumulonimbus clouds forming the Puting Beliung. From this research, it is known that the Puting Beliung occurred around 08.12 UTC. The growth of cyclone-producing clouds occurred rapidly with a reflectivity value between 35 - 45 dBZ and wind speed up to 35 knots. Analysis of satellite imagery showed a significant decrease in cloud peak temperature so that the formation of convective clouds in the form of Cumulonimbus clouds indicates the phenomenon of the Puting Beliung. However, in this case study the Puting Beliung phenomenon is indicated as seen from the mesocyclone pattern of the rotating radial velocity component, and not from the hook echo or bow echo pattern.","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":"88484836","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}
Hail in Pulau Muda Village, Pelalawan Regency, Riau Province, occured on September 23rd 2019 when the entire Riau area was covered by smoke due to forest and land fires phenomenon. The hail was not accompanied by extreme rain and did not cause material harm. However, a study of atmospheric conditions before, during, and after the hail occurred is needed to reference for early warning of future events. The result was analyzed descriptively on the radar products, satellite images and observation parameters. CMAX and VCUT of radar product generated from RainbowV5.9 showed that at 06.02 UTC there was a single CB cloud, which its maximum reflectivity core was 60 dBZ at 5 km height. Both the core reflectivity and its height decreased by the time as the cloud dissipated. Himawari-8 RGB images and cloud top temperature time series processed by SATAID showed a significant decrease of cloud top temperature reached -75oC at 06.00 UTC where the cloud had large ice particles with strong updraft and dissipated within 1 hour into thin cirrus and medium-low clouds. ECMWF ERA5 Reanalysis data processed by GrADS showed that surface air temperature, RH, and CAPE index has a different value from h-1 to h+1 of hail occurrence. Vertical profile showed the single CB cloud had much amount of ice particles and also strong updrafts that the maximum value of updraft was 0,202 m/s at the development stage. In contrast, the maximum value of downdraft was 0,032 m/s in the dissipation stage of the cloud.
{"title":"Atmosphere Condition Analysis on Hail Event (Case Study : Pelalawan on September 23rd, 2019)","authors":"Mari Frystine","doi":"10.31172/jmg.v23i3.813","DOIUrl":"https://doi.org/10.31172/jmg.v23i3.813","url":null,"abstract":"Hail in Pulau Muda Village, Pelalawan Regency, Riau Province, occured on September 23rd 2019 when the entire Riau area was covered by smoke due to forest and land fires phenomenon. The hail was not accompanied by extreme rain and did not cause material harm. However, a study of atmospheric conditions before, during, and after the hail occurred is needed to reference for early warning of future events. The result was analyzed descriptively on the radar products, satellite images and observation parameters. CMAX and VCUT of radar product generated from RainbowV5.9 showed that at 06.02 UTC there was a single CB cloud, which its maximum reflectivity core was 60 dBZ at 5 km height. Both the core reflectivity and its height decreased by the time as the cloud dissipated. Himawari-8 RGB images and cloud top temperature time series processed by SATAID showed a significant decrease of cloud top temperature reached -75oC at 06.00 UTC where the cloud had large ice particles with strong updraft and dissipated within 1 hour into thin cirrus and medium-low clouds. ECMWF ERA5 Reanalysis data processed by GrADS showed that surface air temperature, RH, and CAPE index has a different value from h-1 to h+1 of hail occurrence. Vertical profile showed the single CB cloud had much amount of ice particles and also strong updrafts that the maximum value of updraft was 0,202 m/s at the development stage. In contrast, the maximum value of downdraft was 0,032 m/s in the dissipation stage of the cloud.","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":"88565823","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":"SUHU PERMUKAAN DAN KANDUNGAN PANAS LAUT PERAIRAN INDONESIA DALAM SATU ABAD TERAKHIR","authors":"Mutiara Rachmat Putri","doi":"10.31172/jmg.v23i2.841","DOIUrl":"https://doi.org/10.31172/jmg.v23i2.841","url":null,"abstract":"","PeriodicalId":32347,"journal":{"name":"Jurnal Meteorologi dan Geofisika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86584524","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":"PENGARUH CENS-CT TERHADAP CURAH HUJAN EKSTRIM DAN BANJIR DI KOTA SEMARANG (STUDI KASUS TANGGAL 5 DAN 6 FEBRUARI 2021)","authors":"Zauyik Nana Ruslana","doi":"10.31172/jmg.v23i2.761","DOIUrl":"https://doi.org/10.31172/jmg.v23i2.761","url":null,"abstract":"","PeriodicalId":32347,"journal":{"name":"Jurnal Meteorologi dan Geofisika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77065979","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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