METEOROLOGY CLIMATOLOGY AND GEOPHYSICS AGENCY (BMKG) HAS THE DUTY TO PROVIDE WEATHER INFORMATION INCLUDING RAINFALL. WEATHER IS A WHOLE PHENOMENON THAT OCCURS IN THE EARTH'S ATMOSPHERE. RAINY OR SUNNY WEATHER CONDITIONS GREATLY AFFECT COMMUNITY ACTIVITIES IN DAILY LIFE, ESPECIALLY FOR OUTDOOR ACTIVITIES. RAINFALL CONDITIONS THAT OCCUR CAN BE DETERMINED BY THE PRESENCE OF RAINFALL. BMKG HAS SEVERAL TYPES OF RAINFALL GAUGES, WITH A NUMBER THAT HAS NOT BEEN EVENLY DISTRIBUTED THROUGHOUT THE REGION. THE PRICE OF THE EQUIPMENT IS RELATIVELY EXPENSIVE. THE SOLUTION THAT CAN BE DONE TO INCREASE THE DENSITY OF RAINFALL OBSERVATIONS IS TO UTILIZE EXISTING SOURCES TO OBTAIN WEATHER INFORMATION. THIS RESEARCH WILL UTILIZE CCTV THAT IS SPREAD IN THE JAKARTA AREA TO BE PROCESSED SO AS TO PRODUCE INFORMATION ON RAIN CONDITIONS. THE METHOD USED IS TO DO IMAGE PROCESSING USING THE CONVOLUTIONAL NEURAL NETWORK (CNN) METHOD. CCTV IMAGES WILL BE TAKEN FROM THE INTERNET AUTOMATICALLY BY THE CRAWLING METHOD TO GET DIGITAL IMAGE DATA. THE AVAILABLE IMAGES WILL THEN BE CARRIED OUT A TRAINING PROCESS AND MODEL TESTING TO GET THE MODEL WITH THE BEST ACCURACY. THE RESULTS OF THIS MODEL WILL BE USED FOR RAIN DETECTION ON DIGITAL CCTV IMAGES. THE RAIN DETECTION PROCESS WILL BE DONE AUTOMATICALLY AND IN REAL TIME. THE RESULTS OF THE RAIN DETECTION PROCESS WILL BE DISPLAYED ON THE MAP ACCORDING TO THE LOCATION OF THE INSTALLED CCTV. THIS RESEARCH HAS MADE A CNN MODEL FOR AUTOMATIC RAIN DETECTION WITH 98.8% TRAINING ACCURACY AND 96.4% TESTING ACCURACY AND HAS BEEN EVALUATED WITH BMKG OBSERVATION DATA SO THAT IT HAS AN EVALUATION ACCURACY OF 96.7%.

ON AUGUST, 23 2020, THERE WAS A PHENOMENON OF HAIL IN SUKABUMI FOR 30 MINUTES. THE GROWTH OF CLOUD THAT PRODUCED HAIL CAN BE INTERPRETED WITH HIMAWARI-8 SATELLITE USING THE RGB (RED, GREEN, BLUE) METHOD. IN THIS RESEARCH, THE HAIL WILL BE INTERPRETED WITH RGB METHOD WHICH  PROCESSED USING SATAID SOFTWARE BY UTILIZING 3 PRODUCTS, NAMELY DAY CONVECTIVE STORM (TO DISTINGUISH CONVECTIVE CLOUDS FROM THE OTHER CLOUDS), DAY MICROPHYSICS (FOR CLOUD MICROPHYSIS) AND AIR MASS BY COMBINING SEVERAL HIMAWARI-8 SATELLITE CHANNELS. IN ADDITION, THE CAUSED OF HAIL WILL BE SUPPORTED BY ANALYSIS OF SEA SURFACE TEMPERATURE ANOMALY AND THE 850-700 MB LAYER OF  RELATIVE HUMIDITY WHICH PROCESSED WITH GRADS SOFTWARE. THE RESULTS SHOWS THAT DAY CONVECTIVE STORM PRODUCT COULD DETECT THE DEVELOPMENT OF CB CLOUDS (THE GROWTH, THE MATURE AND THE EXTINCTION PHASE). DAY MICROPHYSICS PRODUCT SHOWS THAT THERE HAD BEEN A STRONG MICROPHYSICAL PROCESS DURING HAIL AND AIR MASS PRODUCT SHOWS THAT THERE WAS A LOT OF AIR MASS CONTENT WHICH COMING FROM THE NORTHEAST-EAST DIRECTION AND INFLUENCING THE GROWTH OF CUMULONIMBUS CLOUD. IN ADDITION, POSITIVE SST ANOMALIES AND WET RH VALUES ??(75-90%) IN THE 850-700 MB LAYER INFLUENCED THE CONVECTIVE CLOUD GROWTH THAT CAUSED HAIL IN SUKABUMI.

RAINFALL CHARACTERISTICS OF INDONESIA'S TROPICAL CLIMATE HAVE HIGH VARIABILITY ACCORDING TO SPACE AND TIME, SO TO DETERMINE THE RAINFALL PATTERN OF A LOCATION, AN IN SITU RAINFALL MEASURING INSTRUMENT (AWS = AUTOMATIC WEATHER STATION) IS NEEDED WITH HIGH DENSITY. THE EXISTENCE OF AWS ALSO REQUIRES RELATIVELY HIGH MAINTENANCE COSTS AND A STANDARD PLACEMENT LOCATION (ACCORDING TO THE RULES OF WMO = WORLD METEOROLOGICAL ORGANIZATION) WHICH IS RELATIVELY BROAD AND IS NOT OBSTRUCTED BY OTHER OBJECTS THAT CAN MAKE THE RESULT OF RAINFALL DATA IS NOT REPRESENTATIVE. WITH THE CONCEPT OF COMPUTER VISION, RESEARCH WILL BE CARRIED OUT TO ESTIMATE THE RAINFALL CONDITION FROM THE CCTV CAMERAS. THE CCTV CAMERA DATA WHICH HAVE QUALITATIVE CHARACTERISTIC INTO RAINFALL DATA WHICH HAVE QUANTITATIVE CHARACTERISTICS. THIS RESEARCH IS ALSO MOTIVATED BY THE LARGE NUMBER OF CCTVS THAT ARE PLACED IN A LOT OF LOCATIONS BY LOCAL GOVERNMENTS ALONG WITH THE SMART CITY PROGRAM IN DISTRICTS AND CITIES THROUGHOUT INDONESIA. THE PRELIMINARY RESEARCH WAS CONDUCTED IN CENTER FOR ATMOSPHERIC SCIENCE AND TECHNOLOGY OFFICE IN BANDUNG. RAINFALL DATA FROM AWS WAS USED TO VALIDATE CCTV DATA WHICH PLACED IN SAME LOCATION. THE PROCESS OF CONVERTING CCTV DATA INTO RAINFALL DATA GOES THROUGH 6 STAGES. THE FIRST IS READING THE IMAGE MAPPING DATA AND AWS (IN RAINFALL ACCUMULATION DATA FORM). SECOND, READ THE IMAGE DATA IN GRAYSCALE. THIRD, EXTRACT THE FEATURES. FOURTH, SPLIT THE REFERENCE AND SAMPLE DATA. FIFTH, CONDUCTS THE K-NN MAPPING REFERENCE IMAGE AND RAINFALL ACCUMULATION DATA. SIXTH IS TO PRAISE K-NN TESTING. THE ACCURACY IS CALCULATE WITH COMPARING THE ESTIMATED NUMBER OF CCTV CAMERAS THAT ARE CORRECT WITH THE TOTAL SAMPLE SIZE. THE EVALUATION RESULT STATES THAT THE HIGHEST ACCURACY IS OBTAINED WITH K = 1. WHEN K=1, THE ACCURACY PERCENTAGE REACHING 94.8%. ACCURACY DECREASES WITH INCREASING VALUE OF K AND DRASTICALLY DECREASES WITH K> 2. IN THE 1-10 DAYS REFERENCE DATA, THE HIGHEST ACCURACY IS OBTAINED BY THE NUMBER OF REFERENCE DATA FOR 10 DAYS, WHICH IS AROUND 97%, STABLE UNTIL THE VALUE OF K = 8. WHILE THE LOWEST ACCURACY IS OBTAINED WHEN THE REFERENCE DATA IS 1 DAY WITH AN ACCURACY VALUE OF ABOUT 43%. BASED ON THE RESULTS OF THIS STUDY, IT CAN BE CONCLUDED THAT RAIN DATA FROM CCTV CAN BE USED TO ESTIMATE THE RAINFALL DATA.

WIND IS A COLLECTION OF AIR MASS FLOW WHICH MOVES BY THE DIFFERENCE OF AIR PRESSURE. THE TRUE WIND IS AN ACTUAL WIND SPEED AND DIRECTION DID NOT EFFECTED BY THE SHIP SPEED AND DIRECTION. MEASUREMENT OF TRUE WIND DIRECTION AND SPEED ON THE SHIP IS AN IMPORTANT THING FOR THE SAFETY OF MARINE TRANSPORTATION AS THE PRIME TRANSPORTATION IN INDONESIA THAT IS AN ARCHIPELAGO COUNTRY. THE AIMS OF THIS RESEARCH ARE TO STAKE AND KNOW THE EFFECTIVENESS OF THE TRUE WIND DIRECTION AND SPEED MEASUREMENTS SYSTEM ON THE SHIP WHICH SHOWN AS REAL TIME DATA MONITORING ON A DISPLAY. THIS SYSTEM WILL MAKE THE SHIP CAPTAIN EASIER FOR MONITORING THE TRUE WIND DATA WITHOUT HAVE TO MANUALLY MEASURE THE TRUE WIND FROM THE APPARENT WIND DATA OF ANEMOMETER. THIS SYSTEM USES TWO MAIN SENSORS SUCH AS GPS AND ANEMOMETER THAT EACH SENSOR HAS THEIR OWN FUNCTION. THE GPS UBLOX NEO M8N PRODUCES THE SHIP SPEED AND THE COORDINATE DATA IN LATITUDE AND LONGITUDE WHEREAS THE ANEMOMETER DELIVER SIGNAL AS THE WIND SPEED AND DIRECTION. THE APPARENT WIND AS THE WIND SPEED AND DIRECTION OF ANEMOMETER BEING AN INPUT SIGNAL WILL AUTOMATICALLY BE MEASURED USING SOME ALGORITHM TO HAVE A RESULT OF TRUE WIND SPEED AND DIRECTION DATA. THIS MEASUREMENT WILL BE PROCESSED BY THE ARDUINO MEGA 2560 AS A MICROCONTROLLER THAT ADDITIONALLY ALSO NEEDS A RTC TO PRODUCE THE TIME DATA. THEN THE OUTPUT DATA SUCH TIME, COORDINATE, ALSO TRUE WIND SPEED AND DIRECTION SAVE ON THE SD CARD AND SHOW ON LCD SETTING UP ON THE LOGGER DATA BOX. ALTHOUGH THE TRUE WIND DATA IS ALSO SENT FROM THE TRANSMITTER TO THE RECEIVER BY 433 MHZ 3DR TELEMETRY COMMUNICATION AND DISPLAY ON THE USER INTERFACE OF PERSONAL COMPUTER AS A REAL TIME DATA. THE CONCLUSION OF THIS RESEARCH IS THAT SYSTEM MAKES THE CAPTAIN OF SHIP MORE EFFECTIVE AND EFFICIENT FOR KNOWING THE REAL TIME TRUE WIND DATA AND THE COMMUNICATION DISTANCE THAT THE TRANSMITTER AND RECEIVER CAN COMMUNICATE ACHIEVE APPROXIMATELY 87,06 METERS. 

 

ABSTRACT. ATTENUATION CAUSES THE QUANTITATIVE PRECIPITATION ESTIMATION (QPE) BY THE C-BAND WEATHER RADAR TO UNDERESTIMATE. ATTENUATION CORRECTION ON C-BAND WEATHER RADAR IS NEEDED TO ELIMINATE PRECIPITATION ESTIMATION ERRORS. IN THIS STUDY, GATE-BY-GATE METHOD ATTENUATION CORRECTION WITH THE KRAEMER’S APPROACH IS APPLIED TO C-BAND WEATHER RADAR DATA FROM THE INDONESIAN AGENCY FOR METEOROLOGY AND GEOPHYSICS (BMKG) WEATHER RADAR NETWORK IN BENGKULU.  THIS METHOD USES REFLECTIVITY AS THE ONLY INPUT. THE ESTIMATED PRECIPITATION ERROR IS OBTAINED BY COMPARING RADAR-BASED ESTIMATES TO 10 OBSERVATION RAIN GAUGES OVER A MONTH. THE DATA ARE PROCESSED USING PYTHON-BASED LIBRARIES WRADLIB AND ARCGIS 10.5. AS A RESULT, THE CORRECTED PRECIPITATION HAD A SMALLER ERROR VALUE (R = 0.88; RMSE = 8.38) THAN THE UNCORRECTED PRECIPITATION (R = 0.83; RMSE = 11.70).

 

KEYWORDS : ATTENUATION CORRECTION, QUANTITATIVE PRECIPITATION ESTIMATION, C-BAND WEATHER RADAR, BENGKULU

 

RAINFALL ESTIMATION BASED ON SATELLITE DATA WITH CONVECTIVE STRATIFORM TECHNIQUE (CST) AND MODIFIED CONVECTIVE STRATIFORM TECHNIQUE (MCST) METHODS (CASE STUDY IN JABODETABEK, 31^ST DECEMBER 2019)

 

RAHMAT NUR RAHMAN^1*, INDRA², AND LEVI RATNASARI³

¹ MARARENA SARMI METEOROLOGICAL STATION, BMKG

² MATHILDA BATLYARI SAUMLAKI METEOROLOGICAL STATION, BMKG

³ ZAINUDDIN ABDUL MADJID METEOROLOGICAL STATION, BMKG

 

* CORRESPONDING E-MAIL: RAHMATTHEKICKERS@GMAIL.COM

 

ABSTRACT. ACCURATE AND REAL TIME RAINFALL INFORMATION IS NEEDED FOR EARLY WARNING OF HYDROMETEOROLOGICAL DISASTERS. IT IS NECESSARY TO HAVE A METHOD FOR ESTIMATION CONSIDERING THAT THE AVAILABILITY OF OBSERVATION POINTS IN INDONESIA IS CURRENTLY STILL LACKING. VARIOUS METHODS OF ESTIMATION RAINFALL TO USE SATELLITES HAVE BEEN CONDUCTED, BUT IN MANY TIMES THE SATELLITE FAILED TO DISTINGUISH BETWEEN CUMULIFORM CLOUDS AND STRATIFORM CLOUDS CONSEQUENTLY, THE ESTIMATED VALUE OF THE ESTIMATED RAINFALL TO UNDERESTIMATE OR OVERESTIMATE. THIS STUDY APPLIES THE CONVECTIVE STRATIFORM TECHNIQUE (CST) AND MODIFIED CONVECTIVE STRATIFORM TECHNIQUE (MCST) METHODS FROM THE HIMAWARI-8 SATELLITE IMAGERY TO ESTIMATE THE RAIN EVENT THAT CAUSED FLOODING IN JABODETABEK ON DECEMBER 31^ST, 2019. BOTH METHODS WERE VERIFIED WITH AWS RAINFALL DATA IN JABODETABEK USING STATISTICAL INDEXES AND CONTINGENCY TABLES. THE BASIC OF THE ALGORITHMS METHODS ARE CARRIED OUT BY INVOLVING THE SEPARATION OF CUMULIFORM AND STRATIFORM CLOUDS, SO THAT THE PROBLEM CAN BE OVERCOME BECAUSE IT CAN IMPROVE THE ESTIMATED VALUE OF UNDERESTIMATE OR OVERESTIMATE RAINFALL. THE RESULTS OF THE TWO METHODS GENERALLY SHOW THAT THE MCST METHOD PRODUCES BETTER RAINFALL ESTIMATES, WHILE CST OFTEN PRODUCES RAINFALL VALUES ??EVEN WHEN THE EVENT DOES NOT RAIN.

 

KEY WORDS: RAINFALL ESTIMATION, SATELLITE, CST, MCST

LAODE BANGSAWAN, MUHAMMAD CHRISNA SATRIAGASA, SYAMSUL BAHRI

ABSTRACT

THE INTEGRATION OF THE AVAILABILITY AND PROCESSING OF CHIRPS SATELLITE DATA BY THE GOOGLE EARTH ENGINE (GEE) PLATFORM WAS USED IN THIS STUDY TO EXTRACT THE ESTIMATED MONTHLY RAINFALL IN SOUTH SULAWESI. SEVERAL AREAS WERE SELECTED BASED ON THE CHARACTERISTICS OF THE RAINY PERIOD CYCLE REPRESENTING SOUTH SULAWESI, NAMELY MAKASSAR, MASAMBA, WAJO, AND BONE. HOWEVER, THE PERFORMANCE OF THE CHIRPS RAINFALL ESTIMATION HAS NOT BEEN MAXIMIZED WITH CORRELATION COEFFICIENT VALUES OF 0.94, 0.63, 0.65, 0.74 FOR EACH OF THESE AREAS SO THAT THE INCREASE IN RAINFALL ESTIMATION PERFORMANCE IS CARRIED OUT BY APPLYING THE MULTIPLE LINEAR REGRESSION METHOD WHICH TAKES INTO ACCOUNT THE OBSERVED RAINFALL, THE LOCATION OF LATITUDE AND LONGITUDE AS WELL AS ELEVATION IN EVERY LOCATION. THE RESULTS SHOWED AN INCREASE IN THE VALUE OF THE CORRELATION COEFFICIENT TO 0.95, 0.74, 0.74, AND 0.87 IN EACH STUDY AREA.

KEYWORDS: GOOGLE EARTH ENGINE (GEE), CHIRPS, MULTIPLE LINEAR REGRESSION.

ONE OF THE OBSERVED CLOUD CONDITION IS TOTAL CLOUD COVER. THE METEOROLOGY AND CLIMATOLOGY AND GEOPHYSICS AGENCY (BMKG) OBSERVES THE AMOUNT OF CLOUD COVER MANUALLY BY OBSERVERS, WHICH MEANS SUCH OBSERVATIONS ARE STILL SUBJECTIVE. THIS RESEARCH AIMS TO BUILD A AUTOMATIC TOTAL CLOUD COVER OBSERVATION SYSTEM USING K-NEAREST NEIGHBOR ALGORITHM. THE PARAMETER OBSERVED BY THIS TOOL IS THE AMOUNT OF CLOUD COVER USING RASPBERRY PI CAMERA V2 WHICH HAS AN IMAGE RESOLUTION OF 1024 X 768 PIXELS AS A SENSOR. THIS SYSTEM USES A RASPBERY PI 3 REV B + MICROCONTROLLER TO DETERMINE THE AMOUNT OF CLOUD COVER AND USES A VGA CABLE AS ITS COMMUNICATION SYSTEM. THE DATA DISPLAYED ON A PC MONITOR. THIS AUTOMATIC CLOUD COVER OBSERVATION SYSTEM HAS A DETERMINATION RANGE FROM 0 TO 8 OCTAVES. THE TEST RESULTS SHOW THAT THE DESIGN OF THE SYSTEM ABLE TO PROVIDE DATA THAT IS DIRECTLY IN THE FORM OF OCTAVES.

KEYWORDS: CLOUD, RASPBERRY PI 3 REV B+, RASPBERRY PI CAMERA V2, K- NEAREST NEIGHBOR.

OBSERVATION NETWORK WILL NEVER BE ENOUGH FOR CREATING GOOD INFORMATION ABOUT MONTHLY RAINFALL. INTERPOLATION METHOD IS ALWAYS NEEDED AND FOR OPERATIONAL PURPOSES, INVERSE DISTANCE WEIGHTING (IDW) METHOD IS USED. IN EAST JAVA, 197 OBSERVATION POINTS ARE INVOLVED THEN IDW'S PARAMETERS USED ARE NEIGHBOR=12 AND POWER=2. THE CONSEQUENCES OF THIS FRAMEWORK ARE INVESTIGATED IN THIS STUDY. BY REVERSING IDW'S FORMULA, TWO KINDS OF RASTER ANALYSIS ARE DEVELOPED,  DISTANCE TO NEIGHBOUR USED (DNU) AND COEFFICIENT FROM POINT (CFP). DNU SHOWS HOW FAR POINTS USED ON IT FOR DOING INTERPOLATION IN SOME AREA BY KILOMETER (KM). MEANWHILE, CFP DESCRIBES AN AREA IMPACTED BY A POINT. DATA USED IN THIS STUDY ARE LONGITUDE AND LATITUDE OF 197 OBSERVATION POINTS. THE SCRIPTS ARE WRITTEN IN R LANGUAGE. ANALYSIS BASED ON LOCAL GOVERNMENTAL REGION SHOWS THAT SUMENEP HAS VERY FAR DNU, IN AVERAGE, FIRST POINT USED LOCATED MORE THAN 25 KM AND ITS TWELFTH IS 112 KM (AVERAGE OTHER REGION DNU1=7 KM AND DNU12=35 KM), IT MEANS THERE SHOULD BE SPECIAL INTERPOLATION MECHANISM FOR SUMENEP. CFP CONFIRMS THAT SOME POINTS GIVE IMPACT IN UNNATURAL WAYS (IMPACTED AREA=5741 KM2 OR WEIGHTING PER AREA=1.81KM-2). WE PROPOSE DNU AND CFP AS ALTERNATIVE QUALITY CONTROL PARAMETERS FOR INVESTIGATING CONSEQUENCES IN INTERPOLATING RAINFALL.

 

KEYWORDS: IDW, RASTER ANALYSIS, DNU, CFP

 

THE UTILIZATION OF HF RADAR DATA FROM 2018-2019 TO STUDY THE CHARACTERISTICS OF SEA SURFACE CURRENT (SSC) IN THE BALI STRAIT HAS BEEN CARRIED OUT. THE DATA PROCESSING METHOD BY CALCULATING THE SPEED AND DIRECTION OF THE SSC OF THE ZONAL AND MERIDIONAL COMPONENTS. FURTHERMORE, THE SSC ANALYSIS CONDUCTED EVERY HOUR AND MONTH BY CALCULATING THE AVERAGE OF THE SAME HOURS AND MONTHS OF THE OVERALL DATA. THE RESULT SHOWED THAT A UNIQUE PATTERN OF SSC IN THE BALI STRAIT OCCURRED ON THE WEST SIDE OF THE ISLAND OF BALI AND THE EAST SIDE OF JAVA ISLAND. ON THE WESTERN SIDE OF THE ISLAND, THERE WAS A DECREASE IN SSC SPEED AT 0:00–7:00 AND 13:00–18:00, AS WELL AS A TWO-TIME INCREASE IN 8:00–12:00 AND 19:00–2:00, BOTH OF WHICH WERE AT A FLUCTUATING SPEED RANGE OF 0-140 CM/S WITH A DOMINANT DIRECTION TOWARDS THE SOUTH. ON THE EASTERN SIDE OF JAVA ISLAND, SSC SPEED RANGES FROM 0-40 CM/S ALL THE TIME WITH THE DOMINANT DIRECTION TOWARD THE EAST TO SOUTHEAST. THE SSC PATTERN EACH MONTH IS ALSO MORE CLEAR IN THIS STUDY, WHERE IN DECEMBER-MARCH THE SSC SPEED IS SMALLER THAN THAT OF JUNE-SEPTEMBER, RANGING FROM 0-20 CM/S AND 40-140 CM/S, RESPECTIVELY. FURTHERMORE, THE TWO SSC PATTERNS ABOVE CAN BE SIMPLIFIED INTO TWO PERIODS, NAMELY RELAXATION AND AGITATION. THIS RESEARCH ALSO APPLIES HF RADAR APPLICATION IN THE CASE OF A SHIPWRECK IN BALI STRAIT.

AIR TEMPERATURE IS ONE OF THE MOST COMMON AND IMPORTANT METEOROLOGICAL PARAMETERS, BECAUSE IT HAS A DIRECT IMPACT ON HUMAN ACTIVITIES. EVERY DATA, INCLUDING AIR TEMPERATURE DATA, NEEDS TO BE VERIFIED, ONE OF WHICH IS 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, IN THIS CASE, THE DIFFERENCE OF AIR TEMPERATURE DATA EVERY HOUR, COMPARED TO THE THRESHOLD VALUE THAT WAS ALREADY DETERMINED, SO 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 TO PRODUCE A THRESHOLD TO BE THE REFERENCE IN CARRYING OUT STEP CHECK QUALITY CONTROL.

THE DATA USED IN THIS STUDY ARE HOURLY AIR TEMPERATURE DATA FROM DRY-BULB THERMOMETER READINGS AND HOURLY WEATHER CONDITION 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 IN ORDER TO OBTAIN A THRESHOLD VALUE ACCORDING TO THE WEATHER CONDITIONS (RAIN AND NO RAIN CONDITIONS), THEN THE MAXIMUM AND MINIMUM VALUE OF AIR TEMPERATURE DIFFERENCES EVERY HOUR ARE BEING CALCULATED.

THE RESULTS SHOWED THAT THERE ARE 88% OF NO RAIN DATA AND 12% OF RAIN DATA. FOR NO RAIN WEATHER CONDITIONS, THE HIGHEST INCREASE IN AIR TEMPERATURE IS 6.4°C/HOUR, WHILE THE HIGHEST DECREASE IN TEMPERATURE IS 5.8°C/HOUR, AND FOR RAINY WEATHER CONDITIONS THE HIGHEST INCREASE IN AIR TEMPERATURE IS 4.2°C/HOUR WHILE THE HIGHEST DECREASE IN AIR TEMPERATURE IS 11.2°C/HOUR. WITH THIS RESULTS, OBSERVERS CAN FIRST CARRY OUT QUALITY CONTROL WITH THE STEP CHECK METHOD BEFORE FILLING IN THE DATA INTO THE SYSTEM DATABASE, SO ANY SUSPECT DATA EITHER FROM READING ERRORS OR TOOL ERRORS CAN BE MINIMIZED AND FINALLY PRODUCE A VALID DATASET.

EL-NINO WHICH OCCURRED IN 2019 IN INDONESIA CAUSED LONGER DRY CONDITIONS. LOW RAINFALL AND VEGETATION DROUGHT CAUSE WIDESPREAD FOREST / LAND FIRES. THIS STUDY AIMS TO KNOW THE RELATIONSHIP BETWEEN DROUGHT CONDITIONS AND FOREST / LAND FIRES FROM THE PARAMETERS OF RAINFALL AND VEGETATION GREENNESS LEVEL. THE STUDY LOCATED IN JAMBI AND CENTRAL KALIMANTAN DURING THE PEAK MONTHS OF FIRES WHICH ARE SEPTEMBER 2019. TO SEE FLUCTUATIONS IN THE PEAK OF FIRES, 8 DAILY DATA WERE TAKEN FOR THIS TIME PERIOD. EXTRACTION OF RAINFALL INFORMATION IS DERIVED FROM THE HIMAWARI-8 INFRARED BAND 1 IMAGE INTO L2 RAINFALL RATE DATA. VEGETATION GREENNESS LEVEL INFORMATION IS DERIVED FROM TERRA / AQUA MODIS RED AND NEAR INFRARED BAND IMAGES INTO L2 ENHANCE VEGETATION INDEX (EVI) DATA. HOTSPOT DATA COMES FROM THE IMAGES OF TERRA, AQUA MODIS, SNPP VIIRS, AND NOAA20. FIRE DATA WAS EXTRACTED FROM HOTSPOT DATA AND DELINEATION OF MODIS RGB IMAGE SMOKE. RAINFALL FLUCTUATION AFFECTS THE NUMBER OF FOREST / LAND FIRE HOTSPOTS. THE DECREASE IN RAINFALL FOLLOWS THE INCREASE IN THE NUMBER OF HOTSPOTS AND THE INCREASE IN RAINFALL AFFECTS THE DECREASE IN THE NUMBER OF HOTSPOTS. IN JAMBI PROVINCE, RAINFALL FROM 0 TO 50 MM WAS FOLLOWED BY AN INCREASE IN THE NUMBER OF HOTSPOTS AND RAINFALL FROM 0 TO 400 MM THE NUMBER OF HOTSPOTS DECREASED. IN JAMBI PROVINCE, THE HIGHEST NUMBER OF HOTSPOTS OCCURRED DURING THE LOWEST RAINS (0 MM) ON SEPTEMBER 6-13, 2019. MEANWHILE, THE TKV VARIABLE HAD LITTLE EFFECT ON THE NUMBER OF HOTSPOTS. THE DRIEST TKV (0.1) ON SEPTEMBER 14-21 2019 WAS INFLUENCED BY THE HIGHEST NUMBER OF FIRE HOTSPOTS IN THE PREVIOUS PERIOD.

FOREST AND LAND FIRE OCCUR EVERY YEAR IN INDONESIA ESPECIALLY IN PEAT LAND AREA AND INCREASING IN DRY SEASON. THESE FIRES GIVE BAD IMPACT TO AIR POLLUTANT FROM FIRE SMOKE EVENTS. FIRE SMOKE DISTRIBUTION NEEDS TO BE IDENTIFIED PERIODICALLY. GCOM-C DATA IS A NEW RELEASE DATA THAT COULD BE USED TO DETECT FIRE SMOKE. THIS STUDY HAS AN OBJECTIVE TO IDENTIFY FIRE SMOKE FROM GCOM-C DATA. GCOM-C DATA HAS WAVELENGTH RANGE FROM 0.38 TO 12 ΜM AND COVERS VISIBLE, NEAR INFRARED, SHORT-WAVE INFRARED AND THERMAL INFRARED. IT IS RELATIVELY SIMILAR TO MODIS OR HIMAWARI-8 IMAGES WHICH COULD IDENTIFY FIRE SMOKE ALSO. THE METHODOLOGY USED WAS VISUAL INTERPRETATION TO DETECT FIRE SMOKE USING NEAR INFRARED BAND (VN08), SHORTWAVE INFRARED BAND (SW03), AND THERMAL BANDS (T01 AND T02). HOTSPOT DATA WAS OVERLAID WITH GCOM-C IMAGE TO KNOW THE LOCATION OF FIRE EVENTS. COMBINATION OF COMPOSITE RGB IMAGE HAS BEEN APPLIED TO DETECT FIRE SMOKE. GCOM-C IMAGE OF VN8 BANDS AND COMBINATION OF THERMAL BAND IN COMPOSITE IMAGE COULD BE USED TO DETECT FIRE SMOKE IN PULANG PISAU, CENTRAL KALIMANTAN.

THE ISSUE OF CLIMATE CHANGE IS A GLOBAL ISSUE  OFTEN DISCUSSED LATELY. BUT DEEPER STUDY IS NEEDED  TO IDENTIFY AND ANALYZE THE RISKS OF CLIMATE CHANGE. THIS  STUDY TAKES  SERANG, BANTEN AS THE STUDY AREA BECAUSE SERANG IS A DENSELY POPULATED CITY . IT IS NECESSARY TO ANALYZE THE IMPACT OF THE CLIMATE CHANGE TO THE CITY. THE CLIMATE CHANGE  IS INDENTIFIED BY RAINFALL PARAMETER ANALYSIS  AND TEMPERATURES DURING  THE BASELINE PERIODS (1981-2015), THE PERIOD OF 2016-2039   AS THE FUTURE (NEAR), THE PERIOD OF 2040 TO 2069 (MIDDLE) AND THE PERIOD OF 2070 TO 2099 (FAR) TAKEN  FROM GCM MODELS. THE OUTPUT OF THIS PROJECTION WILL  BE CORRECTED BY THE EQUATION BENNET AND EISNER. THEN, THE MEAN ABSOLUTE ERROR (MEA) IN EACH DATA MODELING SCENARIOS IS USED  TO FIND THE BEST SCENARIO MODELS IN THE FUTURE. THE RESULTS SHOW THAT IN PROJECTING THREE PERIODS (NEAR, MIDDLE, FAR), EACH CLIMATE PARAMETER HAS  DIFFERENT CORRECTION. THE PROJECTION OF MONTHLY RAINFALL AVERAGE IN THE FUTURE  WILL DECREASE WHILE THE AIR TEMPERATURE (MAXIMUM AND MINIMUM) IS  INCREASING.

 

QUASI LINEAR CONVECTIVE SYSTEM (QLCS) IS A TYPE OF CONVECTIVE SYSTEM THAT IS KNOWN TO HAVE THE POTENTIAL TO CAUSE HAZARDS IN THE AREA OF ITS OCCURRENCES AND PROPAGATIONS. SEVERAL PREVIOUS STUDIES HAVE SHOWN THAT THIS PHENOMENON HAS BEEN IDENTIFIED IN SEVERAL REGIONS OF INDONESIA. BUT THERE HAVE NOT BEEN MANY COMPREHENSIVE STUDIES ON THE OCCURRENCE AND PROPERTIES OF QLCS IN THE INDONESIAN REGION. THUS, THE RELIABLE AND COMPREHENSIVE THEORY THAT IS ABLE TO EXPLAIN THE VARIABILITY AND CHARACTERISTICS OF QLCS IN INDONESIA IS VERY LACKING. THIS OBSERVATIONAL STUDY AIMS TO INITIALLY CONDUCT A COMPREHENSIVE STUDY ON THE OCCURRENCE AND PROPERTIES OF QLCS IN INDONESIA. THIS STUDY TAKES THE CASES OF QLCS EVENTS DURING 2017 IN SURABAYA, MAUMERE, AND PANGKALAN BUN. THESE STUDY AREAS ARE SELECTED BECAUSE THEY HAVE SIMILAR CLIMATES AND RAINFALL PATTERNS AND ARE COVERED BY C-BAND-BASED WEATHER RADAR OBSERVATION NETWORK. OBSERVATIONAL CLASSIFICATIONS ARE PERFORMED TO DETERMINE THE CHARACTERISTICS OF SPATIAL, TEMPORAL, PROPAGATION, LOW-LEVEL WIND PROFILE, AND FORMATION IN THE STUDY AREA. DURING THE STUDY PERIOD, 151 QLCS CASES WERE IDENTIFIED. THERE ARE 23 QLCS CASES FROM THE MAUMERE’S RADAR DATA, 58 QLCS CASES FROM THE SURABAYA’S RADAR DATA, AND 70 CASES FROM PANGKALAN BUN’S RADAR DATA. THE RESULTS SHOW THAT THERE ARE SEVERAL VARIATIONS OF QLCS CHARACTERISTICS AMONG THOSE THREE AREAS DURING THE STUDY PERIOD ALTHOUGH THEY HAVE SIMILAR CLIMATES AND RAINFALL PATTERNS.

 

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 THE 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 AN 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. KEYWORDS: HIMAWARI, TROPICAL SQUALL LINE, MESOSCALE CONVECTIVE VORTICES

 

A HIGH-RESOLUTION-GRIDDED RAINFALL DATASET IS ESSENTIAL FOR MANY PURPOSES.  SUCH AS ANALYSIS OF EXTREME WEATHER CONDITIONS, NATURAL-DISASTER MITIGATION, OR TO BE USED AS AN INPUT TO THE HYDROLOGICAL MODEL. SATELLITE-BASED RAINFALL PRODUCTS (E.G., GSMAP (GLOBAL SATELLITE MAPPING OF PRECIPITATION)) CAN SOLVE THE SPATIAL AND TEMPORAL ISSUE DESPITE THEIR RAINFALL INTENSITY OFTEN BEING UNDER OR OVERESTIMATED. THIS RESEARCH AIMS TO PROVIDE A HIGH-RESOLUTION RAINFALL DATASET BY ADJUSTING THE 0.1 DEG GSMAP RAINFALL DATA TO THE SURFACE RAINFALL DATA FROM SEVERAL OBSERVATION POINTS IN JAKARTA AND THE SURROUNDING AREA. THE ASSESSMENT INCLUDES CALCULATING THE BIAS BETWEEN THE SATELLITE ESTIMATION IN THE NEAREST OBSERVATION POINT AND INTERPOLATING THE ERROR TO THE GSMAP GRID TO OBTAIN THE CORRECTION FACTOR IN EVERY GRID POINT. WE IMPLEMENT THE METHOD FOR AN EXTREME WEATHER EVENT IN THE JABODETABEK AREA IN JANUARY 2020. THE RESULT REVEALS A MORE REALISTIC RAINFALL SPATIAL DISTRIBUTION THAN REGULARLY INTERPOLATING THE OBSERVATION DATA. THE VALIDATION OF ADJUSTED RAINFALL ESTIMATION IN THE OBSERVATION POINT ALSO SHOWS A REDUCTION IN BIAS VALUE.

 

ABSTRACT. MOBILE WEATHER STATIONS ARE NEEDED BECAUSE OF ITS BETTER COVERAGE BALANCE THAN STATIC STATIONS. CENTER FOR CLIMATE RISK AND OPPORTUNITY MANAGEMENT IN SOUTHEAST ASIA PACIFIC (CCROM-SEAP) OF BOGOR AGRICULTURAL UNIVERSITY DEVELOPED A LOW-COST MINI MOBILE OBSERVATION SYSTEM USING DOIT ESP32 DEVKIT V1 MODULE WHICH BASED ON INTERNET OF THINGS TO MONITOR REAL TIME METEOROLOGICAL ELEMENTS (SUCH AS TEMPERATURE, HUMIDITY, AND PRESSURE), CO2, PM2.5, AND PM10 CONCENTRATION FOR BOGOR (CENTER OF BOGOR CITY). THE SYSTEM EQUIPPED WITH AN ARRAY OF GASEOUS AND METEOROLOGICAL SENSORS. WITH FIREBASE (DATABASE SERVICE BY GOOGLE) INTEGRATION, THE SYSTEM RECORDS DATA EVERY 2 SECONDS AND SENT AUTOMATICALLY TO FIREBASE. THE SYSTEM ALSO HAVE A LOCAL WEBSERVER WHICH DISPLAYING REAL-TIME GRAPH FOR EACH ELEMENTS EVERY 5 SECONDS. WE ALSO CREATING AN ANDROID APPLICATION CALLED SERVMO WHICH DISPLAYING THE REALTIME DATA. THIS APPLICATION MAY ALSO CREATE CSV FILE FROM ALL OF THE STORED DATA IN FIREBASE. THIS SYSTEM IS SUITABLE FOR REAL-TIME MOBILE MONITORING PURPOSE FOR BETTER BALANCE OF MEASUREMENTS COVERAGE.

ABSTRACT. NATIONAL INSTITUTE FOR ENVIRONMENTAL STUDIES (NIES) WITH CENTRE FOR CLIMATE RISK AND OPPORTUNITY MANAGEMENT IN SOUTHEAST ASIA PACIFIC (CCROM-SEAP) WORKING TOGETHER TO DEVELOP VERY USEFUL MONITORING SYSTEM FOR MEASURING GREENHOUSE GASES (GHGS) AND AIR POLLUTANT. AIR QUALITY MEASUREMENT IS VERY COMPLEX, BUT THIS TOOL CAN BE MEASURED VERY EASY TO UNDERSTAND WITH REAL-TIME DATA. THIS TOOL CAN COUNT HOW MUCH ANTHROPOGENIC AND NATURE ROLES EMISSION IN THIS REGION. GHGS THAT IS MEASURED IN THE FORM OF PARTICULATE MATTER (PM10 AND PM2.5), OZONE (O3), CARBON DIOXIDE (CO2), METHANE (CH4), CARBON MONOXIDE (CO), NITROGEN OXIDE (NOX), AND SULFUR DIOXIDE (SO2). THIS RESEARCH AIMS TO MEASURED SENSITIVITY FORM THIS TOOL WITH NEAREST TRAFFIC IN BOGOR. THIS RESEARCH EXPECTED TO KNOW HOW BIG THIS TOOL CAN FIND OUT AND EFFECTED TO NEAREST TRAFFIC. MONITORING SYSTEM CAN BE ACCESSED PUBLIC AND SHARE TO ALL PEOPLE WITH THE PURPOSE TO MANAGEMENT AIR QUALITY AND EARLY WARNING IN THIS REGION. THIS TOOL DESIGNED IN THREE PLACES, CCROM-SEAP BOGOR, BPPT SERPONG, AND BMKG CIBEUREUM. THIS TOOL IS PUT WITH PURPOSE TO COMPARE ANTHROPOGENIC AND NATURAL EMISSIONS IN JAKARTA MEGACITY AND SURROUNDING ACTIVITY.