CONVECTIVE CLOUD MONITORING SINCE ITS GROWTH, ESPECIALLY RELATED TO WHEN AND WHERE THE FIRST CONVECTIVE CLOUD INITIATED CALLED CONVECTIVE INITIATION (CI) COULD BE THE MAIN KEY IN PROVIDING AN EARLIER HEAVY RAINFALL EVENT PREDICTION. ONE OF THE SHORT-TERM PREDICTION OR KNOWN AS NOWCASTING ALGORITHMS RELATED TO CI IS SATELLITE CONVECTION ANALYSIS AND TRACKING (SATCAST) ALGORITHM. THIS STUDY AIMS TO ASSESS THE ACCURACY AND LEAD TIME OF THE CI NOWCASTING USING SATCAST ALGORITHM IN PREDICTING THE CI EVENT WITHIN 0-60 MINUTES OVER SURABAYA AND SURROUNDING AREAS USING HIMAWARI-8 SATELLITE DURING JUNE-JULY-AUGUST (JJA) PERIOD IN 2018. THREE MAIN PROCESSES USED IN THIS STUDY CONSIST OF POTENTIAL CLOUD SELECTION THROUGH CLOUD MASKING PROCESS, CLOUD OBJECT TRACKING, AND CI NOWCASTING. TWELVE INTEREST FIELDS ARE UTILIZED AS PREDICTORS BASED ON SIX BANDS OF HIMAWARI-8 SATELLITE WHICH REPRESENT CLOUD PHYSICS ATTRIBUTES SUCH AS CLOUD-TOP HEIGHT, GLACIATION, OR COOLING RATE. THE VERIFICATION RESULTS USING THE SURABAYA WEATHER RADAR SHOW THAT THE PREDICTION CAN ACHIEVES 87.33% ACCURACY FROM THE 3449 CLOUD OBJECTS IN TOTAL. THE PREDICTION HAS POD AND FAR SCORES OF 57.11% AND 52.22%, RESPECTIVELY, WITH CSI SCORE OF 35.16%. THE 32.33 MINUTES OF MEAN LEAD TIME PREDICTION INDICATES THAT CI NOWCASTING OVER SURABAYA AND SURROUNDING AREAS DURING THE JJA PERIOD IS CAPABLE TO DETECT GROWING CUMULUS ABOUT 30 MINUTES PRIOR TO CI EVENT.

FUTURE ENERGY BECOMES A CONCERN ALL OVER THE COUNTRY. THE FOSSIL ENERGY RESOURCES ARE DECREASING NOW, AND THE EXPLOITATION OF THESE RESOURCES LEAVE BEHIND ENVIRONMENTAL PROBLEMS. IT WAS INCREASING THE GAS EMISSION OF CO2 AND AFFECTED GLOBAL WARMING. RENEWABLE AND ENVIRONMENTALLY FRIENDLY ENERGY RESOURCE IS THE RIGHT CHOICE TO SOLVE THE PROBLEM. WAVE POWER IS ONE OF THE MARINE RESOURCES THAT HAVE AN ADVANTAGE IN HIGH DENSITY AND CONTINUITY. THIS RESEARCH AIMS TO INVESTIGATE THE SPATIAL-TEMPORAL DISTRIBUTION OF WAVE POWER POTENCY. THIS STUDY LOCATION BETWEEN 90°E - 150°E; 15°N - 15°S. WE USED A HINDCAST DATA SIMULATION OF WAVEWATCH-III WITH 0.125° (~14 KM) SPATIAL RESOLUTION AND SIX-HOURLY DATA FOR 25 YEARS (1991-2015). THE RESULT SHOWS THAT THE OPEN SEA, SUCH AS THE INDIAN OCEAN AND PACIFIC, CONTAINS HIGHER WAVE POWER DENSITY. THE LEVEL OF STABILITY SHOWS THAT THIS AREA IS MORE STABLE THAN THE INNER SEA. THE POWER DENSITY CHANGES PERIODICALLY CONDUCTED WITH THE MONSOONAL CYCLE. THE HIGHEST ENERGY FLUX IN THE INDIAN OCEAN ACHIEVED WHEN AUSTRALIAN MONSOON AND LOWEST WHEN ASIAN MONSOON, WHEREAS IN THE PACIFIC OCEAN, THE PEAK OF POWER DENSITY REACHES WHEN ASIAN MONSOON ONSET AND THE LOWEST IN JUNE-JULY-AUGUST. THE MOST STABLE LEVEL COHERENT WITH THE HIGHEST POWER DENSITY, AND THE LOWEST LEVEL IS IN THE TRANSITION PERIOD. BASED ON THIS ANALYSIS, THE MOST POTENTIAL AREAS FOR WAVE POWER DEVELOPMENT ARE IN ENGGANO, LAMPUNG, BANTEN, WEST JAVA, CENTRAL JAVA, DIY, EAST JAVA UNTIL BALI.

 

KEYWORDS— SPATIAL-TEMPORAL ANALYSIS, WAVE POWER RESOURCES, WAVEWATCH-III, MONSOON

LOCAL AREA RAIN RADAR DATA ARE INCREASINGLY UTILIZED IN ESTIMATING RAINFALL FOR PREVENTING HYDROMETEOROLOGICAL DISASTERS SUCH AS FLOODS, LANDSLIDES AND EVEN ACCIDENTS IN AIR TRANSPORTATION. HOWEVER, DATA AVAILABILITY OF LOCAL AREA RAIN RADAR OF SANTANU ARE ONLY PROVIDED IN RADAR REFLECTIVITY UNIT. IT IS DIFFICULT TO MAKE THE DIRECT RELATIONSHIP BETWEEN REFLECTIVITY OF RADAR AND RAINFALL INTENSITY. THIS STUDY AIMS TO DETERMINE THE CONSTANT RELATION BETWEEN THE LOCAL AREA RAIN RADAR REFLECTIVITY OF SANTANU AND THE RAIN RATE IN WEST SUMATRA. THE CONSTANT OF Z-R RELATIONSHIP IS CRUCIAL INFORMATION BECAUSE THE VALUE IS DIFFERENT FOR EACH REGION. IT IS NECESSARY TO DETERMINE THE CONSTANT OF RELATION BETWEEN THE RADAR REFLECTIVITY FACTOR AND THE RAIN RATE OF EACH RAIN, BOTH STRATIFORM AND CONVECTIVE. IN THIS STUDY, A THRESHOLD OF 10 MM/HR AND 38 DBZ WERE USED AS THE LIMIT FOR STRATIFORM AND CONVECTIVE RAIN. THERE WERE THREE METHODS USED WHICH WERE THE STATISTICAL, DROP SIZE DISTRIBUTION (DSD), THE PROBABILITY MATCHING METHOD BY REGRESSING Z AND R. THE RESULT SHOWS THE VALUES OF Z-R RELATIONSHIP FOR THE STATISTICAL METHOD OF Z = 234R^1.87, DROP SIZE DISTRIBUTION (DSD) METHOD OF  Z = 345R^1.33, THE PROBABILITY MATCHING METHOD OF Z =  224R^2.56. THE RESULTS SHOWED THAT THE STATISTICAL METHOD HAS A BETTER QUANTITATIVE PRECIPITATION ESTIMATION (QPE) VALUE WHICH IS IN LINE WITH THE ACTUAL DATA WITH A COEFFICIENT OF DETERMINATION OF 0.873.

SEMARANG COAST IS AN AREA OVER NORTHERN JAVA THAT FREQUENTLY STRICKEN BY TIDAL FLOODS. ONE OF THE PHENOMENA TO CHANGE HIGH SEA SURFACE IS ENSO. THIS STUDY WILL DISCUSS THE IMPACT OF ENSO ON THE SEMARANG COAST WITH OBSERVATION DATA FROM APRIL 2002 UNTIL DECEMBER 2012. THE RELATIONSHIP OF ENSO WITH SEA SURFACE HIGH AND THE DETERMINATION OF TIDAL FLOOD INUNDATION AREA DURING ENSO EVENTS BECOME THE MAIN OBJECT. THE TIDAL DATA HAS SIGNIFICANT CORRELATED WITH SOI VALUE. THE METEOROLOGICAL FACTOR OF RAINFALL IS ALSO TAKEN INTO AN ACCOUNT. THE SIMULATION OF TIDAL FLOOD HIGH IS OBTAINED FROM THE TIDAL DATA PROCESSING WITH LEAST SQUARE METHOD IN THE HYDRO-DYNAMICS MODEL. THE SPATIAL ANALYST METHOD IN THE ARCGIS 10.1 SOFTWARE IS USED IN THE FORMATION OF THE TIDAL FLOOD SPATIAL MODEL. THE SOI VALUE HAS A POSITIVE LINEAR RELATIONSHIP TO TIDES + RAINFALL BETTER THAN TIDES ONLY, WITH THE CORRELATION VALUE RESPECTIVELY, I.E. +0.57 AND +0.53. LA NINA CAUSES THE INCREASE OF SEA SURFACE HIGH HAS A VALUE RANGE OF 2 – 16 CM, WHILE EL NINO DOESN’T DECLINE OR RISE TO THE SEA SURFACE HIGH CONSISTENTLY. THE CUBIC REGRESSION EQUATION FOR TIDAL FLOOD INUNDATION AREA PREDICTION BASED ON TIDES + RAINFALL FACTOR IS THE BEST EQUATION.

DURING DRY SEASON 2019, EXTREME BIOMASS BURNING OCCURRED IN JAMBI, INDONESIA AND WAS EXACERBATED WITH EL NINO EVENT.  PEAK BURNING SEASON WAS OBSERVED IN SEPTEMBER WITH TOTAL HOTSPOT REACHED OF 7034. RED SKY HAS BEEN REPORTED ON SEPTEMBER 21ST DURING THE DAY. CIMEL SUNPHOTOMETER MEASUREMENTS IN JAMBI (METEOROLOGICAL STATION) AS ONE OF THE AEROSOL ROBOTIC NETWORK (AERONET) STATIONS IN INDONESIA FROM 1 TO 26 SEPTEMBER 2019 WERE USED TO INVESTIGATE THE RED SKY PHENOMENON. ASSESMENT OF AEROSOL OPTICAL PROPERTIES VARIATION AND SPECTRAL ANALYSIS ARE CONDUCTED. THE STUDY REVEALS THAT THE RED SKY OCCURRED DUE TO FIRSTLY, VERY HIGH AEROSOL LOADING RETRIEVED FROM SUN-PHOTOMETER IN JAMBI. THE AEROSOL OPTICAL DEPTH (AOD) WAS 0.34 AT 500 NM ON NON-HAZY DAY (EARLY SEPTEMBER) AND THEN INCREASED SHARPLY TO 5.74 DURING HAZY DAY. SECONDLY, DURING SEPTEMBER 23^RD ONLY LONGER WAVELENGTHS OF AOD WERE MEASURED AND RETAINED AT 675, 870, 1020, AND 1640 NM; WHILST AOD IN SHORTER WAVELENGTHS CAN NOT BE RETRIEVED. HIGHEST AOD ON SEPTEMBER 23RD WAS 6.19 AT 675 NM WHICH IS ASSOCIATED WITH THE RED SKY IN THE PREVIOUS DAY. THIRDLY, HIGH LEVELS OF FINE PARTICULATE PRESENCE IN THE ATMOSPHERE INDICATED FROM HIGH ANGSTROM EXPONENT AND ITS FINE MODE FRACTION.

HIGH-FREQUENCY (HF) RADAR IS AN INSTRUMENT THAT USES RADIO WAVES TO MEASURE OCEAN CURRENTS AND WAVES REMOTELY. THIS TECHNOLOGY HAS MANY ADVANTAGES, SUCH AS HAS HIGH SPATIAL AND TEMPORAL RESOLUTION, CAN OPERATE IN ANY WEATHER CONDITION, AND DOES NOT POSE A DANGER TO THE MARINE ENVIRONMENT. HOWEVER, HF RADAR'S RESEARCH IS STILL LIMITED IN INDONESIA. THIS RESEARCH AIMS TO ANALYZE OCEAN CURRENTS' CHARACTERISTICS IN THE BALI STRAIT USING HF RADAR DATA IN JULY 2020. RADIAL VELOCITY DATA FROM TWO HF RADAR SITES IN BANYUWANGI ARE COMBINED TO OBTAIN OCEAN CURRENTS VECTORS. THE CALCULATED CURRENT DATA FROM HF RADAR WERE COMPARED WITH ADCP DATA TO MEASURE ITS ACCURACY. OCEAN CURRENT DATA WERE ANALYZED USING THE HARMONIC ANALYSIS TO SEPARATE THE TIDAL AND NONTIDAL CURRENTS. COMPARISON BETWEEN CURRENT DATA FROM HF RADAR AND ADCP SHOWS A SIMILAR PATTERN BUT HF RADAR DATA SLIGHTLY HIGHER THAN ADCP. THE AVERAGE CURRENT VELOCITY AND STANDARD DEVIATION RANGES FROM 0.01 - 1.09 M/S AND 0.24 - 0.82 M/S, RESPECTIVELY. HARMONIC ANALYSIS OF OCEAN CURRENT DATA AT 0.82 M/S STANDARD DEVIATION SHOWS THAT THE DOMINANT CURRENTS ARE TIDAL CURRENT WITH AN AVERAGE VELOCITY OF 1.57 M/S. THERE IS A STRONG NEGATIVE CORRELATION BETWEEN TIDAL CURRENT VELOCITY AND TIDAL ELEVATION OF TIDE GAUGE IN KETAPANG PORT. THIS RESEARCH REVEALS THAT THE HF RADAR IS VERY RELIABLE FOR MEASURING OCEAN CURRENTS.

ONE OF THE IMPORTANT FACTOR IN WEATHER AND CLIMATE DYNAMICS THAT CAN TRIGGER PRECIPITATION ON THE COAST AND THE SURROUNDING AREA IS A SEA BREEZE. SEA BREEZE OCCURS BECAUSE OF DIFFERENCES IN SURFACE TEMPERATURE BETWEEN LAND AND SEA DUE TO SOLAR HEATING WHICH THEN FORMS A PRESSURE GRADIENT THAT LEADS TO A LAND CALLED THE SEA BREEZE CIRCULATION. AN IMPORTANT PART OF SEA BREEZE CIRCULATION IS THE SEA BREEZE FRONT (SBF). SBF IS A BOUNDARY AREA WHERE WIND FROM THE SEA DIRECTION MEETS THE WIND FROM THE LAND DIRECTION, WHICH IS MARKED BY SIGNIFICANT CHANGES IN TEMPERATURE, HUMIDITY, WIND AND CAN TRIGGER CONVECTIVE ACTIVITY. THIS STUDY AIMS TO DETERMINE THE CHARACTERISTICS OF THE SBF ON THE NORTH COAST OF BANTEN - JAKARTA IN 2018 WHICH WERE IDENTIFIED USING A DOPPLER WEATHER RADAR PPI PRODUCT AND CONVECTIVE ACTIVITY USING CMAX PRODUCT. QUALITATIVE AND QUANTITATIVE METHODS ARE USED TO DETERMINE THE SBF PARAMETERS SUCH AS FREQUENCY OF OCCURRENCE, ONSET TIME, DURATION, LENGTH, COLUMN DEPTH AND SBF PENETRATION, AND CONVECTIVE ACTIVITY DURING THE OCCURRENCE OF SBF. THE RESULTS SHOWED THAT SBF WAS DETECTED MORE IN THE RAINY SEASON JANUARY, FEBRUARY AND DECEMBER 2018, AND OCCUR BETWEEN 08.08 LT AND 15.20 LT. SBF CAN TRIGGER THE OCCURRENCE OF CONVECTIVE CLOUDS AND AFFECT THE TEMPERATURE AND HUMIDITY CONDITIONS AROUND THE SBF.

THIS PAPER EVALUATES TEN GRIDDED PRECIPITATION DATASETS IN INDONESIA, NAMELY APHRODITE, CMORPH, CHIRPS, GFD, SA-OBS, TMPA 3B42 V7, PERSIAN-CDR AT 0.25?, MOREOVER GSMAP_NRT V06, GPM-IMERG V06, AND MSWEP V2 AT 0.1?. THE EVALUATION FOCUSES ON TIME SERIES BIAS USING METRICS SUCH AS MEAN, STANDARD DEVIATION, COEFFICIENT OF VARIATION, RELATIVE CHANGE (VARIABILITY), MANN-KENDALL TEST, AND KOLMOGOROV-SMIRNOV TEST (KS-TEST) AT DAILY, MONTHLY, SEASONAL, AND ANNUAL TIME SCALES. THE STATISTICAL RELATIONSHIP BETWEEN THE PRECIPITATION DATA SETS IS ANALYZED WITH REFERENCE OBSERVATIONAL DATA USING TAYLOR DIAGRAMS FOR EVALUATING THE RELATIVE SKILL OF THE PRECIPITATION DATASET. THE RESULT SHOWS MSWEP HAS THE BEST PERFORMANCE IN ALMOST ALL TIME INTERVALS EXCEPT AT THE MONTHLY TIME SCALE THAT IS GSMAP_NRT. THE RELATIVE SKILL OF MONTHLY RAINFALL BASED ON ITS TYPE SHOWS THAT MSWEP IS THE BEST PERFORMED IN REGION A, WHILE CHIRPS IN B REGION, AND GFD IN REGION C. THE APPLICATION OF EXISTING PRECIPITATION DATASETS IS ESSENTIAL TO COPE WITH THE LIMITATION OF RAIN GAUGE OBSERVATIONS. THIS STUDY IMPLICATES THE DEVELOPMENT OF PRECIPITATION PRODUCTS IN THE INDONESIA REGION.

PROVIDING MARITIME METEOROLOGICAL FORECASTS (INCLUDING OCEAN WAVE INFORMATION) IS ONE OF BMKG DUTIES. CURRENTLY, BMKG EMPLOYS WAVEWATCH3 WAVE MODEL (WW3) TO FORECAST OCEAN WAVES IN INDONESIA. EVALUATING THE WAVE FORECASTS IS VERY IMPORTANT TO IMPROVE THE FORECASTS SKILL. THIS PAPER PRESENTS THE EVALUATION OF 7-DAYS-AHEAD BMKG’S WAVE FORECAST. THE EVALUATION WAS PERFORMED BY COMPARING WAVE DATA OBSERVATION AND BMKG WAVE FORECAST. THE OBSERVATION DATA WERE OBTAINED FROM RV MIRAI 1708 CRUISE ON DECEMBER 5TH TO 31ST AT THE INDIAN OCEAN AROUND 04°14`S AND 101°31`E. SOME STATISTICAL PROPERTIES AND RECEIVER OPERATING CHARACTERISTICS (ROC) CURVE WERE UTILIZED TO ASSESS THE MODEL PERFORMANCE. THE EVALUATION PROCESSES WERE CARRIED OUT ON MODEL’S PARAMETERS: SIGNIFICANT WAVE HEIGHT (HS) AND WIND SURFACE FOR EACH 7-DAYS FORECAST STARTED FROM 00 UTC.
THE RESULTS SHOW THAT, IN AVERAGE, WW3 FORECASTS ARE OVER-ESTIMATE THE WAVE HEIGHT THAN THAT OF THE OBSERVATION. THE FORECAST SKILLS DETERMINED FROM THE ROC CURVES ARE GOOD FOR THE FIRST- AND SECOND-DAY FORECAST, WHILE THE THIRD UNTIL SEVENTH DAY DECREASE TO FAIR. THIS PHENOMENON IS SUSPECTED TO BE CAUSED BY THE WIND DATA CHARACTERISTICS PROVIDED BY THE GLOBAL FORECASTS SYSTEM (GFS) AS THE INPUT OF THE MODEL. NEVERTHELESS, ALTHOUGH STATISTICAL CORRELATION IS GOOD FOR UP TO 2 DAYS FORECAST, THE AVERAGE VALUE OF ROOT MEAN SQUARE ERROR (RMSE), ABSOLUTE BIAS, AND RELATIVE ERROR ARE HIGH. IN GENERAL, THIS VERIFIES THE OVERESTIMATE RESULTS OF THE MODEL OUTPUT AND SHOULD BE TAKEN INTO CONSIDERATION BY BMKG TO IMPROVE WAVE MODEL FORECASTS.

AEROSOLS PLAY A SIGNIFICANT ROLE IN CLIMATE CHANGE, BUT THE QUANTITATIVE ESTIMATES OF THEIR EFFECTS ARE STILL UNCERTAIN. TO REDUCE THIS UNCERTAINTY, ONE OF KEY PARAMETERS THAT MUST BE ACCURATELY QUANTIFIED IS AEROSOL OPTICAL DEPTH (AOD). A CONVENTIONAL CLEAR-SKY MINIMUM REFLECTANCE METHOD, WHICH HAS BEEN WIDELY USED FOR AOD RETRIEVAL FROM GEOSTATIONARY SATELLITE, USUALLY HAS POORER ACCURACY OVER URBAN THAN OTHER LAND AREAS. THIS IS PRIMARILY DUE TO COMPLEX URBAN SURFACE PROPERTIES AND MIXED AEROSOL TYPES FROM DIFFERENT EMISSION SOURCES. WHEN THE SURFACE REFLECTANCE IS CALCULATED FROM THE CLEAR-SKY MINIMUM REFLECTANCE, BACKGROUND AEROSOL OPTICAL DEPTH (BOD) IS ASSUMED TO BE ZERO. THIS ASSUMPTION GENERATES LARGER SURFACE REFLECTANCE WHICH IN TURN LEADS TO UNDERESTIMATION OF AOD RETRIEVED. THIS STUDY PROPOSED A CORRECTION FOR BOD VALUE TO BE APPLIED FOR AOD RETRIEVAL PRIMARY OVER URBAN AREAS WHERE THE POLLUTION OR NATURAL SOURCE AEROSOL IS PERSISTENT FOR LONG TERM. THE STUDY AREA INCLUDES BANDUNG, JAMBI, PONTIANAK, AND MAKASAR. THE AOD RETRIEVED FROM A MODIFIED BOD WAS COMPARED TO THE COLLOCATED DATA MEASURED BY AERONET. THE COMPARISON SHOWED THAT THE CORRECTED SURFACE REFLECTANCE IMPROVED THE ACCURACY OF AOD IN THE STUDY AREAS, WITH A CORRELATION COEFFICIENT INCREASE FROM 0.45 TO 0.63 AND THE FRACTION OF ‘GOOD RETRIEVAL’ CHANGE FROM 32% TO 55%.

IOD AND ENSO ARE TWO GLOBAL PHENOMENA THAT ARE QUITE INFLUENCING THE TERRITORY OF INDONESIA. SUMATERA IS ONE OF THE ISLANDS IN INDONESIA THAT HAS UNIQUE LOCAL CHARACTERISTICS AND IS LOCATED IN THE WESTERNMOST. THIS STUDY ATTEMPTS TO ANALYZE HOW MUCH THE IOD AND ENSO HAVE AFFECTED RAINFALL IN THE SUMATERA ISLAND AND WHICH ON IS MORE DOMINANT AND ALSO THE DISTRIBUTION WHICH CAN BE USED AS A REFERENCE IN FORECASTING SEASONS AND RAINFALL. IN A PERIOD OF 37 YEARS (1981-2017), THE INFLUENCE OF IOD AND ENSO WAS ANALYZED USING A CORRELATION METHOD WHICH WAS THEN MAPPED. THE RAINFALL DATA USED WAS OBTAINED FROM CHIRPS WITH A SCALE OF 0.025. DATA IS PROCESSED USING CLIMATE DATA OPERATOR (CDO) AND R-STATISTICS TO OBTAIN CORRELATION MAPS. CORRELATION CALCULATIONS ARE CARRIED OUT ON THE WHOLE DATA AND IN THE SEASON PERIOD. THE EFFECT OF IOD WAS SEEN TO BE SIGNIFICANT IN THE SOUTHERN PART OF SUMATERA WITH CORRELATION REACHING -0.4, WHILE IN THE NORTH AND CENTER THE CORRELATION ONLY REACHED -0.25. THE EFFECT OF ENSO LOOKS MORE EVENLY COMPARED TO THE EFFECT OF IOD, WITH CORRELATION REACHING -0.4 IN THE NORTHERN AND SOUTHERN PARTS OF SUMATERA, WHILE IN THE MIDDLE PART IT SHOWS A SMALLER CORRELATION VALUE WITH RANGES OF -0.1 TO -0.3. IN THE DJF AND MAM SEASON PERIODS, A POSITIVE CORRELATION BETWEEN PRECIPITATION AND IOD OCCURRED THROUGHOUT SUMATRA AND IN THE JJA AND SON, A DOMINANT NEGATIVE CORRELATION OCCURRED. WHILE THE ENSO SHOWS DOMINANT NEGATIVE CORRELATION IN EACH MONTH PERIOD. BASED ON THE CORRELATION, ENSO IS MORE INFLUENTIAL ON SUMATRA COMPARED TO IOD IN OVERALL DATA. HOWEVER, ON SEASONAL DATA, IOD IS MORE INFLUENTIAL THAN ENSO. THE SOUTHERN PART OF SUMATERA ISLAND IS THE MOST AFFECTED BY ENSO AND IOD COMPARED TO OTHER PARTS.

KEYWORDS: PRECIPITATION, IOD, ENSO, MAP CORRELATION, SUMATERA

TITLE: ESTIMATION OF THE SEA BREEZE FRONT VELOCITY OVER COASTAL-URBAN REGIONS USING HIMAWARI-8 SATELLITE IMAGES

AUTHOR: MUHAMMAD REZZA FERDIANSYAH (BMKG) AND ARIE WAHYU WIJAYANTO (STIS)

EMAIL: MUHAMMAD.FERDIANSYAH@BMKG.GO.ID

ABSTRACT:

THE SEA BREEZE IS A METEOROLOGICAL PHENOMENON THAT OCCURS DUE TO THE CONTRAST TEMPERATURE BETWEEN LAND AND OCEANS. THE DIRECTION AND SPEED OF SEA BREEZE PROPAGATION ARE INFLUENCED STRONGLY BY MANY FACTORS, SUCH AS GEOGRAPHICAL FACTORS AND SURFACE CONDITIONS. THE SEA BREEZE FUNCTIONS AS VENTILATION FOR AIR POLLUTION AND MITIGATION OF THE INCREASING AIR TEMPERATURE (KNOWN AS THE URBAN HEAT ISLANDS PHENOMENON) IN THE COASTAL AREAS. THEREFORE, THE SPATIAL DISTRIBUTION OF THE CHARACTERISTICS OF THE SEA BREEZE IS ESSENTIAL. THAT SPATIAL INFORMATION CAN BE RETRIEVED USING REMOTE SENSING TECHNIQUES SUCH AS THE HIMAWARI-8 SATELLITE OBSERVATION. WHEN THE SEA BREEZE PROPAGATES INLAND, UNDER CERTAIN CONDITIONS, A CUMULUS CLOUD-LINE (CLOUDLINE) WILL FORM IN THE VICINITY OF THE SEA BREEZE FRONT. THIS CLOUDINESS FEATURE IS DETECTABLE ON SATELLITE IMAGERY, AND IT CAN BE A PROXY FOR THE LOCATION OF THE SEA BREEZE FRONT (SBF). HENCE, WE CAN TRACE THE SBF PROPAGATION BY TRACING THE CLOUDLINE MOVEMENT. OUR PREVIOUS STUDIES HAVE SUCCESSFULLY DETECTED THE CLOUDLINE AUTOMATICALLY WITH A COMPUTER VISION APPROACH FOR EDGE DETECTION, USING THE MORPHOLOGICAL-SNAKE ALGORITHM. IN THIS PAPER, WE PROPOSE A NEW METHOD TO ESTIMATE THE SBF VELOCITY USING HIMAWARI-8 SATELLITE IMAGES. THE PROPOSED METHOD CREATES A SEGMENTATION OF CLOUDLINE DATA POINTS USING A CLUSTERING APPROACH NAMED MACHINE LEARNING-BASED K-MEANS++ ON THE LEVEL-SET RESULTED FROM THE SNAKE ALGORITHM. WE ESTIMATE THE SBF DIRECTION AND SPEED BY CALCULATING THE HAVERSINE DISTANCE OF THE SEGMENTED CLOUDLINE DATA POINTS THAT PROPAGATE OVER TIME.

A SPATIAL RAIN SCANNER, CALLED SANTANU, HAS BEEN DEVELOPED BASED ON A MARINE RADAR TO SATISFY THE DEMAND OF SPATIAL RAIN INFORMATION FOR HYDROLOGICAL APPLICATIONS. SINCE THE COVERAGE OF SANTANU IS 44 KM IN RADIUS, IT IS NECESSARY TO EXPAND THE COVERAGE BY INSTALLING IT IN TWO SITES THAT INTERSECT EACH OTHER PERFORMING A RADAR NETWORK. FOR THIS PURPOSE, THE FIRST SANTANU HAS BEEN INSTALLED AT THE CENTER FOR ATMOSPHERIC SCIENCE AND TECHNOLOGY (PSTA) IN BANDUNG AND THE SECOND ONE AT THE SPACE AND ATMOSPHERIC OBSERVATION CENTER (BPAA) TANJUNGSARI IN SUMEDANG. THIS PAPER FOCUSES ON THE CALIBRATION OF RADAR OBSERVATIONS WITH RAIN GAUGE DATA IN BANDUNG AREA AND ITS SURROUNDINGS. THE CALIBRATION METHOD IS BY CALCULATING RAINFALL DEPTH (THREE PARAMETERS) INSTEAD OF ONLY THE INTENSITY OF RAINFALL. THE DATA PERIOD USED FOR THIS RESEARCH IS FROM MARCH TO NOVEMBER 2020. TWO CALIBRATION METHODS ARE USED AND THE RESULTS SHOW THAT THE CALIBRATION BY CALCULATING THREE PARAMETERS (ACCUMULATED REFLECTIVITY, DURATION, AND INTENSITY) IN THE LINEAR MODEL IS ABLE TO MEASURE QUANTITATIVE PRECIPITATION ESTIMATION (QPE) BETTER THAN USING A LINEAR MODEL WITH ONE PARAMETER (ACCUMULATED REFLECTIVITY).

 

THE SEA SURFACE CURRENTS CIRCULATION IN THE BALI STRAIT IS INFLUENCED BY VARIOUS FACTORS, INCLUDING SURFACE WIND, GEOGRAPHICAL FACTORS, AND ASTRONOMICAL POSITION, ITS VARIATIONS ARE VERY COMPLEX AND NEED TO BE STUDIED IN DEPTH. THIS STUDY EXAMINES THE COMPARISON OF OCEAN CURRENTS OBSERVATION DATA AS MEASURED BY THE BMKG’S HF RADAR LOCATED AT BOOM AND WARU BEACH, BANYUWANGI AND BMKG’S ADCP WHICH WAS DEPLOYED IN THE BALI STRAIT (8.185532° S 114.395619° E AND 8.198532° S 114.437248° E) ON APRIL 24^TH - MAY 3^RD, 2019. THE ADCP DEPLOYMENTS ARE PART OF THE BALI STRAIT WAVE AND CURRENT SURVEY PROJECT BY BMKG’S RESEARCH AND DEVELOPMENT CENTER. RADIAL VELOCITY AND DIRECTION OF THE OCEAN SURFACE CURRENTS DATA ARE OBTAINED FROM THE HF RADAR, WHILE THE SURFACE TIDAL DATA, RADIAL VELOCITY AND DIRECTION OF THE OCEAN CURRENTS FROM THE SURFACE AND DOWN TO 20 METERS BELOW THE SEA LEVEL ARE OBTAINED FROM THE ADCP. THE COMPARISON ANALYSIS HAS BEEN MADE TO DETERMINE THE TWO INSTRUMENTS PERFORMANCE IN MEASURING THE OCEAN SURFACE CURRENTS AND THE OBSERVED TIDAL TYPES. ADDITIONAL RESEARCH ALSO CONDUCTED TO DETERMINE THE CHARACTERISTICS OF THE RADIAL VELOCITY AND DIRECTION OF OCEAN CURRENTS FOR EACH LAYER OBSERVED BY THE ADCP. THE OBSERVATION DATA SHOWS THE TIDAL TYPES AT THE TIME OF OBSERVATION ARE MIXED TIDE, PREVAILING SEMI-DIURNAL. ALSO, RADIAL VELOCITY DATA OF OCEAN SURFACE CURRENTS FROM HF RADAR AND ADCP ONLY WELL CORRELATED IN THE FIRST HIGH TIDE AND SECOND LOW TIDE PERIOD. BASED ON THE ADCP’S OBSERVATIONS, THE OCEAN CURRENTS RADIAL VELOCITY CHARACTERISTICS IN THE BALI STRAIT SHOW GOOD PERSISTENCE IN THE LOWER LAYER THAN THE UPPER LAYER. GENERALLY, THE DATA DISTRIBUTION OF HF RADAR AND ADCP ON SEA SURFACE CURRENTS’ RADIAL VELOCITY AND DIRECTION TENDS TO BE IN THE SAME RANGE. HOWEVER, THE RADIAL VELOCITY DATA OF THE HF RADAR TENDS TO OVERESTIMATE COMPARED TO THE ADCP DATA. FURTHERMORE, THERE IS A DISTORTION IN THE DIRECTIONAL MEASUREMENT OF HF RADAR BECAUSE OF THE INFLUENCE FROM THE SURFACE WINDS RIGHT ABOVE THE SEA LEVEL.

 

THE CHARACTERISTICS OF CLIMATIC RAINFALL VARIABILITY IN INDONESIA ARE INVESTIGATED BASED ON INTEGRATED MULTI-SATELLITE RETRIEVALS FOR GPM (IMERG) DATA USING A DOUBLE CORRELATION METHOD (DCM). THE ANALYSIS CONCENTRATED ON THE PERIOD OF APRIL 2014 TO MARCH 2019. PRIOR TO REGIONALIZATION, IMERG V06 DATA WERE VALIDATED USING OBSERVATION RAINFALL DATA FROM THE AGENCY FOR METEOROLOGY, CLIMATOLOGY, AND GEOPHYSICS OF THE REPUBLIC OF INDONESIA (BMKG). THE RESULTS SHOWED THAT 96% OF 154 TOTAL VALIDATION LOCATIONS HAVE A HIGH CORRELATION SCORE BETWEEN IMERG AND RAIN GAUGES (R = 0.5 – 0.97). IMERG WAS ABLE TO CORRECTLY IDENTIFY MONTHLY TIME SERIES PATTERNS AND SEASONAL PATTERNS OF RAINFALL IN THE THREE DISTINCT RAINFALL REGIONS IN INDONESIA. BASED ON SATISFACTORY VALIDATION RESULTS, IMERG V06 DATA WITH 0.1X0.1 DEGREE RESOLUTION WAS CONDUCTED TO REGIONALIZE DOMINANT RAINFALL PATTERN IN INDONESIA MARITIME CONTINENT. THIS ANALYSIS OBTAINED FOUR RAINFALL REGIONS IN INDONESIA. REGION A HAS THE MONSOONAL CHARACTERISTIC, COVERS SOUTH AND CENTRAL INDONESIA FROM SOUTH SUMATRA TO NUSA TENGGARA, PARTS OF KALIMANTAN, PARTS OF SULAWESI, AND PARTS OF PAPUA. REGION B HAS AN EQUATORIAL PATTERN (SEMI-MONSOONAL), LOCATED IN THE EQUATORIAL AREA OF INDONESIA AND COVERS THE WEST AND EAST PART OF SUMATRA AND THE NORTH-CENTRAL PART OF KALIMANTAN. REGION C WITH ANTI-MONSOONAL PATTERN COVERS MALUKU, WESTERN-CENTRAL PAPUA AND PARTS OF SULAWESI (CLOSE TO THE WESTERN PACIFIC REGION). REGION D, WITH A COMBINATION OF MONSOONAL AND COLD SURGE CHARACTERISTIC, IS LOCATED ON THE NORTH PART OF SUMATERA AND A SMALL PORTION OF NORTHERN KALIMANTAN TO SOUTH CHINA SEA REGION. BESIDES THE NEW REGION D, THIS RESEARCH ALSO SHOWED FIVE OTHER DIFFERENCES BETWEEN IMERG BASED MAP AND GRIDDED RAIN GAUGES DATA BASED MAP (2003). THE RESULT FROM IMERG WAS MORE DETAILED BECAUSE OF THE 28 TIMES HIGHER SPATIAL RESOLUTION THEN GRIDDED RAIN GAUGES ON THE PREVIOUS STUDY, AND IT INCLUDES MEASUREMENTS OF RAINFALL ON THE OCEAN AREA.

STORM IS DEFINED AS A DISTURBANCE OF THE ATMOSPHERE MARKED BY WINDS AND USUALLY BY RAIN. COASTAL STORMS MUST COMPRISE A MARITIME COMPONENT, SUCH AS WAVES, CURRENTS AND/OR WATER LEVELS. COASTAL STORM DETECTION IS NECESSARY SO THE NUMBER OF CASUALTIES AND LOSSES CAUSED BY THESE EVENTS CAN BE REDUCED. THE METHOD USED IN THIS SYSTEM IS THE SUGIANTO WAVE FORECASTING METHOD WITH STANDARDIZATION OF COASTAL STORMS USING THE BEAUFORT SCALE. THE TIDAL DATA IS PROCESSED USING THE ADMIRALTY METHOD. THIS SYSTEM WAS BUILT USING ARDUINO UNO EQUIPPED WITH ANEMOMETER JL-FS2 AND MB1010 LV-MAXSONAR TO MEASURE WIND, WAVES AND TIDES PARAMETERS. THE POWER SOURCE FROM 100 WP SOLAR PANELS STORED IN A 40 AH ACCUMULATOR. DATA FROM FIELD INSTRUMENT IS STORED TO THE IOT MAPID DATABASE USING NODEMCU ESP8266. THIS SYSTEM IS PLACED IN TIMBULSLOKO, DEMAK. THE RESULTS OF FIELD OBSERVATION THEN VALIDATED USING BMKG AND BIG DATA. THE RESULTS OF FIELD OBSERVATION SHOWED AN AVERAGE WIND SPEED 2,12 M/S; SIGNIFICANT WAVE HEIGHT 0,20 M; SIGNIFICANT WAVE PERIOD 3,51 S; WAVE ENERGY 80,12 J/M2; WIND ENERGY 14,55 W/M2. THE RESULTS OF TIDAL DATA SHOWED MIXED TIDE PREVAILING SEMI DIURNAL.

 

KEYWORDS: COASTAL STORM, METEOROLOGICAL OCEANOGRAPHY,WIND WAVE, ARDUINO, IOT

A LONG-TERM RELIABLE SATELLITE TEMPERATURE (SST) DATA RECORD IS REQUISITE RESOURCES FOR MONITORING IN ORDER TO UNDERSTAND THE CLIMATE VARIABILITY. THIS STUDY USED DINEOF TO OVERCOME MISSING DATA SATELLITE PROBLEMS DUE TO CLOUDS AND SEVERAL ISSUES THAT PRODUCE MISSING DATA IN CREATING A LONG-TERM DATA RECORD, ESPECIALLY FOR CLIMATE VARIABILITY. THIS REQUIRES A COMBINATION OF MULTIPLE SATELLITE PRODUCTS AND ISSUES OF MISSING DATA CLOUDS ARE INEVITABLE. DINEOF (DATA INTERPOLATING EMPIRICAL ORTHOGONAL FUNCTIONS) WITH EOF-BASED TECHNIQUE TO RECONSTRUCT MISSING DATA IN GEOPHYSICAL FIELDS WAS USED TO ATTAIN A COMPLETE AND COHERENT MULTI-SENSOR SST DATA RECORD. THE RECONSTRUCTION OF THESE DATA DEPICTED INTO IMAGES WITHIN A LONG TIME SERIES USING DINEOF CAN LEAD TO LARGE DISCONTINUITIES IN THE RECONSTRUCTION. FILTERING THE TEMPORAL COVARIANCE MATRIX ALLOWS TO REDUCE THIS SPURIOUS VARIABILITY AND THEREFORE, MORE REALISTIC RECONSTRUCTIONS ARE OBTAINED. HOWEVER, THIS APPROACH HAS NOT YET TESTED IN THE TROPICAL AREA, WHICH USUALLY HAS MORE CLOUDS AND SOME MISSING VALUES IN THE SST DATA SETS. THEREFORE, THIS RESEARCH AIMS TO RECONSTRUCT SST DATA IN LOMBOK STRAIT, WHICH HAS A PLETHORA OF MISSING DATA EITHER DUE TO CLOUDS OR SUNGLINTS. THE TAKEN DATA FROM JANUARY 2019 UNTIL NOVEMBER 2020 SHOWED MISSING DATA UP TO 58.16%. IT SUCCESSFULLY RECONSTRUCTED UNTIL THE LAST RECONSTRUCTION, INCLUDING DATA PUT ASIDE FOR CROSS-VALIDATION AFTER 42 ITERATIONS AS THE OPTIMAL EOF MODES WITH THE CONVERGENCE ACHIEVED UP TO 0.9806 X10-3. THE RESULTS HIGHLIGHT THAT THE DINEOF METHOD CAN EFFECTIVELY RECONSTRUCT SST DATA IN LOMBOK STRAIT.

THE MADDEN-JULIAN OSCILLATION (MJO) IS THE DOMINANT MODE OF INTRASEASONAL VARIABILITY OF RAINFALL IN INDONESIA, BUT ITS SIGNAL IS OFTEN OBSCURED IN INDIVIDUAL STATION DATA, WHERE EFFECTS ARE MOST DIRECTLY FELT AT THE LOCAL LEVEL. THE CHARACTERISTIC OF THE MJO DURING ITS PROPAGATION THROUGH THE MARITIME CONTINENT HAS ALWAYS BEEN A CHALLENGE TO COMPREHEND DESPITE DECADES OF RESEARCH ATTEMPTS IN THE REGION. IMPACTS OF THE MJO ON DAILY RAINFALL ANOMALY DURING THE FOUR CLIMATIC SEASONS: DJF, MAM, JJA, AND SON IN INDONESIA HAVE BEEN EVALUATED USING 86 DAILY IN-SITU DATA FROM 1981 - 2012.

THE GREATEST IMPACT OF THE MJO ON RAINFALL OVER INDONESIA OCCURS DURING THE DJF AND MAM SEASONS (AUSTRAL SUMMER), WITH THE MAGNITUDE VARYING ACROSS REGIONS. ENHANCED RAINFALL GENERALLY OCCURS OVER THE WESTERN PARTS OF INDONESIA DURING MJO PHASES 2 TO 4, CENTRAL PARTS OF INDONESIA IN PHASE 4, AND EASTERN PARTS OF INDONESIA IN PHASES 4 TO 5. CONVERSELY, SUPPRESSED RAINFALL GENERALLY OCCURS OVER THE WESTERN PARTS OF INDONESIA DURING MJO PHASES 5 TO 8, CENTRAL PARTS OF INDONESIA IN PHASES 6 TO 8, AND EASTERN PARTS OF INDONESIA DURING MJO PHASES 1 TO 2 AND 6 TO 8.

IN CONTRAST, THE MJO INFLUENCE DURING THE JJA AND SON SEASONS ARE SLIGHTLY LESS THAN DURING THE DJF AND MAM SEASONS AS A RESULT OF THE NORTHWARD SHIFT OF THE INTRASEASONAL OSCILLATION CONVECTIVE ENVELOPE DURING BOREAL SUMMER. PARTICULARLY, ENHANCED RAINFALL OCCURS OVER THE WESTERN AND NORTHERN PARTS OF INDONESIA DURING MJO PHASES 2 AND 3, AND SUPPRESSED RAINFALL IN PHASES 6 AND 7. THE RESULTS INDICATE THAT CONVECTIVELY ACTIVE MJO MAY INCREASE THE POSSIBILITY OF DAILY EXTREME RAINFALL IN PARTICULAR REGIONS IN INDONESIA AT DIFFERENT SEASONS.

THE COASTAL AREA IS AN AREA WITH A VERY HIGH DYNAMIC PROCESS, ONE OF WHICH CAN BE SEEN FROM THE CHANGE IN THE SHAPE OF THE COASTLINE. THE VARIATION IN SHORELINE MORPHODYNAMICS IS CAUSED BY SEVERAL INFLUENCING PROCESSES SUCH AS CLIMATIC CONDITIONS, VARIATIONS IN SEA LEVEL, WAVE ENERGY, AND THE INFLUENCE OF HUMAN ACTIVITIES. NOWADAYS THE IMPACT OF GLOBAL CLIMATE CHANGE HAS PLAYED AN IMPORTANT ROLE IN THE DYNAMICS OF COASTAL AREAS BECAUSE IT HAS CAUSED CHANGES IN COASTLINES AND SEA-LEVEL RISE. THIS STUDY AIMS TO ANALYZE THE DYNAMICS OF THE JEPARA COAST IN TERMS OF CHANGES IN COASTLINE AND SURFACE TEMPERATURE DUE TO CLIMATE CHANGE. THE METHODS AND DATA USED IN THIS STUDY USE REMOTE SENSING ANALYSIS WITH COASTLINE EXTRACTION AND LAND SURFACE TEMPERATURE (LST) FROM THE 1982 LANDSAT MSS SATELLITE IMAGERY, 2000 LANDSAT ETM IMAGERY, AND 2020 LANDSAT 8 OLI / TIRS. THE DYNAMICS OF SHORELINE CHANGES ON THE COAST OF JEPARA ARE DOMINATED BY ACCRETION AND ABRASION PROCESSES. THE RESULTS OF LAND SURFACE TEMPERATURE PROCESSING SHOW A TREND OF CHANGING SURFACE TEMPERATURE WHICH HAS BEEN INCREASING FOR MORE THAN 30 YEARS. THIS RESEARCH IS VERY IMPORTANT TO DO TO FIND OUT THE DYNAMICS OF THE COAST IN JEPARA AS A RESULT OF GLOBAL CLIMATE CHANGE. AS A COASTAL AREA WITH AN EVER-INCREASING POPULATION AND VARIOUS SOCIO-ECONOMIC ACTIVITIES, THE IDENTIFICATION OF SHORELINE CHANGES PROVIDES INFORMATION TO COASTAL SCIENTISTS, ENGINEERS, DECISION-MAKERS, AND STAKEHOLDERS FOR FUTURE MANAGEMENT AND DEVELOPMENT PLANS FOR COASTAL AREAS.

 

TROPOSPHERIC (SURFACE) OZONE CONCENTRATIONS ARE INFLUENCED BY BIOGENIC, CHEMICAL PROCESSES, NATURAL PRECURSOR EMISSIONS, KINETICS, DEPOSITION, AS WELL AS THE DISTRIBUTION OF OZONE AND ITS PRECURSORS. THIS STUDY AIMS TO DETERMINE THE TYPES OF PRECURSORS (CO₂, CH₄, AND CO) THAT MOST INFLUENCE THE TROPOSPHERIC OZONE CONCENTRATION IN RURAL AND REMOTE AREAS. THE STUDY WAS CONDUCTED IN GAW KOTOTABANG STATION (WEST SUMATRA) WHICH REPRESENTS THE REMOTE AREA, AND THE CIBEREUM STATION (WEST JAVA) THAT REPRESENTS THE RURAL AREA. THE DATA USED IN THIS STUDY ARE THE AUTOMATIC OBSERVATION DATA FROM BOTH THE PICARRO G2401 AND THE OZONE ANALYZER TEI-49C IN THE 2019 – 2020 PERIOD. PEARSON CORRELATION METHOD IS USED TO DEPICT THE RELATIONSHIP BETWEEN EACH PRECURSOR AND TROPOSPHERIC OZONE CONCENTRATIONS. IN GENERAL, SURFACE OZONE CONCENTRATIONS IN BOTH KOTOTABANG AND CIBEREUM BEGIN TO INCREASE AT 08 – 09 WIB, FOLLOWING THE INCREASE IN SOLAR RADIATION INTENSITY, AND DECREASE AT 18 – 19 WIB. THIS PATTERN IS DUE TO THE FACT THAT SURFACE OZONE IS A SECONDARY POLLUTANT FORMED BY PHOTOCHEMICAL REACTIONS, IN WHICH THE PHOTOCHEMICAL REACTIONS ARE TRIGGERED BY ENERGY FROM SOLAR RADIATION. CO HAS THE HIGHEST CORRELATION WITH TROPOSPHERIC OZONE CONCENTRATIONS IN BOTH KOTOTABANG (0.34) AND CIBEREUM (0.53) STATIONS. THE RESULTS IN THIS STUDY INDICATE THE EFFECT OF EACH PRECURSOR ON SURFACE OZONE CONCENTRATION AND THE ACCOMPANYING PROCESSES.