EXAMINING OPTIMUM PREDICTION TIME OF RAINFALL DYNAMICS BASED ON CHAOTIC PERSPECTIVE AT DIFFERENT TEMPORAL SCALES: A CASE STUDY IN BOJONEGORO, INDONESIA
AS THE DRIVING FORCE OF THE HYDROLOGICAL SYSTEM, RAIN GIVES SERIOUS IMPACTS WHEN DEALING WITH PETROLEUM MINING ACTIVITIES, ESPECIALLY IN PROTECTING ASSETS AND SAFETY. RAINFALL IS ONE OF METEOROLOGICAL FACTORS WHICH CHARACTERIZED BY HIGH SPATIAL AND TEMPORAL VARIABILITY. DUE TO THIS REASON, LONG-TERM FORECASTING CAN ONLY BE DONE IN A STOCHASTIC WAY. THE HIGHLY NONLINEAR RELATIONSHIPS ON RAINFALL DYNAMICS THEN EXAMINED USING LYAPUNOV EXPONENT METHOD IN ORDER TO ANALYZE THE CHAOTIC BEHAVIOR ON RAINFALL TIME SERIES DATA. THE STUDY OF RAINFALL DYNAMICS HAS BEEN DONE IN THREE DIFFERENT TEMPORAL SCALE, I.E. DAILY, 5-DAY, AND 10-DAY, OBSERVED OVER A PERIOD OF 6 YEARS FROM 2012 – 2017 AT ONE OF THE LARGEST PETROLEUM MINING SITES IN BOJONEGORO, INDONESIA. THE TIME DELAY (Τ) OBTAINED BY USING AVERAGE MUTUAL INFORMATION (AMI) METHOD FOR THE THREE-RAINFALL SERIES (3, 2, 3, RESPECTIVELY). THE FINITE CORRELATION DIMENSIONS OBTAINED FOR ALL THREE-RAINFALL SERIES DATA IS AROUND M=4, INDICATE POSSIBLE EXISTENCE OF CHAOTIC BEHAVIOR IN RAINFALL OBSERVED IN BOJONEGORO AT THE THREE SCALES. THE MAXIMUM LYAPUNOV EXPONENT ΛMAXFOR EACH OF THREE-RAINFALL SERIES IN BOJONEGORO IS 0.111, 0.057, 0.062, RESPECTIVELY. THESE VALUES WERE ANALYZED IN ORDER TO FIND THE OPTIMUM PREDICTION TIME OF RAINFALL OCCURRENCE TO PERFORM BETTER FORECASTING. THE RESULT SHOWS THAT THE OPTIMUM RANGE OF PREDICTION TIME FOR DAILY, 5-DAY, AND 10-DAY HAVE 9, 18, AND 16 TIMES LONGER THAN THEIR TEMPORAL SCALE.
1CENTER FOR RESEARCH AND DEVELOPMENT, INDONESIAN AGENCY FOR METEOROLOGY CLIMATOLOGY AND GEOPHYSICS (BMKG), JL ANGKASA I NO 2, KEMAYORAN, JAKARTA PUSAT 10610, INDONESIA
 
 
ABSTRACT
RAINFALL INTENSITY THRESHOLDS ONLY DO NOT TAKE ADVANTAGE OF THE AWARENESS OF THE SLOPE'S HYDROLOGICAL PROCESSES, SO THEY APPEAR TO PRODUCE LARGE FALSE AND MISSED ALERT RATES, DECREASING THE CREDIBILITY OF EARLY WARNING SYSTEMS FOR LANDSLIDES. IN THIS STUDY, WE ANALYZE THIS DILEMMA BY MODELING THE BEHAVIOR OF SLOPES TO PRECIPITATION, INCLUDING THE POTENTIAL EFFECT OF SOIL MOISTURE UNCERTAINTY GIVEN BY NUMERICAL MODELING. FOR THE SIMULATION OF SOIL MOISTURE DURING THE STUDY PERIOD AND EVENT RAINFALL THRESHOLDS OF AN EXCESSIVE EVENT USED TO DESCRIBE THE INTENSITY OF A RAINFALL EVENT, THE WEATHER RESEARCH AND FORECASTING (WRF) MODEL IS USED. THE THREE DAYS SIMULATION WAS CONDUCT DURING LANDSLIDE EVENT IN SAMIGALUH, KULON PROGO 28 NOVEMBER 2018. THE FOUR PLANETARY BOUNDARY LAYER (PBL) PARAMETERS IN THE WRF MODEL ARE COMPARED TO UNDERSTAND EACH CHARACTER, I.E., YONSEI UNIVERSITY (YSU), MELLOR-YAMADA-JANJIC (MYJ), SHIN-HONG (SH) AND BOUGEAULT-LACARR&EGRAVE;RE (BL). IN GENERAL, ALL PARAMETERS HAVE AN UNDERESTIMATION OF PRECIPITATION. EACH PBL PARAMETER'S RESPONSE TO RAINFALL IS DIFFERENT. BOTH MYJ AND SH SCHEMES ARE CLOSER TO OBSERVATION THAN OTHERS FOR DAY 1 AND DAY 2 OF SIMULATION, DAILY PRECIPITATION. FOR ALL PBL SCHEMES, INCREASED SOIL MOISTURE IS SEEN, SUGGESTING THAT THE SOIL IS WETTER AND MORE VULNERABLE TO LANDSLIDE EVENTS.AS AN EARLY WARNING PREDICTOR OF LANDSLIDES IN TERMS OF RAINFALL PARAMETERS, THE SH METHOD IS VERY USEFUL IN THIS ANALYSIS. FOR EARLY WARNING OF LANDSLIDES, A SHORT PERIOD (<6 HOURS) OF PRECIPITATION WITH A HIGH ACCUMULATION OF PRECIPITATION WOULD BE VERY BENEFICIAL.
KEYWORDS: SOIL MOISTURE, RAINFALL, LANDSLIDE, WRF MODEL
EARLY WARNING OF HEAVY RAINFALL EVENT USING TIME-LAGGED ENSEMBLE PREDICTION SYSTEM (CASE STUDY: FEBRUARY, 15TH 2019)
CONVECTIVE CLOUDS CAN BE RELATED TO THE DEVELOPMENT OF STRONG STORMS THAT PRODUCE VARIOUS EXTREME WEATHER. IN THE DEVELOPMENT OF EXTREME WEATHER COULD INVOLVE STRONG NONLINEAR INTERACTIONS OF MANY FACTORS IN THE ATMOSPHERE, SO THE ABILITY TO FORECAST AN EXTREME WEATHER ESPECIALLY HEAVY RAINFALL AND ISSUED AN EARLY WARNING BECOMES VERY IMPORTANT. BMKG HAS DEVELOPED TIME-LAGGED ENSEMBLE PREDICTION SYSTEM BY UTILIZING THE INITIAL TIME DIFFERENCE WHICH IS CONSIDERED CAPABLE OF PROVIDING DATA UPDATES MORE CLOSELY TO THE FORECASTS FINAL RESULTS. THIS STUDY EXAMINES THE COMBINATION OF PERCENTILE CLASSIFICATION METHOD INSIDE THE ENSEMBLE PREDICTION SYSTEM, TO LOOK FOR THE EXTREME VALUES DISTRIBUTION, THEN USED IT AS EXTREME THRESHOLD. THE EXTREME THRESHOLD TESTED IN HEAVY RAIN CASES ON 168, 72, AND 24 HOURS BEFORE IT HAPPENED, WHICH IS ALSO THE TIME OF ISSUED AN EARLY WARNING. BASED ON THIS RESEARCH, IT WAS FOUND THAT THE USE OF THE 90TH AND 95TH PERCENTILE CLASSIFICATION METHOD WAS ABLE TO SHOW A SIGNAL OF EXTREME EVENTS ON 168 AND 72 HOURS BEFORE THE EVENTS WITH CONSISTENT PROBABILITY PATTERN. IN 24 HOURS PREDICTION PERIOD, THE PROBABILITY VALUE INCREASES AND THE AVERAGE PRECIPITATION VALUE EXCEEDS THE EXTREME THRESHOLD.
KEYWORDS: HEAVY RAINFALL, TIME-LAGGED ENSEMBLE, PREDICTION, PERCENTILE
BAYESIAN MODEL AVERAGING (BMA) TO IMPROVE PREDICTION QUALITY OF SEA SURFACE TEMPERATURE (SST) IN NINO34 REGION
PREDICTION OF SEA SURFACE TEMPERATURE (SST) ANOMALY IN NINO34 REGION IS ESSENTIAL TO DETERMINE THE EL NI&NTILDE;O SOUTHERN OSCILLATION (ENSO), I.E. EL NI&NTILDE;O, LA NI&NTILDE;A AND NEUTRAL CONDITION FOR COMING MONTHS. FURTHERMORE, IN ORDER TO FIND OUT THE RESPONSE OF ENSO PHENOMENON TO RAINFALL OVER THE INDONESIA REGION, WE NEED MORE ACCURATE SST PREDICTION IN NINO34. SST PREDICTIONS ARE ROUTINELY RELEASED BY INSTITUTIONS SUCH AS THE EUROPEAN CENTER FOR MEDIUM-RANGE WEATHER FORECASTS (ECMWF). HOWEVER, SST PREDICTIONS FROM THE DIRECT OUTPUT (RAW) OF THE GLOBAL MODEL OF THE ECMWF SEASONAL PREDICTION, NAMELY SEAS5-RAW, IS SUFFERING FROM BIAS THAT AFFECTS THE LOW QUALITY OF SST PREDICTIONS. AS A RESULT, IT ALSO INCREASES THE POTENTIAL ERRORS IN PREDICTING THE ENSO CATEGORY THAT WILL OCCUR IN THE NEXT SEASON.
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THIS STUDY USES THE MONTHLY DATA OF THE SST SEAS5-RAW ENSEMBLE PREDICTION. THE DATA WAS DOWNLOADED FROM THE COPERNICUS CLIMATE CHANGE SERVICE (C3S), PERIOD OF 1993-2020. THEN, ONE VALUE REPRESENTING SST IN NINO34 WAS CALCULATED FOR EACH LEAD-TIME (LT), LT0-LT6. IN THIS STUDY, WE USE BAYESIAN MODEL AVERAGING (BMA) METHOD TO IMPROVE THE PREDICTION QUALITY OF SEAS5-RAW. THE ADVANTAGE OF BMA OVER OTHER POST-PROCESSING METHODS IS ITS ABILITY TO QUANTIFY THE UNCERTAINTY IN ENSEMBLE PREDICTIONS WHICH IS EXPRESSED IN THE FORM OF CALIBRATED AND SHARP PREDICTIVE PROBABILITY DENSITY FUNCTION (PDF).
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WE FOUND THAT THE BMA CALIBRATION PROCESS BETWEEN SST SEAS5-RAW AGAINST THE OBSERVED VALUE, WAS OPTIMAL WITH A LENGTH OF TRAINING PERIOD OF 160 MONTHS. THE RESULT SHOWS THE PREDICTION QUALITY OF SST NINO34 OF SEAS5-BMA (BMA OUTPUT) IS SUPERIOR TO SEAS5-RAW, ESPECIALLY FOR LT0, LT1 AND LT2. THIS QUALITY IS KNOWN FROM DECREASING IN THE ROOT MEAN SQUARE ERROR (RMSE), INCREASING IN PROPORTION OF CORRECT (PC) AND DECREASING IN THE AVERAGE OF ERROR RATE WHICH IS SHOWN BY BRIER SCORE (BS) FOR THE EL NINO, LA NINA AND NEUTRAL EVENT CATEGORIES.
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KEYWORDS: BAYESIAN MODEL AVERAGING, SEAS SURFACE TEMPERATURE, SEAS5
MONTHLY RAINFALL FORECAST OVER INDONESIA USING MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE ENSEMBLE
MONTHLY RAINFALL FORECAST OVER INDONESIA USING MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE ENSEMBLE
HASTUADI HARSA[1], MUHAMMAD NAJIB HABIBIE, ALFAN SUKMANA PRAJA, SRI PUJI RAHAYU,
THAHIR DANIEL HUTAPEA, YUNUS SWARINOTO, RONI KURNIAWAN, SRI NOVIATI
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[1]HASTUADI@GMAIL.COM
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ABSTRACT
A MONTHLY RAINFALL FORECAST METHOD IS PRESENTED IN THIS PAPER. THE METHOD PROVIDES SPATIAL FORECAST OVER INDONESIA AND EMPLOYS ENSEMBLE OF VARIOUS MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE ALGORITHMS AS ITS FORECAST MODELS. EACH SPATIAL GRID IN THE FORECAST OUTPUT IS PROCESSED AS AN INDIVIDUAL DATASET. THEREFORE, EACH LOCATION IN THE FORECAST OUTPUT HAS DIFFERENT STACKED ENSEMBLE MODELS AS WELL AS THEIR MODEL PARAMETER SETTINGS. FURTHERMORE, THE BEST ENSEMBLE MODEL IS CHOSEN FOR EACH SPATIAL GRID. THE INPUT DATASET OF THE MODEL CONSISTS OF EIGHT CLIMATE DATA (I.E. EAST AND WEST DIPOLE MODE INDEX, OUTGOING LONGWAVE RADIATION, SOUTHERN OSCILATION INDEX, AND NINO 1.2, 3, 4, 3.4) AND MONTHLY RAINFALL REANALYSIS DATA, RANGING FROM JANUARY 1982 UNTIL DECEMBER 2019. THERE ARE FOUR ASSESSMENT PROCEDURES PERFORMED ON THE MODELS: MONTHLY RAINFALL ESTABLISHMENT AS A RESPONSE FUNCTION OF CLIMATE PATTERNS, AND ONE-UP TO THREE-MONTH LEAD FORECAST. THE RESULTS SHOW THAT, BASED ON THEIR PERFORMANCE, THESE NON-PHYSICAL MODELS ARE CONSIDERABLE TO COMPLEMENT THE EXISTING FORECAST MODELS.
KEYWORDS: DEEP LEARNING, XGBOOST, DIPOLE MODE INDEX, OUTGOING LONGWAVE RADIATION, SOUTHERN OSCILLATION INDEX, NINO SEA SURFACE TEMPERATURE INDICES.
FOUR-DIMENSIONAL VARIATIONAL (4DVAR) PERFORMANCE TEST WITH ASSIMILATION SATELLITE AND RADAR DATA (CASE STUDY HEAVY RAINFALL BENGKULU MARCH 4, 2019))
ABSTRACT.&NBSP;FOUR-DIMENSIONAL VARIATIONAL (4DVAR) IS ONE OF THE ASSIMILATION TECHNIQUES CONSIDERING TIME INTEGRATION TO DISTRIBUTE OBSERVATIONAL DATA AT TIME WINDOW INTERVALS. IN THIS STUDY, WE AIM TO EVALUATE THE 4DVAR ASSIMILATION TECHNIQUE USING SATELLITE AND RADAR DATA TO SIMULATE A HEAVY RAINFALL CASE IN BENGKULU ON MARCH 4, 2019. THE RESULT SHOWS THAT RADAR DATA ASSIMILATION (DA-RAD) IS ABLE TO IMPROVE RAINFALL PATTERN OVER BENGKULU MAINLAND AREAS, WHILE THE SATELLITE DATA ASSIMILATION (DA-SAT) ENHANCES RAINFALL OVER THE OCEAN. IN ADDITION, FOR TEMPORAL ANALYSIS, THE DA-RAD SUCCESSFULLY CORRECT THE INITIAL TIME OF THE EVENT, PRODUCING THE SMALLEST ERROR AND THE BEST CORRELATION IN STATISTICAL VERIFICATION, ALSO A SMALL BIAS AND HIGHER ACCURACY FOR DISCRETE VERIFICATION. HOWEVER, DA-SAT IS MORE CAPABLE TO IMPROVE RAINFALL ACCUMULATION WITH THE LOWEST FAR VALUE. IN CONCLUSION, COMPARED TO OTHERS, BOTH SATELLITE AND RADAR CAN BE USED AS ASSIMILATION DATA FOR 4DVAR METHODS AS THEY HAVE DIFFERENT ROLES IN INCREASING THE QUALITY OF MODEL PERFORMANCE.
KEYWORDS: ASSIMILATION, 4DVAR, SATELLITE, RADAR, HEAVY RAIN
THE IMPACT OF LAND COVER CHANGES ON TEMPERATURE PARAMETERS IN NEW CAPITAL OF INDONESIA (IKN)
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THE IMPACT OF LAND COVER CHANGES ON TEMPERATURE PARAMETERS IN NEW CAPITAL OF INDONESIA (IKN)
R A F DENRYANTO1ANDR H VIRGIANTO2*
1DEPARTMENT OF METEOROLOGY, SCHOOL OF METEROLOGY, CLIMATOLOGY AND GEOPHYSICS, JAKARTA, INDONESIA
2DEPARTMENT OF CLIMATOLOGY, SCHOOL OF METEROLOGY, CLIMATOLOGY AND GEOPHYSICS, JAKARTA, INDONESIA
*EMAIL: RISTA.VIRGIANTO@STMKG.AC.ID
ABSTRACT. NORTH PENAJAM PASER REGENCY AND KUTAI KARTANEGARA REGENCY WHICH ARE LOCATED IN EAST KALIMANTAN PROVINCE ARE TWO LOCATIONS THAT ARE PLANNED AS THE NEW CAPITAL OF INDONESIA (IKN). THIS HAS BECOME ONE OF THE FACTORS CHANGING LAND COVER FROM VEGETATION LAND TO URBAN LAND, SO THAT IT CAN CONTRIBUTE TO TEMPERATURE CHANGES. IN THIS WORK, WE ANALYZE IMPACTS OF LAND USE CHANGE ON TEMPERATURE AND RAINFALL IN THE NEW CAPITAL CITY. THE CHANGE WILL SIMULATE LAND COVER CHANGES USING THE WEATHER RESEARCH AND FORECASTING (WRF) MODEL WITH TWO SCENARIOS OF LAND COVER CHANGE FROM VEGETATION LAND TO 547% AND 1222% URBAN LAND. IT WAS FOUND THAT SCENARIO I AND II WITH THE ADDITION OF 547% AND 1222% IN URBAN AREAS, RESPECTIVELY, WILL INCREASE THE TEMPERATURE UP TO 0.95 AND 1.35 CELSIUS. THIS INDICATES THAT THE ADDITION OF MORE URBAN AREAS RESULTS IN AN INCREASE IN TEMPERATURE. THE QUANTITATIVE FEATURES OF THIS RELATIONSHIP WILL BE USEFUL FOR URBAN PLANNERS TO CONTROL THE DEVELOPMENT OF NEW CAPITALS WITHOUT HAVING A SIGNIFICANT IMPACT ON CLIMATE CHANGE.
ABSTRACT. DESIGNING CLIMATE CHANGE ADAPTATION ACTIONS ARE CONSIDERABLY A CHALLENGE, AS THE ACTIONS SHOULD BE TARGETED UNIQUELY ADDRESSING CLIMATE CHANGE IMPACTS. ONE OF THE CHALLENGES IS TO TARGET CLIMATE CHANGE ADAPTATION SITES THAT SHOULD CLEARLY REFLECT THE IMPACTS OF CLIMATE CHANGE. THE COMPLEXITY RAISES CONSIDERING CLIMATE CHANGE IMPACT A WIDE RANGE OF ECONOMIC SECTORS, WHICH REQUIRE A LOT OF RESOURCES TO CONDUCT CLIMATE CHANGE ASSESSMENTS. THIS STUDY PROPOSES THE USE OF CLIMATE CHANGE HOTSPOTS AS AN INITIATIVE TO FIRSTLY CONSIDER THE POTENTIAL TARGETS OF ADAPTATION SITES. THE TARGET OF GLOBAL EFFORTS TO MAINTAIN AIR TEMPERATURE UNDER 2&DEG;C WAS EMPLOYED AS A CLUE TO PRIORITIZE AREAS THAT AIR TEMPERATURE ARE INCREASING BEYOND THE THRESHOLDS TO WHICH CAN AFFECT HUMAN ACTIVITIES. THIS STUDY EMPLOYED SPATIAL AND THRESHOLD ANALYSIS TO DEVELOP CLIMATE CHANGE HOTSPOTS OF PROJECTED TEMPERATURE CHANGE FOR 2021-2050 OVER INDONESIA. THE THRESHOLDS WERE DEFINED BY CONSIDERING THE EFFECTS OF BASE TEMPERATURE OF 32, 35, AND 38 &DEG;C ON AGRICULTURE, ENVIRONMENT, AND HUMAN HEALTH IN COMBINATION WITH ELEVATED TEMPERATURE FROM 0.75 TO 2 &DEG;C. THE INITIATIVE METHOD WAS APPLIED THE BASELINE AND PROJECTED AIR TEMPERATURE OBTAINED FROM HIGHER RESOLUTION OF CLIMATE MODEL OUTPUTS SIMULATED UNDER REPRESENTATIVE CARBON PATHWAY SCENARIO OF 4.5 (RCP 4.5) AS A CASE STUDY. THE MAPS OF CLIMATE CHANGE HOTSPOTS PROVIDE THE POTENTIAL TARGETED AREAS FOR CLIMATE CHANGE ADAPTATION ACTIONS. THE MAPS CAN ALSO BE COMBINED WITH THE OTHER MAPS RELATED TO CLIMATE CHANGE ANALYSES, WHICH ARE AVAILABLE IN INDONESIA SUCH AS SIDIK AND INARISK TO REFINE THE PRIORITY AREAS AND/OR MORE GENERAL GEOGRAPHIC INFORMATION SUCH AS CITY LOCATION. AS AN EXAMPLE, THE OVERLAY OF CLIMATE CHANGE HOTSPOTS AND CITY LOCATION CAN PROVIDE EARLY ANTICIPATION ON WHICH CITY WILL EXPERIENCE URBAN HEAT ISLAND. THE DEVELOPMENT OF CLIMATE CHANGE HOTSPOTS NATIONALLY ARE ALSO EXPECTED TO INITIATE CLIMATE CHANGE SERVICES THAT CAN BE PROVIDED TO THE END USERS TO EASE THEM IN DEFINING SUITABLE ACTIONS TO ADAPT TO THE IMPACTS OF CLIMATE CHANGE.
KEYWORDS: CLIMATE CHANGE, ADAPTATION, HOTSPOTS, TARGET SITE, AIR TEMPERATURE
THE SKILL OF PROBABILISTIC FORECAST OF THE COMMENCEMENT OF RAINY SEASON OVER JAVA ISLAND BASED ON THE APPLICATION OF CA (CONSTRUCTED ANALOGUE) METHOD ON THE PRODUCTS OF CFSV2 (CLIMATE FORECAST SYSTEM VERSION 2) MODEL
COMMENCEMENT OF RAINY SEASON IS ONE OF THE FORECAST PRODUCTS THAT IS ISSUED REGULARLY BY THE INDONESIAN METEOROLOGY, CLIMATOLOGY, AND GEOPHYSICAL AGENCY (BMKG), WITH DETERMINISTIC INFORMATION ABOUT IN WHICH DECAD OF WHICH MONTH THE COMMENCEMENT WILL OCCUR IN EACH A DESIGNATED AREA. ON THE OTHER HAND, STATE-OF-THE-ART SEASONAL FORECASTING METHODS SUGGEST THAT PROBABILISTIC FORECAST PRODUCTS ARE POTENTIALLY BETTER FOR DECISION MAKING. THE PROBABILISTIC FORECAST IS ALSO MORE SUITABLE FOR INDONESIA BECAUSE OF LARGE RAINFALL VARIABILITY THAT ADDS UP TO UNCERTAINTY IN CLIMATE MODEL SIMULATIONS, BESIDES COMPLEX GEOGRAPHICAL FACTORS. THIS RESEARCH ATTEMPTED TO DEVELOP A METHOD TO PRODUCE A PROBABILISTIC FORECAST OF THE COMMENCEMENT OF THE RAINY SEASON, AS WELL AS MONSOON ONSET, BY UTILIZING THE FREELY AVAILABLE SEASONAL MODEL OUTPUT OF CFSV2 (CLIMATE FORECAST SYSTEM VERSION 2) OPERATED BY THE US NATIONAL OCEANIC AND ATMOSPHERIC ADMINISTRATION (NOAA). IN THIS CASE, THE OUTPUT OF THE GLOBAL MODEL IS DOWNSCALED USING MODIFIED CONSTRUCTED ANALOGUE (CA) METHOD WITH OBSERVATIONAL RAINFALL DATABASE FROM 26 BMKG STATIONS AND TRMM 3B43 GRIDDED DATASET. THIS METHOD WAS THEN APPLIED TO PERFORM HINDCAST USING CFS-R (RE-FORECAST) FOR THE 2011-2014 PERIOD. THE RESULTS SHOW THAT DOWNSCALED CFS PREDICTIONS WITH INITIAL DATA IN SEPTEMBER (LEAD-1) GIVE SUFFICIENT ACCURACY, WHILE THAT INITIALIZED IN AUGUST (LEAD-2) HAVE LARGE ERRORS FOR BOTH COMMENCEMENT OF THE RAINY SEASON AND MONSOON ONSET. FURTHER ANALYSIS OF FORECAST SKILL USING THE BRIER SCORE INDICATES THAT THE CA SCHEME USED IN THIS STUDY SHOWED GOOD PERFORMANCE IN PREDICTING THE COMMENCEMENT OF THE RAINY SEASON WITH SKILL SCORE IN THE RANGE OF 0.2. THE PROBABILISTIC SKILL SCORES INDICATE THAT PREDICTION FOR EAST JAVA IS BETTER THAN WEST- AND CENTRAL-JAVA REGIONS. IT IS ALSO FOUND THAT THE RESULTS OF CA DOWNSCALING CAN CAPTURE YEAR TO YEAR VARIATIONS, INCLUDING DELAYS IN THE COMMENCEMENT OF THE RAINY SEASON.
VERIFICATION OF 24-HOUR ACCUMULATION PRECIPITATION PARAMETERS ON NUMERICAL WEATHER MODELS IN 2020
THIS STUDY VERIFIES THE PERFORMANCE OF 4 (FOUR) NUMERICAL WEATHER MODELS, NAMELY ECMWF-IFS, NCEP-GFS, ARPEGE AND WRF-ARW IN THE INDONESIAN REGION IN 2020. THE PARAMETERS TESTED WERE THE ACCUMULATION OF RAINFALL FOR THE PREDICTION OF THE NEXT 24 HOURS WITH THE VERIFIER USED WAS THE DAILY RAINFALL OBSERVED BY MEASURING RAIN OBSERVATIONS AT THE BMKG PROVINCIAL STATION. THIS ANALYSIS AIMS TO DETERMINE THE PERFORMANCE OF EACH MODEL AS WELL AS RECOMMENDATIONS FOR FORECASTER IN 34 PROVINCES TO DETERMINE THE REFERENCE MODEL IN EACH REGION OR EACH SEASON. THE VERIFICATION METHOD USED IN THIS STUDY IS THE DICHOTOMOUS, MULTI CATEGORY, AND VERIFICATION METHOD FOR CONTINOUS VARIABLE, I.E MEAN ERROR (ME) AND ROOT MEAN SQUARE ERROR (RMSE). THE VERIFICATION INDEX ANALYZED IS ACCURACY FOR 24 HOUR ACCUMULATION RAINFALL PARAMETER IN 2020 AT 34 POINT THAT REPRESENTS EACH PROVINCE IN INDONESIA REGION. AS THE RESULT, THE IFS MODEL SHOWS THE BEST PERFORMANCE AMONG THE 3 OTHER NUMERICAL WEATHER MODELS VERIFIED IN THIS STUDY. THE IFS MODEL IS PROVEN TO BE THE MOST SUITABLE NUMERICAL MODEL FOR THE PREDICTION OF 24-HOUR PRECIPITATION PARAMETERS IN MOST STUDY POINTS AND IN MOST SEASON.
ABSTRACT. WEATHER RESEARCH AND FORECASTING (WRF) IS AN OPEN-SOURCE NUMERICAL WEATHER PREDICTION MODEL THAT CAN BE USED FOR HIGH-RESOLUTION RAINFALL PREDICTIONS. BESIDES THESE ADVANTAGES, WRF OUTPUT ACCURACY CAN BE AFFECTED BY THE INITIAL CONDITION. THE ACCURACY OF THE WRF MODEL CAN BE IMPROVED BY DATA ASSIMILATION. DATA ASSIMILATION IS COMBINES OBSERVATION DATA WITH MODEL DATA TO IMPROVE THE INITIAL STATE OF ATMOSPHERIC FLOW. THIS STUDY AIMS TO INVESTIGATE THE EFFECT OF ASSIMILATION WEATHER RADAR IN MODELS USING WRF FOR PREDICTING RAINFALL EVENTS IN THE PALEMBANG REGION ON 12 OCTOBER AND 12 NOVEMBER 2018. THIS STUDY USES RADAR RADIAL VELOCITY DATA AS INPUT DATA FOR ASSIMILATION. THE ASSIMILATION TECHNIQUE USES THE 3DVAR AND USED A WARM START PROCEDURE WITH SPIN-UP (12 HOURS) AND SPIN-UP (6 HOURS). AND IMPLEMENTED A RAPID UPDATE CYCLE (UPDATES ASSIMILATION RADAR DATA PER 1 HOUR, 3 HOURS, AND 6 HOURS) REFERRING TO GUSTARI'S RESEARCH (2014) FOR IMPROVING INITIAL DATA OR MODEL INITIAL CONDITIONS BEFORE SIMULATING EXTREME RAIN EVENTS IN THE RELATED CASE STUDY. THE OUTPUT OF THE MODEL WAS VERIFIED USING GLOBAL SATELLITE MAPPING OF PRECIPITATION (GSMAP) DATA AND USING RAIN GAUGE DATA FOR POINT VERIFICATION. BASED ON THE EIGHT MODEL SCENARIOS, THE 12 HOUR SPIN-UP WITH THE IMPLEMENTATION OF THE 1-HOUR RAPID UPDATE CYCLE SHOWS CONSISTENCY IN THE IMPROVEMENT OF THE MODEL COMPARED TO THE MODEL WITHOUT ASSIMILATION.
MONTHLY RESERVOIR INFLOW PREDICTION BASED ON ARTIFICIAL NEURAL NETWORK OVER SAGULING CATCHMENT AREA
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IMPACT BASED FORECAST (IBF) IS A FORECASTING SYSTEM THAT CONNECTS THE GAP BETWEEN HYDROMETEOROLOGICAL FORECAST PRODUCTS AND AN UNDERSTANDING OF THEIR POTENTIAL IMPACTS IN VARIOUS SECTORS OF LIFE, ONE OF&NBSP;THE&NBSP;IMPORTANT ONE IS THE HYDROLOGICAL SECTOR. IN LINE WITH IBF, OVER THE YEARS A HYDROLOGICAL PREDICTION MODEL HAS BEEN DEVELOPED TO PROVIDE MORE ACCURATE PREDICTIONS TO SUPPORT WATER RESOURCES MANAGEMENT IN THE EFFICIENT OPERATION OF WATER INFRASTRUCTURE AND MITIGATE THE IMPACT OF NATURAL DISASTERS AND CLIMATE VARIABILITY. THE PROPOSED METHOD WAS APPLIED TO BUILD A PREDICTION MODEL FOR THE SAGULING RESERVOIR MONTHLY INFLOW BASED ON THE ARTIFICIAL NEURAL NETWORK (ANN) METHOD BY UTILIZING THE OBSERVATION MONTHLY RAINFALL DATA (P) OWNED BY THE METEOROLOGY, CLIMATOLOGY AND GEOPHYSICS AGENCY (BMKG) AROUND THE SAGULING CATCHMENT AREA AND THE MONTHLY INFLOW (Q) OBSERVED BY PT. INDONESIA POWER POMU IN THE SAGULING RESERVOIR AS A PREDICTOR MODEL. THE FIRST STEP IS DATA PREPROCESSING, THIS STEP IS USEFUL TO INVESTIGATE THE COMBINATION OF EFFICIENT DATA INPUT. THE BEST DATA INPUT WITH THE HIGHEST CORRELATION COEFFICIENT WITH DATA OUTPUT ARE&NBSP; PT, PT-1, PT-11, QT, QT-1, AND QT-11. THE NEXT STEP IS COMBINING THE DATA INPUT INTO THREE DIFFERENT INPUT DATASET COMBINATIONS TO APPLY WITH THREE VARIATIONS IN THE NUMBER OF HIDDEN LAYERS IN THE ANN MODEL ARCHITECTURE. SO THAT WE HAVE NINE ARCHITECTURAL MODEL SCENARIOS ARE BUILT TO BE APPLIED USING THE ANN BACKPROPAGATION ALGORITHM MODEL.BY THE TRAINING, TESTING ANG VALIDATING MODEL OBTAINED THE BEST MONTHLY INFLOW SAGULING RESERVOIR MODEL I.E BEST MODEL UNDER THE NORMAL PERIOD IS MODEL NINE WITH RMSE 31,23 AND R 0,88. THIS MODEL IS ALSO THE BEST MODEL UNDER LA NINA CONDITION WITH RMSE 30,01 AND R 0,83. THEN FOR CONDITIONS UNDER EL NINO WE GET MODEL FOUR FOR THE BEST MODEL WITH RMSE 30,77 AND R 0,92.
THE REAL-TIME MULTIVARIATE MJO (RMM) INDEX BASED ON ARIMA TECHNIQUE
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THE REAL-TIME MULTIVARIATE MJO (RMM) INDEX BASED ON ARIMA TECHNIQUE
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EDDY HERMAWAN1 AND DHEOLIVIAN ARIESTA PUTRA2
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1CENTER FOR ATMOSPHERIC SCIENCE & TECHNOLOGY OF LAPAN BANDUNG
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E-MAIL: EDDY.HERMAWAN@LAPAN.GO.ID
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2FACULTY OF MATHEMATICS AND NATURAL SCIENCES OF YOGYAKARTA STATE UNIVERSITY
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E-MAIL: DHEOLIVIAN.ARIESTA2016@STUDENT.UNY.AC.ID
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ABSTRACT
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AS WE KNOW, MADDEN-JULIAN OSCILLATION IS RECOGNIZED AS ONE OF THE LEADING SOURCES OF SUBSEASONAL PREDICTABILITY. THE MJO IS AN ORGANIZED ENVELOPE OF TROPICAL CONVECTION WITH A LIFE CYCLE OF ABOUT 40-50 DAYS, FITTING NEATLY WITHIN THE SUBSEASONAL TIME SCALE (30-90 DAYS). IT IS CHARACTERIZED BY A VAST ZONAL SCALE (WAVENUMBER 1-3) AND DOMINAN EASTWARD PROPAGATION OVER THE TROPICAL INDO-PACIFIC BASIN, PARTICULARLY DURING THE BOREAL WINTER SEASON. BASED ON THE ABOVE MOTIVATION THIS STUDY IS MAINLY CONCERNED TO DEVELOP THE RMM INDEX USING THE STATISTICAL ANALYSIS WITH EMPHASIS THAT MODEL IS VALIDATED DURING THE LA-NINA EVENT 2020 (PERIOD OF JULY 15 TO NOVEMBER 18, 2020). WE SELECT THIS PERIOD SINCE WE ARE INTEREST TO INVESTIGATE THE CHARACTERISRICS OF RMM INDEX MODEL DURING UBNORMAL CONDITION. BASED ON THE LONGER THE DAILY OF RMM INDEX DATA FOR PERIOD OF JANUARY 2017 TO FEBRUARY 2020, WE GOT THE BEST ARIMA MODEL IS ARIMA (2,1,2) FOR RMM 1 WITH THE FORMULA : ZT = 0,6134ZT-1 + 0.319ZT-2 &NDASH; 0.0453ZT-3 &NDASH; 0.00012 + 0.8570AT-1 + 0.2704AT-2. WHILE, FOR THE RMM2 WE GOT ARIMA (1,1,3) WITH THE FORMULA ZT = (1-0.9123)ZT-1 + 1.4478ZT-2 - 0,0074 + 1.4478AT-1 + 0.7117AT-2&NBSP; + 0.1710AT-3 FOR PERIOD OF JULY 15 TO NOVEMBER 18, 2020. NOT ONLY FOR THE AMPLITUDE, THIS STUDY ALSO CONCERN ON THE PHASE OF MJO. WE FOUND AN A GOOD AGREEMENT BETWEEN OUR MODEL COMPARING THE THE POAMA AUSTRALIAN MODEL. THOSE ALL ABOVE RESULTS WILL BE DISCUSSED IN THIS FULL PAPER.
IMPROVEMENTS TO THE ACCURACY OF NUMERICAL WEATHER PREDICTIONS IN NORTH SUMATRA USING THE ASSIMILATION METHOD
NUMERICAL WEATHER PREDICTION PLAYS A VERY IMPORTANT ROLE IN RESPONDING TO THE INCREASING NEEDS OF WEATHER FORECASTING. AN OPEN-SOURCE AND EXTENSIBLE NUMERICAL WEATHER PREDICTION PLATFORM IS THE WEATHER RESEARCH FORECASTING (WRF) MODEL. HOWEVER, THE WRF MODEL DOES NOT FULLY PRODUCE ACCURATE WEATHER PREDICTIONS. ONE OF THE PROBLEMS THAT CAUSE THE WRF MODEL TO BE INACCURATE IN PREDICTING RAIN EVENTS IS THE INACCURACY OF THE INITIAL MODEL DATA. ONE OF THE TECHNIQUES IN FORMING A MODEL OF INITIAL CONDITIONS THAT ARE CLOSER TO THE ACTUAL DATA OF THE ATMOSPHERE IS THE ASSIMILATION OF THE THREE DIMENSIONAL VARIATION (3DVAR) TECHNIQUE. THIS STUDY AIMS TO DETERMINE THE EFFECT OF THE ASSIMILATION OF UPPER AIR AND SURFACE OBSERVATION DATA (CONV) AND SATELLITE (SATA) AND THEIR COMBINATION (BOTH) ON THE ACCURACY OF HEAVY RAIN PREDICTIONS IN THE NORTH SUMATRA REGION IN 2020 FOR EACH PERIOD OF THE DJF-MAM-JJA-SON SEASON. THE COMPARATIVE DATA IN THIS STUDY ARE GSMAP RAINFALL DATA AND SYNOPTIC OBSERVATION DATA FROM BMKG STATIONS IN THE NORTH SUMATRA REGION. THE RESEARCH RESULTS SHOW THAT THE CONV AND BOTH EXPERIMENTAL MODELS PROVIDE THE MOST SIGNIFICANT CHANGES IN THE PREDICTIVE MODEL DATA FOR ALL PARAMETERS IN EACH RAIN CASE. THE SATA EXPERIMENT GAVE SIGNIFICANT RESULTS ON TEMPERATURE AND HUMIDITY PARAMETERS. THE MODEL'S PERFORMANCE IN PREDICTING HEAVY RAIN EVENTS IN ORDER OF THE BEST IS THE SATA, CONV, AND BOTH EXPERIMENTS. FURTHERMORE, THE SATA EXPERIMENT WAS ABLE TO IMPROVE THE MODEL'S PERFORMANCE IN PREDICTING RAINFALL INCIDENCE BY ABOUT 14%. MEANWHILE, THE BOTH AND CONV EXPERIMENTS WERE ABLE TO REDUCE THE MODEL PREDICTION ERROR IN PREDICTING RAIN EVENTS BY ABOUT 6% AND 2%, RESPECTIVELY. ALSO, THE CONV EXPERIMENT WAS ABLE TO REDUCE THE RAIN BIAS FROM 1.26 TO 0.97. MEANWHILE, THE MODEL'S PERFORMANCE IN ESTIMATING PARAMETERS OF SURFACE AIR TEMPERATURE AND SURFACE AIR HUMIDITY IN ORDER OF THE BEST IS BOTH, SATA, AND CONV.
ANALYSIS AND PREDICTION OF BLACK CARBON EMISSION FROM 2019 FOREST FIRES IN RIAU PROVINCE, INDONESIA
ANALYSIS AND PREDICTION OF BLACK CARBON EMISSION FROM 2019 FOREST FIRES IN RIAU PROVINCE, INDONESIA
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WILIN JULIAN SARI, WALUYO EKO CAHYONO, AND ROSIDA
CENTER OF ATMOSPHERIC SCIENCE AND TECHNOLOGY, INDONESIA NATIONAL INSTITUTE OF AERONAUTICS AND SPACE (LAPAN), BANDUNG, INDONESIA
WILIN.JULIAN@LAPAN.GO.ID
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ABSTRACT. FOREST FIRES ARE AN ANTHROPOGENIC PHENOMENON WHICH CONTRIBUTES TO TEMPORAL CHANGE OF ATMOSPHERIC COMPOSITION. THIS TEMPORAL CHANGE IS OWING TO THE PRESENCE OF AIR POLLUTANTS IN THE FORM OF, ONE OF WHICH, PARTICULATE MATTERS SUCH AS BLACK CARBON WHICH RESULTED FROM THE INCOMPLETE COMBUSTION OF FOREST BIOMASS. DUE TO ITS SIZE AND ITS LIGHT ABSORPTION ABILITY, BLACK CARBON IS KNOWN TO HAVE BAD IMPACTS ON HUMAN HEALTH, ESPECIALLY TO THE RESPIRATORY SYSTEM, AND ON THE CLIMATE. THIS STUDY AIMS TO ANALYSE AND ESTIMATE THE EFFECT OF FOREST FIRES THAT HAPPENED IN RIAU PROVINCE, INDONESIA IN SEPTEMBER 2019 TOWARDS THE PRODUCTION OF BLACK CARBON, AS WELL AS TO COMPUTATIONALLY PREDICT THE BACKWARD AND FORWARD AIR MOVEMENT TRAJECTORIES IN ORDER TO CONFIRM THE AIR MASS SOURCES OF THE BLACK CARBON. THE DATA OF BLACK CARBON CONCENTRATION USED IN THIS STUDY IS AN HOURLY TEMPORAL DATA GENERATED BY THE UNITED STATE (US) NATIONAL AERONAUTICS AND SPACE ADMINISTRATION (NASA)&RSQUO;S MERRA 2 MODEL AND RETRIEVED FROM NASA&RSQUO;S GIOVANNI WEBSITE WITH A PERIOD OF 01 JANUARY 2019 TO 31 DECEMBER 2019 AND A SPATIAL RESOLUTION OF 0.5 X 0.625?, WHILE THE TRAJECTORY CALCULATION USES THE US NATIONAL OCEANIC AND ATMOSPHERIC ADMINISTRATION (NOAA)&RSQUO;S HYSPLIT TRAJECTORY MODEL. DATA ANALYSIS OF THIS STUDY SHOWS THAT THERE WAS AN INCREASE IN BLACK CARBON SURFACE MASS CONCENTRATION IN SEPTEMBER COMPARED TO THE OTHER MONTHS WITH A MAXIMUM CONCENTRATION OF 3.5E-09 KG/M3 OCCURRED ON 11 SEPTEMBER. THIS IS IN AN AGREEMENT WITH THE HOTSPOTS DATA RETRIEVED FROM THE INDONESIA NATIONAL INSTITUTE OF AERONAUTICS AND SPACE (LAPAN)&RSQUO;S TERRA/AQUA THAT SHOWS THERE WERE MORE HOTSPOTS IN RIAU IN SEPTEMBER, I.E. 1055 HOTSPOTS, COMPARED TO THE OTHER MONTHS WITH MAXIMUM NUMBERS OF HOTSPOTS WERE ALSO PRESENT ON 11 SEPTEMBER.
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KEYWORDS: FOREST FIRE, HOTSPOT, BLACK CARBON, MERRA 2, HYSPLIT.
VERIFICATION OF FLASH FLOOD THREAT PRODUCTS OF SOUTHEASTERN ASIA-OCEANIA FLASH FLOOD GUIDANCE SYSTEM (SAOFFGS) ON INDONESIA FLASH FLOOD EARLY WARNING SYSTEM
SOUTHEASTERN ASIA-OCEANIA FLASH FLOOD GUIDANCE SYSTEM (SAOFFGS) AT INDONESIAN AGENCY FOR METEOROLOGY CLIMATOLOGY AND GEOPHYSICS IS DESIGNED TO GUIDE THE FORECASTER IN ISSUING FLASH FLOOD WARNINGS IN INDONESIA. IT IS ESSENTIAL TO DETERMINE SYSTEM PERFORMANCE IN SUPPORTING FLASH FLOOD MITIGATION STRATEGIES. HOWEVER, VERIFICATION OF SAOFFGS PRODUCTS HAS NOT BEEN INVESTIGATED. THIS STUDY AIMED TO VERIFY IMMINENT FLASH FLOOD THREAT (IFFT) AND PERSISTENCE FLASH FLOOD THREAT (PFFT) PRODUCTS FROM SAOFFGS WITH THE FLOOD EVENTS IN ALL REGIONS OF INDONESIA FROM SEPTEMBER TO DECEMBER 2018.THE VERIFICATION SCORES ARE CALCULATED USING CONTINGENCY TABLES TO GET PROPORTION CORRECT (PC), PROBABILITY OF DETECTION (POD), FALSE ALARM RATIO (FAR), PROBABILITY OF FALSE DETECTION (POFD), AND CRITICAL SUCCESS INDEX (CSI) VALUES. IFFT AND PFFT PRODUCT'S PERFORMANCE WAS ANALYZED BASED ON THE VERIFICATION SCORES TO IDENTIFY FLASH FLOOD EVENTS ON A NATIONAL AND REGIONAL SCALE. THE RESULT INDICATES THAT BOTH IFFT AND PFFT HAVE A DEFICIENT PERFORMANCE TO IDENTIFY FLASH FLOOD EVENTS ON A NATIONAL SCALE. THE FAR VALUE IS QUITE LARGE COMPARED TO POD AND CSI VALUES, AND IT REPRESENTS THE LACK OF MODEL SENSITIVITY IN DETECTING FLASH FLOODS IN CERTAIN AREAS. NEVERTHELESS, THE REGIONS WITH THE BEST VERIFICATION SCORES IN THE SEQUENCE ARE KALIMANTAN, JAWA, SUMATERA, SULAWESI, BALI-NUSA TENGGARA, AND MALUKU-PAPUA. QUALITATIVE ANALYSIS CAPABILITY IS DEEMED NECESSARY TO OBTAIN AN EFFICIENT FLASH FLOOD WARNING.
ESTIMATION OF SPATIAL DISTRIBUTION OF MAXIMUM PM10 AND PM2.5 CONCENTRATION IN BANDUNG CITY AND SURROUNDING COUNTRIES USING WRF-CHEM MODEL
BANDUNG IS ONE OF BIG CITY IN INDONESIA WITH HIGH ACTIVITIES ON INDUSTRIAL AND TRANSPORTATION THAT WILL INCREASE THE AIR POLLUTANT EMISSION AND CAUSES ADVERSELY AFFECT THE PUBLIC HEALTH. BASED ON THAT MATTER, MONITORING OF AIR POLLUTANT CONCENTRATION IS URGENTLY NEEDED TO PREDICT THE DIRECTION OF POLLUTANT DISPERSION AND TO ANALYZE WHICH LOCATIONS ARE VULNERABLE TO MAXIMUM EXPOSURE OF THE POLLUTANT. FIELD MONITORING OF AIR POLLUTANT CONCENTRATION NEED MUCH TIME AND HIGHLY COST BUT MODELING COULD HELP FOR THIS. WEATHER RESEARCH FORECASTING/CHEMISTRY (WRF-CHEM) IS ONE OF THE MODELS THAT COULD USE TO ANALYZE AIR POLLUTANT DISPERSION BECAUSE IT IS ALREADY COMBINE METEOROLOGY AND AIR QUALITY MODEL. OUTPUT OF THE WRF-CHEM RUNNING MODEL ON JULY AND OCTOBER 2018 WHICH HAS BEEN ANALYZED VISUALLY SHOWED THAT DISPERSION PATTERN OF PM10 AND PM2.5 IS SPREAD MOSTLY TO THE WEST, NORTHWEST, AND NORTH FOLLOWING THE WIND DIRECTION. ACCORDING TO OUTPUT OF THE WRF-CHEM MODEL, BANDUNG KULON IS THE MOST POLLUTED SUBDISTRICT BY PM10 DAN PM2.5 WITH THE EXPOSURE FREQUENCY OF 22 HOURS (PM10), 24 HOURS (PM2.5) ON JULY 2018 AND 19 HOURS (PM10), 14 HOURS (PM2.5) ON OCTOBER 2018. THE HIGHEST CONCENTRATION OF PM10 DURING THE RUNNING PERIOD ON JULY AND OCTOBER 2018 ON BANDUNG KULON IS AT 545 &MICRO;G/M3 (JULY) AND 458 &MICRO;G/M3 (OCTOBER). THIS CONCENTRATION IS OVERPASS THE NATIONAL AMBIENT AIR-QUALITY STANDAR FOR PM10 IN 24 HOUR WHICH IS 150 &MICRO;G/M3.&NBSP;THE HIGHEST CONCENTRATION OF PM2.5 DURING THE RUNNING PERIOD ON JULY AND OCTOBER 2018 ON BANDUNG KULON IS AT 191 &MICRO;G/M3 (JULY) AND 209 &MICRO;G/M3 (OCTOBER). THIS CONCENTRATION IS OVERPASS THE NATIONAL AMBIENT AIR-QUALITY STANDAR FOR PM2.5 IN 24 HOUR WHICH IS 65 &MICRO;G/M3.
DETERMINING THE INFLUENCE OF METEOROLOGICAL FACTORS ON AIR POLLUTION CONCENTRATION TREND OVER JAKARTA MEGACITY
METEOROLOGICAL FACTORS PLAY AN IMPORTANT ROLE IN INFLUENCING THE CONCENTRATION OF POLLUTANTS IN THE ATMOSPHERE. IT IS VERY DIFFICULT DETERMINING THE TRENDS IN AIR POLLUTANTS WHETHER CHANGES IN CONCENTRATION ARE DUE TO EMISSIONS OR METEOROLOGICAL FACTORS ESPECIALLY IN JAKARTA MEGACITY. THE OBJECTIVE OF THIS RESEARCH IS TO QUANTIFY THE STRENGTH OF WEATHER PARAMETERS EFFECTS (E.G. RAINFALL, AIR TEMPERATURE, WIND SPEED AND DIRECTION) ON THE PM2.5 CONCENCRATION OVER JAKARTA REGION.
WE&RSQUO;RE CONDUCTING THE DE-WEATHERING MODELING METHOD IN THIS RESEARCH. DE-WEATHERING IS A METHOD USED TO REMOVE METEOROLOGICAL FACTORS / VARIATIONS FROM AIR QUALITY TIME SERIES DATA. IN THIS RESEARCH, WE ARE USING PARTICULATE MATTER DATA WITH THE DIAMETER SIZE LESS THAN 2.5 &MICRO;G/M3, PM2.5 HOURLY OBSERVATION DATA SERIES FROM BETA ATTENUATION METHOD (BAM) INSTRUMENT LOCATED AT U.S EMBASSY AT THE CENTRAL JAKARTA FOR THE LAST 3 YEARS (2018 - 2020) PERIOD. IN ADDITION, BMKG HEADQUARTERS&RSQUO; METEOROLOGICAL DATA IS ALSO USED WITH PARAMETERS OF RAINFALL, TEMPERATURE, WIND DIRECTION AND SPEED WITH THE ASSOCIATED TIME PERIOD.
IN THIS RESEARCH HAS DEMONSTRATED THAT AIR QUALITY TREND IN JAKARTA WAS 32.1% INFLUENCED BY TEMPORAL FACTOR (WEEKLY). IT IS INDICATED THAT THE WEEKLY PATTERN IS ASSOCIATED WITH HUMAN ACTIVITY THAT PLAYED A FAIRLY HIGH ROLE IN INFLUENCING PM2.5 CONCENTRATION. ADDITIONALLY, FOR THE METEOROLOGICAL FACTORS: AIR TEMPERATURE, WIND DIRECTION, AND WIND SPEED HAD AN INFLUENCE ON THE CONCENTRATION OF PM2.5 TO 5.6%, 5.3%, AND 1%, RESPECTIVELY.
PRELIMINARY VERIFICATION FOR SUBSEASONAL TO SEASONAL PRECIPITATION MODEL ON FOUR SPECIFIC CONDITIONS OVER WESTERN INDONESIA
PRELIMINARY VERIFICATION OF SUB SEASONAL TO SEASONAL REFORECAST PRECIPITATION MODEL (S2S) WAS CONDUCTED TO ANALYZE THE PERFORMANCE OF THE MODEL OVER WESTERN INDONESIA ON FOUR CONDITIONS. THE ECMWF S2S MODEL WAS COMPARED TO QUALITY CONTROLLED DAILY PRECIPITATION DATA FROM 643 OBSERVATION POINTS OVER THE REGION. THREE EARLIEST TIME STEP AND THE THREE LONGEST ONE WERE UTILIZED TO OBTAIN THE BEST PERFORMANCE COMPARISON. THE ANALYSIS WAS CONDUCTED IN THE TERM OF MONTH, MJO EVENTS, COLD SURGE EVENTS, AS WELL AS WHEN THE MJO AND COLD SURGES WERE ACTIVE IN THE SAME TIME. THE RESULTS SHOW THAT EVEN THOUGH THE EARLIEST TIME STEP IS ABLE TO CAPTURE HIGHER AND LOWER PRECIPITATION OVER SOME SPECIFIC AREA, THE STATISTICAL CORRELATION FOR MONTHLY PERIOD IS MOSTLY UNDER 0.6 WITH LOWER RMSE OBSERVED OVER JAVA. THE CORRELATION INCREASES IN THE EVENT OF NCS OVER SOME POINTS ON NATUNA ISLANDS, WEST KALIMANTAN, AND THE NORTHERN COAST OF WESTERN JAVA BUT WITH INCREASING RMSE. ON THE EVENTS OF MJO, HIGHER CORRELATION IS FOUND ON SOME POINTS OVER NORTH SUMATERA, WEST KALIMANTAN, AND ALONG THE JAVA. MEANWHILE, WHEN THE MJO AND NCS ACTIVE, HIGHER CORRELATIONS ARE EASIER TO BE ACHIEVED OVER THE REGION. BETTER RESULT IS ACHIEVED BY THE EARLIEST TIME STEP AS HIGHER POSITIVE CORRELATIONS ARE SPOTTED ALONG WITH BETTER SPATIAL PATTERN SIMILARITY.
THE ANOMALOUSLY-WET DRY SEASONS OVER THE WESTERN MARITIME CONTINENT (MC) HAVE BEEN PREVIOUSLY FOUND WERE DETERMINED BY THE OCEANIC AREA TO THE WEST OF INDONESIA. THIS STUDY EXPLORES SUBREGIONS PROCESSES OF SEA-AIR INTERACTION OVER WESTERN MC BY SIMULATING DIURNAL PRECIPITATION USING CUBIC CONFORMAL ATMOSPHERIC MODEL (CCAM) WITH A SPATIAL RESOLUTION OF 15 KM DURING THE MJJAS PERIOD OF 2020, WHICH IS CONFIRMED BY PRECIPITATION DATA FROM TROPICAL RAINFALL MEASURING MISSION (TRMM) SATELLITE OBSERVATION. THE RESULTS SHOW THAT REGIONAL ANOMALOUS PRECIPITATION OVER WESTERN MC IS INDUCED BY ANOMALOUS CIRCULATION PATTERNS OVER 3 KEYS OF SUBREGIONS, I.E., KARIMATA STRAIT, SOUTHERN SUMATRA (LAMPUNG AND SUNDA STRAIT), AND JAVA. THE ANOMALOUS CIRCULATION ALSO MODULATES ANOMALOUS LOCAL CIRCULATION AND ENHANCE SURFACE WATER VAPOR BY AN INCREASE OF SURFACE LATENT HEAT FLUX. &NBSP;
KEYWORDS: WESTERN MARITIME CONTINENT, DAILY PRECIPITATION, ANOMALOUSLY-WET DRY SEASON
FLASH FLOOD DETECTION IN CILACAP REGENCY USING SOUTHEASTERN ASIA-OCEANIA FLASH FLOOD GUIDANCE SYSTEM (SAOFFGS) PRODUCTS
FLASH FLOOD IS ONE TYPE OF FLOOD THAT IS KNOWN TO HAVE THE POTENTIAL TO CAUSE DISASTROUS EFFECTS. THIS PHENOMENON IS MAINLY CAUSED BY HEAVY OR EXCESSIVE RAINFALL IN A SHORT PERIOD OF TIME. BASED ON THE INDONESIAN NATIONAL DISASTER MANAGEMENT AUTHORITY RECORDS, CILACAP IS ONE OF THE REGENCIES WHICH HAS A HIGH NUMBER OF FLOODING CASES TRIGGERED BY HEAVY RAINFALL. HOWEVER, THERE WERE NOT MANY FLASH FLOOD MODELING STUDIES THAT HAVE BEEN ABLE TO PREDICT THE FLASH FLOOD EVENTS IN THIS REGION PROPERLY. THE SOUTHEASTERN ASIA-OCEANIA FLASH FLOOD GUIDANCE SYSTEM (SAOFFGS), AS A PART OF GLOBAL FLASH FLOOD GUIDANCE SYSTEM (FFGS), QUANTIFYING BASIN HYDROLOGICAL PARAMETERS IN THE SOUTHEASTERN ASIA AND OCEANIA REGION USING NEAR-REAL-TIME ESTIMATED RAINFALL VALUES AND THE OUTPUT OF NUMERICAL WEATHER PREDICTION (NWP) AS THE INPUT. BY COMBINING HYDROLOGICAL MODELS AND NUMERICAL WEATHER PREDICTION OUTPUTS IN THE FRAME OF THE NEAR-REAL-TIME DATA UPDATE APPROACH, SAOFFGS ANSWERS THE NEED FOR INFORMATION ON POTENTIAL FLASH FLOODS THAT ARE COMPREHENSIVE AND NEAR-REAL-TIME. THIS STUDY AIMED TO INVESTIGATE THE MODEL PERFORMANCE IN DETECTING CILACAP&RSQUO;S FLASH FLOOD EVENTS FROM OCTOBER TO DECEMBER 2020USING PRODUCTS FROM SAOFFGS. THE PRODUCTS USED IN THIS STUDY ARE THE 6-HOURLY MAP (MEAN AREA PRECIPITATION), FFG (FLASH-FLOOD GUIDANCE), IMMINENT FLASH FLOOD THREAT (IFFT), AND 12-HOURLY ALSO 24-HOURLY FLASH-FLOOD RISK (FFR). THE RESULT INDICATES THAT THE SAOFFGS PRODUCTS WERE SUCCESSFULLY CAPTURED SIGNALS OF THE POTENTIAL FLASH FLOOD OCCURRENCE FOR EACH CASE. THE SAME RESULTS SHOW THAT THE FLASH FLOOD EVENTS WERE WELL-DETECTED USING IMMINENT FLASH FLOOD THREAT (IFFT) PRODUCT AND THE FLASH FLOOD SIGNALS IN THE STUDY PERIOD COULD BE DETECTED USING THE FLASH FLOOD RISK (FFR) PRODUCTS AT 12-24 HOURS BEFORE THE FLASH FLOOD OCCURRED.
DOES THE URBANISATION HAVE CONTRIBUTION IN INCREASING EXTREME PRECIPITATION EVENT OVER JAKARTA
EXTREME PRECIPITATION IN JAKARTA EXHIBITS TRENDS RELATED TO LOCAL TEMPERATURE, SEASONAL TROPICAL MONSOON CIRCULATIONS AND POTENTIALLY OTHER DRIVERS, BOTH IN FREQUENCY AND INTENSITY. THE HEAVIEST 1% OF ALL PRECIPITATION EVENTS EXHIBITS AN STRONGER&NBSP;TREND DURING THE WET SEASON; IN CONTRAST TO THE ANNUAL MEAN PRECIPITATION&NBSP;THAT RELATIVELY UNCHANGED. A NEW RECORD-BREAKING OF EXTREME PRECIPITATION INDUCED JAKARTA BIG FLOOD WAS ON 1 JANUARY&NBSP;2020, WITH THE HIGHEST RECORD OF DAILY RAINFALL OF&NBSP;377 MM/DAY. BESIDE METEOROLOGICAL FACTOR AND CLIMATE CHANGE,&NBSP;POTENTIALLY OTHER DRIVERS THAT MAY ATTRIBUTE TO INCREASING TREND OF JAKARTA EXTREME PRECIPITATION IS URBANISATION. RECENT STUDIES SUGGEST THAT THE RAPID URBANIZATION OF JAKARTA IN THE 20TH CENTURY COULD HAVE MODIFIED THE INTENSITY OF EXTREME PRECIPITATION EVENTS. THIS STUDY INVESTIGATED THE POTENTIAL URBAN INFLUENCES OF JAKARTA, INDUCED BY THE URBAN HEAT ISLAND AND THE ENHANCED SURFACE ROUGHNESS, ON THE INTENSITY AND SPATIAL DISTRIBUTION OF PRECIPITATION IN THE 2013, 2014, AND 2015&NBSP;EXTREME PRECIPITATION EVENT. THE WRF-JAKARTA URBAN SCENARIO WAS&NBSP;IMPLEMENTED BY TWO DIFFERENT LAND COVER SETUP. THE RESULTS SHOWED THAT THE SPATIAL EXTENT OF HIGH PRECIPITATION INCREASE UP TO 30% IN THE URBANISED AREA, ESPECIALLY IN THE NIGHT AND MORNING TIME, WHILE THE INTENSITY OF PRECIPITATION EXTREMES IN THE FULLY-URBANISED SCENARIO EXHIBIT 3 - 4 TIMES STRONGER THAN WITHIN LESS-URBANISED SCENARIO.&NBSP;