ON APRIL 27, 2018 HEAVY RAIN HAS OCCURED IN PALANGKARAYA. BASED ON SURFACE DATA OBSERVATIONS AT TJILIK RIWUT METEOROLOGICAL STATION, THE PEAK OF RAIN OCCURS BETWEEN 18-21 UTC, WHICH IS 54 MM WITHIN 3 HOURS. AS A RESULT, FLOOD OCCURED IN THE FOLLOWING DAY. THIS STUDY AIMS TO EXAMINE THE CAUSES OF HEAVY RAIN USING WRF MODEL OF THE 3DVAR TECHNIQUE THAT ASSIMILATED WITH AMSU-A SATELLITE WHICH USING THE TROPICAL PHYSIC SUITE PARAMETERIZATION SCHEME AND HIMAWARI-8 SATELLITE DATA WHICH PROCESSED BY PYTHON PROGRAMMING. BASED ON THE RESULTS OF WRF MODELLING, WRF OF 3DVAR MODEL IS NOT REPRESENTATIVE ENOUGH IN RELATION TO HEAVY RAIN EVENT. HOWEVER, THERE ARE SEVERAL WEATHER DISTURBANCES THAT SHOWS THE POTENCY FOR SEVERE WEATHER OCCURRENCE. THESE ARE INDICATED BY THE SHEAR LINE AND EDDY CIRCULATION AT 18 AND 21 UTC, AND TIME SERIES OF AIR PRESSURE DECREASES WITH A 0.5 MB TENDENCY BETWEEN 15 TO 18 UTC. MOREOVER, THE CLOUD TOP TEMPERATURE GRAPH FROM HIMAWARI-8 SATELLITE DATA SHOWS A DRASTIC REDUCTION IN TEMPERATURE TO -61.4323 AT 18.20 UTC, WHICH SUPPORT THE PROCESS OF THE HEAVY RAIN. BASED ON THE RESULTS OF THE WEATHER ANALYSIS, IT SHOWS THE CAUSED OF HEAVY RAIN IN PALANGKARAYA.

 

KEYWORDS : WRF 3DVAR, HIMAWARI-8, PYTHON PROGRAMMING, FLOOD, HEAVY RAIN

 

 

 

TURBULENCE ANALYSIS ON THE FLIGHT OF ETIHAD AIRWAYS IN BANGKA ISLAND USING THE WRF CASE STUDY MAY 4, 2016

 

B R T ANDARI^1,2, N J TRILAKSONO² AND M A MUNANDAR³

¹MASTER PROGRAM IN EARTH SCIENCE, FACULTY OF EARTH SCIENCES AND TECHNOLOGY, INSTITUT TEKNOLOGI BANDUNG, ID

²WEATHER AND CLIMATE PREDICTION LABORATORY, FACULTY OF EARTH SCIENCES AND TECHNOLOGY, INSTITUT TEKNOLOGI BANDUNG, ID

³AVIATION METEOROLOGICAL CENTER, BADAN METEOROLOGI KLIMATOLOGI DAN GEOFISIKA, ID

 

ANDARIBAYU@GMAIL.COM

 

ABSTRACT. THE INCREASING OF SAFETY OF AVIATION OPERATIONS IN INDONESIA SHOULD BE SUPPORTED BY ACCURATE WEATHER FORECASTS. THIS WEATHER FORECAST IS NEEDED, ESPECIALLY IN DETECTING TURBULENCE, CONSIDERING THAT GEOGRAPHICALLY INDONESIA HAS EFFECTIVE SOLAR RADIATION RESULTING TO CONVECTIVE CLOUDS FORMATION. CONVECTIVE CLOUDS CAN TRIGGER TURBULENCE THEN PRODUCE DISRUPTION AND EVEN ACCIDENTS ON FLIGHTS. THIS RESEARCH USES A CASE STUDY ON THE ETIHAD AIRWAYS FLIGHT ON THE BANGKA ISLAND AT MAY 4, 2016. AT THE TIME OF THE INCIDENT, THERE WAS TURBULENCE AT 39,000 FEET ALTITUDE AND THE AIRCRAFT DID NOT ENTER OVERCAST AREA. THE TURBULENCE IN THIS STUDY IS SIMULATED USING THE WEATHER RESEARCH AND FORECASTING (WRF) MODEL WHICH IS DOWNSCALED UP TO 3 KM WITH PARAMETERIZATION WRF SINGLE-MOMENT 6 CLASS (WSM6). THE RESULTS THEN VERIFIED USING CORRELATION AND LINEAR REGRESSION FOR TEMPERATURE, WIND DIRECTION, WIND SPEED, AND PATTERN RESEMBLANCE BETWEEN CLOUD FRACTION AND THE CONVECTIVE CORE DISTRIBUTION. THE TURBULENCE IS ANALYZED FROM THE SOUTH-NORTH AND WEST EAST VERTICAL AIR FLOW. THE TURBULENCE SPOTTED AT 06.40 UTC WHEN THERE IS FAIRLY STRONG UPDRAFT WHICH CAN CAUSE TURBULENCE. THE TURBULENCE PARAMETERS USED SUCH AS EDDY DISSIPATION RATE (EDR), RICHARDSON NUMBER AND TURBULENCE INDEX 1 (TI1) INDICATE THAT THIS CASE IS IN THE STRONG CATEGORY. TURBULENCE THAT OCCURS ON THIS STUDY IS IDENTIFIED AS A NEAR CLOUD TURBULENCE (NCT) EVENT DUE TO CLOUD FORMATION OBSERVED IN THE WEST OF THE TURBULENCE AND STRONG UPDRAFT ACTIVITY AT THE LOCATION OF TURBULENCE.

 

KEYWORDS: TURBULENCE, CAUSES OF TURBULENCE, WRF, INTENSITY OF TURBULENCE

ONE OF METHOD TO CREATE GOOD FORECAST WITH WRF-ARW MODELLING IS TUNING FOR PARAMETERIZATION, BUT THIS METHOD CAN’T GIVE PROBABILITY EVENT OF RAINFALL. THE RESULT WAS THERE HAVE BEEN ABLE TO SIMULATE AND PREDICT SOME WEATHER PARAMETERS. HOWEVER, FROM THE RESULTS OF THE VERIFICATION THERE ARE SOME WEATHER PARAMETERS ARE STILL LOW ACCURACY. BECAUSE OF THE LOW ACCURACY ON SOME PARAMETERS OF THE WEATHER, THE AUTHORS ARE INTERESTED IN PERFORMING POST-PROCESSING METHODS IN PREDICTING THE WEATHER IN EXTREME WEATHER AT PATTIMURA AMBON METEOROLOGICAL STATION. IN THIS STUDY WILL USE MULTI-PHYSICS ENSEMBLE PREDICTION SYSTEM (MEPS) BY COMBINING 20 WRF-ARW PARAMETERIZATION SCHEME, WHICH WILL BE PROCESSED ENSEMBLE MEAN, ENSEMBLE SPREAD, AND BASIC PROBABILITY TO GET THE UNCERTAINTY FROM EACH WEATHER PARAMETERS. VERIFICATION PROCESS USING SPREADS AND SKILL METHOD AND ROC CURVES. THE MEPS PRODUCTS HAVE THE BETTER SKILL COMPARED TO THE FORECAST CONTROL, THE CORRELATION VALUE OF MEPS PRODUCTS IS LARGER AND WITH THE LOWEST ERROR VALUE. THE RESULT OF  ROC CURVES SHOWS THE MEPS HAS AN ABILITY TO PREDICT WEATHER CONDITION FROM CLOUDY AND EXTREME RAIN.

ABSTRACK

RAINFALL OBSERVATION AND RESEARCH IN THE KOLAKA REGENCY AREA CONTINUES TO DEVELOP IN LINE WITH THE NEED FOR MINING, WATER RESOURCE MANAGEMENT AND AS AN EFFORT TO MITIGATE HYDROMETEOROLOGICAL DISASTERS. KOLAKA REGENCY IS INCLUDED IN THE NON-SEASON ZONE AREA, NAMELY AN AREA THAT DOES NOT HAVE A CLEAR CLIMATOLOGICAL BOUNDARY BETWEEN THE RAINY SEASON AND THE DRY SEASON. THIS SITUATION CAUSES THE POTENTIAL FOR HYDROMETEOROLOGICAL DISASTERS SUCH AS FLOODS AND LANDSLIDES TO CONTINUE TO INCREASE. RAINFALL LEVEL MODELING IS CARRIED OUT TO SEE THE RELATIONSHIP BETWEEN LOCAL WEATHER PARAMETERS AND RAINFALL IN THE REGION. THE DATA USED IN THIS STUDY INCLUDE THE AVERAGE SEA SURFACE TEMPERATURE (SST) AND GLOBAL SST ANOMALIES FROM 2000-2019 WITH A RESOLUTION OF 0.083° LATITUDE X 0.083° LONGITUDE IN THE BONE BAY AREA, DATA ON AVERAGE AIR TEMPERATURE, RELATIVE HUMIDITY (RH), DIRECTION AND WIND SPEED AND DAILY RAINFALL OF KOLAKA REGENCY IN 2019. WITH THE MULTINOMIAL LOGISTIC REGRESSION METHOD, IT WAS FOUND THAT SST CONDITIONS WERE A FACTOR THAT INFLUENCED THE RAINFALL CATEGORY, BASED ON PROCESSING RESULTS, THE ODDS RATIO FOR SST WAS EXP (-0.3254) = 0.7222. THIS MEANS THAT THE GREATER THE SST WILL HAVE A TENDENCY OF 0.7222 TIMES SMALLER IN THE RAINFALL CATEGORY, IN OTHER WORDS, THE HIGHER THE SST VALUE CAUSES THE AIR CONDITION TO BECOME DRIER SO THAT THE OTHER RAIN CLOUD FORMING FACTORS ARE GETTING SMALLER, THEREBY REDUCING THE TENDENCY FOR RAIN TO OCCUR. RH CONDITIONS ARE ALSO A SIGNIFICANT FACTOR AFFECTING THE RAINFALL CATEGORY, BASED ON ODDS RATIO PROCESSING, THE RH VARIABLE IS EXP (0.1678) = 1.1827, MEANING THAT EVERY INCREASE OF ONE HUMIDITY UNIT WILL HAVE A TENDENCY OF 1.1827 TO HAVE A HIGHER CATEGORY OF RAINFALL THAN IF THE CONDITION RH IS SMALLER ONE UNIT, IN OTHER WORDS THE INCREASING HUMIDITY WILL INCREASE THE TENDENCY FOR RAIN TO OCCUR.

KEYWORDS: RAINFALL LEVEL, NON SEASON ZONE, MULTINOMIAL LOGISTIC REGRESSION

THE WEATHER RESEARCH AND FORECAST (WRF) MODEL WAS USED TO FORECAST THE MOST INTENSIVE STAGE OF CEMPAKA TROPICAL CYCLONE ON 27 - 29 NOVEMBER 2017. THIS STUDY AIMS TO EVALUATE THE COMBINATION OF CUMULUS AND MICROPHYSICS PARAMETERIZATION AND THE EFFICIENCY OF ASSIMILATION METHOD TO PREDICT PRESSURE VALUES AT THE CENTER OF THE CYCLONE, MAXIMUM WIND SPEED, AND CYCLONE TRACK. THIS STUDY TESTED 18 COMBINATIONS OF CUMULUS AND MICROPHYSICS PARAMETERIZATION SCHEMES TO OBTAIN THE BEST COMBINATION OF BOTH PARAMETERIZATION SCHEMES WHICH LATER ON CALLED AS CONTROL MODEL (CTL). AFTERWARD, ASSIMILATION SCHEMES USING 3DVAR CYCLES OF 1, 3, 6 HOURS, AND 4DVAR NAMELY RUC01, RUC03, RUC06, AND 4DV, RESPECTIVELY WERE EVALUATED FOR TWO DOMAINS WITH GRID SIZES OF EACH 30 AND 10 KM. GFS DATA OF 0.25-DEGREE AND THE YOGYAKARTA DOPPLER RADAR DATA WERE USED AS THE INITIAL DATA AND ASSIMILATION DATA INPUT, RESPECTIVELY. THE RESULT OF THE PARAMETERIZATION TEST SHOWS THAT THERE IS NO COMBINATION OF PARAMETERIZATION SCHEMES THAT CONSTANTLY OUTPERFORM ALL VARIABLES. HOWEVER, THE COMBINATION OF KAIN-FRITSCH AND THOMPSON CAN PRODUCE THE BEST PREDICTION OF TROPICAL CYCLONE TRACK COMPARED TO OTHER COMBINATIONS. WHILST, THE RUC03 ASSIMILATION SCHEME NOTED AS THE MOST EFFICIENT METHOD BASED ON THE ACCURACY OF TRACK PREDICTION AND DURATION OF MODEL TIME INTEGRATION.

THE WEATHER FORECAST IS VERY IMPORTANT TO PROTECT LIFE AND PROPERTY. FORECAST OF TEMPERATURE IS IMPORTANCE TO AGRICULTURE SECTOR. FURTHERMORE, IT CAN HELP METEOROLOGIST TO FORECAST THE OTHER ATMOSPHERE CONDITION SUCH AS HUMIDITY, EVAPORATION, ETC.  THE PURPOSE OF THIS RESEARCH IS TO PREDICT THE TEMPERATURE CONDITION IN THE NEXT DAY USING ARTIFICIAL NEURAL NETWORK MODEL (ANN). IN ORDER TO MAKE MODELLING OF ANN, THE AVERAGE TEMPERATURE THAT MEASURED IN METEOROLOGICAL STATION IN URBAN AND COASTAL AREA OF JAKARTA OVER 2010 – 2019 WERE USED AS TRAINING DATA. THE TESTING DATA USING SURFACE TEMPERATURE DURING JANUARY – DECEMBER 2020. THIS MODEL IS USING VARIATION NUMBER OF NEURON IN HIDDEN LAYER BETWEEN 3 AND 15. BASED ON THE RESULT, ANN MODEL IS GOOD ENOUGH TO PREDICT THE TEMPERATURE CONDITION IN JAKARTA AND SURROUNDING AREA WITH CORRELATION BETWEEN 0.631 – 0.669 AND MEAN ABSOLUTE ERROR (MAE) BETWEEN 0.546 – 0.581^OC. THE BEST MODEL PREDICTION WAS OBTAINED WHEN THE NUMBER OF NEURON IS 13 IN URBAN AREA OF JAKARTA AND 9 IN COASTAL AREA OF JAKARTA. 

IN THIS STUDY, PREDICTION OF TROPICAL CYCLONES USING THE WEATHER RESEARCH AND FORECASTING (WRF) MODEL WAS USED TO TEST THE DOUBLE-MOMENT (DM) AND SINGLE-MOMENT (SM) MICROPHYSICAL PARAMETERIZATION SCHEMES IN EVENT OF LILI AND MANGGA TROPICAL CYCLONES. MODELS WITH MICROPHYSICAL PARAMETERIZATION SCHEMES WDM5, WDM6, WSM5, WSM6, AND WITHOUT MICROPHYSICAL PARAMETERIZATION SCHEMES (CTL) WERE EACH TESTED AGAINST TRACK PREDICTIONS, THE PRESSURE VALUE, AND MAXIMUM WIND SPEED. THE RESULTS OF TRACK PREDICTION SHOW THAT THE BEST SCHEMES IN THE TROPICAL CYCLONE CASE OF LILI AND MANGGA IS WSM6 AND WDM6, RESPECTIVELY, WITH AN AVERAGE ERROR VALUE OF 78.1 AND 80.1 KM. BASED ON THE TAYLOR DIAGRAM, THE PREDICTION RESULTS OF THE PRESSURE VALUE AND THE MAXIMUM WIND SPEED IN CASE OF LILI TROPICAL CYCLONES GET THE WDM6 SCHEME AS THE BEST SCHEME. MEANWHILE, THE RESULTS OF THE PRESSURE PREDICTION AT THE CYCLONE CENTER IN THE CASE OF MANGGA TROPICAL CYCLONES SHOW THAT THE WDM6 SCHEME IS THE BEST. HOWEVER, THE PREDICTION OF MAXIMUM WIND SPEED IN MANGGA TROPICAL CYCLONES PRODUCES THE CTL SCHEME AS THE BEST SCHEME. THIS STUDY SHOWS THAT DM DAN SM MICROPHYSICAL PARAMETERIZATION SCHEMES HAVE A BIG IMPACT ON TRACK PREDICTION COMPARE TO PRESSURE VALUE AND MAXIMUM WIND SPEED VARIABLE.

JAKARTA IS REGION WITH HIGH NUMBER OF COVID-19 CASES IN INDONESIA. THIS STUDY INVESTIGATES THE IMPACT OF THE COVID-19 PANDEMIC AND THE RESULTING LARGE SCALE SOCIAL RESTRICTION ON AIR POLLUTION LEVELS IN JAKARTA, INDONESIA BY STUDYING PARTICULATE MATTER (PM₁₀) LEVELS. THIS STUDY EMPLOYS ARIMA INTERVENTION USING DAILY COVID-19 CASE DATA FROM JANUARY 1, 2020 TO SEPTEMBER 30, 2020 (THE PERIOD BEFORE AND AFTER THE FIRST CASE OF COVID-19 IN INDONESIA ON MARCH 2, 2020). THE ANALYSIS SHOWS COVID-19 STARTED TO HAVE IMPACT ON PM₁₀ IN JAKARTA ON 11^TH DAY AFTER THE CONFIRMATION OF FIRST COVID-19 CASE IN INDONESIA, WHICH IS INDICATED BY UNORDINARY INCREASE IN PM₁₀ LEVEL. HOWEVER, ON 12^TH DAY AFTER INTERVENTION, PM₁₀ LEVEL DECREASES. THIS OCCURRED AT BEGINING OF PERIOD WHEN LARGE-SCALE SOCIAL RESTRICTIONS ARE IMPOSED. HOWEVER, ONE MONTH AFTER INTERVENTION, PM₁₀ INCREASES AGAIN AND CONTINUE TO INCREASE UNTIL THE END OF PERIOD OF STUDY. THIS IS ALLEGEDLY BECAUSE PEOPLE ARE ACCUSTOMED TO BEING IGNORANT AND BORED WITH PANDEMIC SITUATION SO THAT SOCIAL RESTRICTIONS AND MOVEMENTS ARE NO LONGER EFFECTIVE WHICH RESULTS IN RISING OF PM₁₀ LEVELS AGAIN. HENCE, IT CAN BE CONCLUDED THAT COVID-19 HAVE IMPACT ON AIR QUALITY IN JAKARTA EVEN THOUGH IMPACT IS VERY SMALL.

KEYWORDS: COVID-19, AIR POLLUTION, PM₁₀, ARIMA INTERVENTION

THE INFLUENCE OF HYBRID MASS COORDINATE IS BETTER TO REPRESENT TURBULENCE IN THE AMERICA THAN BASIC MASS COORDINATE. THEREFORE, IT IS NECESSARY TO RESEARCH THE EFFECT OF THESE COORDINATES ON TURBULENCE SIMULATIONS IN INDONESIA DUE TO ANALYZE DIFFERENT ATMOSPHERIC CONDITIONS FROM THE AMERICA.

IN THIS RESEARCH, TWO EXPERIMENT ARE PERFORMED USING TWO DIFFERENT VERTICAL COORDINATES WITH A CASE STUDY FLIGHT TURBULENCE FROM BATIK AIRLINES ON OCTOBER 24TH, 2017. THE TWO DIFFERENT VERTICAL COORDINATES ARE HYBRID SIGMA COORDINATE AND BASIC MASS COORDINATE. THE DATA USED ARE NCEP-FNL, HIMAWARI-8 SATELLITE IMAGE DATA, REANALYSIS ERA-5 DATA, AND SOUNDING DATA. 

BASED ON THE RESULT OF THIS RESEARCH, SIMULATION USING HYBRID SIGMA COORDINATE SHOWS ISENTROPIC LINES THAT HAVE THE POTENTIAL TURBULENCE DURING AND AFTER TURBULENCE EVENT. ACCORDING TO THE RICHARDSON NUMBER VALUE AND INTENSITY OF THE ENERGY DISSIPATION RATE, THE HYBRID MASS COORDINATE SIMULATION SHOWS TURBULENCE POTENTIAL GREATER THAN THE BASIC MASS COORDINATE.

AIR POLLUTION IS AN INEVITABLE PROBLEM IN THE MEGAPOLITAN CITY OF JAKARTA WITH PM2.5 AS ONE OF THE MOST IMPORTANT INDICATORS OF AIR POLLUTION AFFECTING HUMAN HEALTH. JAKARTA HAS A SIGNIFICANT DIFFERENCE IN WEATHER DURING THE RAINY SEASON AND DRY SEASON, BUT THE DIFFERENCE IN VARIABILITY OF PARTICULATE CONCENTRATION BETWEEN THE SEASONS IS NOT WIDELY KNOWN. NUMERICAL SIMULATIONS OF PM2.5 CONCENTRATIONS USING WRF-CHEM WERE CARRIED OUT IN THE TWO MOST IMPORTANT PERIODS IN 2019, NAMELY IN THE WETTEST PERIOD (JANUARY, 3RD DASARIAN) AND THE DRIEST PERIOD (JULY, 1ST DASARIAN) IN JAKARTA. SIMULATIONS WERE CARRIED OUT USING THE TWO WRF-CHEM MODEL SCHEMES, NAMELY CMBZ-MOSAIC AND T1-MOZCART. THE PURPOSE OF THIS STUDY IS TO OBTAIN THE RESULTS OF THE PARTICULATE CONCENTRATION MODEL SPATIALLY AND TEMPORALLY IN JAKARTA AREA DURING THE STUDY PERIOD. THE ACCURACY LEVEL OF THE MODEL OUTPUT IS ALSO COMPARED WITH THE GROUND BASED DATA IN THREE OBSERVATION LOCATIONS, WHICH IS LOCATED IN SOUTH JAKARTA, CENTRAL JAKARTA, AND AT BMKG HEADQUARTER OFFICE. IT CAN BE SEEN HOW THE PERFORMANCE OF THE TWO MODEL SCHEMES IN TWO DIFFERENT SEASONS. THE SIMULATION RESULTS ARE EXPECTED TO BE ABLE TO SHOW CHANGES IN PM2.5 CONCENTRATIONS BOTH SPATIALLY AND TEMPORALLY DURING THE RAINY AND DRY SEASONS IN JAKARTA.

COASTAL INUNDATION HAS A GREAT IMPACT ON THE ENVIRONMENT, SUCH AS DAMAGE TO INFRASTRUCTURE AND POLLUTION OF LAND AND WATER. ONE OF THE EFFORTS TO PREVENT COASTAL INUNDATION IS TO PREDICT THE WATER LEVEL. DELFT3D IS A HYDRODYNAMIC MODEL THAT'S ABLE TO SIMULATE THE WATER LEVEL. COASTAL INUNDATION RESEARCH USING DELFT3D MODEL IS STILL RARELY DONE IN INDONESIA, ESPECIALLY IN THE EAST COAST OF SUMATRA. THIS RESEARCH IS CONDUCTED IN BELAWAN COASTAL AREA BY SIMULATING THE WATER LEVEL THAT CAUSED THE COASTAL INUNDATION USING DELFT3D MODEL. THE BEST BATHYMETRY FOR THE PREDICTION OF WATER LEVEL AND THE MAGNITUDE OF THE WIND EFFECT WAS OBTAINED FROM THE SIMULATION. THE FINAL STEP IS TO PREDICT THE WATER LEVEL IN BELAWAN COASTAL AREA. THE RESULT OF THIS SHOW THAT DELFT3D MODEL CAN STIMULATE THE WATER LEVEL WHICH CAUSES THE COASTAL INUNDATION IN BELAWAN COASTAL AREA. THE CORRELATION OF DELFT3D MODEL IS 0.9, WHILE FOR THE RMSE, RMSE OF GEBCO BATHYMETRY IS 0.39 METERS AND NOAA BATHYMETRY IS 0.46 METERS. SO, GEBCO BATHYMETRY IS BETTER THAN NOAA BATHYMETRY ON DESCRIBING THE WATER LEVEL IN BELAWAN COASTAL AREA BECAUSE OF THE RMSE. THE WIND EFFECT ON THE WATER LEVEL SIMULATIONS IS NOT SIGNIFICANT BECAUSE THE COEFFICIENT OF DETERMINATION IS 0.47%. IN ADDITION, THE DELFT3D MODEL WITH GEBCO BATHYMETRY INPUT IS ABLE TO PREDICT THE WATER LEVEL WHICH CAUSES THE COASTAL INUNDATION WITH CORRELATION REACHES 0.92 AND RMSE IS 0.39 METERS. 

SPATIAL FRACTION VERIFICATION OF HIGH RESOLUTION MODEL

I MADE KEMBAR TIRTANEGARA^1* AND FITRIA PUSPITA SARI¹

¹DEPARTMENT OF METEOROLOGY, COLLEGE OF METEOROLOGY, CLIMATOLOGY AND GEOPHYSICS, SOUTH TANGERANG, INDONESIA

 

*EMAIL: KEMBARTIRTANEGARA@GMAIL.COM

^ 

ABSTRACT. FRACTION SKILL SCORE (FSS) IS ONE OF SPATIAL VERIFICATION METHOD TO EVALUATE MODEL PERFORMANCE ON SPATIAL SCALE VARIATIONS. THE METHOD WAS APPLIED TO ASSESS THE WEATHER RESEARCH AND FORECASTING (WRF) MODEL USING 2 KM (MODEL2KM) AND 6 KM (MODEL6KM) GRID SIZE. CLOUD TOP TEMPERATURE (CTT) DATA FROM HIMAWARI-8 SATELLITE WAS UTILISED AS A GROUND TRUTH DATA. THIS STUDY AIMS TO EVALUATE THE MOST FITTED THRESHOLD OF CONVECTIVE CLOUD SIMULATION FOR THREE HEAVY RAIN EVENTS. THE THRESHOLD CONSIDERS THE EVALUATION OF ABSOLUTE AND PERCENTILE ASPECT. THE RESULT SHOWS THAT THERE IS NO SIGNIFICANT CHANGE IN THE FSS VALUE FOR RESOLUTION INCREASE OF MODEL2KM COMPARED TO MODEL6KM. ALSO, THE EVENTS OF HEAVY RAIN HAVING A LOWER CTT GENERATE A HIGHER FSS VALUE FOR ABSOLUTE THRESHOLD. WHILST, THE PERCENTILE THRESHOLD FOR THREE CASES HAVE A GREATER FSS VALUE, THOUGH IT CANNOT PROVIDE THE INFORMATION OF CTT ABSOLUTE TEMPERATURE VALUE.

                        KEYWORD: SPATIAL VERIFICATION, FSS, WRF, COULD TOP TEMPERATURE, HIMAWARI-8

 

MANY LAND AND FOREST FIRES IN INDONESIA OCCURRED DURING THE DROUGHT SEASON, WHICH IS AROUND THE MIDDLE OF THE YEAR. THESE INCIDENTS OCCUR FREQUENTLY IN THE PROVINCES OF SOUTH KALIMANTAN, EAST KALIMANTAN, CENTRAL KALIMANTAN, WEST KALIMANTAN, LAMPUNG AND SOUTH SUMATRA. HOWEVER, THERE WERE ALSO EPISODES OF LAND AND FOREST FIRES AT THE BEGINNING OF THE YEAR IN SEVERAL AREAS, SUCH AS IN RIAU PROVINCE. THE DROUGHT SEASON IN INDONESIA IS MOSTLY INFLUENCED BY THE MOVEMENT OF WINDS FROM THE AUSTRALIAN CONTINENT, KNOWN AS THE AUSTRALIAN MONSOON. IN ADDITION, LOCAL CLOUD FORMATION THAT INFLUENCED BY SEA SURFACE TEMPERATURE (SST) AROUND INDONESIA AFFECTS THE SEVERITY OF DROUGHT SEASON IN EACH YEAR. MOREOVER, IT AFFECTS SEVERITY OF LAND AND FOREST FIRES ITSELF INDIRECTLY. THE OBJECTIVE OF THIS RESEARCH IS TO EXAMINE THE ASSOCIATION OF THE AUSTRALIAN MONSOON AND LOCAL SST WITH LAND AND FOREST FIRES IN INDONESIA. THIS RESEARCH USE THE AUSTRALIAN MONSOON INDEX (AUSMI) AS AN INDICATOR FOR THE AUSTRALIAN MONSOON, AS WELL AS SST IN THE KARIMATA STRAIT AND SST IN THE JAVA SEA AS INDICATORS OF LOCAL SST. INDICATOR OF LAND AND FOREST FIRES THAT WILL BE USED IS THE NUMBER OF HOTSPOTS. HETEROGENEOUS CORRELATION MAP (HCM) WILL BE USED TO DESCRIBE THE NUMBER OF HOTSPOTS ASSOCIATION WITH AUSMI AND LOCAL SST. HCM IS OBTAINED BY CALCULATING THE PEARSON CORRELATION OF THE SINGULAR VALUE DECOMPOSITION (SVD) RESULT USING HOTSPOT AND AUSMI INDICATORS, AS WELL AS HOTSPOT AND LOCAL SST INDICATORS. THE ANALYSIS SHOWS THAT AUSMI WITH AN EAST WIND PATTERN IS A PATTERN THAT ASSOCIATE WITH THE NUMBER OF HOTSPOTS IN INDONESIA, ESPECIALLY IN YEARS WHEN ZONAL WINDS ENTER AN UPWARD PHASE MORE SLOWLY. SST IN THE KARIMATA STRAIT IS ASSOCIATE WITH A HOTSPOT IN THE COASTAL PART OF RIAU PROVINCE THAT OCCURRED AT THE BEGINNING OF THE YEAR. MEANWHILE, SST IN THE JAVA SEA IS ASSOCIATE WITH THE NUMBER OF HOTSPOTS IN THE PROVINCES OF LAMPUNG, SOUTH SUMATRA, JAMBI, WEST KALIMANTAN, CENTRAL KALIMANTAN, SOUTH KALIMANTAN AND EAST KALIMANTAN.

 

KEYWOARD: SEA SURFACE TEMPERATURE, HCM, HOTSPOT, INDONESIA, SVD

AS PART OF THE LUNGS OF THE WORLD, THE FOREST COVERS SUMATRA ISLAND HAS A SIGNIFICANT IMPACT ON WORLD OXYGEN PRODUCERS AND THE ABSORPTION OF CARBON DIOXIDE. DROUGHT OVER SUMATRA ISLAND OFTEN CAUSES FOREST FIRES THAT CAN DAMAGE THE FUNCTION OF FORESTS AS THE WORLD'S LUNGS. PREDICTION OF THE SEASONALITY OF FOREST FIRES IS NEEDED TO PREVENT AND OVERCOME FOREST FIRES THAT WILL OCCUR NEXT MONTH. THIS STUDY UTILIZES SEASONAL RAINFALL PREDICTIONS TO PREDICT THE INCIDENCE OF FOREST FIRES BASED ON THE DROUGHT INDEX OBTAINED. THE RESULT SHOWS THAT ECMWF SEAS5 HAS GOOD PERFORMANCE TO PREDICT RAINFALL OVER SUMATERA ISLAND FOR THE FIRST UNTIL THE FOURTH MONTHS (LEAD TIME 0 - 3). THE NEGATIVE STANDARDIZED PRECIPITATION INDEX (SPI) COINCIDES WITH INCREASE NUMBER OF THE HOTSPOT. A LINEAR EQUATION HAS APPLIED TO THE CALCULATED NUMBER OF HOTSPOTS BASE ON SPI FROM ECMWF

NOWADAYS, MANY RESEARCHERS ARE STARTING TO PAY MORE ATTENTION TO THE ASSOCIATION BETWEEN PM_2.5 CONCENTRATION AND RESPIRATORY DISEASES. PM_2.5 IS THE MOST THREATENING AIR POLLUTANT FOR HUMAN HEALTH IN CITIES, AND CAN CAUSE A RISING NUMBER OF DEATHS. IN RECENT YEARS, PM_2.5 CONCENTRATION CONTINUES TO SHOW A POSITIVE TREND, ESPECIALLY IN JAKARTA AS ONE OF THE MEGA COASTAL CITIES WHICH IS REPORTED AS THE CITY WITH THE WORST AIR QUALITY IN INDONESIA. HOWEVER, THE LIMITATION OF OBTAINING DETAILED PM_2.5 CONCENTRATION DATA IS ONE OF THE PROBLEMS THAT HINDER IN ANALYZING THE RELATIONSHIP BETWEEN PM_2.5 AND RESPIRATORY SYSTEM ENGRAVER. THEREFORE, THIS STUDY AIMS TO IDENTIFY METHODS TO ESTIMATE THE SPATIAL DISTRIBUTION OF PM_2.5 CONCENTRATION IN JAKARTA AT ITS BEST. WE WILL COMPARE THE ESTIMATED SPATIAL DISTRIBUTION RESULTS OF PM_2.5 CONCENTRATIONS FROM THREE DIFFERENT INTERPOLATION METHODS, THERE ARE KRIGING, SPLINE, AND IDW (INVERSE DISTANCE WEIGHTED), AS WELL AS IDENTIFY WHICH METHOD IS MOST REALISTIC IN GENERATING THE ESTIMATED CONCENTRATION VALUE OF PM_2.5. THE DATA USED IN THIS STUDY IS THE LATEST OBSERVATION DATA OF PM_2.5 MEASUREMENT RESULTS IN SEVEN POINTS SPREAD ACROSS JAKARTA. THE DATA IS A TWO-YEAR TIME SERIES DATA IN THE PERIOD 2019-2020 WHICH WILL THEN BE PROCESSED INTO ANNUAL AVERAGE DATA. WE EXPECT THAT, THE BEST TOOL USED IN THE METHOD THAT CAN PRODUCE THE MOST REALISTIC PM_2.5 CONCENTRATION VALUE IS THE KRIGING INTERPOLATION TOOL. 

KELVIN WAVES PROPAGATION WERE IDENTIFIED BY FILTERING OLR VALUES FOR SPECIFIC WAVENUMBER AND PERIOD. SATELLITE DATA ASSIMILATION ON WEATHER RESEARCH AND FORECASTING (WRF) MODEL WAS USED TO PROVIDE A BETTER SIMULATION OF KELVIN WAVES. THIS RESEARCH WAS CONDUCTED BY SIMULATE THE KELVIN WAVE ON ASSIMILATED WRF OUTPUT USING AMSU-A SATELLITE DATA, AND THE RESULTS WERE VALIDATED BY REANALYSIS DATA FROM NOAA AND ECMWF, OBSERVATION DATA, AND GSMAP SATELLITE DATA. OUTGOING LONGWAVE RADIATION (OLR), ZONAL WIND, TEMPERATURE, AND RAINFALL WERE USED TO IDENTIFY AND COMPARE THE PROPAGATION AND HORIZONTAL-VERTICAL STRUCTURE OF WAVES AND THE MODULATING RAINFALL INFLUENCE BY KELVIN WAVES ON THE OUTPUT OF ASSIMILATED WRF MODELS AND REANALYSIS DATA. THE RESULTS SHOWED THE ASSIMILATED WRF OUTPUT COULD IDENTIFY KELVIN WAVE PROPAGATION THAT RESEMBLES REANALYSIS DATA BETTER COMPARE TO NON ASSIMILATED DATA OUTPUT. FURTHERMORE, ASSIMILATED WRF OUTPUT CAPTURE THE HORIZONTAL AND VERTICAL STRUCTURE THAT GENERALLY CONSISTENT WITH THE LINEAR THEORY OF THE KELVIN WAVE. HOWEVER, MODEL UNDERESTIMATE ON PREDICTS RAINFALL AND FAILED TO INDICATE RAINFALL MODULATION BY THE KELVIN WAVE, RESPECTIVELY.

PRECIPITATION PREDICTION BASED ON TIME SERIES ANALYSIS OF PRECIPITATION AND AIR TEMPERATURE DATA USING THE LONG SHORT-TERM MEMORY METHOD

CASE STUDY: EAST JAVA PROVINCE

 

NOVVRIA SAGITA¹, MARINDA NUR AULIYA^2*, RAGA ALAM PRABU NATA², RAJAB PRIMA², REZQI FAUZY² 

 

¹LECTURER OF METEOROLOGY MAJORS, SCHOOL OF METEOROLOGY CLIMATOLOGY AND GEOPHYSICS

²STUDENT OF METEOROLOGY MAJORS, SCHOOL OF METEOROLOGY CLIMATOLOGY AND GEOPHYSICS, JL. PERHUBUNGAN 1 NO. 5 KOMPLEK METEOROLOGI BMKG PONDOK BETUNG, BINTARO-TANGERANG SELATAN, BANTEN, INDONESIA, 15221, *EMAIL: MARINDAAULIYA@GMAIL.COM

 

 

ABSTRACT

 

ABSTRACT - INDONESIA IS AN AGRICULTURAL COUNTRY. ACCORDING TO BPS, INDONESIA'S RICE FIELD AREA IN 2015 WAS 8,087,393 HA, WITH THE BIGGEST RICE AREA LOCATION IN EAST JAVA PROVINCE. THE RESULTS OF AGRICULTURAL MANUFACTURING IN INDONESIA ARE MOTIVATED BY USING CLIMATE FACTORS, IN PARTICULAR PRECIPITATION. GLOBAL WARMING IS THE BOOM INSIDE THE COMMON TEMPERATURE OF THE EARTH'S SURFACE AS A RESULT OF THE BOOM IN THE QUANTITY OF GREENHOUSE GASOLINE EMISSIONS WITHIN THE SURROUNDINGS (IDAYATI, 2007). GLOBAL WARMING CAN CAUSE CHANGES IN PRECIPITATION PATTERNS (NURHAYATI ET AL., 2020). THE DATA USED IN THIS STUDY ARE PRECIPITATION DATA AND SURFACE AIR TEMPERATURE FROM 1990 TO 2019. THE DATA IS SOURCED FROM THE ECMWF AND IS LOCATED IN EAST JAVA PROVINCE. THIS STUDY USES THE LONG SHORT-TERM MEMORY (LSTM) METHOD BASED ON TIME SERIES ANALYSIS. THE RESULTS SHOWED THE BEST RESULTS IN DATA MODELING WITH THE NUMBER OF EPOCH 100 WHICH RESULTED IN AN RMSE VALUE OF 2.3776965. DATA PROCESSING IS DIVIDED INTO TWO, NAMELY PREDICTION OF PRECIPITATION BASED ON PRECIPITATION DATA TO DETERMINE NORMAL PRECIPITATION AND PREDICTION OF PRECIPITATION BASED ON PRECIPITATION AND AIR TEMPERATURE DATA TO DETERMINE PRECIPITATION AFFECTED BY GLOBAL WARMING EVENTS. THIS COMPARISON SHOWS THAT THERE IS A DIFFERENCE IN THE AMOUNT OF PRECIPITATION BETWEEN THE TWO DATA AND THERE IS AN INCREASE IN THE AMOUNT OF MONTHLY PRECIPITATION DUE TO THE GLOBAL WARMING EVENT IN EAST JAVA PROVINCE.

 

KEY WORDS: PRECIPITATION, AIR TEMPERATURE, LONG SHORT-TERM MEMORY

INDONESIAN CLIMATE IS STRONGLY EFECTED BY EL NIÑO-SOUTHERN OSCILLATION (ENSO) AS ONE OF STRONGEST CLIMATE DRIVEN FACTOR. ENSO PREDICTION DURING THE UPCOMING MONTHS OR YEAR IS CRUCIAL FOR GOVERNMENT IN ORDER TO DESIGN THE NEXT STRATEGICPOLICY. BESIDES PRODUCING ITS OWN ENSO PREDICTION, , BMKG ALSO REGULARLY RELEASE THE STATUS AND ENSO PREDICTION COLLECTED FROM ANOTHER CLIMATE CENTERS, SUCH AS JAPAN METEOROLOGICAL AGENCY (JMA) AND NATIONAL OCEANIC AND ATMOSPHERIC ADMINISTRATION (NOAA). HOWEVER, SKILL OF THESE PRODUCTS IS NOT CLEARLY KNOWN YET. THE AIM OF THIS STUDY IS TO CONDUCT A SIMPLE ASSESSMENT ON THE SKILL OF JMA ENSEMBLE PREDICTION SYSTEM (EPS) AND NOAA CLIMATE FORECAST SYSTEM VERSION 2 (CFSV2) ENSO PREDICTION USING WORLD METEOROLOGICAL ORGANIZATION (WMO) STANDARD VERIFICATION SYSTEM FOR LONG RANGE FORECAST (SVS-LRF) METHOD. BOTH ENSO PREDICTION RESULTS ALSO COMPARED EACH OTHER USING STUDENT'S T-TEST. THE ENSO PREDICTIONSDATA WERE OBTAINED FROM THE ENSO JMA AND ENSO NCEP FORECAST ARCHIVE FILES, WHILE OBSERVED NINO 3.4 WERE CALCULATED FROM CENTENNIAL IN SITU OBSERVATION-BASED ESTIMATES (COBE) SEA SURFACE TEMPERATURE ANOMALY (SSTA) . BOTH ENSO PREDICTION ISSUED BY JMA AND NCEP HAS A GOOD SKILL ON 1 TO 3 MONTHS LEAD TIME, INDICATED BY HIGH CORRELATION COEFFICIENT AND POSITIVE VALUE OF MEAN SQUARE SKILL SCORE (MSSS). HOWEVER, THE SKILL OF BOTH SKILLS SIGNIFICANLY REDUCED FOR MAY-AUGUST TARGET MONTH. FURTHER CAREFUL INTERPRETATION IS NEEDED FOR ENSO PREDICTIONISSUED ON THIS MENTIONED PERIOD. MORE COMPREHENSIVE RESULT RELATED THIS ASSESSMENT CAN BE FOUND ON FULL PAPER.
KEYWORDS: VERIFICATION, ENSO PREDICTION, SVS-LRF, JMA-EPS,  NCEP CFSV2

TWO INDICES (RMM1 AND RMM2) ARE USED TO REPRESENT THE MJO. TOGETHER THEY DEFINE THE MJO BASED ON 8 PHASES AND CAN REPRESENT BOTH THE PHASE AND AMPLITUDE OF THE MJO. REAL-TIME MULTIVARIATE MJO SERIES 1 (RMM1) AND 2 (RMM2) IS BASED ON A PAIR OF EMPIRICAL ORTHOGONAL FUNCTIONS (EOFS) OF THE COMBINED FIELDS OF NEAR-EQUATORIALLY AVERAGED 850- HPA ZONAL WIND, 200-HPA ZONAL WIND, AND SATELLITE-OBSERVED OUTGOING LONGWAVE RADIATION (OLR) DATA. THIS STUDY USED LONG SHORT-TERM MEMORY (LSTM) RECURRENT NEURAL NETWORKS (RNN) TO PREDICT RMM AS SUPPORTING FACTORS TO IMPROVE THE WEATHER PREDICTION QUALITY IN THE FUTURE. LSTM HAS AN EXCESS IN UNDERSTANDING TEMPORAL DEPENDENCIES. THE DATA USED ARE RMM DAILY DATA PERIOD 1^ST JANUARY 1981–2^ND OCTOBER 2020. THE MODEL RESULTS SHOW THE PERFORMANCE OF 0.07 FOR RMM1 AND 0.06 FOR RMM2 IN TERMS OF ROOT MEAN SQUARE ERROR (RMSE).