Identifikasi Kekuatan dan Kelemahan Komponen Sistem Informasi Iklim(strength And Weakness Identification Of Climate Information Component)

Based on the survey of climate information application in many sectors showed that climate informations are inaccurate, lately received, abstrused and not meet to the user activities. There is a big gaps between climate information producer and user, it needs a bridging to handle a problem in interpreting information. These conditions caused to not optimally climate risk anticipation, so that there were still a lot of failures in some sectors, i.e. crops failure, urban floods, food and water shortage, health crisis, forest fire, etc. There are many activities have been done to increase skill to intepret and react to climate information. Providing climate information is one of the methods to minimize the climate risk. By understanding the climate information, climate risk could be managed optimally and it can minimize negative impact of climate extreme and get benefit from good climate conditions. Boer, 2009, said that there are five primary components as a key to climate information application in manage a risk, 1) climate data observation and data analysis, 2) climate forecast/prediction system, 3) climate information production and evaluation system, 4) communication and dissemination system, and 5) climate information system. Valuation of strength and weakness of five components above relatively depends on which angel be used. It needs an objective indicator to evaluate those components. In this paper, strength and weakness of climate information components will be identified. Data was collected from Meteorological, Climatological and Geophysical Agency's stations and some institutions in Banten Province as climate information users by distributing questionaire. Furthermore, based on the components identification it could be created a strategy how to increase the capacity of climate information applications

IDENTIFIKASI KEKUATAN DAN KELEMAHAN KOMPONEN SISTEM INFORMASI IKLIM(STRENGTH AND WEAKNESS IDENTIFICATION OF CLIMATE INFORMATION COMPONENT)

Based on the survey of climate information application in many sectors showed that climate informations are inaccurate, lately received, abstrused and not meet to the user activities. There is a big gaps between climate information producer and user, it needs a bridging to handle a problem in interpreting information. These conditions caused to not optimally climate risk anticipation, so that there were still a lot of failures in some sectors, i.e. crops failure, urban floods, food and water shortage, health crisis, forest fire, etc. There are many activities have been done to increase skill to intepret and react to climate information. Providing climate information is one of the methods to minimize the climate risk. By understanding the climate information, climate risk could be managed optimally and it can minimize negative impact of climate extreme and get benefit from good climate conditions. Boer, 2009, said that there are five primary components as a key to climate information application in manage a risk, 1) climate data observation and data analysis, 2) climate forecast/prediction system, 3) climate information production and evaluation system, 4) communication and dissemination system, and 5) climate information system. Valuation of strength and weakness of five components above relatively depends on which angel be used. It needs an objective indicator to evaluate those components. In this paper, strength and weakness of climate information components will be identified. Data was collected from Meteorological, Climatological and Geophysical Agency’s stations and some institutions in Banten Province as climate information users by distributing questionaire. Furthermore, based on the components identification it could be created a strategy how to increase the capacity of climate information applications

Maximum Temperature Forecast Using NWP Output and Station Data in Equatorial Region: Preliminary Result for West Java, Indonesia

Model Output Statistics (MOS) is one of the statistical downscaling methods in post-processing of Numerical Weather Prediction (NWP) output to get weather forecasts at a point of observation stations. The problem in MOS is how to determine the spatial domain of NWP to be used as predictor in the development stage. This paper uses methods for determining the optimal NWP spatial domain and to predict maximum temperature in the Jabodetabek area using NWP output from Global Forecast System (GFS) generated by the National Oceanic and Atmospheric Administration (NOAA)

Acceleration Response Spectra for M 7.4 Donggala Earthquake and Comparison with Design Spectra

A 7.4 magnitude earthquake have strucked Donggala on September 28th 2018, followed by tsunami and liquefaction which hit Palu, Central Sulawesi, a few minutes later. This event had resulted in damage to buildings, and caused more than 2,000 people were killed and injured. Indonesia already have a building code in form of SNI 1726:2002 which had been updated to SNI 1726:2012. This paper analyses the hazard level caused by the 2018 Donggala earthquake compared to the existing design spectra, as mentioned in SNI 1726:2002 and SNI 1726:2012. A simple analysis was carried out by comparing Donggala earthquake’s acceleration response spectra with the existing design spectra, at the MPSI accelerograph station. The site class at MPSI station is hard soil (SC). The seismic hazard in Palu and Donggala refers to SNI 1726:2002 is included in the earthquake area 4. The maximum earthquake response factor for earthquake area 4 is about 0.6 for hard soil type (SC). The MPSI station recorded peak ground acceleration of Donggala earthquake around 0.14 g. The acceleration response spectra recorded at the MPSI station showed a peak value of around 0.71 g for the N component. This value is actually still below the design spectra referring to SNI 1726:2012, which the peak value is 0.88 g for SC, but, it exceeded the design spectra of SNI 1726:2002

Interannual rainfall variability over the Indonesian maritime continent and its relation to the Asian winter monsoon

Interannual rainfall variability over the Indonesian maritime continent is well related to the Indian Ocean Dipole (IOD) and El Nino-Southern Oscillation (ENSO) events [1]. Droughts conditions during the dry season (May?October) in northwestern Jawa occur in conjunction with simultaneous development of positive IOD and El Nino events, whereas wet conditions tend to appear in negative IOD rather than single La Nina events. On the other hand, interannual rainfall variation in the rainy season (November?April) is not closely related to ENSO/IOD, but rainfall tends to be abundant in neutral (non-ENSO/IOD) years. Correlation and composite analysis suggested that the rainy season rainfall would be influenced by the Asian winter monsoon strength and/or variability. In this study, we aim to investigate effects of Asian winter monsoon, especially for the cross-equatorial northerly surges (CENS) events over South China Sea and Jawa Sea, to interannual rainfall variability in the rainy season over northwestern Jawa. The CENS event was defined as the area-averaged northerly wind exceeding 5 m/s over South China Sea and Jawa Sea (105°E?115°E, 5°S?EQ) based on the QuikSCAT sea surface wind data [2]. During the analysis period (December 1999-March 2008), 53 CENS events were defined. We used surface daily rainfall data to investigate the rainfall variability and its relation to the CENS events. The occurrence frequency of CENS events was about 20%, whereas the contribution of CENS rainfall amount to the total rainfall amount in the rainy season was about 30?40%. The CENS events and rainfall peaks were well-corresponded including the Jakarta flood events in January 2002 and February 2007. Interannual variations of CENS events rainfall were well-corresponded to the interannual variations of the rainy season rainfall (correlation coefficient is 0.82). Obtained results suggested that CENS rainfall is one of the important factors to determine rainy season rainfall. It will be also suggested the CENS events would influence the rainfall variability in the rainy season over the southern part of the maritime continent, especially for the northern coastal region of the islands.Proceedings of GRENE 3rd Workshop (17-19 March, 2014, Bali, Indonesia

PENENTUAN DOMAIN SPASIAL NWP DALAM PEMBANGUNAN MODEL OUTPUT STATISTICS

Model Output Statistics (MOS) adalah salah satu metoda statistical downscaling pada tahap post processing luaran Numerical Weather Prediction (NWP) untuk mendapatkan nilai prakiraan parameter cuaca di sebuah titik stasiun pengamatan. Permasalahan yang timbul dalam MOS adalah penentuan domain spasial NWP yang akan digunakan sebagai prediktor. Pada makalah ini disajikan metoda penentuan domain spasial untuk memprakirakan suhu maksimum di wilayah Jabodetabek menggunakan data luaran NWP Global Forecast System (GFS) dari National Oceanic and Atmospheric Administration (NOAA). Data pengamatan suhu maksimum diambil dari delapan stasiun di Wilayah Jakarta, Jawa Barat dan Banten yang digunakan untuk kalibrasi. Pada tahap awal domain spasial NWP ditentukan berukuran 8x8 grid, selanjutnya dicobakan untuk beberapa domain, yaitu berukuran 2x2, 3x3, 3x4, 4x4 dan 5x5 grid. Tiga metoda digunakan untuk menentukan domain spasial, yaitu metoda analisis korelasi spasial, singular value decomposition (SVD) dan partial least square regression (PLSR). Analisis ketiga metoda secara umum menunjukkan hasil yang hampir sama, yaitu domain dengan ukuran 3x3 adalah yang paling baik. Analisis korelasi spasial menunjukkan luasan dengan korelasi lebih besar dari 0,4 hanya meliputi domain maksimal 3x3. Analisis SVD menunjukkan bahwa keeratan hubungan secara simultan antara data observasi dengan NWP hampir sama, yaitu pada ekspansi pertama. Sedangkan hasil verifikasi analisis PLSR menggunakan korelasi dan root mean square error (RMSE) menunjukkan bahwa grid berukuran 3x3 adalah domain terbaik.   Model Output Statistics (MOS) is one of statistical downscaling method in post-processing of Numerical Weather Prediction (NWP) output to get weather forecasts at a point of observation stations. The problems in MOS is how to determine the spatial domain of NWP which will be used as predictor in development stage. This paper presented  the methods for determining NWP spatial domain to predict the maximum temperature in the Greater Jakarta area using NWP output of Global Forecast System (GFS) producted by National Oceanic and Atmospheric Administration (NOAA). Maximum temperature observation data was taken in eight stations, around West Java, Banten and Jakarta. In the first stage, spatial domain of NWP was defined as 8x8 grids, then attempted for some domains, i.e. 2x2, 3x3, 3x4, 4x4 and 5x5grids. Three methods for determining spatial domain were spatial correlation analysis, singular value decomposition (SVD) and partial least square regression (PLSR). Those three analysis method generally showed similar results,  spatial domains with size 3x3 is the most excellent. Spatial correlation analysis shows that the size of the area which have correlation greater than 0.4 was only covers a maximum of 3x3 domain. SVD analysis suggests that the simultaneous relationship between the observation data with NWP is almost the same in the first expansion. While the results of the verification PLSR analysis using correlation and root mean square error (RMSE) indicates that 3x3 grid is the best domain

Observational study on key environmental conditions for the occurrence of nighttime offshore-ward migration of precipitation area off the west coast of Sumatra Island

http://www.godac.jamstec.go.jp/darwin/cruise/mirai/mr15-04/

Strengthening multi-hazards early warning system in the pacific through BMKG-UNESCAP collaboration pilot projects

On the period of September to December 2017, three pilot projects were implemented in Tonga, Papua New Guinea, and Solomon Islands aiming to strengthen the multi-hazards early warning system in the respective countries through close collaboration between the Indonesian Agency for Meteorology Climatology and Geophysics (BMKG) and United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP). Main activities during the implementation phase were tailored based on gap analysis and risk assessments conducted beforehand. Thus, installation of high-resolution numerical weather, ocean wave, and climate prediction and forecasting tools were chosen to fill in the assessed gaps. These activities were incorporated with capacity building activities and high-level meetings with related stakeholders in disaster risk management using the concept of Fast-Leveraging-Easy-Economical-Sustain (FLEES). All three pilot projects had successfully proven to achieve their objectives by improving the capacities of National Meteorological Services in those three countries to produce multi-hazards early warning in higher resolution at a regional scale for disaster management in their respective countries