Analisis Tingkat Kenyamanan Di DKI Jakarta Berdasarkan Indeks THI (Temperature Humidity Index)

Climate phenomenon affects physiological comfortableness in residential area. Analysis of thermal comfort level in DKI Jakarta were conducted using THI (Temperature Humidity Index). Based on climate data stations in Kemayoran, Tanjung Priok, Halim, Cengkareng dan Pondok Betung during 1985-2012 showed that the average percentage of daily thermal comfort level with categories uncomfortable were 22,1% (81 days per year), half comfortable 71 % (259 days per year) and comfortable 7,1% (26 days per year). The study showed that the greater percentage uncomfortable level, the closer into the center of the city and during 1985 to 2012 the THI index tend to increasing with significant level more than 50% meant that the thermal comfort level tend to more uncomfortable.Keywords: thermal comfort level, temperature humidity index, urban heat islandCitation: Wati, T dan Fatkhuroyan. (2017). Analisis Tingkat Kenyamanan Di DKI Jakarta Berdasarkan Indeks THI (Temperature Humidity Index). Jurnal Ilmu Lingkungan, 15(1), 57-63, doi:10.14710/jil.15.1.57-6

Comparison Pan Evaporation Data with Global Land-surface Evaporation GLEAM in Java and Bali Island Indonesia

This paper evaluates the variability of pan evaporation (Epan) data in Java and Bali during 2003-2012 and compares to GLEAM (Global Land-surface Evaporation: the Amsterdam Methodology) data version v3.b namely actual evaporation (E) and potential evaporation (Ep) in the same period with statistical method. Gleam combines a wide range of remotely sensed observations to the estimation of terrestrial evaporation and root-zone soil moisture at a global scale (0.25-degree). The aim is to assess the accuracy of Gleam data by examining correlation, mean absolute error, Root mean square error and mean error between Epan and Gleam data in Java and Bali Island. The result shows the correlation between Epan with Ep Gleam is higher than Epan with E Gleam. Generally, the accuracy of Gleam data is a good performance to estimate the land evaporation in Java and Bali at annual and monthly scale. In daily scale, the correlation is less than 0.50 both between Epan with E Gleam and between Epan with Ep Gleam. In daily scale, the average errors ranging from 0.15 to 3.09 mm according to RMSE, MAE, and ME.The result of this study is essential in providing valuable recommendation for choosing alternative evaporation data in regional or local scale from satellite data

Characteristic of Soil Moisture in Indonesia Using ESA CCI Satellites Products

Soil moisture (SM) is one of the energy and water exchange main drivers between the atmosphere and land surface. The study aims to analyze the soil moisture characteristics in Indonesia on monthly and seasonal time scales. The analysis uses mapping of monthly and seasonal ESA CCI SM satellite products of mean daily from 1979 to 2016. The results showed the spatial and temporal variability of SM in Indonesia. Sumatera has SM values > 0.3 m3/m3 almost throughout the year. Besides, Java has SM values > 0.3 m3/m3 from January to April and October to December while 0.2-0.3 m3/m3 from May to September. In Borneo, the SM value > 0.3 m3/m3 from February to June and November to December, while from July to September are 0.2-0.3 m3/m3. Sulawesi has SM values > 0.3 m3/m3 from January to July, on December, and 0.2-0.3 m3/m3 from august to November. Bali to Nusa Tenggara have SM values between 0.2-0.3 m3/m3 throughout the year, except 0.3 m3/m3 throughout the year, except in Jayawijaya Mountain and South Papua. The ESA CCI SM product is essential for monitoring SM in Indonesia

Improving Numerical Weather Prediction of Rainfall Events Using Radar Data Assimilation

Data assimilation is one of method to improve initial atmospheric conditions data in numerical weather prediction. The assimilation of weather radar data that has quite extensive and tight data is considered to be able to improve the quality of weather prediction and analysis. This study aims to investigate the effect of assimilation of Doppler weather radar data in Weather Research Forecasting (WRF) numerical model for the prediction of heavy rain events in the Jabodetabek area with dates representing four seasons respectively on 20 February 2017, 3 April 2017, 13 June 2017, and 9 November 2017. For this purpose, the reflectivity (Z) and radial velocity (V) data from Plan Position Indicator (PPI) product and reflectivity (Z) data from Constant Altitude PPI (CAPPI) product were assimilated using WRFDA (WRF Data Assimilation) numerical model with 3DVar (The Three Dimensional Variational) system. The output of radar data assimilation and without assimilation of the numerical model of WRF is verified by spatial with GSMaP data and by point with precipitation observation data. In general, WRF radar assimilation provides a better simulation of spatial and point rain events compared to the WRF model without assimilation which is improvements of rain prediction from WRF radar data assimilation would be more visible in areas close to radar sources and not echo-blocked from fixed objects, and more visible during the rainy seaso

ANALISIS INDEKS IKLIM UNTUK ASURANSI PERTANIAN TANAMAN PADI DI KABUPATEN CIREBON DALAM RANGKA ADAPTASI PERUBAHAN IKLIM

Analisis indeks iklim untuk asuransi pertanian tanaman padi telah dilakukan di empat kecamatan di wilayah Kabupaten Cirebon yaitu di kecamatan Gegesik, Susukan, Klangenan dan Babakan untuk mengurangi kerugian gagal panen akibat bencana iklim kekeringan. Analisis tersebut menggunakan dua metode yaitu metode Historical Burn Analysis (HBA) untuk kejadian kekeringan dan metode statistik korelasi antara curah hujan dengan produksi dan luas panen tanaman padi. Hasil analisis indeks iklim dengan parameter curah hujan berdasarkan metode HBA menghasilkan dua indeks yaitu indeks trigger dan indeks exit, indeks trigger merupakan besaran curah hujan sebagai batasan untuk pembayaran klaim asuransi yang dibayarkan sebagian dan indeks exit yaitu besaran curah hujan untuk pembayaran klaim asuransi dibayarkan penuh dengan indeks window(jendela waktu) bulan Juni hingga September. Indeks iklim berdasarkan korelasi curah hujan dengan produksi dan luas panen tanaman padi, diperoleh satu indeks exit yang merupakan batasan pembayaran klaim asuransi sepenuhnya dengan periode 5 tahun, 10 tahun dan 20 tahun yaitu masing-masing sebesar 317 mm, 242 mm dan 180 mm di kecamatan Gegesik dengan indeks window bulan Maret-April-Mei, sedangkan kecamatan lainnya memiliki korelasi yang lemah sehingga tidak dapat ditentukan besaran indeks exitnya. Analysis of climate indices for paddy crop agricultural insurance was conducted for four subdistricts of Cirebon district namely Gegesik, Susukan, Klangenan and Babakan, to reduce crop failure due to drought as climate disaster. The analysis employed two methods : the Historical Burn Analysis (HBA) of drought and statistical method which analyzed the correlation between rainfall with paddy crop yield and harvest areas. The HBA method resulted in two kind of indices : trigger index and exit index. Trigger index is the treshold of rainfall for the insurance claim with partial payment while exit index is the rainfall treshold for full payment of the insurance claim with window indices during June to September. Another method resulted in only one index : exit indices values during 5, 10 and 20 years period of insurance that were 317 mm, 242 mm and 180 mm in Gegesik with window indices during March-April-May periods. Unfortunately, exit indices for other subdistricts could not be determined because of the weak correlation between its rainfall with yield and harvest areas of paddy crop

Validation of Satellite Daily Rainfall Estimates Over Indonesia

Rainfall is the most important factor in the Earth’s water and energy cycles. The aim of this research is to evaluate the accuracy of Global Satellite Mapping of Rainfall (GSMaP) data by referencing daily rain-gauged rainfall measurements across the Indonesian Maritime Continent. We compare the daily rainfall data from GSMaP Moving Kalman Filter (MVK) to readings from 152 rain-gauge observation stations across Indonesia from March 2014 to December 2017. The results show that the correlation coefficient (CC) provides better validation in the rainy season while root mean square error (RMSE) is more accurate in the dry season. The highest proportion correct (PC) value is obtained for Bali-NTT, while the highest probability of detection (POD) and false alarm ratio (FAR) values are obtained for Kalimantan. GSMaP-MVK data is over-estimated compared to observations in Indonesia, with the mean accuracy for daily rainfall estimation being 85.47% in 2014, 85.74% in 2015, 82.73 in 2016, and 82.59% in 2017

Using 3D-Var Data Assimilation for Improving the Accuracy of Initial Condition of Weather Research and Forecasting (WRF) Model in Java Region (Case Study : 23 January 2015)

Weather Research and Forecasting (WRF) is a numerical weather prediction model developed by various parties due to its open source, but the WRF has the disadvantage of low accuracy in weather prediction. One reason of low accuracy  of model is inaccuracy initial condition model to the actual atmospheric conditions. Techniques to improve the initial condition model is the observation data assimilation. In this study, we used three-dimensional variational (3D-Var) to perform data assimilation of some observation data. Observational data used in data assimilation are observation data from basic stations, non-basic stations, radiosonde data, and The Binary Universal Form for the Representation of meteorological data (BUFR) data from the National Centers for Environmental Prediction (NCEP) , and aggregate observation data from all stations. The aim of this study compares the effect of data assimilation with different data observation on January 23, 2015 at 00.00 UTC for Java island region. The results showed that changes root mean square error (RMSE) of surface temperature from 2° C to 1.7° C - 2.4° C, dew point from 2.1o C to 1.9o  C - 1.4o C, relative humidity from 16.1% to 3.5% - 14.5% after the data assimilation

PENGOLAHAN DAN PEMULIHAN DATA RADAR CUACA MENGGUNAKAN WRADLIB BERBASIS PYTHON

Informasi dari radar cuaca sangatlah penting bagi BMKG dalam memberikan pelayanan terkait prakiraan cuaca jangka pendek (near real time). Perangkat lunak wradlib berbasis python dapat menjadi salah satu solusi alternatif untuk pemanfaatan, pengolahan dan pemulihan data radar cuaca di BMKG. Beberapa kelebihan yang dimiliki wradlib-python adalah berlisensi sumber terbuka sehingga mengurangi ketergantungan terhadap perangkat lunak dari produsen radar tertentu, dapat mengolah dan menampilkan data radar cuaca secara masif, memulihkan dan menyimpan luaran data radar dalam koordinat kartesian (tidak dalam koordinat polar) dan format NetCDF sehingga memudahkan pengguna dalam pengolahan data radar lebih lanjut. Studi ini hanya memfokuskan pengolahan data radar volumetric (.vol) luaran produk Gematronik dan data NetCDF (.nc) luaran produk Enterprise Electronics Corporation (EEC). Beberapa skrip berbasis python telah dirancang untuk membaca Plan Position Indicator (PPI) dan menghitung nilai Constant Altitude PPI (CAPPI) dari data reflektifitas radar per 10 menit dan memulihkannya menjadi luaran data NetCDF dalam koordinat kartesian. Dalam proses pengolahannya dibutuhkan waktu sekitar 1-3 menit menggunakan Personal Computer (PC) dengan spesifikasi processor setara Intel(R) Core(TM) i7 dengan ukuran luaran data sebesar 4-7 MB tergantung kepada radius jangkauan radar, jumlah PPI dan jumlah ketinggian CAPPI. Oleh karena itu, studi ini merekomendasikan penggunaaan wradlib sebagai alternatif solusi untuk pengolahan dan visualisasi data radar cuaca di Pusat Meteorologi Publik dan untuk pemulihan dan penyimpanan data radar cuaca di Pusat Database BMKG

Pengaruh Kejadian Meteorologi Dan Geofisika Di Sekitar Jawa Barat Terhadap Pergeseran Jembatan Cisomang Km 100

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