ABSTRAKFenomena iklim mempengaruhi kenyamanan fisiologis di daerah pemukiman. Analisa tingkat kenyamanan di DKI Jakarta dilakukan menggunakan THI (Temperature Humidity Index). Berdasarkan data iklim periode 1985 -2012 stasiun Kemayoran, Tanjung Priok, Halim, Cengkareng dan Pondok Betung, hasil penelitian menunjukkan rata-rata prosentase tingkat kenyamanan harian dengan kategori tidak nyaman sebesar 22,1 % (81 hari per tahun), sebagian nyaman 71 % (259 hari per tahun) dan nyaman 7,1% (26 hari per tahun). Tingkat kenyamanan menunjukkan semakin ke tengah kota semakin besar prosentase tidak nyaman. Selama periode tersebut terjadi kecenderungan peningkatan indeks THI dengan signifikansi > 50% menunjukkan tingkat kenyamanan di DKI Jakarta cenderung semakin tidak nyaman.Kata kunci: tingkat kenyamanan, temperature humidity index, urban heat island ABSTRACTClimate 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 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.

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 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. Abstrak. Curah hujan adalah faktor terpenting dari siklus air dan energi bumi. Tujuan dari penelitian ini adalah untuk mengevaluasi akurasi dari GSMaP (Global Satellite Mapping of hujan dan memiliki nilai RMSE terbaik di musim kemarau. Selain itu, nilai tertinggi Proportion over-estimasi terhadap hasil pengamatan di Indonesia dengan akurasi rata-rata untuk prakiraan Kata kunci: GSMaP, curah hujan, rain-gauge.

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ABSTRAKInformasi prakiraan cuaca berdasarkan data radar sangat penting bagi BMKG dalam memberikan peringatan dini cuaca ekstrim. Saat ini, BMKG memiliki setidaknya ada tiga format data radar cuaca yang berasal dari tiga produsen radar yakni Gematronik, Enterprise Electronics Corporation (EEC) dan Baron yang hanya dapat diolah menggunakan perangkat lunak dari masing-masing produsen radar. Perangkat lunak wradlib berbasis python dapat mengolah ketiga format data radar tersebut dan menyimpannya dalam format data yang sama. Kelebihan wradlib-python lainnya adalah berlisensi sumber terbuka (open-source) sehingga dapat di-install di berbagai sistem operasi secara gratis, mengurangi ketergantungan terhadap perangkat lunak dari produsen radar, dapat mengolah dan menampilkan multi format data radar cuaca secara masif, menyimpan data radar dalam format NetCDF koordinat kartesian sehingga memudahkan pengolahan data radar lebih lanjut seperti input untuk asimilasi data. Studi ini memfokuskan pengolahan data radar volumetric (.vol) luaran produk Gematronik dan Baron serta data radar NetCDF (.nc) luaran produk EEC. Wradlibpython dapat mengekstrak secara otomatis data Plan Position Indicator (PPI) dan menghitung nilai Constant Altitude PPI (CAPPI) dari data reflektifitas radar. Walaupun intensitas reflektifitas citra radar luaran wradlib-python relatif lebih tinggi dari luaran perangkat lunak dari produsen radar, luaran tersebut memiliki pola spasial yang relatif sama. Oleh karena itu, wradlib-python dapat menjadi salah satu solusi alternatif untuk pengolahan, penyimpanan dan visualisasi data radar cuaca di BMKG. ABSTRACTWeather forecast information based on weather radar is very important for BMKG in providing early warning services for extreme weather. Currently, BMKG has at least three weather radar data format from three radar companies i.e. Gematronik, Enterprise Electronics Corporation (EEC) and Baron which can only be extracted and processed using their original software from each company. The python-based library wradlib can extract and process these three data format and save them into the same data format. Advantages of wradlib-python include having an open-source license so it can be freely installed in multi operating system, reducing dependency on original weather radar software, be able to process and visualize massive multi weather radar data format and be able to save radar data in cartesian coordinate and NetCDF format which make it easier for further data processing such as input for data assimilation. This study only focuses on processing weather radar volumetric data (.vol) from Gematronik and Baron, and NetCDF data (.nc) from EEC. Wradlib-python can automatically extract Plan Position Indicator (PPI) data and calculate Constant Altitude PPI (CAPPI) values from reflectivity data. Although radar images generated by wradlib-python relatively have a greater reflectivity intensity than ones from the original softwares, they have a relatively similar spatial pattern. Therefore, wradlib-python can be one of altern...

<p>To explore the characteristics of Northerly Cold Surge during Years of the Maritime Continent Campaign 2021, intensive observation was used to detect the modification processes of the air mass at the head of cold surge, convection development, and severe weather including torrential rainfall using several methods such as the intensive upper-air observation at Jakarta and Pangkal Pinang, vapor variation observation with GNSS network, and precipitation radar network. During this campaign, 7 CENS (Cross-Equatorial Northerly Surge) events were observed according to Hattori’s criteria. The results of the intensive observation show that all of 7 CENS events occurred in association with the negative SST anomaly over the Java Sea with CENS6 (18 – 21 February 2021) induced extreme rainfall (over 150 mm/day) in the southern part of Jakarta. The significant negative SST anomaly was continued over the inland & marginal seas of Indonesia under the strong northerly surge condition during this campaign.</p>