On the basis of detailed analysis of a case study and long-term climatology, it is shown that equatorial waves and their interactions serve as precursors for extreme rain and flood events in the central Maritime Continent region of southwest Sulawesi, Indonesia. Meteorological conditions on January 22, 2019 leading to heavy rainfall and devastating flooding in this area are studied. It is shown that a convectively coupled Kelvin wave (CCKW) and a convectively coupled equatorial Rossby wave (CCERW) embedded within the larger-scale envelope of the Madden-Julian Oscillation (MJO) enhanced convective phase, contributed to the onset of a mesoscale convective system which developed over the Java Sea. Low-level convergence from the CCKW forced mesoscale convective organization and orographic ascent of moist air over the slopes of southwest Sulawesi. Climatological analysis shows that 92% of December-January-February floods and 76% of extreme rain events in this region were immediately preceded by positive low-level westerly wind anomalies. It is estimated that both CCKWs and CCERWs propagating over Sulawesi double the chance of floods and extreme rain event development, while the probability of such hazardous events occurring during their combined activity is eight times greater than on a random day. While the MJO is a key component shaping tropical atmospheric variability, it is shown that its usefulness as a single factor for extreme weather-driven hazard prediction is limited.

The probability forecast simulation of Rainy Season Onset and the Lenght of Season based on Indonesia SST, Nino34 SST, and IODM SST Anomalies as predictors, have been done over Seasonal Forecast Area (SFA) 126 Denpasar. This SFA was chosen as a case in relation to it's monsoonal rainfall pattern. In this SFA there were clearly different condition between Rainy and Dry Seasons. Basically the peak of Rainy and Dry Seasons only happen once a year. The peak of Rainy Season commonly takes place during early year but Dry Season occurs in the middle year. The probability forecast simulation of SFA 126 Denpasar was computed base on predictors condition at once. Time lag of 3, 2, 1 month(s) of predictors preceded the earliest onset of season have been examined. Time lags used data which have the most significant value of determination coefficient R 2 (taken from multi linear regression equation) should be denoted as input for providing the probability forecast simulations. Results show that Indonesia SSTA has significantly played a role for determining the onset of seasons over SFA 126 Denpasar, whether earlier onset or longer ones, especially during the weak of Nino34 and IODM SSTAs. Meanwhile the other predictors condition were denoted to strengthen and weaken the onset of seasons. ABSTRAK Simulasi prediksi probabilitas Awal Musim Hujan (AMH) dan Panjang Musim Hujan (PMH) terkait kondisi Indonesia SSTA, Nino34 SSTA, dan IODM SSTA sebagai prediktor telah dilakukan di Zona Musim (ZOM) 126 Denpasar. Lokasi ini dipilih karena memiliki pola curah hujan dasarian maupun bulanan pola monsunal, sehingga terdapat perbedaan yang jelas antara kondisi Musim Hujan (MH) dengan kondisi Musim Kemarau (MK). Kejadian puncak dan lembah curah hujan pada lokasi ini berlangsung sekali dalam satu tahun. Puncak hujan berlangsung bersamaan dengan MH dan sebaliknya lembah hujan berlangsung bersamaan dengan MK. Untuk ZOM 126 Denpasar, simulasi prediksi probabilitas ini dihitung berdasaran pada kondisi seluruh prediktor sebagaimana tersebut di atas. Time lag 3, 2, dan 1 bulan digunakan pada data dasarian awal yang diolah. Data yang memiliki nilai koefisien determinasi R 2 terbesar dari persamaan regresi multi linear yang dibentuk berdasarkan pada kondisi prediktor di atas selanjutnya digunakan dalam menentukan simulasi prediksi probabilitas AMH dan PMH. Hasil menunjukkan bahwa kondisi Indonesia SSTA sangat berperan dalam menentukan nilai probabilitas maju-mundur AMH dan panjang-pendek PMH di ZOM 126 Denpasar, khususnya pada saat Nino34 dan IODM SSTA lemah. Sementara itu kondisi Nino34 SSTA dan IODM Anomaly memiliki peran sebagai penguat/ pelemah terhadap probabilitas kejadian AMH dan PMH di ZOM 126 Denpasar.

Informasi spasial curah hujan dibutuhkan oleh berbagai sektor namun karena keterbatasan pengamatan, proses interpolasi harus dilakukan. Metode interpolasi spasial terbaik untuk suatu tempat perlu ditentukan secara khusus. Penggunaan metode interpolasi Inverse Distance Weight (IDW) P=5 di Stasiun Klimatologi Malang perlu dikaji ulang. Tujuan penelitian ini adalah mencari justifikasi parameter interpolasi, membandingkan hasil interpolasi, dan pada akhirnya menentukan metode interpolasi terbaik untuk curah hujan bulanan Jawa Timur. Tiga metode yang diperbandingkan adalah IDW, Ordinary Kriging (OK), dan Regression Kriging (RK). Data curah hujan bulanan yang digunakan adalah 197 titik selama 204 bulan. Prediktor RK menggunakan ketinggian, kelerengan, dan estimasi curah hujan satelit. Parameter interpolasi seperti ukuran piksel, jumlah pencarian (NN), model variogram, dan power IDW dijustifikasi terlebih dahulu. Korelasi spasial digunakan untuk membandingkan hasil interpolasi. Validasi silang lipat sepuluh digunakan untuk menghasilkan galat. Galat interpolasi yang digunakan berupa nilai dan selisih kategori warna peta standar. RMSE dan MAE digunakan sebagai parameter validasi. Analisis waktu komputasi juga dilakukan. Piranti lunak R Statistics dan QGIS digunakan untuk membentuk bahan maupun mencari parameter interpolasi sedangkan interpolasi dilakukan menggunakan SAGA. Parameter interpolasi ditentukan sebagai berikut: ukuran piksel=0,01; NN=9; model variogram sperikal dengan Nugget=0, Sill=1, dan range bervariasi; power IDW=1,5. Hasil interpolasi RK jauh berbeda dari IDW maupun OK. Secara umum, IDW memiliki galat paling kecil (MAE kategori=0,871) dibandingkan OK (0,890) maupun RK (1,188).

In 2017, BMKG has 41 weather radars covering most of Indonesia region and most of its data are automatically sent to the BMKG headquarter every 10 minutes. There are four different weather radar brands with its specific data format and software analysis. In recent years, the weather radar community has developed open-source software to handle several radar data formats. Based on this, BMKG has developed the Indonesia In-House Radar Integration System (InaRAISE) of BMKG using the open-source weather radar software. InaRAISE has been developed using Python-based libraries Wradlib and Py-ART for processing weather radar data. BMKG radar data have been successfully extracted and transformed into Cartesian coordinates for post-processing. The multiple radars have been successfully composited by comparing column-maximum reflectivity. Web-based near real-time radar images has been experimentally operated, but not officially launched. The main constraint is the susceptible communication network between radar sites and BMKG headquarter causing real-time data transfer problems. InaRAISE serves feasible data radar extraction for data assimilation in the numerical weather prediction model. InaRAISE could serve as a supporting of the existing radar integration system or possibly as a replacement.

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