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All Journal Seminar Nasional Informatika (SEMNASIF) Policy Brief Pertanian, Kelautan, dan Biosains Tropika Jurnal Meteorologi dan Geofisika
Wido Hanggoro
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Energy Environmental Science Physics Social Sciences

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PENDISTRIBUSIAN DATA NUMERICAL WEATHER PREDICTION (NWP) DENGAN GrADS DATA SERVER Wido Hanggoro; Iis Widya Harmoko; Setyawan Widyarto
Seminar Nasional Informatika (SEMNASIF) Vol 1, No 1 (2012): Computation And Instrumentation
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penggunaan software ‘Numerical Weather Prediction’ (NWP) diperlukan untuk memberikan informasi cuaca harian secara spasial beresolusi tinggi. Data keluaran produk-produk NWP mempunyai ukuran yang sangat besar sehingga sulit dipertukarkan untuk dapat ditampilkan dalam bentuk peta cuaca maupun untuk analisis lebih lanjut. Pemanfaatan teknologi ‘online storage’ menggunakan GrADS Data Server (GDS) merupakan salah satu solusi untuk mendistribusikan data-data NWP yang berukuran besar. Selain menyediakan koneksi yang stabil dan aman, GDS juga dapat membaca berbagai format data cuaca seperti GRIB, Binary, NetCDF, HDF, BUFR, dan GrADS station data. Walaupun diperlukan keahlian tambahan untuk mengakses data tersebut, tetapi penggunaannya sebanding dengan kemudahan akses serta kemungkinan analisa data untuk keperluan lebih lanjut.
Strategi Mitigasi Urban Heat Island (UHI) di Kawasan Metropolitan Sari, Dyah Lukita; June, Tania; Hidayat, Rahmat; Perdinan; Hanggoro, Wido; Arifin, Hadi Susilo
Policy Brief Pertanian, Kelautan, dan Biosains Tropika Vol 6 No 2 (2024): Policy Brief Pertanian, Kelautan dan Biosains Tropika
Publisher : Direktorat Kajian Strategis dan Reputasi Akademik IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/agro-maritim.0602.838-843

Abstract

Kekhawatiran terhadap paparan suhu tinggi dalam jangka waktu lama yang dapat berdampak serius terhadap kesehatan manusia, produktivitas dan infrastruktur terjadi di banyak negara berkembang terutama yang terletak di wilayah tropis. Kawasan metropolitan menghadapi risiko tambahan akibat dampak UHI ini dikarenakan kondisi kepadatannya, dan desain pemukiman yang tidak terencana. Sementara itu, penduduknya kurang mempunyai kemampuan finansial untuk memitigasi dampak. Kemampuan untuk menghindari, mengelola dan membangun ketahanan terhadap dampak UHI di masa depan akan tergantung pada keputusan yang diambil saat ini. Policy brief ini menyoroti peluang-peluang utama untuk mitigasi UHI dalam bidang perencanaan kota, energi, dan penghijauan diantaranya dengan instalasi permukaan reflektif (cool roof, cool pavement, dan cool wall) serta infrastruktur hijau (green roof dan kanopi tanaman). Desain perkotaan dan investasi infrastruktur, kesenjangan sosial ekonomi, dan risiko perubahan iklim harus dikelola secara bersamaan. Tindakan yang diperlukan termasuk mereformasi standar bangunan, melakukan tinjauan kerentanan, dan berinvestasi pada infrastruktur yang dibangun untuk menahan serta meminimalkan paparan panas guna mewujudkan “cool city”, kota yang lebih sejuk.
Perbandingan Prediksi Asimilasi data Radiasi Satelit Pada Kejadian Siklon Tropis Seroja Menggunakan WRFDA CRTM dan RTTOV Kurniati, Ranti; Sagita, Novvria; Hanggoro, Wido
Jurnal Meteorologi dan Geofisika Vol. 25 No. 1 (2024)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v25i1.932

Abstract

Tropical cyclone prediction is essential for the process of mitigating the resulting disasters. Several numerical weather models have been developed but still produce errors in tropical cyclone predictions. Data assimilation is one method that can improve the initial condition values of numerical weather prediction models so that they can approach actual atmospheric conditions to reduce tropical cyclone prediction errors. Due to the limited meteorological parameter data used for data assimilation at the location of tropical cyclone events, most of which occur in ocean areas, satellite data is needed. Radiation data is initial data from satellite data, which is then transformed into meteorological parameter data using the radiative transfer model (RTM). The Weather Research and Forecasting (WRF) model data assimilation system (WRFDA) is an open-based numerical weather and data assimilation model that has 2 RTM options, namely the Radiative Transfer Model for TIROS Operational Vertical Sounder (RTTOV) and the Community Radiative Transfer Model (CRTM). This research uses two different RTMs to compare the prediction results of Tropical Cyclone Seroja, including minimum pressure, maximum wind speed, and trajectory. The research results show that predictions of minimum pressure, maximum wind speed, and trajectory of tropical cyclone Seroja by a numerical weather model assimilated with satellite radiation data are better than without assimilation. Furthermore, the assimilation of radiation data with RTTOV has the best accuracy in predicting the maximum wind speed and minimum pressure of tropical cyclone Seroja. Meanwhile, the assimilation of radiation data with CRTM can produce a minimum error in the trajectory of tropical cyclone Seroja. Future research requires adding satellite radiation data from various sensors and satellites.
Calibration Indonesian-Numerical Weather Prediction using Geostatistical Output Perturbation Sutikno, Sutikno; Cahyoko, Fajar Dwi; Putra, Fernaldy Wananda; Makmur, Erwin Eka Syahputra; Hanggoro, Wido; Taufik, Muhamad Rifki; Aza, Vestiana
Jurnal Meteorologi dan Geofisika Vol. 24 No. 2 (2023)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v24i2.1037

Abstract

Indonesian-Numerical Weather Prediction (INA-NWP) is a numerical-based weather forecast method that has been developed by the Meteorology, Climatology and Geophysics Agency. However, the forecast is still unable to produce accurate weather forecasts. Geostatistical Output Perturbation (GOP) is a weather forecast method derived from only one deterministic output. GOP takes into consideration the spatial correlation among multiple locations simultaneously. GOP is capable to identify spatial dependency patterns that are associated with error models. This study aims to obtain calibrated forecasts for daily maximum and minimum temperature variables using GOP at 10 meteorological stations in Surabaya and surrounding areas. The stages in performing temperature forecasts using GOP are obtaining regression coefficient estimators, then calculating empirical semivariograms and estimating spatial parameters. Based on several weather forecast indicators, such as RMSE and CRPS, GOP is better than INA-NWP in terms of precision and accuracy.
Co-Authors Aza, Vestiana Cahyoko, Fajar Dwi Dyah Lukita Sari, Dyah Lukita Hadi Susilo Arifin Iis Widya Harmoko Kurniati, Ranti Makmur, Erwin Eka Syahputra Novvria Sagita P. Perdinan Putra, Fernaldy Wananda Rahmat Hidayat Setyawan Widyarto Sutikno Sutikno Tania June Taufik, Muhamad Rifki