Akurasi Prediksi Curah Hujan Harian Operasional Di Jabodetabek : Perbandingan Dengan Model WRF

Gustari, Indra; Hadi, Tri Wahyu; Hadi, Safwan; Renggono, Findy

doi:10.31172/jmg.v13i2.126

Cited by 31 publications

(25 citation statements)

References 10 publications

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“…BMKGled Tanjungpinang Meteorological Station to make an aerodrome warning for extreme weather conditions at Raja Haji Fisabilillah International Airport. Precipitation forecasting was one of the difficult parts to predict and was still being studied [9]. It could be done subjectively based on the forecaster point of view and objective by using a statistical or numerical methods.…”

Section: Results and Discussion A Tanjungpinang Meteorological Stationmentioning

confidence: 99%

Aerodrome Warning Verification Using Quality Measurement of Contingency Table (Case Study in Jakarta and Tanjungpinang)

Anggoro¹,

Ninggar²,

Widomurti³

2020

Proceedings of the Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bog

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Improvement in Aerodrome Warning (AW) nowcasts need better prediction for supporting the safety and security of air traffic from extreme weather. AW consists of weather conditions, wind direction and wind speed, and visibility with observing time and validity time of forecast. Weather forecast verification is important for all stakeholders in the airport, so they can prepare and have plans to mitigate undesirable activity disturbance. AW and Automated Weather Observing System (AWOS) data are from Soekarno-Hatta Meteorological Station (07L) and Tanjungpinang Meteorological Station for January to April2019. Statistic test using hits, false alarm, misses, the correct negative is to find the score of POD, Bias, FAR, TS, and HSS which is to measure the magnitude of AW. Overall, AW has good accuracy to predict the extreme weather in the aerodrome.

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Section: Results and Discussion A Tanjungpinang Meteorological Stationmentioning

confidence: 99%

Aerodrome Warning Verification Using Quality Measurement of Contingency Table (Case Study in Jakarta and Tanjungpinang)

Anggoro¹,

Ninggar²,

Widomurti³

2020

Proceedings of the Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bog

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“…Keterbatasan jumlah titik pengamatan stasiun dan kemampuan komputasi yang dimiliki oleh BMKG menjadi kendala utama untuk dapat menyediakan informasi prakiraan cuaca pada skala lokal. Hasil verifikasi akurasi prediksi cuaca operasional BMKG memperlihatkan skor yang sangat rendah dalam memprediksi hujat lebat (Gustari, 2012). Oleh karena itu kerjasama serta dukungan pemerintah daerah perlu digalakan untuk menggagas kemandirian teknologi tanggap bencana di tingkat lokal.…”

Section: Pendahuluanunclassified

Perbaikan Prediksi Cuaca Numerik Kejadian Curah Hujan Lebat Terkait Dengan Kejadian Longsor Di Banjarnegara Menggunakan Asimilasi Data Satelit

Mulsandi

Kristianto

Zakir

2018

JSTMC

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Wilayah Banjarnegara terekspos dengan kejadian tanah longsor yang terjadi hampir setiap tahun. Hujan lebat merupakan salah satu faktor penting pemicu terjadinya longsor yang paling mungkin untuk diprediksi, sehingga prediksi hujan lebat yang akurat sangat dibutuhkan dalam sistem peringatan dini longsor. Namun demikian, keterbatasan peralatan pengamatan cuaca di Banjarnegara memberikan kendala tersendiri sehingga dibutuhkan teknik lain dalam pembuatan informasi prediksi cuaca di wilayah ini. Penelitian ini dibuat untuk memberikan kontribusi landasan ilmiah dalam membuat prakiraan cuaca menggunakan model Weather Research and Forecasting (WRF) dengan mengintegrasikan data pengamatan satelit menggunakan WRF Data Assimilation (WRF-DA) untuk memperbaiki kualitas data awal model. Hasil penelitian menunjukan bahwa prosedur asimilasi data satelit cuaca dapat memperbaiki data awal kandungan uap air di atmosfer (+60%) beberapa jam sebelum kejadian hujan lebat. Sehingga hasil prediksi model cuaca numerik dengan menggunakan asimilasi data satelit (DA-SAT) menjadi lebih baik dibandingkan dengan yang tidak menggunakan asimilasi data (Non_DA). Hal ini ditunjukan dengan nilai bias model yang mengecil (-32%) jika dibandingkan dengan data pengamatan penakar hujan stasiun. Hasil perbandingan data series waktu akumulasi curah hujan antara DA-SAT dan Non-DA memperlihatkan adanya perbedaan waktu tercapainya hujan maksimum dan juga perbedaan intensitasnya dimana skema Non-DA lebih lambat (+5 jam) dengan bias (-40%) sementara DA-SAT lebih lambat 0.5 jam dengan bias (+8%). Dapat disimpulkan bahwa asimilasi data satelit dapat memperbaiki kesalahan prediksi jumlah hujan dan waktu kejadiannya. Hasil penelitian ini dapat dijadikan rekomendasi penggunaan asimilasi data satelit dalam pembuatan informasi prediksi cuaca numerik di wilayah Banjarnegara.

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“…The prediction of accurate heavy rain is important in order to build a flood early warning system. However, operational weather prediction capabilities of Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG) are only good at forecasts of rain or not rain dichotomies, while predictions of heavy and hefty rainfall have low predictive abilities (Gustari et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Improving Numerical Weather Prediction of Rainfall Events Using Radar Data Assimilation

Hastuti¹,

Paski²,

Fatkhuroyan³

2019

IJG

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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 season

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Akurasi Prediksi Curah Hujan Harian Operasional Di Jabodetabek : Perbandingan Dengan Model WRF

Cited by 31 publications

References 10 publications

Aerodrome Warning Verification Using Quality Measurement of Contingency Table (Case Study in Jakarta and Tanjungpinang)

Aerodrome Warning Verification Using Quality Measurement of Contingency Table (Case Study in Jakarta and Tanjungpinang)

Perbaikan Prediksi Cuaca Numerik Kejadian Curah Hujan Lebat Terkait Dengan Kejadian Longsor Di Banjarnegara Menggunakan Asimilasi Data Satelit

Improving Numerical Weather Prediction of Rainfall Events Using Radar Data Assimilation

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