Spatial evaluation rainfall estimation on weather radar using Marshall-Palmer reflectivity-rainfall rate in Banten

• Autorzy: Hartanto , Humaidi Syahrul , Frida Erna , Ananda Naufal , Sinambela Marzuki

Identyfikatory

DOI

10.24425/jwld.2024.151567

• Języki publikacji: EN

Abstrakty

EN

Based on data from the National Disaster Management Agency (Ind.: Badan Nasional Penanggulangan Bencana - BNPB), throughout 2022, more than 91% of disaster events were hydrometeorological disasters, with floods at 43% and landslides at 17%. One of the factors for floods and landslides is high rainfall intensity. Automatic rain gauge (ARG) is a rainfall observation instrument that can accurately measure rainfall at observation points. However, it has problems such as communication systems that cause delays in data transmission, low instrument density, and inability to cover a wide spatial area, which can affect the accuracy of rainfall information. Weather radar is a remote sensing instrument that can estimate rainfall spatially so that weather radar observations can reach areas of the region that do not have ARG. However, before being used as rainfall information, estimation rainfall needs to be evaluated or calibrated. Evaluation of rainfall estimation on weather radar to ARG in Banten at a 30-120 km distance range, shows a coefficient of determination above 0.8. Based on the studies that have been conducted, increase of root mean square error (RMSE) is due to influence of radar observation range and observation distance on ARG. Adjustment of rainfall estimation improves the accuracy of rainfall estimation. Adjusting rainfall estimation can reduce RMSE by 50%.

Słowa kluczowe

EN

estimation; Marshall-Palmer; rainfall; reflectivity; weather radar

• Wydawca: Wydawnictwo ITP
• Czasopismo: Journal of Water and Land Development
• Rocznik: 2024
• Tom: no. 62
• Strony: 193--200
• Opis fizyczny: Bibliogr. 34 poz., mapa, rys., tab., wykr.

Twórcy

autor

Hartanto

• Universitas Sumatera Utara, FMIPA, Post Graduate Program (Physics), Bioteknologi St No. 1, USU, 20155, Medan, Indonesia

• https://orcid.org/0009-0008-9287-072X

autor

Humaidi Syahrul

• Universitas Sumatera Utara, FMIPA, Post Graduate Program (Physics), Bioteknologi St No. 1, USU, 20155, Medan, Indonesia

• https://orcid.org/0000-0003-2183-3282

autor

Frida Erna

• Universitas Sumatera Utara, FMIPA, Post Graduate Program (Physics), Bioteknologi St No. 1, USU, 20155, Medan, Indonesia

• https://orcid.org/0000-0001-8953-9499

autor

Ananda Naufal

• Institut Teknologi Bandung, Graduate Student in Instrumentation & Control Program, Ganesha St No. 1, 40132, Bandung, Indonesia

• https://orcid.org/0009-0009-9965-4402

autor

Sinambela Marzuki

• Sekolah Tinggi Meteorologi Klimatologi dan Geofisika, Program in Applied of Instrumentation, MKG, Meteorologi No. 5 Tanah Tinggi, 15119, Tangerang, Banten, Jakarta, Indonesia

• https://orcid.org/0000-0003-3363-4440

Bibliografia

• Ali, A., Deranadyan, G. and Umam, I.H. (2020) “An enhancement to the quantitative precipitation estimation using radar-gauge merging,” International Journal of Remote Sensing and Earth Sciences, 17(1), pp. 65–74. Available at: https://doi.org/10.30536/j.ijreses.2020.v17.a3316.
• Ali, A. et al. (2021) “Preliminary study of a radio frequency interference filter for non-polarimetric c-band weather radar in Indonesia (case study: Tangerang Weather Radar),” International Journal of Remote Sensing and Earth Sciences, 18(2), pp. 189–202.
• Ali, A., Lubis, F., and Sa’adah U. (2023) “Komparasi Algoritma Asimilasi Data Radar-Rain Gauge Dalam Peningkatan Akurasi Quantitative Precipitation Estimation (QPE): mean field bias (MFB) dan brandes spatial adjustment (BRA) [Comparison of radar-rain gauge data assimilation algorithms in improving accuracy of quantitative precipitation estimation (QPE): mean field bias (MFB) and brandes spatial adjustment (BRA)].” Jurnal Sains & Teknologi Modifikasi Cuaca 24(1), pp. 35–42.
• Ananda, N., Hartanto, H. and Kurniadi, D. (2023) “Preliminary evaluation of weather radar rainfall estimation in Bandung City,” 2023 8th International Conference on Instrumentation, Control, and Automation (ICA), pp. 76–80. Available at: https://doi.org/10.1109/ICA58538.2023.10273091.
• Aziding, K. et al. (2023) “Enhanced technique for prediction of Z-R relationship in tropical region,” Journal of Physics: Conference Series, 2559(1), 012009. Available at: https://doi.org/10.1088/1742-6596/2559/1/012009.
• Binetti, M.S. et al. (2022) “The use of weather radar data: Possibilities, challenges and advanced applications,” Earth, 3(1), pp. 157–171. Available at: https://doi.org/10.3390/earth3010012.
• Curzio Di, D. et al. (2022) “Comparing rain gauge and weather RaDAR data in the estimation of the pluviometric inflow from the Apennine Ridge to the Adriatic Coast (Abruzzo Region, Central Italy),” Hydrology, 9(12), 225. Available at: https://doi.org/10.3390/hydrology9120225.
• Daliakopoulos, I.N. and Tsanis, I.K. (2011) “A weather radar data processing module for storm analysis,” Journal of Hydroinformatics, 14(2), pp. 332–344. Available at: https://doi.org/10.2166/hydro.2011.118.
• Dinh, T.-L. et al. (2023) “A new approach for quantitative precipitation estimation from radar reflectivity using a gated recurrent unit network,” Journal of Hydrology, 624, 129887. Available at: https://doi.org/10.1016/j.jhydrol.2023.129887.
• EEC (2013) C-BAND: DWSR-2501C, DWSR-2501C/K, DWSR-3501C, DWSR-5001C, DWSR-10001C Magnetron and Klystron models / Single and dual-polarity configurations 250 kW to 1MW of radiated power. Enterprise: Enterprise Electronics Corporation. Available at: https://www.eecweathertech.com/pdf/EEC-C-Band-Systems.pdf (Accessed: April 12, 2024).
• Gyasi-Agyei, Y. (2020) “Identification of the optimum rain gauge network density for hydrological modelling based on radar rainfall analysis,” Water, 12(7), 1906. Available at: https://doi.org/10.3390/w12071906.
• Harisuseno, D. and Cahya, E.N. (2020) “Determination of soil infiltration rate equation based on soil properties using multiple linear regression,” Journal of Water and Land Development, pp. 77–88.Availableat: https://doi.org/10.24425/jwld.2020.135034.
• Hartanto, N. et al. (2023) “Evaluation of meteorological radar precipitation forecast in Banten,” 2023 International Conference on Information Technology and Computing (ICITCOM), pp. 297–300. Availableat: https://doi.org/10.1109/ICIT-COM60176.2023.10442051.
• Hutapea, T.D.F. et al. (2021) “Modifikasi konstanta persamaan Z-R radar Surabaya Untuk Peningkatan Akurasi Estimasi Curah Hujan [Modification of Surabaya radar Z-R equation constants for improved rainfall estimation accuracy],” Jurnal Meteorologi Dan Geofisika, 21(2), pp. 91–97. Available at: https://doi.org/10.31172/jmg.v21i2.545.
• Imhoff, R.O. et al. (2020) “Spatial and temporal evaluation of radar rainfall nowcasting techniques on 1,533 events,” Water Resources Research, 56(8). Available at: https://doi.org/10.1029/2019wr026723.
• Kalesse-Los, H. et al. (2023) “The Virga-Sniffer – a new tool to identify precipitation evaporation using ground-based remote-sensing observations,” Atmospheric Measurement Techniques, 16(6), pp. 1683–1704. Available at: https://doi.org/10.5194/amt-16-1683-2023.
• Mahavik, N., Tantanee, S. and Masthawee, F. (2021) “Investigation of Z-R relationships during tropical storm in GIS using implemented mosaicking algorithms of radar rainfall estimates from ground-based weather radar in the Yom River basin, Thailand,” Applied Geomatics, 13(4), pp. 645–657. Available at: https://doi.org/10.1007/s12518-021-00383-2.
• Mapiam, P.P. and Sriwongsitanon, N. (2008) “Climatological Z-R relationship for radar rainfall estimation in the upper Ping river basin,” ScienceAsia, 34(2), 215. Available at: https://doi.org/10.2306/scienceasia1513-1874.2008.34.215.
• Marengo, J.A. et al. (2021) “Extreme rainfall and Hydro-Geo-Meteorological disaster risk in 1.5, 2.0, and 4.0°C global warming scenarios: An analysis for Brazil,” Frontiers in Climate, 3. Available at: https://doi.org/10.3389/fclim.2021.610433.
• Marshall, J.S. and Palmer, W.McK. (1948) “The distribution of raindrops with size,” Journal of Meteorology, 5(4), pp. 165–66. Available at: https://doi.org/10.1175/1520-0469(1948)005<0165:TDORWS>2.0.CO;2.
• Nsabagwa, M. et al. (2019) “Towards a robust and affordable automatic weather station,” Development Engineering, 4, 100040. Available at: https://doi.org/10.1016/j.deveng.2018.100040.
• Ozkaya, A. and Akyurek, Z. (2019) “Evaluating the use of bias-corrected radar rainfall data in three flood events in Samsun, Turkey,” Natural Hazards, 98(2), pp. 643–674. Available at: https://doi.org/10.1007/s11069-019-03723-z.
• Pappa, A. et al. (2021) “Analysis of the Z-R relationship using X-Band weather radar measurements in the area of Athens,” Acta Geophysica, 69(4), pp. 1529–1543. Available at: https://doi.org/ 10.1007/s11600-021-00622-5.
• Qiu, Q. et al. (2020) “Evaluation of the radar QPE and rain gauge data merging methods in northern China,” Remote Sensing, 12(3), 363. Available at: https://doi.org/10.3390/rs12030363.
• Radjab, A.F. et al. (2020) “Partnership in weather observation using the crowdsourcing method,” IOP Conference Series Earth and Environmental Science, 499(1), 012019. Available at: https://doi.org/10.1088/1755-1315/499/1/012019.
• Sevruk, B., Ondrás, M. and Chvíla, B. (2009) “The WMO precipitation measurement intercomparisons,” Atmospheric Research, 92(3), pp. 376–380. Available at: https://doi.org/10.1016/j.at-mosres.2009.01.016.
• Sharif, R.B., Habib, E.H. and ElSaadani, M. (2020) “Evaluation of radar-rainfall products over coastal Louisiana,” Remote Sensing, 12(9), 1477. Available at: https://doi.org/10.3390/rs12091477.
• Sokol, Z. et al. (2021) “The role of weather radar in rainfall estimation and its application in meteorological and hydrological modelling — A review,” Remote Sensing, 13(3), 351. Available at: https://doi.org/10.3390/rs13030351.
• Suwarno, I. et al. (2021) “IoT-based lava flood early warning system with rainfall intensity monitoring and disaster communication technology,” Emerging Science Journal, 4, pp. 154–166. Available at: https://doi.org/10.28991/esj-2021-sp1-011.
• Tahir, W. et al. (2022) “Mean field bias correction to Radar QPE as input to flood modeling for Malaysian river basins,” International Journal of Integrated Engineering, 14(5). Available at: https://doi.org/10.30880/ijie.2022.14.05.019.
• Tiwi, D.A. et al. (2023) “Post-disaster rapid assessment of Sunda Strait tsunami on 24th–25th December 2018 in the Regencies of Serang and Pandeglang, Province of Banten, Indonesia,” IOP Conference Series Earth and Environmental Science, 1173(1), 012015. Available at: https://doi.org/10.1088/1755-1315/1173/1/012015.
• Urban, G. and Strug, K. (2021) “Evaluation of precipitation measurements obtained from different types of rain gauges,” Meteorologische Zeitschrift, 30(5), pp. 445–463. Available at: https://doi.org/10.1127/metz/2021/1084.
• Xia, Q. et al. (2020) “Quantification of precipitation using polarimetric radar measurements during several typhoon events in Southern China,” Remote Sensing, 12(12), 2058. Available at: https://doi.org/10.3390/rs12122058.
• Yang, Z., Liu, P. and Yang, Y. (2019) “Convective/stratiform precipitation classification using ground-based Doppler radar data based on the K-nearest neighbor algorithm,” Remote Sensing, 11(19), 2277. Available at: https://doi.org/10.3390/rs11192277.