1 Japan Weather Association, Tokyo170–6055, Japan; firstname.lastname@example.org
2 Japan Meteorological Business Support Center, Tokyo101-0054, Japan; email@example.com
3 Aero–Meteorological Observatory, Hanoi 10000, Vietnam; firstname.lastname@example.org; email@example.com; nguyenminhcuong_T59@hus.edu.vn
4 Japan International Cooperation Agency, Tokyo102–0084, Japan; firstname.lastname@example.org
*Correspondence: email@example.com; Tel.: +84–829761096
Real–time monitoring of quantitative precipitation distribution is essential to prevent natural disasters caused by heavy rainfall. Precipitation distribution by rain gauge network or combined with radar/satellite data is operationally used in Viet Nam. Previously, meteorological radar data was simply converted to precipitation amount by using simple Z–R relationship. In order to get the accurate quantitative precipitation estimation (QPE) data, converted precipitation amount from radar should be corrected by rain gauge data. In the ongoing JICA technical cooperation project, preliminary development of the QPE product has been conducted by utilizing the data from the automatic rain gauge network and meteorological radar network in Viet Nam. The fundamental part of this QPE algorithm has been used and updated in Japan Meteorological Agency (JMA) for more than 25 years. This is the first attempt to get quantitative precipitation distribution with precise resolution by combining radar and rain gauge data in Viet Nam. This paper describes each process to introduce this QPE method to Viet Nam and indicates some preliminary results. Several issues to improve its accuracy is also proposed.
Cite this paper
Kimpara, C.; Tonouchi, M.; Hoa, B.T.K.; Hung, N.V.; Cuong, N.M.; Akaeda, K. Quantitative Precipitation Estimation by Combining Rain gauge and Meteorological Radar Network in Viet Nam. VN J. Hydrometeorol. 2020, 5, 36-50.
1. Tonouchi, M.; Kasuya, Y.; Tanaka, Y.; Akatsu, K.; Akaeda, K.; Nguyen, V.T. Activities of JICA on disaster prevention and achievement of JICA project in Period 1. VN J. Hydrometeorol., 2020, 5, 1–12.
2. Vicente, G.; Scofield, R. A.; Mensel, W.P. The operational GOES infrared rainfall estimation technique. Bull. Amer. Meteor. Soc. 1998, 79, 1881–1898. doi: 10.1175/1520-0477(1998)079<1883:TOGIRE>2.0.CO;2
3. Vasiloff, S.V.; Seo, D.J.; Howard, K.W.; Zhang, J.; Kitzmiller, D.H.; Mullusky, M.G.; Krajewski, W.; Brandes, E.A.; Rabin, R.M.; Berkowitz, D.S.; Brooks, H.E.; Mcginley, J.; Kuligowski, R.J.; Brown, B.G. Improving QPE and very short term QPF. Bull. Amer. Meteor. Soc., 2007, 88, 1899–1911. doi: 10.1175/BAMS–88–12–1899
4. Makihara, Y. A method for improving radar estimates of precipitation by comparing data from radars and rain gauges, J. Meteor. Soc. Japan, 1996, 74, 459–480. doi: 10.2151/jmsj1965.74.4_459
5. Makihara, Y. Algorithms for precipitation nowcasting focused on detailed analysis using radar and rain gauge data, Technical Reports of the MRI, 2000, 39, 63–111. doi:10.11483/mritechrepo.39
6. Observation Department. Radar/rain gauge-analyzed precipitation, Weather Radar Application Technology at the Japan Meteorological Agency, 2017, pp. 62–70.
7. Makihara, Y. Quantitative Precipitation Estimation, Training for QPE derived from the combination of radar and rain gauge data, Hanoi, Training Material, 2020, ##5.
8. JICA and JMBSC. The Project for Strengthening Capacity in Weather Forecasting and Flood Early Warning System in Vietnam, Final report (First period), 2020, pp. 147.