Articles | Volume 26, issue 3
https://doi.org/10.5194/hess-26-775-2022
https://doi.org/10.5194/hess-26-775-2022
Research article
 | 
11 Feb 2022
Research article |  | 11 Feb 2022

Citizen rain gauges improve hourly radar rainfall bias correction using a two-step Kalman filter

Punpim Puttaraksa Mapiam, Monton Methaprayun, Thom Bogaard, Gerrit Schoups, and Marie-Claire Ten Veldhuis

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Cited articles

Amitai, E.: Systematic Variation of Observed Radar Reflectivity–Rainfall Rate Relations in the Tropics, J. Appl. Meteorol., 39, 2198–2208, https://doi.org/10.1175/1520-0450(2001)040<2198:SVOORR>2.0.CO;2, 2000. 
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Anhert, P., Krajewski, W. F., and Johnson, E. R.: Kalman Filter estimation of radar-rainfall mean field bias, in: 23rd Radar Meteorology Conf., Amer. Meteor. Soc., 33–37, https://www.jstor.org/stable/26225332 (last access: 2 February 2022), 1986. 
Arai, K., Liang, X., and Liu, Q.: Method for estimation of rain rate with Rayleigh and Mie scattering assumptions on the ZR relationship for different rainfall types, Adv. Space Res., 36, 813–817, https://doi.org/10.1016/j.asr.2005.04.102, 2005. 
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Short summary
The density of rain gauge networks plays an important role in radar rainfall bias correction. In this work, we aimed to assess the extent to which daily rainfall observations from a dense network of citizen scientists improve the accuracy of hourly radar rainfall estimates in the Tubma Basin, Thailand. Results show that citizen rain gauges significantly enhance the performance of radar rainfall bias adjustment up to a range of about 40 km from the center of the citizen rain gauge network.