Abstract
Abstract. The low density of conventional rain gauge networks is often a limiting factor for radar rainfall bias correction. Citizen rain gauges offer a promising opportunity to collect rainfall data at a higher spatial density. In this paper, hourly radar rainfall bias adjustment was applied using two different rain gauge networks: tipping buckets, measured by Thai Meteorological Department (TMD), and daily citizen rain gauges. The radar rainfall bias correction factor was sequentially updated based on TMD and citizen rain gauge data using a two-step Kalman filter to incorporate the two gauge datasets of contrasting quality. Radar reflectivity data from the Sattahip radar station, gauge rainfall data from the TMD, and data from citizen rain gauges located in the Tubma Basin, Thailand, were used in the analysis. Daily data from the citizen rain gauge network were downscaled to an hourly resolution based on temporal distribution patterns obtained from radar rainfall time series and the TMD gauge network. Results show that an improvement in radar rainfall estimates was achieved by including the downscaled citizen observations compared with bias correction based on the conventional rain gauge network alone. These outcomes emphasize the value of citizen rainfall observations for radar bias correction, in particular in regions where conventional rain gauge networks are sparse.
Highlights
Hydrometeorological hazards, like flash floods and landslides, cause severe damage to economies, properties, and human lives worldwide
The question that we set out to answer is as follows: to what extent do the downscaled citizen rainfall observations improve the accuracy of hourly radar rainfall estimates? Several scenarios of hourly rainfall distribution patterns were applied for downscaling in order to investigate the most suitable technique for hourly radar rainfall assessment
These results indicate that the parameter r1, the lag-one correlation coefficient of the logarithmic mean field bias, ranges from 0.15 to 0.53, depending on the hourly downscaling approach, whereas σβ2, representing the stationary variance of the logarithmic mean field bias, remains relatively invariant over the same period of simulation
Summary
Hydrometeorological hazards, like flash floods and landslides, cause severe damage to economies, properties, and human lives worldwide. In this context, flood forecasting and warning systems are a valuable nonstructural measure to mitigate damage. Flood forecasting and warning systems are a valuable nonstructural measure to mitigate damage Such systems require the input of rainfall data at a high spatial and temporal resolution. Weather radar, which can better capture the variation in rainfall fields at fine spatial and temporal resolutions, could be used as an alternative rainfall product to improve the accuracy of flash flood estimates and warnings. Various sources of error affect radar rainfall estimates, mainly errors in reflectivity measurements and reflectivity–rainfall conversion (Jordan et al, 2000). The calibrated parameter A in the Z–R relationship will include any errors in the radar system caused by the electrical calibration of the radar (Seed et al, 2002)
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