With the availability of an increased number of ground-based weather radars, the development of composite radar rainfall estimates has become common practice. In mountainous terrain, weather radar measurements often encounter beam blockage effects, resulting in erroneous estimates of rainfall. This study introduces a novel relative radar quality index based on the radar reflectivity fraction to enhance the radar composite product. Additionally, we develop an improved mean field bias adjustment technique by including the spatial variability of the bias adjustment factors associated with the quality of radar observations. Radar reflectivity data from a network of single-polarization S-band radars, the Sattahip and Phimai radar stations in Thailand, and automatic rain gauges within the composite area, were used for the analysis. Three independent datasets were employed: (1) 51 storm events (2016–2022) for evaluating radar composite performance and QI-based bias adjustment; (2) hourly data from August–October 2020 to assess bias factor uncertainty; and (3) three heavy storms (2016, 2017, and 2020) to examine the QI method's effectiveness in beam-blocked basins. Our analysis explored seven combinations of hourly radar composite products. Subsequently, the performance of radar rainfall estimates obtained from applying the proposed mean field bias was evaluated by comparing them with the conventional technique. Results show the potential of integrating combined multiple quality indices to improve rainfall estimates, particularly for heavy rainfall events in mountainous regions.

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.