Evaluation of radar-gauge merging methods for quantitative precipitation estimates

Abstract

Abstract. Accurate quantitative precipitation estimates are of crucial importance for hydrological studies and applications. When spatial precipitation fields are required, rain gauge measurements are often combined with weather radar observations. In this paper, we evaluate several radar-gauge merging methods with various degrees of complexity: from mean field bias correction to geostatical merging techniques. The study area is the Walloon region of Belgium, which is mostly located in the Meuse catchment. Observations from a C-band Doppler radar and a dense rain gauge network are used to retrieve daily rainfall accumulations over this area. The relative performance of the different merging methods are assessed through a comparison against daily measurements from an independent gauge network. A 3-year verification is performed using several statistical quality parameters. It appears that the geostatistical merging methods perform best with the mean absolute error decreasing by 40% with respect to the original data. A mean field bias correction still achieves a reduction of 25%. A seasonal analysis shows that the benefit of using radar observations is particularly significant during summer. The effect of the network density on the performance of the methods is also investigated. For this purpose, a simple approach to remove gauges from a network is proposed. The analysis reveals that the sensitivity is relatively high for the geostatistical methods but rather small for the simple methods. The geostatistical methods give the best results for all network densities except for a very low density of 1 gauge per 500 km2 where a range-dependent adjustment complemented with a static local bias correction performs best.

Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review

“…For instance, the US Next Generation Radar (NEXRAD) Weather Surveillance Radar-1988 Doppler (WSR-88D) provides up to 4 km spatial and 6 min temporal resolution QPE [36]. This makes up for the spatial poverty of gauged rainfall through radar-rain gauge fusion [37,[40][41][42][43][44][45]. Although radar stations can provide relatively high spatiotemporal resolution QPE, a single station can only be suitable for small range applications.…”

“…One idea is to improve the precipitation estimation directly, e.g., to improve the identification of Z-R relationship and bright band so as to improve radar rainfall estimates and streamflow simulations [72]. Another solution, as mentioned before, is to reduce uncertainties in radar QPE by incorporating rain gauge records [37,[40][41][42][43][44][45]. The radar-rain gauge merged QPEs can then be used for streamflow/flood modelling, and it has been found that these combined products generally result in an optimal streamflow prediction compared to the use of a single product [61,68,69,72].…”

A Study of Spatial Soil Moisture Estimation Using a Multiple Linear Regression Model and MODIS Land Surface Temperature Data Corrected by Conditional Merging

“…In rainfall research, various strategies for combining satellite-based and observed data have been widely used to overcome the limitation of areal representativeness of point scale measurements and the high variability in satellite-based datasets. This method, which is called "conditional merging (CM)" in Sinclari et al [28], uses the radar field to estimate the error associated with the ordinary kriging method based on rain gauges and to correct it [29]. In general, previous studies that have used the CM method yielded reasonable results compared to ground-based measurements and exhibited improved spatial and temporal variability [30].…”

The role of precipitation size distributions in km‐scale NWP simulations of intense precipitation: evaluation of cloud properties and surface precipitation

“…The underlying assumption of this method is that radar estimates are affected mainly by a uniform multiplicative error, due to bad electronic calibration or an erroneous coefficient in the reflectivity-rain-rate relation. A four-year verification of this method against an independent set of rain-gauge stations found the absolute error of this technique to be about 1.8 mm (Goudenhoofdt and Delobbe, 2009). This uncertainty is associated with many issues regarding the quality of the returned power radar signal, such as beam blocking by intense convective cells or beam broadening and attenuation at large distances from the radar.…”

“…Radar-based precipitation estimates are derived from a pseudo-CAPPI (Constant Altitude Plan Position Indicator) at 1500 m above sea level, extracted from a five-elevation scan. The processing of the radar data and strategies for merging radar observations with rain-gauge measurements are presented in Goudenhoofdt and Delobbe (2009). The 24 h precipitation accumulations for convective and stratiform events were calculated using a simple meanfield bias adjustment and were aggregated to the ARPS grid.…”