Combining radar and rain gauge rainfall estimates using conditional merging

Abstract

The Hydrologist's traditional tool for measuring rainfall is the rain gauge. Rain gauges are relatively cheap, easy to maintain and provide a direct and suitably accurate estimate of rainfall at a point. What rain gauges fail to capture well is the spatial variability of rainfall with time, an important aspect for the credible modelling of a catchment's response to rainfall. This spatial variability is particularly evident at short timescales of up to several days. As the period of accumulation increases, the expected spatial variability is reduced and rain gauges provide improved spatial rainfall estimates. Because of the fractal variability of rainfall in space, simple interpolation between rain gauges does not provide an accurate estimate of the true spatial rainfall field, at short time scales.Weather radar provides (with a single instrument) a highly detailed representation of the spatial structure and temporal evolution of rainfall over a large area. Estimated rainfall rates are derived indirectly from measurements of reflectivity and are therefore subject to a combination of systematic and random errors.This article describes a recently proposed merging technique and presents an application to simulated rainfall fields. The technique employed is Conditional Merging (Ehret, 2002), which makes use of Kriging to extract the optimal information content from the observed data. A mean field based on the Kriged rain gauge data is adopted, while the spatial detail from the radar is retained, reducing bias, but keeping the spatial variability observed by the radar. The variance of the estimate is reduced in the vicinity of the gauges where they are able to provide good information on the true rainfall field.

Development of a precipitation–area curve for warning criteria of short-duration flash flood

“…Additionally, reliable rainfall forecasts with adequate lead time and accuracy are essential for flash flood forecasting. In general, the gauged rainfall, radar data (Sinclair and Pegram, 2005;Mazzetti and Todini, 2009) and satellite data (Sorooshian et al, 2000;Kubota et al, 2007) have been used for quantitative precipitation estimates, and some studies have used multiple precipitation sources (Sokol, 2006;Chiang et al, 2007). Therefore, this method is necessary for assessing the applicability of using rainfall data obtained from various sources.…”

A non-stationary stochastic ensemble generator for radar rainfall fields based on the short-space Fourier transform

“…An interesting question is how to condition the unconditional rainfall ensembles to rain gauges, e.g. as done in the conditional merging of Sinclair and Pegram (2005) or the random mixing approach of Bardossy and Hörning (2016). It is worth mentioning that Velasco-Forero et al (2009) and Schiemann et al (2011) also took advantage of the WienerKhintchine theorem to obtain 2-D positive definite correlograms directly from radar rainfall images for improved blending with rain gauge measurements.…”

“…In addition, we believe that current radar rain gauge merging and adjustment techniques (e.g. Sideris et al, 2014) can be further extended to generate QPE ensembles conditioned onto the rain gauges, for example by following the conditional merging approach of Sinclair and Pegram (2005).…”

The value of weather radar data for the estimation of design storms – an analysis for the Hannover region

“…CM -Conditional merging interpolation (Sinclair and Pegram, 2005) is applied using data from recording stations and radar data without adjustment (Radar).…”