Impact of Gauge Representative Error on a Radar Rainfall Uncertainty Model

Abstract

In modeling the radar rainfall uncertainty, rain gauge measurement is generally regarded as the areal “true” rainfall. However, the inconsistent scales between radar and gauge may introduce a new uncertainty (also known as gauge representative uncertainty), which is erroneously identified as radar rainfall uncertainty and therefore called pseudouncertainty. It is crucial to comprehend what percentage of the estimated radar rainfall uncertainty actually stems from such pseudouncertainty rather than radar rainfall itself. For this reason, based on a fully formulated radar rainfall uncertainty model, this study aims to explore how the gauge representative error affects the distribution, spatial dependence, and temporal dependence of hourly accumulated radar rainfall uncertainty, and consequently affects the produced radar rainfall uncertainty band. Three group scenarios that delineate various degrees of gauge representative errors were designed to configure and run the uncertainty model. In the setting of a long-term analysis (almost 7 years) of the Brue catchment in the United Kingdom, we found that the gauge representative error affected the simulation of the marginal distribution of radar rainfall error, and had a considerable effect on temporal dependence estimation of radar rainfall uncertainty. The spread of the rainfall uncertainty band decreased with the growth of the gauge density in a radar pixel. The scenario with the lowest representative error only had 78% uncertainty spread of the scenario that has the largest error. This indicated there was a large impact of the representative error on radar rainfall uncertainty models. It is hoped that more catchments with diverse climate and geographical conditions and more radar data with various spatial scales could be explored by the research community to further investigate this crucial issue.

Cross-Examination of Similarity, Difference and Deficiency of Gauge, Radar and Satellite Precipitation Measuring Uncertainties for Extreme Events Using Conventional Metrics and Multiplicative Triple Collocation

“…Traditionally, rainfall is estimated by gauges as a direct measurement but only at a point scale [13]. Even though gauge data are often treated as the "ground truth" for rainfall measurement, they are still not impeccable due to splash-out during heavy rainfall, lack of sensitivity to light rain rates, under-catching by wind drift, and evaporation [14][15][16]. Especially in heavy rain events, multiple studies have demonstrated that the error caused by these inherent factors is not trivial [15,16].…”

“…The wind from tropical cyclones would substantially affect the performance of rain gauges as well, with the relative bias ranging from 5% to 80% [17]. Besides the systematic error, when interpolating point samples, errors caused by spatial interpolation accounts for 50% to 80% of the total difference depending upon the gauge quality and density [14,18]. Stampoulis and Anagnostou [19] discovered that the convective nature of rainfall can also increase gauge-interpolation uncertainties.…”

“…However, since it is an indirect measurement of rain rate, radar technology itself produces and propagates errors in its end products. The uncertainties of radar-based precipitation estimation can be categorized as incorrect calibration, sampling representativeness, non-weather echoes, and uncertainties in Z-R relations [14,17,[20][21][22][23]. These inherent systematic errors are challenging to mitigate by improving radar technology alone.…”

Evaluation of Radar Quantitative Precipitation Estimates (QPEs) as an Input of Hydrological Models for Hydrometeorological Applications

“…Even though the radar QPEs had been used for nearly 30 years in hydrological applications, the use of radar QPEs as an input of hydrological models is still controversial mainly due to the associated errors (Rabiei and Haberlandt 2015;Wang et al 2015). The accuracy is contentious due to errors in measurements and reflectivityrain intensity conversions and causes uncertainties in hydrological models (Dai et al 2018). Since radar provides indirect rainfall measurements, errors can be induced by the variability of the drop size distribution (Maki et al 2005), attenuation (Park et al 2005), ground clutter (Hubbert et al 2009), radar miscalibration (Ayat et al 2018), partial beam blocking (P. C.…”

Effect of rainfall variability and gauge representativeness on satellite rainfall accuracy in a small upland watershed in southern Ethiopia

“…Many studies have used less than 10 years of rainfall data (e.g. Maggioni et al 2017, Dai et al 2018 and Haile et al (2009) and Tang et al (2018), for example, used 2 years and 3 months of rainfall data, respectively.…”