Radar rainfall uncertainty modelling influenced by wind

Abstract

Abstract:Radar-based estimates of rainfall are affected by many sources of uncertainties, which would propagate through the hydrological model when radar rainfall estimates are used as input or initial conditions. An elegant solution to quantify these uncertainties is to model the empirical relationship between radar measurements and rain gauge observations (as the 'ground reference'). However, most current studies only use a fixed and uniform model to represent the uncertainty of radar rainfall, without consideration of its variation under different synoptic regimes. Wind is such a typical weather factor, as it not only induces error in rain gauge measurements but also causes the raindrops observed by weather radar to drift when they reach the ground. For this reason, as a first attempt, this study introduces the wind field into the uncertainty model and designs the radar rainfall uncertainty model under different wind conditions. We separate the original dataset into three subsamples according to wind speed, which are named as WDI (0-2 m/s), WDII (2-4 m/s) and WDIII (>4 m/s). The multivariate distributed ensemble generator is introduced and established for each subsample. Thirty typical events (10 at each wind range) are selected to explore the behaviours of uncertainty under different wind ranges. In each time step, 500 ensemble members are generated, and the values of 5th to 95th percentile values are used to produce the uncertainty bands. Two basic features of uncertainty bands, namely dispersion and ensemble bias, increase significantly with the growth of wind speed, demonstrating that wind speed plays a considerable role in influencing the behaviour of the uncertainty band. On the basis of these pieces of evidence, we conclude that the radar rainfall uncertainty model established under different wind conditions should be more realistic in representing the radar rainfall uncertainty. This study is only a start in incorporating synoptic regimes into rainfall uncertainty analysis, and a great deal of more effort is still needed to build a realistic and comprehensive uncertainty model for radar rainfall data.

Radar and rain gauge rainfall discrepancies driven by changes in atmospheric conditions

“…Nevertheless, detailed radar data information along with layer selection should be implemented in further analysis. Besides the relative humidity, other meteorological variables (such as wind, which have been investigated on wind-induced error) [Collier, 1999;Nešpor and Sevruk, 1999;Duchon and Essenberg, 2001;Mittermaier et al, 2004;Lack and Fox, 2007;Fortin et al, 2008;Lauri et al, 2012;Dai et al, 2013Dai et al, , 2015Dai and Han, 2014] may also play important roles in resulting in radar-gauge rainfall discrepancies. Dai and Han [2014] proposed a scheme in tackling wind effects on radar-gauge comparison and found that it could be helpful in radar rainfall adjustment to some extent.…”

Exploration of discrepancy between radar and gauge rainfall estimates driven by wind fields

“…Dai et al . [] investigated how the radar rainfall uncertainties vary with the wind field through analyzing and comparing the characteristics of the generated uncertainty bands under different wind situations. It is concluded that the wind field plays a vital role in radar rainfall uncertainty and this uncertainty may propagate in the hydrological model and consequently contaminate the model output.…”

Evaluation of Remotely Sensed Soil Moisture for Landslide Hazard Assessment

“…During the validation period, 45 rainfall events are selected as the test cases for the threshold's performances. To separate rainfall events from rainfall datasets, a one-day dry period (i.e., a period without rainfall) is used [49][50][51]. Since most of the landslides included in the datasets are shallow which respond quickly to rainfall, therefore a one-day approach is suitable here.…”