Radar rainfall uncertainty modelling influenced by wind

• Published in: Hydrological processes 2015-03, Vol.29 (7), p.1704-1716
• Main Authors: Dai, Qiang, Han, Dawei, Rico-Ramirez, Miguel A., Zhuo, Lu, Nanding, Nergui, Islam, Tanvir
• Format: Article
• Language: English
• Subjects: copula; Empirical analysis; ensemble generator; Error analysis; Estimates; Gages; Gauges; Hydrologic data; Hydrologic models; Hydrology; Initial conditions; Meteorological radar; Modelling; Precipitation; Radar; Radar data; Radar measurement; Radar rainfall; Rain; Rain gauges; Raindrops; Rainfall; Rainfall data; Uncertainty; Uncertainty analysis; Weather; Weather radar; Wind; wind effects; Wind speed; wind-induced error
• ISSN: 0885-6087; 1099-1085
• DOI: 10.1002/hyp.10292

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. Copyright © 2014 John Wiley & Sons, Ltd.