International audienceUrban catchments are typically characterised by high spatial variability and fast runoff processes resulting in short response times. Hydrological analysis of such catchments requires high resolution precipitation and catchment information to properly represent catchment response. This study investigated the impact of rainfall input resolution on the outputs of detailed hydrodynamic models of seven urban catchments in North-West Europe. The aim was to identify critical rainfall resolutions for urban catchments to properly characterise catchment response. Nine storm events measured by a dual-polarimetric X-band weather radar, located in the Cabauw Experimental Site for Atmospheric Research (CESAR) of the Netherlands, were selected for analysis. Based on the original radar estimates, at 100m and 1min resolutions, 15 different combinations of coarser spatial and temporal resolutions, up to 3000m and 10min, were generated. These estimates were then applied to the operational semi-distributed hydrodynamic models of the urban catchments, all of which have similar size (between 3 and 8km2), but different morphological, hydrological and hydraulic characteristics. When doing so, methodologies for standardising model outputs and making results comparable were implemented. Results were analysed in the light of storm and catchment characteristics. Three main features were observed in the results: (1) the impact of rainfall input resolution decreases rapidly as catchment drainage area increases; (2) in general, variations in temporal resolution of rainfall inputs affect hydrodynamic modelling results more strongly than variations in spatial resolution; (3) there is a strong interaction between the spatial and temporal resolution of rainfall input estimates. Based upon these results, methods to quantify the impact of rainfall input resolution as a function of catchment size and spatial-temporal characteristics of storms are proposed and discussed. © 2015 The Authors

Radar Doppler spectra that deviate from a Gaussian shape were observed from a tornadic supercell on 10 May 2003, exhibiting features such as a dual peak, flat top, and wide skirt in the nontornadic region. Motivated by these observations, a spectral model of a mixture of two Gaussian components, each defined by its three spectral moments, is introduced to characterize different degrees of deviation from Gaussian shape. In the standard autocovariance method, a Gaussian spectrum is assumed and biases in velocity and spectrum width estimates may result if this assumption is violated. The impact of non-Gaussian weather spectra on these biases is formulated and quantified in theory and, consequently, verified using four experiments of numerical simulations.Those non-Gaussian spectra from the south region of the supercell are further examined and a nonlinear fitting algorithm is proposed to estimate the six spectral moments and compare to those obtained from the autocovariance method. It is shown that the dual-Gaussian model can better represent observed spectra for those cases. The authors' analysis suggests that vertical shear may be responsible for the flat-top or the dualpeak spectra in the lower elevation of 0.58 and their transition to the single-peak and wide-skirt spectra in the next elevation scan of 1.58.

Phased-array radars (PARs) have the capability of instantaneously and dynamically controlling beam position on a pulse-by-pulse basis, which allows a single radar to perform multiple functions, such as tracking multiple storms or weather and aviation surveillance. Moreover, these tasks can be carried out with different update times to achieve the goal of better characterizing and forecasting the storms of interest. However, these tasks usually compete for finite radar resources, and scheduling algorithms are often needed to address resource contention. To capitalize on the PAR capabilities, an algorithm based on the concept of time balance (TB) is developed for adaptive weather sensing. Two quality measures are introduced to quantify the gain of adaptive sensing relative to standard scanning patterns used by the Weather Surveillance Radar-1988 Doppler (WSR-88D). A simulation experiment is performed to demonstrate the advantages of adaptive sensing and to test and verify the performance of the TB scheduling algorithm. It is shown that the gain of adaptive sensing can be realized by the TB scheduler; that is, storms of interest can be revisited more frequently within a relatively short period time compared to conventional scanning.

One of the most beneficial polarimetric variables may be the specific differential phase KDP because of its independence from power attenuation and radar miscalibration. However, conventional KDP estimation requires a substantial amount of range smoothing as a result of the noisy characteristic of the measured differential phase ΨDP. In addition, the backscatter differential phase δhv component of ΨDP, significant at C- and X-band frequency, may lead to inaccurate KDP estimates. In this work, an adaptive approach is proposed to obtain accurate KDP estimates in rain from noisy ΨDP, whose δhv is of significance, at range resolution scales. This approach uses existing relations between polarimetric variables in rain to filter δhv from ΨDP while maintaining its spatial variability. In addition, the standard deviation of the proposed KDP estimator is mathematically formulated for quality control. The adaptive approach is assessed using four storm events, associated with light and heavy rain, observed by a polarimetric X-band weather radar in the Netherlands. It is shown that this approach is able to retain the spatial variability of the storms at scales of the range resolution. Moreover, the performance of the proposed approach is compared with two different methods. The results of this comparison show that the proposed approach outperforms the other two methods in terms of the correlation between KDP and reflectivity, and KDP standard deviation reduction.