An elegant solution to characterise the residual errors in radar precipitation estimates is to generate an ensemble of precipitation fields. The paper proposes a radar ensemble generator designed for usage in the Alps using LU decomposition (REAL), and presents first results from a real-time implementation coupling the radar ensemble with a semi-distributed rainfall-runoff model for flash flood modelling in a steep Alpine catchment. Each member of the radar ensemble is a possible realisation of the unknown true precipitation field given the observed radar field and knowledge of the space-time error structure of radar precipitation estimates. Feeding the alternative realisations into a hydrological model yields a distribution of response values, the spread of which represents the sensitivity of runoff to uncertainties in the input radar precipitation field.The presented ensemble generator is based on singular value decomposition of the error covariance matrix, stochastic simulation using the LU decomposition algorithm, and autoregressive filtering. It allows full representation of spatial dependence of the mean and covariances of radar errors. This is of particular importance in a mountainous region with large uncertainty in radar precipitation estimates and strong dependence of error structure on location. The real-time implementation of the radar ensemble generator coupled with a semi-distributed hydrological model in the framework of the forecast demonstration project MAP D-PHASE is one of the first experiments of this type worldwide, and is a fully novel contribution to this evolving area of applied research.

Because echoes caused by nonmeteorological targets significantly affect radar scans, contaminated bins must be identified and eliminated before precipitation can be quantitatively estimated from radar measurements.Under mean propagation conditions, clutter echoes (mainly caused by targets such as mountains or large buildings) can be found in almost fixed locations. However, in anomalous propagation conditions, new clutter echoes may appear (sometimes over the sea), and they may be difficult to distinguish from precipitation returns. Therefore, an automatic algorithm is needed to identify clutter on radar scans, especially for operational uses of radar information (such as real-time hydrology).In this study, a new algorithm is presented based on fuzzy logic, using volumetric data. It uses some statistics to highlight clutter characteristics (namely, shallow vertical extent, high spatial variability, and low radial velocities) to output a value that quantifies the possibility of each bin being affected by clutter (in order to remove those in which this factor exceeds a certain threshold).The performance of this algorithm was compared against that of simply removing mean clutter echoes. Satisfactory results were obtained from an exhaustive evaluation of this algorithm, especially in those cases in which anomalous propagation played an important role.

No abstract

Nowcasting precipitation is a key element in the anticipation of floods in warning systems. In this framework, weather radars are very useful because of the high resolution of their measurements both in time and space. The aim of this study is to assess the performance of a recently proposed nowcasting technique (S-PROG) from a hydrological point of view in a Mediterranean environment. S-PROG is based on the advection of weather radar fields according to the motion field derived with an algorithm based on tracking radar echoes by correlation (TREC), and it has the ability of filtering out the most unpredictable scales of these fields as the forecasting time increases. Validation of this nowcasting technique was done from two different perspectives: (i) comparing forecasted precipitation fields against radar measurements, and (ii) by means of a distributed rainfall runoff model, comparing hydrographs simulated with a hydrological model using rainfall fields forecasted by S-PROG against hydrographs generated with the model using the entire series of radar measurements. In both cases, results obtained by a simpler nowcasting technique are used as a reference to evaluate improvements. Validation showed that precipitation fields forecasted with S-PROG seem to be better than fields forecasted using simpler techniques. Additionally, hydrological validation led the authors to point out that the use of radar-based nowcasting techniques allows the anticipation window in which flow estimates are forecasted with enough quality to be sensibly extended.

This second part of a two-paper series compares deterministic precipitation forecasts from the Storm-Scale Ensemble Forecast System (4-km grid) run during the 2008 NOAA Hazardous Weather Testbed (HWT) Spring Experiment, and from the Canadian Global Environmental Multiscale (GEM) model (15 km), in terms of their ability to reproduce the average diurnal cycle of precipitation during spring 2008. Moreover, radar-based nowcasts generated with the McGill Algorithm for Precipitation Nowcasting Using SemiLagrangian Extrapolation (MAPLE) are analyzed to quantify the portion of the diurnal cycle explained by the motion of precipitation systems, and to evaluate the potential of the NWP models for very short-term forecasting.The observed diurnal cycle of precipitation during spring 2008 is characterized by the dominance of the 24-h harmonic, which shifts with longitude, consistent with precipitation traveling across the continent. Time-longitude diagrams show that the analyzed NWP models partially reproduce this signal, but show more variability in the timing of initiation in the zonal motion of the precipitation systems than observed from radar.Traditional skill scores show that the radar data assimilation is the main reason for differences in model performance, while the analyzed models that do not assimilate radar observations have very similar skill.The analysis of MAPLE forecasts confirms that the motion of precipitation systems is responsible for the dominance of the 24-h harmonic in the longitudinal range 1038-858W, where 8-h MAPLE forecasts initialized at 0100, 0900, and 1700 UTC successfully reproduce the eastward motion of rainfall systems. Also, on average, MAPLE outperforms radar data assimilating models for the 3-4 h after initialization, and nonradar data assimilating models for up to 5 h after initialization.