Search

An algorithm for the detection of horizontal wind shear at low levels was developed. The algorithm makes use of data collected by all radars from the Application Radar à la Météorologie Infra-Synoptique (ARAMIS) operational network, in order to build a complete mosaic of wind shear over metropolitan France. The product provides an estimation of the maximum horizontal wind shear detected in the low levels, between 0 and 2 km AGL. Examination of the wind shear mosaic for different cases shows that the product is able to retrieve small-scale wind shear signatures that can be linked to either convergence lines ahead of convective cells, which are indicative of gust fronts, or strong convergence areas inside intense cells. A statistical evaluation of the wind shear mosaic was performed, by comparing horizontal wind shear observed inside the area defined by convective objects with wind gusts recorded along their trajectory by weather stations. A link between those different observations was clearly established. Therefore, the use of wind shear for wind gust prediction was tested in combination with other parameters: an estimation of the energetic potential of density currents, the cell surface with reflectivity over 51 dB Z, relative helicity, and cell propagation speed. Different wind gust warning rules were tested on 468 convection nowcasting objects (CONOs). The results clearly highlighted the benefits of using wind shear for wind gust estimation, and also demonstrated the improvement in forecasting skill when combining different parameters. The wind shear mosaic will be produced operationally before the end of 2013 and will be used to improve wind gust warnings provided to end users. [ABSTRACT FROM AUTHOR]

Summary: A method to estimate antecedent soil moisture conditions is proposed to improve the accuracy of flash flood forecasts at ungauged locations. The method combines two indexes: a ‘climatic’ temporal index calculated in each cell using an uncalibrated soil moisture accounting (SMA) scheme and a spatial ‘statistical’ index giving the “average saturation state” usually encountered before a flood. The proposed method was carried out on two different models: an event-based distributed model, and a REGRESSION model. Simulation results were analysed for 562 individual events, issued from 160 catchments located in the South of France. The presented method improved the efficiency of both models. This indicates that the both proposed indexes give relevant information for estimating antecedent soil moisture conditions at ungauged locations. Furthermore, a contingency statistical analysis based on a 2-year flood threshold showed that alerts were better forecasted by the distributed model. These encouraging results suggest that further efforts should be made for the development of the method and its operational application. [ABSTRACT FROM AUTHOR]

Summary: In the general context of field experiment design, this paper presents a modeling study that quantifies the respective impact of rainfall estimation and soil variability on the simulated discharge for an extreme event in southern France. The CVN distributed hydrological model, built within the LIQUID® modeling platform is used. The method is illustrated for two medium sized catchments, Saumane (99km2) and Uzès (88km2) using raingauges and two radar estimates. The soil properties are extracted from an existing soil database provided for the whole region. The model parameter specification uses available observation and a priori hydrological knowledge. No parameter adjustment is performed. For model evaluation on the regional scale, simulated maximum peak discharges are compared with post-flood estimations for 32 catchments. The area of these catchments ranges from 2.5 to 99km2 and model results are satisfactory. Then, the study focuses on the Saumane and Uzès catchments. A sensitivity analysis highlights the role of the Manning roughness coefficient on the simulated hydrographs dynamics. The impact of the bottom boundary condition of the infiltration and water redistribution module is also shown for the gauged Saumane catchment. Then the impact of rainfall input and soil spatial variability is presented. The results show that (i) the use of radar data is necessary to properly simulate the flood dynamics; (ii) although radar volume-scanning strategy has been shown to give more accurate results on a pixel/gauge comparison of the rainfall estimations, it is not necessarily the case when catchment averaged amounts are considered, especially for catchments in mountainous areas; (iii) the impact of the variability in soil properties on the simulated discharges is of the same order of magnitude as the impact of differences in rainfall estimation; (iv) the flood dynamics presents two phases: the first one, mainly controlled by the soil properties and the second one, since the soils are saturated, controlled by the rainfall variability. Therefore, uncertainties on both observations need to be mitigated in order to improve flash-flood understanding. [Copyright &y& Elsevier]

Summary: Characterisation of the error structure of radar quantitative precipitation estimation (QPE) is a major issue for applications of radar technology in hydrological modelling. Due to the variety of sources of error in radar QPE and the impact of correction algorithms, the problem can only be addressed practically by comparison of radar QPEs with reference values derived from ground-based measurements. Using the radar and raingauge datasets of the Bollène-2002 experiment, a preliminary investigation of this subject has been carried out within a geostatistical framework in the context of the Cévennes–Vivarais Mediterranean Hydrometeorological Observatory. First, raingauge measurements were critically analysed using variograms to detect erroneous squared differences between pairs of raingauge values. The anisotropic block kriging technique was then used to compute and select the most reliable reference values. The statistical distribution and the spatial and temporal structure of the residuals between radar and reference values was established and analysed. The error variance separation concept was also tested to estimate the variance of the residuals between the radar estimates and the true unknown rainfall. A preliminary version of the radar QPE error model was established for 1-km2 domains and time steps ranging from 1 to 12h using the limited data sample of the Bollène-2002 experiment. The error model is dependent on the time scales considered and needs to be conditioned on the rain rate and rainfall type as well as on the radar range. [ABSTRACT FROM AUTHOR]