Sampling errors in rainfall measurements by weather radar

International audienceRadar rainfall data are affected by several types of error. Beside the error in the measurement of the rainfall reflectivity and its transformation into rainfall intensity, random errors can be generated by the temporal spacing of the radar scans. The aim of this work is to analize the sensitivity of the estimated rainfall maps to the radar sampling interval, i.e. the time interval between two consecutive radar scans. This analysis has been performed employing data collected with a polarimetric C-band radar in Rome, Italy. The radar data consist of reflectivity maps with a sampling interval of 1min and a spatial resolution of 300m, covering an area of 1296km2. The transformation of the reflectivity maps in rainfall fields has been validated against rainfall data collected by a network of 14 raingauges distributed across the study area. Accumulated rainfall maps have been calculated for different spatial resolutions (from 300m to 2400m) and different sampling intervals (from 1min to 16min). The observed differences between the estimated rainfall maps are significant, showing that the sampling interval can be an important source of error in radar rainfall measurements

Forest cover influence on regional flood frequency assessment in Mediterranean catchments

The paper aims at evaluating to what extent the forest cover can explain the component of runoff coefficient as defined in a regional flood frequency analysis based on the application of the rational formula coupled with a regional model of the annual maximum rainfall depths. The analysis is addressed to evaluate the component of the runoff coefficient which cannot be captured by the catchment lithology alone. Data mining is performed on 75 catchments distributed from South to Central Italy. Cluster and correlation structure analyses are conducted for distinguishing forest cover effects within catchments characterized by hydro-morphological similarities. We propose to improve the prediction of the runoff coefficient by a linear regression model, exploiting the ratio of the forest cover to the catchment critical rainfall depth as dependent variable. The proposed regression enables a significant bias correction of the runoff coefficient, particularly for those small mountainous catchments, characterised by larger forest cover fraction and lower critical rainfall depth

The draft common frame of reference (DCFR):A giant with feet of clay

This chapter summarizes the lessons drawn from the work of the Economic Impact Group (EIG), a part of the CoPECL Network of Excellence funded by the EU to prepare a Draft Common Frame of Reference (DCFR). First, it revisits basic principles which are central to the work of the whole group. For one, contract law is not just about remedying market failures; it is fundamentally a basic condition for markets to exist at all. Moreover, law and economics analysis looks for Pareto-efficiency and total welfare, without taking distributional considerations into account. Second, it draws general conclusions from contributions to the EIG. As regards the first question before the EIG (desirability of harmonization at European level), the costs of harmonization have been downplayed, so that the case for harmonization has probably been exaggerated, certainly as regards areas such a non-contractual liability where the DCFR cannot simply be an optional regime. As regards the second question assessed by the EIG (appropriateness of the provisions chosen in the DCFR), the work of the EIG reveals shortcomings: among others, rules have been formulated without a complete assessment of their rationales and the ex-ante impact of the DCFR has been ignored. The drafters of the DCFR could have derived more added value from an economic analysis of their work than they seemed to acknowledge. Accordingly, while the DCFR is a momentous work of scholarship, it rests on fragile foundations

Family versus Non-Family Firm Franchisors: Behavioral and Performance Differences

Drawing from resource-based theory, we argue that family firm franchisors behave and perform differently compared to non-family firm franchisors. Our theorizing suggests that compared to a non-family firm franchisor, a family firm franchisor cultivates stronger relationships with franchisees and provides them with more training. Yet, we predict that a family firm franchisor achieves lower performance than a non-family firm franchisor. We argue, however, that this performance relationship reverses itself when family firm franchisors are older and larger. We test our hypotheses with a longitudinal dataset including a matched-pair sample of private U.S. family and non-family firm franchisors