Abstract. The Special Observation Period (SOP1

During the first HyMeX Special Observation Period (SOP1) field campaign, the target site of north‐eastern Italy (NEI) experienced a large amount of precipitation, locally exceeding the climatological values and distributed among several heavy‐rainfall episodes. In particular, two events that occurred during the last period of the campaign drew our attention. These events had common large‐scale patterns and a similar mesoscale setting, characterised by southerly low‐level flow interacting with the Alpine orography, but the precipitation distribution was very different. During Intensive Observing Period IOP18 (31 October–1 November 2012), convective systems were responsible for intense rainfall mainly located over a flat area of the eastern Po Valley, well upstream of the orography. Conversely, during IOP19 (4/5 November 2012), heavy precipitation affected only the Alpine area. In addition to IOP18 and IOP19, the present study analyses other heavy‐precipitation episodes that display similar characteristics and which occurred over NEI during the autumn of recent years. A high‐resolution (2 km grid spacing) non‐hydrostatic NWP model and available observations are used for this purpose. The two different observed precipitation patterns are explained in terms of interaction between the impinging flow and the Alps. Depending on the thermodynamic profile, convection can be triggered when the impinging flow is forced to rise over a pre‐existing cold‐air layer at the base of the orography. In this situation a persistent blocked‐flow condition and upstream convergence are responsible for heavy rain localized over the plain. Conversely, if convection does not develop, flow‐over conditions are established and heavy rain affects the Alps. Numerical parameters proposed in the literature are used to support the analysis. Finally, the role of evaporative cooling beneath the convective systems is evaluated. It turns out that the stationarity of the systems upstream of the Alps is mainly attributable to persistent blocked‐flow conditions, while convective outflow slightly modifies the location of precipitation.

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In this work the hailpad data collected during the April-September period of the years 1992-2009 by a network of approximately 360 stations on the plain of the Friuli Venezia Giulia region (northeast Italy) were analyzed. The frequency distribution of the hail cases is studied for the different years, for the yearday, and for the time of day, considering four 6-h-long periods of the day. Hail is found to be more frequent during the 1100-2300 UTC time interval and in the months of June and July even though during the 18 years analyzed there does not seem to be a defined trend in hail probability. The spatial distribution shows a maximum of probability along the foothills in particular in the northwest. In the middle of this area there is also the UdineCampoformido radiosounding station, which performed four soundings per day. Each case (6-h-long period) is then associated with the 52 indices derived from the sounding launched at the beginning of that period. These sounding-derived indices are not all statistically independent, as found by looking at their correlation matrix and aggregating the most-correlated indices in three groups. A diagnostic bivariate analysis between each index and the hit hailpads in 6 h was done as a preliminary attempt to evaluate the utility of these indices for forecasting hail. It is found that some measures of instability (like updraft, hail diameter, and lifted index) seem to have more skill than the other indices when classifying the hail-event occurrence. When estimating the number of hit hailpads, the best correlation is obtained by the indices belonging to the ''lifted index family.''

Binary classifiers are obtained from a continuous predictor using a threshold to dichotomize the predictor value into event occurrence and nonoccurrence classes. A contingency table is associated with each threshold, and from this table many statistical indices (like skill scores) can be computed. This work shows that the threshold that maximizes one of these indices [the Peirce skill score (PSS)] has some important properties. In particular, at that threshold the ratio of the two likelihood distributions is always 1 and the event posterior probability is equal to the event prior probability. These properties, together with the consideration that the maximum PSS is the point with the "most skill" on the relative operating characteristic curve and the point that maximizes the forecast value, suggest the use of the maximum PSS as a good scalar measure of the classifier skill. To show that this most skilled point is not always the best one for all the users, a simple economic cost model is presented.