Abstract

Hail is one of the most fearsome meteorological phenomena for agricultural areas. The harvest of the whole year can be destroyed in only a few minutes. A pilot project to characterise hail events and identify hail with the help of radar observations is described in this paper. This pilot project was carried out in Terres de Ponent, an area of about 200,000 ha in Lleida (Catalonia), in the NE of the Iberian Peninsula. The aim of the project was to characterise hail events, directly by radiosounding data and radar images, and indirectly by the evaluation of the radiosonde forecasted by the Numerical Weather Prediction (NWP) Mesoscale Atmospheric Simulation System (MASS) and several instability indices. In the first stage of the project several instability indices were calculated during the 5 months of the campaign and a comparison of these indices with those obtained with radiosounding data from Barcelona and Zaragoza was performed. An operative image of the probability of hail distribution in Catalonia (every 6 min, hourly and daily) was also made using the Waldvogel method for its detection. As a starting point, an empirical fit of POH (probability of hail) obtained recently in the Netherlands (POH = 0.319 + 0.133·Δ H, where Δ H is the difference between the 45 dBZ echo top height from radar image and the isozero forecast). Complementary to this, to detect the hail at the beginning of spring, the process was repeated with the 35 dBZ echo top. In a second stage of the project, the calculated POH was compared with the hail events measured by 171 hailpads of the observational network of the Agrupació de Defensa Vegetal (ADV) of Terres de Ponent and a new fit of the POH formula was obtained. It was also the aim of this second stage to validate the forecasted radiosoundings by the NWP MASS model in Lleida and Barcelona. Finally, a comparison between the radiosounding data from Barcelona, Lleida and Zaragoza was made to verify which radiosounding (Barcelona or Zaragoza) is more representative of the Lleida area.

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