Mesoscale analyses and diagnostic parameters for deep convection nowcasting

We study the contribution of surface data to convection nowcasting over reg ions of modest orography and under weak synoptic forcing. Hourly mesoscale analyses are performed using the CANARI optimal interpolation analysis sche me, which combines first-guess fields from the fine mesh (10 km) ALADIN mod el with hourly routine observations arising from a mesonet of automated gro und stations. These analyses then allow the computation of diagnostic param eters that quantify convective instability, low-level lifting processes and moisture supply: these are the convective available potential energy (CAPE ) and the moisture convergence (MOCON). A tuning of the analysis scheme is needed first for it to fit the meso-beta-scale. Then, the skill of the comp uted diagnostics for convection nowcasting is evaluated by comparing their fields with radar reflectivities observed between one and four hours after the analysis time. This is done for four selected convective situations. Wi th regard to thunderstorm triggering, results show that this usually happen s over area of persistently high values of CAFE which undergo convergence c ontinuously from four to one hour before the event; on the other hand, area s of persistent divergence are never associated with convective development s. In addition, the proposed criteria allow a significant reduction in the areal extent of predicted thunderstorms (i.e. decreasing the false-alarm ra te) compared with what can be currently, done on an operational basis, whil e maintaining a low non-detection rate. As to convection monitoring, we fin d that the organization of convective Systems into a reflectivity, line is preceded by a similar organization in the MOCON field from one to three hou rs ahead.