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This study shows the application of a total lightning data assimilationtechnique to the RAMS (Regional Atmospheric Modeling System) forecast. Themethod, which can be used at high horizontal resolution, helps to initiateconvection whenever flashes are observed by adding water vapour to the modelgrid column. The water vapour is added as a function of the flash rate, localtemperature, and graupel mixing ratio. The methodology is set up to improvethe short-term (3 h) precipitation forecast and can be used in real-timeforecasting applications. However, results are also presented for the dailyprecipitation for comparison with other studies.  The methodology is applied to 20 cases that occurred in fall 2012, whichwere characterized by widespread convection and lightning activity. Forthese cases a detailed dataset of hourly precipitation containing thousandsof rain gauges over Italy, which is the target area of this study, isavailable through the HyMeX (HYdrological cycle in the MediterraneanExperiment) initiative. This dataset gives the unique opportunity to verifythe precipitation forecast at the short range (3 h) and over a wide area (Italy).  Results for the 27 October case study show how the methodology works and itspositive impact on the 3 h precipitation forecast. In particular, the modelrepresents better convection over the sea using the lightning dataassimilation and, when convection is advected over the land, theprecipitation forecast improves over the land. It is also shown that theprecise location of convection by lightning data assimilation improves theprecipitation forecast at fine scales (meso-<i>β</i>).  The application of the methodology to 20 cases gives a statisticallyrobust evaluation of the impact of the total lightning data assimilation onthe model performance. Results show an improvement of all statisticalscores, with the exception of the bias. The probability of detection (POD)increases by 3–5 % for the 3 h forecast and by more than 5 % for dailyprecipitation, depending on the precipitation threshold considered.  Score differences between simulations with or without data assimilation aresignificant at 95 % level for most scores and thresholds considered,showing the positive and statistically robust impact of the lightning dataassimilation on the precipitation forecast

Improvement of RAMS precipitation forecast at the short-range through lightning data assimilation