Improvement of RAMS precipitation forecast at the short range through lightning data assimilation
Abstract. This study shows the application of a total lightning data assimilation technique to the RAMS (Regional Atmospheric Modeling System) forecast. The method, which can be used at high horizontal resolution, helps to initiate convection whenever flashes are observed by adding water vapour to the model grid column. The water vapour is added as a function of the flash rate, local temperature and graupel mixing ratio. The methodology is set-up to improve the short-term (3 h) precipitation forecast and can be used in real-time forecasting applications. However, results are also presented for the daily precipitation for comparison with other studies. The methodology is applied to twenty cases occurred in fall 2012, that were characterized by widespread convection and lightning activity. For these cases a detailed dataset of hourly precipitation containing thousands of raingauges over Italy, which is the target of this study, is available through the HyMeX (HYdrological cycle in the Mediterranean Experiment) initiative. This dataset gives the unique opportunity to verify the 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 its positive impact on the 3 h precipitation forecast. In particular, the model represents better the convection over the sea using the lightning data assimilation and, when convection is advected over the land, the precipitation forecast improves over the land. It is also shown that the precise location of the convection by lightning data assimilation, improves the precipitation forecast at fine scales (meso-β). The application of the methodology to twenty cases gives a statistically robust evaluation of the impact of the total lightning data assimilation on the model performance. Results show an improvement of all statistical scores, 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 daily precipitation, depending on the precipitation threshold considered. Score differences between simulations with or without data assimilation are significant at 95 % level for most scores and thresholds considered, showing the positive and statistically robust impact of the lightning data assimilation on the precipitation forecast.