A cloud-scale total lightning data assimilation technique for the WRF-ARW model

To improve forecasts of convection, a new technique for assimilating total lightning data into the WRF-ARW model at cloud-resolving scales has been developed. Assimilated lightning data forces deep, moist precipitating convection to occur in the model using a nudging function for the total lightning data, which locally increases the water vapor mixing ratio and virtual buoyancy via a computationally inexpensive, smooth continuous function. The nudging function also features the simulated graupel mixing ratio as input variable. The assimilation of gridded pseudo-GOES-R resolution (9 km) flash rate for only a few hours prior to the forecast initialization significantly improved the representation of the convection at the initial analysis time and at the 1-hour forecast within the convection-permitting (5 km) and-resolving (2 km) grids. This simple, computationally inexpensive assimilation technique has also been implemented into the real-time operational 4-km CONUS WRF/NSSL forecast testbed, promising results of which will be briefly reviewed.