A Lightning Data Assimilation Technique for Mesoscale Forecast Models

Abstract

Abstract Lightning observations have been assimilated into a mesoscale model for improvement of forecast initial conditions. Data are used from the National Lightning Detection Network (cloud-to-ground lightning detection) and a Lightning Mapping Array (total lightning detection) that was installed in western Kansas–eastern Colorado. The assimilation method uses lightning as a proxy for the presence or absence of deep convection. During assimilation, lightning data are used to control the Kain–Fritsch (KF) convection parameterization scheme. The KF scheme can be forced to try to produce convection where lightning indicated storms, and, conversely, can optionally be prevented from producing spurious convection where no lightning was observed. Up to 1 g kg−1 of water vapor may be added to the boundary layer when the KF convection is too weak. The method does not employ any lightning–rainfall relationships, but rather allows the KF scheme to generate heating and cooling rates from its modeled convection. The method could therefore easily be used for real-time assimilation of any source of lightning observations. For the case study, the lightning assimilation was successful in generating cold pools that were present in the surface observations at initialization of the forecast. The resulting forecast showed considerably more skill than the control forecast, especially in the first few hours as convection was triggered by the propagation of the cold pool boundary.