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Antti T. Pessi and Steven Businger


In this paper, the potential of lightning data assimilation to improve NWP forecasts over data-sparse oceans is investigated using, for the first time, a continuous, calibrated lightning data stream. The lightning data employed in this study are from the Pacific Lightning Detection Network/Long-Range Lightning Detection Network (PacNet/LLDN), which has been calibrated for detection efficiency and location accuracy. The method utilizes an empirical lightning–convective rainfall relationship, derived specifically from North Pacific winter storms observed by PacNet/LLDN. The assimilation method nudges the model’s latent heating rates according to rainfall estimates derived from PacNet/LLDN lightning observations. The experiment was designed to be employed in an operational setting. To illustrate the promise of the approach, lightning data from a notable extratropical storm that occurred over the northeast Pacific Ocean in late December 2002 were assimilated into the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5). The storm exhibited a very electrically active cold front with most of the lightning observed 300–1200 km away from the storm center. The storm deepened rapidly (12 hPa in 12 h) and was poorly forecast by the operational models. The assimilation of lightning data generally improved the pressure and wind forecasts, as the validation of the model results using available surface and satellite data revealed. An analysis is presented to illustrate the impact of assimilation of frontal lightning on the storm development and dynamics. The links among deep convection, thermal wind along the front, and cyclogenesis are explicitly explored.

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