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Antti T. Pessi and Steven Businger

Abstract

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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Antti T. Pessi and Steven Businger

Abstract

Lightning data from the Pacific Lightning Detection Network (PacNet) and Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite were compared with TRMM precipitation radar products and latent heating and hydrometeor data. Three years of data over the central North Pacific Ocean were analyzed. The data were divided into winter (October–April) and summer (June–September) seasons. During the winter, the thunderstorms were typically embedded in cold fronts associated with eastward-propagating extratropical cyclones. Summer thunderstorms were triggered by cold upper-level lows associated with the tropical upper-tropospheric trough (TUTT). Concurrent lightning and satellite data associated with the storms were averaged over 0.5° × 0.5° grid cells and a detection efficiency correction model was applied to quantify the lightning rates. The results of the data analysis show a consistent logarithmic increase in convective rainfall rate with increasing lightning rates. Moreover, other storm characteristics such as radar reflectivity, storm height, ice water path, and latent heat show a similar logarithmic increase. Specifically, the reflectivity in the mixed-phase region increased significantly with lightning rate and the lapse rate of Z decreased; both of these features are well-known indicators of the robustness of the cloud electrification process. In addition, the height of the echo tops showed a strong logarithmic correlation with lightning rate. These results have application over data-sparse ocean regions by allowing lightning-rate data to be used as a proxy for related storm properties, which can be assimilated into NWP models.

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