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 0.58 3 0.58 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.

The waveguide between the earth's surface and the ionosphere allows very low-frequency (VLF) emissions generated by lightning, called sferics, to propagate over long distances. The new Pacific Lightning Detection Network (PacNet), as a part of a larger long-range lightning detection network (LLDN), utilizes this attribute to monitor lightning activity over the central North Pacific Ocean with a network of groundbased lightning detectors that have been installed on four widely spaced Pacific islands (400-3800 km). PacNet and LLDN sensors combine both magnetic direction finding (MDF) and time-of-arrival (TOA)-based technology to locate a strike with as few as two sensors. As a result, PacNet/LLDN is one of the few observing systems, outside of geostationary satellites, that provides continuous real-time data concerning convective storms throughout a synoptic-scale area over the open ocean.The performance of the PacNet/LLDN was carefully assessed. Long-range lightning flash detection efficiency (DE) and location accuracy (LA) models were developed with reference to accurate data from the U.S. National Lightning Detection Network (NLDN). Model calibration procedures are detailed, and comparisons of model results with lightning observations from the PacNet/LLDN in correlation with NASA's Lightning Imaging Sensor (LIS) are presented. The daytime and nighttime flash DE in the northcentral Pacific is in the range of 17%-23% and 40%-61%, respectively. The median LA is in the range of 13-40 km. The results of this extensive analysis suggest that the DE and LA models are reasonably able to reproduce the observed performance of PacNet/LLDN.The implications of this work are that the DE and LA model outputs can be used in quantitative applications of the PacNet/LLDN over the North Pacific Ocean and elsewhere. For example, by virtue of the relationship between lightning and rainfall rates, these data also hold promise as input for NWP models as a proxy for latent heat release in convection. Moreover, the PacNet/LLDN datastream is useful for investigations of storm morphology and cloud microphysics over the central North Pacific Ocean. Notably, the PacNet/LLDN lightning datastream has application for planning transpacific flights and nowcasting of squall lines and tropical storms.

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.

Abstract. The focus of this article is to improve the precipitation accumulation analysis, with special focus on the intense precipitation events. Two main objectives are addressed: (i) the assimilation of lightning observations together with radar and gauge measurements, and (ii) the analysis of the impact of different integration periods in the radar-gauge correction method. The article is a continuation of previous work by Gregow et al. (2013) in the same research field.A new lightning data assimilation method has been implemented and validated within the Finnish Meteorological Institute -Local Analysis and Prediction System. Lightning data do improve the analysis when no radars are available, and even with radar data, lightning data have a positive impact on the results.The radar-gauge assimilation method is highly dependent on statistical relationships between radar and gauges, when performing the correction to the precipitation accumulation field. Here, we investigate the usage of different time integration intervals: 1, 6, 12, 24 h and 7 days. This will change the amount of data used and affect the statistical calculation of the radar-gauge relations. Verification shows that the realtime analysis using the 1 h integration time length gives the best results.

raduate students' research often depends on the instruction and direction of their advisors. Many graduates are isolated from the field data-collecting aspects of research. A scientific cruise, designed entirely and staffed primarily by the graduate students of the School of Ocean and Earth Science and Technology (SOEST) at the University of Hawaii, provided students with a rare opportunity to garner experience in group planning, writing, editing, and defending a proposal, and dealing with unexpected events while aboard the research vessel. This immensely valuable experience aids us as we embark on our academic journey into the realm of scientific research. PROPOSAL COMPETITION AND CRUISE PREPARATION. The SOEST student cruise provided a unique opportunity for graduate students to compete for valuable commodities, including research funding and ship time. A notification released to SOEST graduate students detailed the availability of a grant supporting four days (16-20