The development of a new observational system called LISDAD (Lightning Imaging Sensor Demonstration and Display) has enabled a study of severe weather in central Florida. The total flash rates for storms verified to be severe are found to exceed 60 flashes/rain, with some values reaching 500 flashes/min. Similar to earlier results for thunderstorm microbursts, the peak flash rate precedes the severe weather at the ground by 5-20 minutes. A distinguishing feature of severe storms is the presence of lightning "jumps"-abrupt increases in flash rate in advance of the maximum rate for the storm. The systematic total lightning precursor to severe weather of all kinds-wind, hail, tornadoes-is interpreted in terms of the updraft that sows the seeds aloft for severe weather at the surface and simultaneously stimulates the ice microphysics that drives the intracloud lightning activity.

Two approaches are used to characterize how accurately the north Alabama Lightning Mapping Array (LMA) is able to locate lightning VHF sources in space and time. The first method uses a Monte Carlo computer simulation to estimate source retrieval errors. The simulation applies a VHF source retrieval algorithm that was recently developed at the NASA Marshall Space Flight Center (MSFC) and that is similar, but not identical to, the standard New Mexico Tech retrieval algorithm. The second method uses a purely theoretical technique (i.e., chi-squared Curvature Matrix Theory) to estimate retrieval errors. Both methods assume that the LMA system has an overall rms timing error of 50 ns, but all other possible errors (e.g., anomalous VHF noise sources) are neglected. The detailed spatial distributions of retrieval errors are provided. Even though the two methods are independent of one another, they nevertheless provide remarkably similar results. However, altitude error estimates derived from the two methods differ (the Monte Carlo result being taken as more accurate). Additionally, this study clarifies the mathematical retrieval process. In particular, the mathematical difference between the first-guess linear solution and the Marquardt-iterated solution is rigorously established thereby explaining why Marquardt iterations improve upon the linear solution.

Abstract. An analytic framework is developed in which to analyze climatological VHF (66 MHz) radiation measurements taken by the Kennedy Space Center Lightning Detection and Ranging (LDAR) network. A 19 month noise-filtered sample of LDAR observations is examined using this framework. It is found that the climatological impulsive VHF source density as observed by LDAR falls off •10 dB every 71 km of ground range away from the network centroid (a 31 km e-folding scale). The underlying vertical distribution of impulsive VHF sources is approximately normally distributed with a mean altitude of 9 km and a standard deviation of 2.7 km; this implies that the loss of below-horizon sources has a negligible effect on column-integrated source densities within a 200 km ground range. At medium to far ranges, location errors are primarily radial and have a slightly asymmetric distribution whose standard deviation increases as r 2. Error moments estimated from observed lightning are significantly higher than those from aircraft-based signal generator or analytic estimates. LDAR bulk flash detection efficiency is predicted to be above 90% to 94-113 km range from the network centroid and to fall below 10% at ranges greater than 200-240 km.

Test beds have become an integral part of the weather enterprise, bridging research and forecast services by transitioning innovative tools and tested methods that impact forecasts and forecast users. O ver roughly the last decade, a variety of "test beds" have come into existence focused on high-impact weather and the core tools of meteorology-observations, models, and fundamental understanding of the underlying physical processes. They have entered the proverbial "valley of death" between research and forecast operations (NAS 2000), Develop and introduce new ideas, data, etc. Input Revise and iterate Experiment and demonstrate End testing Output Test and refine loop V Assess impacts and evaluate and have survived. This paper provides a brief background on how this happened; summarizes test bed origins, methods, and selected accomplishments; and provides a perspective on the future of test beds in our field. Dabbert et al. (2005) provides a useful description of test beds from early in their development and Fig.