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Jennifer G. Ward and Francis J. Merceret

Abstract

An automated cloud-edge detection algorithm was developed and extensively tested. The algorithm uses in situ cloud physics data measured by a research aircraft coupled with ground-based weather radar measurements to determine whether the aircraft is in or out of cloud. Cloud edges are determined when the in/out state changes, subject to a hysteresis constraint. The hysteresis constraint prevents isolated transient cloud puffs or data dropouts from being identified as cloud boundaries. The algorithm was verified by detailed manual examination of the dataset in comparison to the results from application of the automated algorithm.

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Francis J. Merceret, Jennifer G. Ward, Douglas M. Mach, Monte G. Bateman, and James E. Dye

Abstract

Electric-field measurements made in and near clouds during two airborne field programs are presented. Aircraft equipped with multiple electric-field mills and cloud physics sensors were flown near active convection and into thunderstorm anvil and debris clouds. The magnitude of the electric field was measured as a function of position with respect to the cloud edge to provide an observational basis for modifications to the lightning launch commit criteria (LLCC) used by the U.S. space program. These LLCC are used to reduce the risk that an ascending launch vehicle will trigger a lightning strike that could cause the loss of the mission or vehicle. Even with fields of tens of kV m−1 inside electrically active convective clouds, the fields external to these clouds decay to less than 3 kV m−1 within 15 km of cloud edge. Fields that exceed 3 kV m−1 were not found external to anvil and debris clouds.

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Winifred C. Lambert, Francis J. Merceret, Gregory E. Taylor, and Jennifer G. Ward

Abstract

The accuracy and availability of data from a network of 915-MHz boundary layer wind profilers operated by the U.S. Air Force on the Eastern Range are assessed using an automated quality control (QC) algorithm developed by the authors. The accuracy and reliability of the automated algorithm is assessed using the results of an extensive manual examination of the same data used for the assessment of the instruments. The details of the automated algorithm and the manual screening process are provided.

Data were collected over a 647-day period from five profilers configured to produce one profile every 15 min, resulting in about 200 000 measurements. The results indicate that the instruments provide reliable, accurate data except when maintenance problems or heavy precipitation are present. Precipitation affected as much as 25% of the measurements in the dataset. The automated QC algorithm proved extremely effective in identifying unacceptable data. Only 0.03% of the data passing automated QC were identified as bad by manual review. While some valid data were identified as bad, the automated algorithm appears to provide exceptional performance for use in automated operational assimilation of boundary profiler data for model initialization and data visualization.

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