2B.5 Combining Gradient and Profile Fit Method for an Advanced Ceilometer-based Boundary Layer Height Detection Algorithm

Thursday, 26 January 2017: 11:30 AM
Conference Center: Yakima 1 (Washington State Convention Center )
Christoph Münkel, Vaisala GmbH, Hamburg, Germany; and R. Roininen

For over a decade, scientists have been using backscatter information from eye-safe lidar ceilometers for observing boundary layer structures such as the daytime mixing layer. For users interested in the physical height of these structures, detection algorithms have been developed that retrieve this information throughout the day. One of those algorithms is BL-VIEW 1.0, designed for the Vaisala ceilometers CL31 and CL51, and introduced at AMS Annual Meeting 2010. It is based on the gradient method and features a cloud, fog and precipitation filter designed to avoid false hits, a noise and range dependent averaging scheme, and a variable detection threshold. While useful in many geographic locations, ceilometer based retrievals have been known to have difficulties in situations where multiple layers are detected within the lowest few kilometers of the backscatter profile and also in pristine atmospheres where the instruments receive little backscatter information. To address these difficulties, a new boundary layer detection algorithm has been developed. The algorithm provides a single value for boundary layer height at all times during the day, with a special focus on detection of the daytime mixed layer. It uses the result of the BL-VIEW 1.0 algorithm, combines it with a profile fit method that examines the entire profile, and finally uses the time of day for improved investigation of the mixing decays after sunset and the determination of the depth of stable nighttime layers. The Vaisala software product BL-VIEW 2.0 will work with this new algorithm; it will also feature comfortable standardized data handling based on NetCDF for easy post-processing with other algorithms like STRAT+. The presentation will include measuring examples from geographically diverse locations with special focus on critical situations and algorithm performance validation with radiosondes. One example with overlaid radiosonde temperature and humidity profiles, and ground PM2.5 readings is shown on the attached graph.
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