Paper
26 October 2011 Adding confidence levels and error bars to mixing layer heights detected by ceilometer
Author Affiliations +
Proceedings Volume 8177, Remote Sensing of Clouds and the Atmosphere XVI; 817708 (2011) https://doi.org/10.1117/12.898122
Event: SPIE Remote Sensing, 2011, Prague, Czech Republic
Abstract
Eye-safe lidar ceilometers are reliable tools for unattended boundary layer structure monitoring around the clock. A single lens optical design enables precise assessment of inversion layers and nocturnal stable layers below 200 m. This design has been chosen for the Vaisala Ceilometers CL31 and CL51. Based on the gradient method, an automatic algorithm for online retrieval of boundary layer depth and additional residual structures has been developed. This robust all weather algorithm is part of the Vaisala boundary layer reporting and analysis tool BL-VIEW. The data averaging intervals used depend on range and signal noise; detection thresholds vary with signal amplitude. All layer heights reported are accomponied by a quality index. In most cases the lowest of these layers is a good measure for the mixing layer height. The continuous knowledge of this atmospheric parameter is supporting the understanding of processes directing air quality. The utility of mixing layer height values for air quality forecast can be further increased by additionally utilizing unaveraged profiles for gradient minima detection. Based on their variation from the result of the BL-VIEW algorithm, confidence levels and error bars can be calculated. Results are presented from campaigns at three different sites. Validation with mixing layer height values derived from co-located radiosoundings confirm the applicability of this novel method.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Christoph Münkel, Klaus Schäfer, and Stefan Emeis "Adding confidence levels and error bars to mixing layer heights detected by ceilometer", Proc. SPIE 8177, Remote Sensing of Clouds and the Atmosphere XVI, 817708 (26 October 2011); https://doi.org/10.1117/12.898122
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Cited by 10 scholarly publications.
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KEYWORDS
Backscatter

Clouds

Signal detection

Humidity

Algorithm development

Aerosols

Interference (communication)

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