Kotthaus, S. and Grimmond, C. S. B.
ORCID: https://orcid.org/0000-0002-3166-9415
(2018)
Atmospheric boundary layer characteristics from ceilometer measurements. Part 1: a new method to track mixed layer height and classify clouds.
Quarterly Journal of the Royal Meteorological Society, 144 (714).
pp. 1525-1538.
ISSN 0035-9009
doi: 10.1002/qj.3299
Abstract/Summary
The use of Automatic Lidars and Ceilometers (ALC) is increasingly extended beyond monitoring cloud base height to the study of atmospheric boundary layer (ABL) dynamics. Therefore, long-term sensor networks observations require robust algorithms to automatically detect the mixed layer height (ZML). Here, a novel automatic algorithm CABAM (Characterise the Atmospheric Boundary layer based on ALC Measurements) is presented . CABAM is the first, non-proprietary mixed layer height algorithm specifically designed for the commonly deployed Vaisala CL31 ceilometer. The method: tracks ZML, takes into account precipitation, classifies the ABL based on cloud cover and cloud type, and determines the relation between ZML and cloud base height. CABAM relies solely on ALC measurements. Results perform well against independent reference (AMDAR: Aircraft Meteorological Data Relay) measurements and supervised ZML detection. AMDAR derived temperature inversion heights allow ZML evaluation throughout the day. Very good agreement is found in the afternoon when the mixed layer height extends over the full ABL. However, during night or the morning transition the temperature inversion is more likely associated with the top of the residual layer. From comparison with SYNOP reports, the ABL classification scheme generally correctly distinguishes between convective and stratiform boundary layer clouds, with slightly better performance during daytime. Applied to six years of ALC observations in central London, Kotthaus and Grimmond (2018) demonstrate CABAM results are valuable to characterise the urban boundary layer over London, UK, where clouds of various types are frequent.
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| Item Type | Article |
| URI | https://reading-pure-test.eprints-hosting.org/id/eprint/76370 |
| Identification Number/DOI | 10.1002/qj.3299 |
| Refereed | Yes |
| Divisions | Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology Central Services |
| Download/View statistics | View download statistics for this item |
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