Journal cover Journal topic
Earth System Science Data The Data Publishing Journal
https://doi.org/10.5194/essd-2018-43
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
20 Apr 2018
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Earth System Science Data (ESSD).
Using CALIOP to estimate cloud-field base height and its uncertainty: the Cloud Base Altitude Spatial Extrapolator (CBASE) algorithm and dataset
Johannes Mülmenstädt1, Odran Sourdeval1, David S. Henderson2, Tristan S. L'Ecuyer2, Claudia Unglaub1, Leonore Jungandreas1, Christoph Böhm3, Lynn M. Russell4, and Johannes Quaas1 1Institute of Meteorology, Universität Leipzig, Leipzig, Germany
2University of Wisconsin at Madison, Madison, Wisconsin, USA
3Institute for Geophysics and Meteorology, Universität zu Köln, Köln, Germany
4Scripps Institution of Oceanography, University of California, San Diego, San Diego, California, USA
Abstract. A technique is presented that uses attenuated backscatter profiles from the CALIOP satellite lidar to estimate cloud base heights of lower-troposphere liquid clouds (cloud base height below approximately 3 km). Even when clouds are thick enough to attenuate the lidar beam (optical thickness τ ≳ 5), the technique provides cloud base heights by treating the cloud base height of nearby thinner clouds as representative of the surrounding cloud field. Using ground-based ceilometer data, uncertainty estimates for the cloud base height product at retrieval resolution are derived as a function of various properties of the CALIOP lidar profiles. Evaluation of the predicted cloud base heights and their predicted uncertainty using a second, statistically independent, ceilometer dataset shows that cloud base heights and uncertainties are biased by less than 10 %. Geographic distributions of cloud base height and its uncertainty are presented. In some regions, the uncertainty is found to be substantially smaller than the 480 m uncertainty assumed in the A-Train surface downwelling longwave estimate, potentially permitting the most uncertain of the radiative fluxes in the climate system to be better constrained. The cloud base dataset is available at https://doi.org/10.1594/WDCC/CBASE.
Citation: Mülmenstädt, J., Sourdeval, O., Henderson, D. S., L'Ecuyer, T. S., Unglaub, C., Jungandreas, L., Böhm, C., Russell, L. M., and Quaas, J.: Using CALIOP to estimate cloud-field base height and its uncertainty: the Cloud Base Altitude Spatial Extrapolator (CBASE) algorithm and dataset, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2018-43, in review, 2018.
Johannes Mülmenstädt et al.
Johannes Mülmenstädt et al.

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Using CALIOP to estimate cloud-field base height and its uncertainty: the Cloud Base Altitude Spatial Extrapolator (CBASE) algorithm and dataset J. Mülmenstädt, O. Sourdeval, D. S. Henderson, T. S. L'Ecuyer, C. Unglaub, L. Jungandreas, C. Böhm, L. M. Russell, and J. Quaas https://doi.org/10.1594/WDCC/CBASE
Johannes Mülmenstädt et al.

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Short summary
One of the key pieces of information about a cloud is how high its base is. Unlike cloud top, cloud base is hard to observe from a satellite's perspective – the cloud blocks the view. But without using satellites, it is difficult to compile global datasets. Here we describe how we got around the limitations of a laser cloud detector aboard a satellite to observe global cloud base heights. This dataset will expand our knowledge of the cloudy atmosphere and its interaction with the surface below.
One of the key pieces of information about a cloud is how high its base is. Unlike cloud top,...
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