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  • 标题:Using CALIOP to estimate cloud-field base height and its uncertainty: the Cloud Base Altitude Spatial Extrapolator (CBASE) algorithm and dataset
  • 本地全文:下载
  • 作者:Johannes Mülmenstädt ; Odran Sourdeval ; David S. Henderson
  • 期刊名称:Earth System Science Data (ESSD)
  • 印刷版ISSN:1866-3508
  • 电子版ISSN:1866-3516
  • 出版年度:2018
  • 卷号:10
  • 期号:4
  • 页码:2279-2293
  • DOI:10.5194/essd-10-2279-2018
  • 出版社:Copernicus
  • 摘要: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.
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