Publication Abstracts
Grosvenor et al. 2018
Grosvenor, D.P., O. Souderval, P. Zuidema, , , R. Bennartz, R. Boers, , J.C. Chiu, M. Christensen, H. Deneke, M. Diamond, G. Feingold, , A. Hünerbein, C. Knist, P. Kollias, A. Marshak, D. McCoy, D. Merk, D. Painemal, J. Rausch, D. Rosenfeld, H. Russchenberg, P. Seifert, , P. Stier, , M. Wendisch, F. Werner, R. Wood, Z. Zhang, and J. Quaas, 2018: Remote sensing of cloud droplet number concentration: Review of current and perspectives for new approaches. Rev. Geophys., 56, no. 2, 409-453, doi:10.1029/2017RG000593.
The cloud droplet number concentration (Nd) is of central interest to improve the understanding of cloud physics and for quantifying the effective radiative forcing by aerosol-cloud interactions. Current standard satellite retrievals do not operationally provide Nd, but it can be inferred from retrievals of cloud optical depth (τc) cloud droplet effective radius (re) and cloud top temperature. This review summarizes issues with this approach and quantifies uncertainties. A total relative uncertainty of 78% is inferred for pixel-level retrievals for relatively homogeneous, optically thick and unobscured stratiform clouds with favorable viewing geometry. The uncertainty is even greater if these conditions are not met. For averages over 1°×1° regions the uncertainty is reduced to 54% assuming random errors for instrument uncertainties. In contrast, the few evaluation studies against reference in-situ observations suggest much better accuracy with little variability in the bias. More such studies are required for a better error characterization. Nd uncertainty is dominated by errors in re and, therefore, improvements in re retrievals would greatly improve the quality of the Nd retrievals. Recommendations are made for how this might be achieved. Some existing Nd datasets are compared and discussed, and best practices for the use of Nd data from current passive instruments (e.g., filtering criteria) are recom- mended. Emerging alternative Nd estimates are also considered. Firstly, new ideas to use additional information from existing and upcoming spaceborne instruments are discussed, and secondly, approaches using high-quality ground-based observations are examined.
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BibTeX Citation
@article{gr03110j,
author={Grosvenor, D. P. and Souderval, O. and Zuidema, P. and Ackerman, A. S. and Alexandrov, M. D. and Bennartz, R. and Boers, R. and Cairns, B. and Chiu, J. C. and Christensen, M. and Deneke, H. and Diamond, M. and Feingold, G. and Fridlind, A. and Hünerbein, A. and Knist, C. and Kollias, P. and Marshak, A. and McCoy, D. and Merk, D. and Painemal, D. and Rausch, J. and Rosenfeld, D. and Russchenberg, H. and Seifert, P. and Sinclair, K. and Stier, P. and van Diedenhoven, B. and Wendisch, M. and Werner, F. and Wood, R. and Zhang, Z. and Quaas, J.},
title={Remote sensing of cloud droplet number concentration: Review of current and perspectives for new approaches},
year={2018},
journal={Reviews of Geophysics},
volume={56},
number={2},
pages={409--453},
doi={10.1029/2017RG000593},
}
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RIS Citation
TY - JOUR ID - gr03110j AU - Grosvenor, D. P. AU - Souderval, O. AU - Zuidema, P. AU - Ackerman, A. S. AU - Alexandrov, M. D. AU - Bennartz, R. AU - Boers, R. AU - Cairns, B. AU - Chiu, J. C. AU - Christensen, M. AU - Deneke, H. AU - Diamond, M. AU - Feingold, G. AU - Fridlind, A. AU - Hünerbein, A. AU - Knist, C. AU - Kollias, P. AU - Marshak, A. AU - McCoy, D. AU - Merk, D. AU - Painemal, D. AU - Rausch, J. AU - Rosenfeld, D. AU - Russchenberg, H. AU - Seifert, P. AU - Sinclair, K. AU - Stier, P. AU - van Diedenhoven, B. AU - Wendisch, M. AU - Werner, F. AU - Wood, R. AU - Zhang, Z. AU - Quaas, J. PY - 2018 TI - Remote sensing of cloud droplet number concentration: Review of current and perspectives for new approaches JA - Rev. Geophys. JO - Reviews of Geophysics VL - 56 IS - 2 SP - 409 EP - 453 DO - 10.1029/2017RG000593 ER -
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