TY - JOUR
T1 - Remote Sensing of Droplet Number Concentration in Warm Clouds
T2 - A Review of the Current State of Knowledge and Perspectives
AU - Grosvenor, Daniel P.
AU - Sourdeval, Odran
AU - Zuidema, Paquita
AU - Ackerman, Andrew
AU - Alexandrov, Mikhail D.
AU - Bennartz, Ralf
AU - Boers, Reinout
AU - Cairns, Brian
AU - Chiu, J. Christine
AU - Christensen, Matthew
AU - Deneke, Hartwig
AU - Diamond, Michael
AU - Feingold, Graham
AU - Fridlind, Ann
AU - Hünerbein, Anja
AU - Knist, Christine
AU - Kollias, Pavlos
AU - Marshak, Alexander
AU - McCoy, Daniel
AU - Merk, Daniel
AU - Painemal, David
AU - Rausch, John
AU - Rosenfeld, Daniel
AU - Russchenberg, Herman
AU - Seifert, Patric
AU - Sinclair, Kenneth
AU - Stier, Philip
AU - van Diedenhoven, Bastiaan
AU - Wendisch, Manfred
AU - Werner, Frank
AU - Wood, Robert
AU - Zhang, Zhibo
AU - Quaas, Johannes
N1 - Funding Information:
This work was inspired by discussions within the Aerosols-Clouds-Precipitation and Climate (ACPC) initiative (acpcinitaitive.org) and at a workshop in October 2016 organized by the Leipzig Graduate School for Clouds, Aerosols and Radiation. D. P. G. was funded by both the University of Leeds under Paul Field and from the NERC funded ACSIS programme via NCAS. The work by J. Q. was funded by the European Research Council (Starting grant 306284 “QUAERERE”). O. S. was funded by the Federal Ministry for Education and Research in Germany (BMBF) in the High Definition Clouds and Precipitation for Climate Prediction (HD(CP)2) project (FKZ 01LK1503A and 01LK1505E). The contribution of P. S. was supported with funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme project RECAP with grant agreement 724602. F. W. was supported by NASA grants NNX14AJ25G and NNX15AC77G. D. T. M. acknowledges support from the PRIMAVERA project, funded by the European Union’s Horizon 2020 programme, grant agreement 641727. A. F. was supported by the U.S. Department of Energy Office of Science (BER) agreement DE-SC0016237 and NASA Radiation Sciences Program. P. Z. was supported by NASA NNX15AF98G. We thank Bjorn Stevens and Edward Gryspeerdt for providing stimulating ideas to this review. The MODIS instrument is operated by the National Aeronautics and Space Agency (NASA), which we acknowledge for their data, which were obtained from NASA’s Level 1 and Atmosphere Archive and Distribution System (LAADS, http://ladsweb. nascom.nasa.gov/). The satellite cloud droplet concentration data set denoted as “GW14” is available from the CEDA (Centre for Environmental Data) archive at http://catalogue.ceda.ac.uk/uuid/ cf97ccc802d348ec8a3b6f2995dfbbff. The Centre for Environmental Data Analysis (CEDA; http://www.ceda.ac.uk, ESA, 2014) provides the AATSR satellite calibrated radiances and cloud data products. The code to process the ORAC cloud products can be obtained via https://github.com/ORAC-CC/ORAC (ORAC, 2017). The data used are listed in the references, tables, supporting information, or in the acknowledgments.
PY - 2018/6
Y1 - 2018/6
N2 - 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 data sets are compared and discussed, and best practices for the use of Nd data from current passive instruments (e.g., filtering criteria) are recommended. Emerging alternative Nd estimates are also considered. First, new ideas to use additional information from existing and upcoming spaceborne instruments are discussed, and second, approaches using high-quality ground-based observations are examined.
AB - 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 data sets are compared and discussed, and best practices for the use of Nd data from current passive instruments (e.g., filtering criteria) are recommended. Emerging alternative Nd estimates are also considered. First, new ideas to use additional information from existing and upcoming spaceborne instruments are discussed, and second, approaches using high-quality ground-based observations are examined.
KW - cloud droplet concentrations
KW - lidar
KW - passive retrievals
KW - radar
KW - remote sensing
KW - satellite
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U2 - 10.1029/2017RG000593
DO - 10.1029/2017RG000593
M3 - Article
AN - SCOPUS:85050376815
VL - 56
SP - 409
EP - 453
JO - Reviews of Geophysics
JF - Reviews of Geophysics
SN - 8755-1209
IS - 2
ER -