Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 409-453 |
| Number of pages | 45 |
| Journal | Reviews of Geophysics |
| Volume | 56 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jun 1 2018 |
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Keywords
- cloud droplet concentrations
- lidar
- passive retrievals
- radar
- remote sensing
- satellite
ASJC Scopus subject areas
- Geophysics
Cite this
Remote Sensing of Droplet Number Concentration in Warm Clouds : A Review of the Current State of Knowledge and Perspectives. / Grosvenor, Daniel P.; Sourdeval, Odran; Zuidema, Paquita; Ackerman, Andrew; Alexandrov, Mikhail D.; Bennartz, Ralf; Boers, Reinout; Cairns, Brian; Chiu, J. Christine; Christensen, Matthew; Deneke, Hartwig; Diamond, Michael; Feingold, Graham; Fridlind, Ann; Hünerbein, Anja; Knist, Christine; Kollias, Pavlos; Marshak, Alexander; McCoy, Daniel; Merk, Daniel; Painemal, David; Rausch, John; Rosenfeld, Daniel; Russchenberg, Herman; Seifert, Patric; Sinclair, Kenneth; Stier, Philip; van Diedenhoven, Bastiaan; Wendisch, Manfred; Werner, Frank; Wood, Robert; Zhang, Zhibo; Quaas, Johannes.
In: Reviews of Geophysics, Vol. 56, No. 2, 01.06.2018, p. 409-453.Research output: Contribution to journal › Article
}
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
PY - 2018/6/1
Y1 - 2018/6/1
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
UR - http://www.scopus.com/inward/record.url?scp=85050376815&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050376815&partnerID=8YFLogxK
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 -