Remote Sensing of Droplet Number Concentration in Warm Clouds: A Review of the Current State of Knowledge and Perspectives

Journal Article · 27 June 2018 · Reviews of Geophysics (1985)

DOI: https://doi.org/10.1029/2017RG000593 · OSTI ID:1457505

Grosvenor, Daniel P.  ^[1]; Sourdeval, Odran ^[2]; Zuidema, Paquita  ^[3]; Ackerman, Andrew  ^[4]; Alexandrov, Mikhail D. ^[5]; Bennartz, Ralf ^[6]; Boers, Reinout  ^[7]; Cairns, Brian ^[4]; Chiu, J. Christine  ^[8]; Christensen, Matthew ^[9]; Deneke, Hartwig ^[10]; Diamond, Michael  ^[11]; Feingold, Graham  ^[12]; Fridlind, Ann  ^[4]; Hünerbein, Anja ^[10]; Knist, Christine ^[13]; Kollias, Pavlos  ^[14]; Marshak, Alexander  ^[15]; McCoy, Daniel ^[1]; Merk, Daniel ^[10] ; Painemal, David  ^[16]; Rausch, John ^[17]; Rosenfeld, Daniel  ^[18]; Russchenberg, Herman ^[19]; Seifert, Patric  ^[10]; Sinclair, Kenneth ^[20]; Stier, Philip  ^[21]; van Diedenhoven, Bastiaan  ^[22]; Wendisch, Manfred  ^[2]; Werner, Frank  ^[23]; Wood, Robert  ^[11]; Zhang, Zhibo  ^[24]; Quaas, Johannes  ^[2]

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1. School of Earth and Environment University of Leeds Leeds UK
2. Leipzig Institute for Meteorology Universität Leipzig Leipzig Germany
3. Department of Atmospheric Sciences Rosenstiel School of Marine and Atmospheric Science Miami FL USA
4. NASA Goddard Institute for Space Studies New York NY USA
5. NASA Goddard Institute for Space Studies New York NY USA, Department of Applied Physics and Applied Mathematics Columbia University New York NY USA
6. Department of Earth and Environmental Sciences Vanderbilt University Nashville TN USA, Space Science and Engineering Center University of Wisconsin‐Madison Madison WI USA
7. Royal Netherlands Meteorological Institute De Bilt The Netherlands
8. Department of Atmospheric Science Colorado State University Fort Collins CO USA
9. Rutherford Appleton Laboratory Harwell UK, Department of Physics University of Oxford Oxford UK
10. Leibniz Institute for Tropospheric Research Leipzig Germany
11. Department of Atmospheric Sciences University of Washington Seattle WA USA
12. Chemical Sciences Division, Earth System Research Laboratory National Oceanic and Atmospheric Administration Boulder CO USA
13. Deutscher Wetterdienst Lindenberg Germany
14. School of Marine and Atmospheric Sciences Stony Brook University Stony Brook NY USA
15. NASA Goddard Space Flight Center Greenbelt MD USA
16. NASA Langley Research Center Hampton VA USA
17. Department of Earth and Environmental Sciences Vanderbilt University Nashville TN USA
18. Institute of Earth Sciences The Hebrew University of Jerusalem Jerusalem Israel
19. Department of Geoscience and Remote Sensing Delft University of Technology Delft The Netherlands
20. NASA Goddard Institute for Space Studies New York NY USA, Department of Earth and Environmental Engineering Columbia University New York NY USA
21. Department of Physics University of Oxford Oxford UK
22. NASA Goddard Institute for Space Studies New York NY USA, Center for Climate Systems Research Columbia University New York NY USA
23. Joint Center for Earth Systems Technology Baltimore MD USA
24. Physics Department UMBC Baltimore MD USA

Abstract The cloud droplet number concentration ( N _d ) 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 N _d , but it can be inferred from retrievals of cloud optical depth ( τ _c ) cloud droplet effective radius ( r _e ) 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. N _d uncertainty is dominated by errors in r _e , and therefore, improvements in r _e retrievals would greatly improve the quality of the N _d retrievals. Recommendations are made for how this might be achieved. Some existing N _d data sets are compared and discussed, and best practices for the use of N _d data from current passive instruments (e.g., filtering criteria) are recommended. Emerging alternative N _d 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.

• Sponsoring Organization: USDOE
• Grant/Contract Number: SC0016237
• OSTI ID: 1457505
• Journal Information: Reviews of Geophysics (1985), Journal Name: Reviews of Geophysics (1985) Journal Issue: 2 Vol. 56; ISSN 8755-1209
• Publisher: American Geophysical Union (AGU)Copyright Statement
• Country of Publication: United States
• Language: English