Remote Sensing of Droplet Number Concentration in Warm Clouds: A Review of the Current State of Knowledge and Perspectives.
Daniel P Grosvenor, Odran Sourdeval, Paquita Zuidema, Andrew Ackerman, Mikhail D Alexandrov, Ralf Bennartz, Reinout Boers, Brian Cairns, J Christine Chiu, Matthew Christensen, Hartwig Deneke, Michael Diamond, Graham Feingold, Ann Fridlind, Anja Hünerbein, Christine Knist, Pavlos Kollias, Alexander Marshak, Daniel McCoy, Daniel Merk, David Painemal, John Rausch, Daniel Rosenfeld, Herman Russchenberg, Patric Seifert, Kenneth Sinclair, Philip Stier, Bastiaan van Diedenhoven, Manfred Wendisch, Frank Werner, Robert Wood, Zhibo Zhang, Johannes Quaas
Author Information
Daniel P Grosvenor: School of Earth and Environment University of Leeds Leeds UK. ORCID
Odran Sourdeval: Leipzig Institute for Meteorology Universität Leipzig Leipzig Germany.
Paquita Zuidema: Department of Atmospheric Sciences Rosenstiel School of Marine and Atmospheric Science Miami FL USA. ORCID
Andrew Ackerman: NASA Goddard Institute for Space Studies New York NY USA. ORCID
Mikhail D Alexandrov: NASA Goddard Institute for Space Studies New York NY USA.
Ralf Bennartz: Department of Earth and Environmental Sciences Vanderbilt University Nashville TN USA.
Reinout Boers: Royal Netherlands Meteorological Institute De Bilt The Netherlands. ORCID
Brian Cairns: NASA Goddard Institute for Space Studies New York NY USA.
J Christine Chiu: Department of Atmospheric Science Colorado State University Fort Collins CO USA. ORCID
Matthew Christensen: Rutherford Appleton Laboratory Harwell UK.
Hartwig Deneke: Leibniz Institute for Tropospheric Research Leipzig Germany.
Michael Diamond: Department of Atmospheric Sciences University of Washington Seattle WA USA. ORCID
Graham Feingold: Chemical Sciences Division, Earth System Research Laboratory National Oceanic and Atmospheric Administration Boulder CO USA. ORCID
Ann Fridlind: NASA Goddard Institute for Space Studies New York NY USA. ORCID
Anja Hünerbein: Leibniz Institute for Tropospheric Research Leipzig Germany.
Christine Knist: Deutscher Wetterdienst Lindenberg Germany.
Pavlos Kollias: School of Marine and Atmospheric Sciences Stony Brook University Stony Brook NY USA. ORCID
Alexander Marshak: NASA Goddard Space Flight Center Greenbelt MD USA. ORCID
Daniel McCoy: School of Earth and Environment University of Leeds Leeds UK.
Daniel Merk: Leibniz Institute for Tropospheric Research Leipzig Germany.
David Painemal: NASA Langley Research Center Hampton VA USA. ORCID
John Rausch: Department of Earth and Environmental Sciences Vanderbilt University Nashville TN USA.
Daniel Rosenfeld: Institute of Earth Sciences The Hebrew University of Jerusalem Jerusalem Israel. ORCID
Herman Russchenberg: Department of Geoscience and Remote Sensing Delft University of Technology Delft The Netherlands.
Patric Seifert: Leibniz Institute for Tropospheric Research Leipzig Germany. ORCID
Kenneth Sinclair: NASA Goddard Institute for Space Studies New York NY USA.
Philip Stier: Department of Physics University of Oxford Oxford UK. ORCID
Bastiaan van Diedenhoven: NASA Goddard Institute for Space Studies New York NY USA. ORCID
Manfred Wendisch: Leipzig Institute for Meteorology Universität Leipzig Leipzig Germany. ORCID
Frank Werner: Joint Center for Earth Systems Technology Baltimore MD USA. ORCID
Robert Wood: Department of Atmospheric Sciences University of Washington Seattle WA USA. ORCID
Zhibo Zhang: Physics Department UMBC Baltimore MD USA. ORCID
Johannes Quaas: Leipzig Institute for Meteorology Universität Leipzig Leipzig Germany. ORCID
The cloud droplet number concentration (N ) 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 , but it can be inferred from retrievals of cloud optical depth (τ ) cloud droplet effective radius (r ) 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 uncertainty is dominated by errors in r , and therefore, improvements in r retrievals would greatly improve the quality of the N retrievals. Recommendations are made for how this might be achieved. Some existing N data sets are compared and discussed, and best practices for the use of N data from current passive instruments (e.g., filtering criteria) are recommended. Emerging alternative N 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.
