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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.
Afiliação
  • Grosvenor DP; School of Earth and Environment University of Leeds Leeds UK.
  • Sourdeval O; Leipzig Institute for Meteorology Universität Leipzig Leipzig Germany.
  • Zuidema P; Department of Atmospheric Sciences Rosenstiel School of Marine and Atmospheric Science Miami FL USA.
  • Ackerman A; NASA Goddard Institute for Space Studies New York NY USA.
  • Alexandrov MD; NASA Goddard Institute for Space Studies New York NY USA.
  • Bennartz R; Department of Applied Physics and Applied Mathematics Columbia University New York NY USA.
  • Boers R; Department of Earth and Environmental Sciences Vanderbilt University Nashville TN USA.
  • Cairns B; Space Science and Engineering Center University of Wisconsin-Madison Madison WI USA.
  • Chiu JC; Royal Netherlands Meteorological Institute De Bilt The Netherlands.
  • Christensen M; NASA Goddard Institute for Space Studies New York NY USA.
  • Deneke H; Department of Atmospheric Science Colorado State University Fort Collins CO USA.
  • Diamond M; Rutherford Appleton Laboratory Harwell UK.
  • Feingold G; Department of Physics University of Oxford Oxford UK.
  • Fridlind A; Leibniz Institute for Tropospheric Research Leipzig Germany.
  • Hünerbein A; Department of Atmospheric Sciences University of Washington Seattle WA USA.
  • Knist C; Chemical Sciences Division, Earth System Research Laboratory National Oceanic and Atmospheric Administration Boulder CO USA.
  • Kollias P; NASA Goddard Institute for Space Studies New York NY USA.
  • Marshak A; Leibniz Institute for Tropospheric Research Leipzig Germany.
  • McCoy D; Deutscher Wetterdienst Lindenberg Germany.
  • Merk D; School of Marine and Atmospheric Sciences Stony Brook University Stony Brook NY USA.
  • Painemal D; NASA Goddard Space Flight Center Greenbelt MD USA.
  • Rausch J; School of Earth and Environment University of Leeds Leeds UK.
  • Rosenfeld D; Leibniz Institute for Tropospheric Research Leipzig Germany.
  • Russchenberg H; NASA Langley Research Center Hampton VA USA.
  • Seifert P; Department of Earth and Environmental Sciences Vanderbilt University Nashville TN USA.
  • Sinclair K; Institute of Earth Sciences The Hebrew University of Jerusalem Jerusalem Israel.
  • Stier P; Department of Geoscience and Remote Sensing Delft University of Technology Delft The Netherlands.
  • van Diedenhoven B; Leibniz Institute for Tropospheric Research Leipzig Germany.
  • Wendisch M; NASA Goddard Institute for Space Studies New York NY USA.
  • Werner F; Department of Earth and Environmental Engineering Columbia University New York NY USA.
  • Wood R; Department of Physics University of Oxford Oxford UK.
  • Zhang Z; NASA Goddard Institute for Space Studies New York NY USA.
  • Quaas J; Center for Climate Systems Research Columbia University New York NY USA.
Rev Geophys ; 56(2): 409-453, 2018 Jun.
Article em En | MEDLINE | ID: mdl-30148283
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.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Guideline Idioma: En Revista: Rev Geophys Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Guideline Idioma: En Revista: Rev Geophys Ano de publicação: 2018 Tipo de documento: Article