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End date: 2023 × Start date: 2020 × Publisher: Hoboken, NJ : Wiley × Subject: lidar × Subject: remote sensing ×

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Now showing 1 - 2 of 2
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    Remote Sensing of Droplet Number Concentration in Warm Clouds: A Review of the Current State of Knowledge and Perspectives
    (Hoboken, NJ : Wiley, 2018) 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
    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.
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    Californian Wildfire Smoke Over Europe: A First Example of the Aerosol Observing Capabilities of Aeolus Compared to Ground‐Based Lidar
    (Hoboken, NJ : Wiley, 2021) Baars, Holger; Radenz, Martin; Floutsi, Athena Augusta; Engelmann, Ronny; Althausen, Dietrich; Heese, Birgit; Ansmann, Albert; Flament, Thomas; Dabas, Alain; Trapon, Dimitri; Reitebuch, Oliver; Bley, Sebastian; Wandinger, Ulla
    In September 2020, extremely strong wildfires in the western United States of America (i.e., mainly in California) produced large amounts of smoke, which was lifted into the free troposphere. These biomass-burning-aerosol (BBA) layers were transported from the US west coast toward central Europe within 3–4 days turning the sky milky and receiving high media attention. The present study characterizes this pronounced smoke plume above Leipzig, Germany, using a ground-based multiwavelength-Raman-polarization lidar and the aerosol/cloud product of ESA’s wind lidar mission Aeolus. An exceptional high smoke-AOT >0.4 was measured, yielding to a mean mass concentration of 8 μg m−3. The 355 nm lidar ratio was moderate at around 40–50 sr. The Aeolus-derived backscatter, extinction and lidar ratio profiles agree well with the observations of the ground-based lidar PollyXT considering the fact that Aeolus’ aerosol and cloud products are still preliminary and subject to ongoing algorithm improvements.