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

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.

Improved Aerosol Processes and Effective Radiative Forcing in HadGEM3 and UKESM1

“…For the analysis here the 2.1‐μm effective radius values are used to compute N d from MODIS. While acknowledging the large uncertainties in satellite retrievals of N d (Grosvenor et al, ) and the sparsity of retrievals at high latitudes in winter months a distinct seasonal cycle in N d over the Southern Ocean is observed. Peak concentrations occur in the austral summer months and coincide with peak marine biogenic emissions (McCoy et al, ).…”

“…Retrieved data have been removed north of 60°N and south of 60°S where retrievals are most uncertain. The simulated N d distributions from GA7 and GA7.1 are in very good agreement with Grosvenor et al () with highest N d concentrations over land and downwind of key source regions. The stratocumulus cloud regions also appear well represented although N d is lower in this region in Bennartz and Rausch ().…”

“…Figure evaluates the simulated PD seasonal cycle of N d over the Southern Ocean (averaged between 35°S and 55°S) against Moderate Resolution Imaging Spectroradiometer (MODIS) satellite retrievals following McCoy et al (), which was based on the N d data set described in Grosvenor and Wood (). As McCoy et al () use only 1 year of MODIS observations we extend the evaluation to also include multiannual retrievals of MODIS N d (Grosvenor et al, ) from 2003 to 2014. This better reflects the interannual variability in Southern Ocean N d .…”

Anthropogenic Air Pollution Delays Marine Stratocumulus Breakup to Open Cells

“…We used SEVIRI-based cloud products (CF, cloud optical depth c , drop effective radius r e , and LWP) generated with the Meteosat Second Generation-Cloud Physical Properties algorithm developed at the Royal Netherlands Meteorological Institute available via http://data.knmi.nl (Roebeling et al, 2006). Potential uncertainties in N d arise from the high sensitivity of N d retrieval to r e (Grosvenor et al, 2018;Szczodrak et al, 2001), especially when r e is relatively small. In order to reduce uncertainty in cloud property retrieval, pixels having c and r e smaller than 3 are excluded from the analysis as these retrievals are less reliable (Sourdeval et al, 2015).…”

Ice crystal number concentration estimates from lidar-radar satellite remote sensing. Part 1: Method and evaluation

“…For liquid clouds, N d can for instance be inferred through relationships between satellite retrievals of τ c and the droplet r eff (Brenguier et al, 2000). These relationships rely on strong assumptions that have shortcomings 25 (Grosvenor et al, 2018) but nonetheless provide N d values that compare well against in situ observations (Painemal and Zuidema, 2011) and can be used to establish climatologies (Bennartz and Rausch, 2017). Such relationships are less trivial for ice clouds due to the high complexity and variability of ice nucleation processes Lohmann, 2002, 2003;Ickes et al, 2015).…”