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.

A detailed description is given of how the liquid water content (LWC) and the ice water content (IWC) can be determined accurately and absolutely from the measured water Raman spectra of clouds. All instrumental and spectroscopic parameters that affect the accuracy of the water-content measurement are discussed and quantified, specifically, these are the effective absolute differential Raman backscattering cross section of water vapor (π)/dΩ, and the molecular Raman backscattering efficiencies ηliq and ηice of liquid and frozen microparticles, respectively. The latter two are determined following rigorous theoretical approaches combined with RAMSES measurements. For ηice, this includes a new experimental method which assumes continuity of the number of water molecules across the vertical extent of the melting layer. Examples of water-content measurements are presented, including supercooled liquid-water clouds and melting layers. Error sources are discussed, one effect that stands out is interfering fluorescence by aerosols. Aerosol effects and calibration issues are the main reasons why spectral Raman measurements are required for quantitative measurements of LWC and IWC. The presented study lays the foundation for cloud microphysical investigations, and for the evaluation of cloud models or the cloud data products of other instruments. As a first application, IWC retrieval methods are evaluated that are based on either lidar extinction or radar reflectivity measurements. While the lidar-based retrievals show unsatisfactory agreement with the RAMSES IWC measurements, the radar-based IWC retrieval which is used in the Cloudnet project performs reasonably well. On average, retrieved IWC agrees within 20% to 30% (dry bias) with measured IWC.

<p>Numerical weather prediction is expected to profit considerably of an improved knowledge of the still underdetermined state of the at<span>mo</span><span>s</span><span>pheric boundary layer. </span><span>As of late, the s</span><span>patially and temporally sparse </span><span>existing </span><span>measurements </span><span>of e.g. radiosondes can be complemented </span><span>with</span> <span>wind, temperature, and humidity profiles </span><span>of ground-based </span><span>remote-sensing </span><span>instruments. </span><span>The DWD evaluates </span><span>several of those instruments </span><span>for operational deployment </span><span>i</span><span>n the framework of the project “Pilotstation”. </span><span>Her</span><span>e, </span><span>we</span><span> will present the results of assimilating observations of the most mature </span><span>of those </span><span>systems, </span><span>i.e. </span><span>microwave radiometer </span><span>(MWR) </span><span>and Doppler lidar, </span><span>into</span><span> the </span><span>I</span><span>CON/KENDA assimilation system </span><span>of the DWD</span><span>.</span></p><p><span>The</span> <span>MWR</span> <span>measures</span><span> brightness temperatures and thus, the profiles provided by the ICON model have to be transformed to observation space using the forward operator RTTOV-gb. </span><span>We ran s</span><span>everal assimilation experiments, especially with regard to </span><span>the vertical localisation of the MWR channels</span><span>.</span> <span>We will demonstrate how </span><span>t</span><span>his </span><span>localisati</span><span>o</span><span>n</span><span>, </span><span>t</span><span>ogether with the proper handling of interchannel cross-correlations, </span> <span>was </span><span>key for obtaining </span><span>a positive impact on the </span><span>upper-air </span><span>forecast statistics.</span></p><p><span>The Doppler lidar provides horizontal wind measurements, which exhibit a similar quality as the existing radar-wind profiler (RWP) observations and which can be assimilated directly. We will present the results of different assimilation experiments and discuss the impact in comparison with the RWP.</span></p>

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