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.

Cloud fraction, liquid and ice water contents derived from long-term radar, lidar, and microwave radiometer data are systematically compared to models to quantify and improve their performance.

Within the framework of the international field campaign COPS (Convective and Orographically-induced Precipitation Study), a large suite of state-of-the-art meteorological instrumentation was operated, partially combined for the first time. This includes networks of in situ and remote-sensing systems such as the Global Positioning System as well as a synergy of multi-wavelength passive and active remote-sensing instruments such as advanced radar and lidar systems. The COPS field phase was performed from 01 June to 31 August 2007 in a low-mountain area in southwestern Germany/eastern France covering the Vosges mountains, the Rhine valley and the Black Forest mountains. The collected data set covers the entire evolution of convective precipitation events in complex terrain from their initiation, to their development and mature phase until their decay. Eighteen Intensive Observation Periods with 37 operation days and eight additional Special Observation Periods were performed, providing a comprehensive data set covering different forcing conditions. In this article, an overview of the COPS scientific strategy, the field phase, and its first accomplishments is given. Highlights of the campaign are illustrated with several measurement examples. It is demonstrated that COPS research provides new insight into key processes leading to convection initiation and to the modification of precipitation by orography, in the improvement of quantitative precipitation forecasting by the assimilation of new observations, and in the performance of ensembles of convection-permitting models in complex terrain.

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Quantitative precipitation forecasting (QPF) in low-mountain regions is a great challenge for the atmospheric sciences community. On the one hand, orographic enhancement of precipitation in these regions can result in severe flash-flood events. On the other hand, the relative importance of forcing mechanisms leading to convection initiation (CI) is neither well understood nor adequately reproduced by weather forecast models. This results in poor QPF skill, both in terms of the spatial distribution of precipitation and its temporal evolution.Two prominent systematic errors of state-of-theart mesoscale models are identified. Figure 1 shows the difference between a 1-month average of 24-h integrated precipitation forecasted with the Consortium for Small-Scale Modeling (COSMO)-EU Model (formerly known as Lokalmodell) of the German Meteorological Service (DWD) and the corresponding observational data. Shown on this figure is the Black Forest low-mountain region in southwestern Germany. Strong systematic errors are found on both the windward and the lee sides. On the windward side, the model strongly overestimates precipitation, whereas on the lee side it is underestimated, which we call the "windward/lee effect." To our knowledge, this error is found in all mesoscale models for both weather prediction and climate simulations, which require convection parameterization, such as in COSMOCH7 of Meteo Swiss, ARPEGE and ALADIN of Meteo France, as well as in the mesoscale models MM5 and ETA. Although we show a summertime example here, Baldauf and Schulz previously demonstrated that this error structure exists during all seasons.Another key problem is the inadequate simulation research campaign AffiliAtions: