Large‐eddy simulations (LES) with the new ICOsahedral Non‐hydrostatic atmosphere model (ICON) covering Germany are evaluated for four days in spring 2013 using observational data from various sources. Reference simulations with the established Consortium for Small‐scale Modelling (COSMO) numerical weather prediction model and further standard LES codes are performed and used as a reference. This comprehensive evaluation approach covers multiple parameters and scales, focusing on boundary‐layer variables, clouds and precipitation. The evaluation points to the need to work on parametrizations influencing the surface energy balance, and possibly on ice cloud microphysics. The central purpose for the development and application of ICON in the LES configuration is the use of simulation results to improve the understanding of moist processes, as well as their parametrization in climate models. The evaluation thus aims at building confidence in the model's ability to simulate small‐ to mesoscale variability in turbulence, clouds and precipitation. The results are encouraging: the high‐resolution model matches the observed variability much better at small‐ to mesoscales than the coarser resolved reference model. In its highest grid resolution, the simulated turbulence profiles are realistic and column water vapour matches the observed temporal variability at short time‐scales. Despite being somewhat too large and too frequent, small cumulus clouds are well represented in comparison with satellite data, as is the shape of the cloud size spectrum. Variability of cloud water matches the satellite observations much better in ICON than in the reference model. In this sense, it is concluded that the model is fit for the purpose of using its output for parametrization development, despite the potential to improve further some important aspects of processes that are also parametrized in the high‐resolution model.

A review of remote sensing technology for lower tropospheric thermodynamic (TD) profiling is presented with focus on high accuracy and high temporal‐vertical resolution. The contributions of these instruments to the understanding of the Earth system are assessed with respect to radiative transfer, land surface‐atmosphere feedback, convection initiation, and data assimilation. We demonstrate that for progress in weather and climate research, TD profilers are essential. These observational systems must resolve gradients of humidity and temperature in the stable or unstable atmospheric surface layer close to the ground, in the mixed layer, in the interfacial layer—usually characterized by an inversion—and the lower troposphere. A thorough analysis of the current observing systems is performed revealing significant gaps that must be addressed to fulfill existing needs. We analyze whether current and future passive and active remote sensing systems can close these gaps. A methodological analysis and demonstration of measurement capabilities with respect to bias and precision is executed both for passive and active remote sensing including passive infrared and microwave spectroscopy, the global navigation satellite system, as well as water vapor and temperature Raman lidar and water vapor differential absorption lidar. Whereas passive remote sensing systems are already mature with respect to operational applications, active remote sensing systems require further engineering to become operational in networks. However, active remote sensing systems provide a smaller bias as well as higher temporal and vertical resolutions. For a suitable mesoscale network design, TD profiler system developments should be intensified and dedicated observing system simulation experiments should be performed.

The science guiding the EUREC⁴A campaign and its measurements is presented. EUREC⁴A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. Through its ability to characterize processes operating across a wide range of scales, EUREC⁴A marked a turning point in our ability to observationally study factors influencing clouds in the trades, how they will respond to warming, and their link to other components of the earth system, such as upper-ocean processes or the life cycle of particulate matter. This characterization was made possible by thousands (2500) of sondes distributed to measure circulations on meso- (200 km) and larger (500 km) scales, roughly 400 h of flight time by four heavily instrumented research aircraft; four global-class research vessels; an advanced ground-based cloud observatory; scores of autonomous observing platforms operating in the upper ocean (nearly 10 000 profiles), lower atmosphere (continuous profiling), and along the air–sea interface; a network of water stable isotopologue measurements; targeted tasking of satellite remote sensing; and modeling with a new generation of weather and climate models. In addition to providing an outline of the novel measurements and their composition into a unified and coordinated campaign, the six distinct scientific facets that EUREC⁴A explored – from North Brazil Current rings to turbulence-induced clustering of cloud droplets and its influence on warm-rain formation – are presented along with an overview of EUREC⁴A's outreach activities, environmental impact, and guidelines for scientific practice. Track data for all platforms are standardized and accessible at https://doi.org/10.25326/165 (Stevens, 2021), and a film documenting the campaign is provided as a video supplement.

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A lidar polychromator design for the measurement of atmospheric temperature profiles in the presence of clouds with the rotational Raman method is presented. The design utilizes multicavity interference filters mounted sequentially at small angles of incidence. Characteristics of this design are high signal efficiency and adjustable center wavelengths of the filters combined with a stable and relatively simple experimental setup. High suppression of the elastic backscatter signal in the rotational Raman detection channels allows temperature measurements independent of the presence of thin clouds or aerosol layers; no influence of particle scattering on the lidar temperature profile was observed in clouds with a backscatter ratio of at least 45. The minimum integration time needed for temperature profiling with a statistical temperature error of ±1 K at, e.g., 20-km height and 960-m height resolution is 1.5 h.

The lidar of the Radio Science Center for Space and Atmosphere (RASC; Kyoto, Japan) makes use of two pure rotational Raman (RR) signals for both the measurement of the atmospheric temperature profile and the derivation of a temperature-independent Raman reference signal. The latter technique is new and leads to significantly smaller measurement uncertainties compared with the commonly used vibrational Raman lidar technique. For the measurement of temperature, particle extinction coefficient, particle backscatter coefficient, and humidity simultaneously, only four lidar signals are needed: the elastic Cabannes backscatter signal, two RR signals, and the vibrational Raman water vapor signal. The RASC lidar provides RR signals of unprecedented intensity. Although only 25% of the RR signal intensities can be used with the present data-acquisition electronics, the 1-s statistical uncertainty of nighttime temperature measurements is lower than for previous systems and is <1K up to 11-km height for, e.g., a resolution of 500 m and 9 min. In addition, RR measurements in daytime also have become feasible.