Large-eddy simulations (LES) with the newThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. R. Heinze et al.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.

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Atmospheric reanalyses covering the European region are mainly available as part of relatively coarse global reanalyses. The aim of this article is to present the development and evaluation of a next generation regional reanalysis for the European CORDEX EUR-11 domain with a horizontal grid spacing of approximately 6 km. In this context, a reanalysis is understood to be an assimilation of heterogeneous observations with a physical model such as a numerical weather prediction (NWP) model. The reanalysis system presented here is based on the NWP model COSMO by the German Meteorological Service (Deutscher Wetterdienst) using a continuous nudging scheme. In order to assess the added value of data assimilation, a dynamical downscaling experiment has been conducted, i.e. an identical model set-up but without data assimilation. Both systems have been evaluated for a 1 year test period, employing standard measures such as analysis increments, biases, or log-odds ratios, as well as tests for distributional characteristics. An important aspect is the evaluation from different perspectives and with independent measurements such as satellite infrared brightness temperatures using forward operators, integrated water vapour from GPS stations, and ceilometer cloud cover. It can be shown that the reanalysis better resolves local extreme events; this is basically an effect of the higher spatio-temporal resolution, as known from dynamical downscaling approaches. However, an important criterion for regional reanalyses is the coherence with independent observations of high temporal and spatial resolution, resulting in significant improvement over dynamical downscaling. The system is intended to become operational within a year, continuously reprocessing and evaluating longer time periods. The reanalysis data are planned to become available to the research community within a year.

[1] This paper investigates the influence of cloud model statistics on the accuracy of statistical multiple-frequency liquid water path (LWP) retrievals for a ground-based microwave radiometer. Statistical algorithms were developed from a radiosonde data set in which clouds were modeled by using a relative humidity threshold and a modified adiabatic assumption. Evaluation of the algorithms was then performed by applying the algorithms to four data sets in which clouds were generated in different ways (i.e., threshold method, gradient method, and cloud microphysical model). While classical twochannel algorithms, in this case using frequencies at 22.985 and 28.235 GHz, do not show a significant dependency on the cloud model, the inclusion of an additional 50-GHz channel can introduce significant systematic errors. The addition of a 90-GHz frequency to the two-channel algorithm leads to a larger increase in LWP accuracy than in case of the 50-GHz channel and is less sensitive to the choice of cloud model. A drizzle case from the cloud microphysical model shows no significant loss of accuracy for the microwave radiometer algorithms, in contrast to simple cloud radar retrievals of liquid water. In case of rain, however, the results deteriorate when the total liquid water path is larger than 700 g m À2 .INDEX TERMS: 3360 Meteorology and Atmospheric Dynamics: Remote sensing; 3394 Meteorology and Atmospheric Dynamics: Instruments and techniques; 6969 Radio Science: Remote sensing; 1640 Global Change: Remote sensing; 1655 Global Change: Water cycles (1836); KEYWORDS: microwave radiometer, cloud liquid water, sensor synergy, ground-based remote sensing Citation: Löhnert, U., and S. Crewell, Accuracy of cloud liquid water path from ground-based microwave radiometry, 1, Dependency on cloud model statistics,

Trade-wind cumuli constitute the cloud type with the highest frequency of occurrence on Earth, and it has been shown that their sensitivity to changing environmental conditions will critically influence the magnitude and pace of future global warming. Research over the last decade has pointed out the importance of the interplay between clouds, convection and circulation in controling this sensitivity. 123Surv Geophys (2017) 38:1529-1568 https://doi.org/10.1007/s10712-017-9428-0 cumuli at cloud base is very sensitive to changes in environmental conditions, while process models suggest the opposite. To understand and resolve this contradiction, we propose to organize a field campaign aimed at quantifying the physical properties of tradecumuli (e.g., cloud fraction and water content) as a function of the large-scale environment. Beyond a better understanding of clouds-circulation coupling processes, the campaign will provide a reference data set that may be used as a benchmark for advancing the modelling and the satellite remote sensing of clouds and circulation. It will also be an opportunity for complementary investigations such as evaluating model convective parameterizations or studying the role of ocean mesoscale eddies in air-sea interactions and convective organization.