The Cloudnet project aims to provide a systematic evaluation of clouds in forecast and climate models by comparing the model output with continuous ground-based observations of the vertical profiles of cloud properties. In the models, the properties of clouds are simplified and expressed in terms of the fraction of the model grid box, which is filled with cloud, together with the liquid and ice water content of the clouds. These models must get the clouds right if they are to correctly represent both their radiative properties and their key role in the production of precipitation, but there are few observations of the vertical profiles of the cloud properties that show whether or not they are successful. Cloud profiles derived from cloud radars, ceilometers, and dual-frequency microwave radiometers operated at three sites in France, Netherlands, and the United Kingdom for several years have been compared with the clouds in seven European models. The advantage of this continuous appraisal is that the feedback on how new versions of models are performing is provided in quasi-real time, as opposed to the much longer time scale needed for in-depth analysis of complex field studies. Here, two occasions are identified when the introduction of new versions of the ECMWF and Météo-France models leads to an immediate improvement in the representation of the clouds and also provides statistics on the performance of the seven models. The Cloudnet analysis scheme is currently being expanded to include sites outside Europe and further operational forecasting and climate models.

A frequency-modulated continuous-wave (FMCW) radar interference mitigation technique using the interpolation of beat frequencies in the short-time Fourier transform (STFT) domain, phase matching, and reconfigurable linear prediction coefficients estimation for Coherent Processing Interval processing is proposed. The technique is noniterative and does not rely on algorithm convergence. It allows the usage of the fast Fourier transform (FFT) as the radar's beat-frequency estimation tool, for reasons such as real-time implementation, noise linearity after the FFT, and compatibility with legacy receiver architectures. Verification is done in range and in range-Doppler using radar experimental data in two ways: first by removing interferences from interference-contaminated data and second by using interference-free data as the reference data, and processing it--as if it had interferences--using the proposed technique, inverse cosine windowing and zeroing for comparison. We found that processing with the proposed technique closely matches the reference-data and outperforms the inverse cosine windowing and zeroing techniques in 2-D cross correlation, amplitude, and phase average errors and phase root-mean-square error. It is expected that the proposed technique will be operationally deployed on the TU Delft simultaneous-polarimetric PARSAX radar.

Herein is proposed a set of nearly orthogonal waveforms based on offset linear frequency modulated (LFM) signals for multiple-input, multiple-output frequency modulated continuous wave (MIMO FMCW) radar operation. The orthogonality of sounding signals is a critical point for many multi-channel radar applications because the interference between signals can significantly limit the radar's ability for observation of weak targets in the presence of strong targets and clutter. High orthogonality for the proposed waveforms can be obtained by using de-ramping processing in the MIMO FMCW radar receiver. The experimental results presented demonstrate that a high orthogonality level is achievable for a specific multi-channel FMCW radar.

This paper describes advances in ground-based thermodynamic profiling of the lower troposphere through sensor synergy. The well-documented integrated profiling technique (IPT), which uses a microwave profiler, a cloud radar, and a ceilometer to simultaneously retrieve vertical profiles of temperature, humidity, and liquid water content (LWC) of nonprecipitating clouds, is further developed toward an enhanced performance in the boundary layer and lower troposphere. For a more accurate temperature profile, this is accomplished by including an elevation scanning measurement modus of the microwave profiler. Height-dependent RMS accuracies of temperature (humidity) ranging from ∼0.3 to 0.9 K (0.5–0.8 g m−3) in the boundary layer are derived from retrieval simulations and confirmed experimentally with measurements at distinct heights taken during the 2005 International Lindenberg Campaign for Assessment of Humidity and Cloud Profiling Systems and its Impact on High-Resolution Modeling (LAUNCH) of the German Weather Service. Temperature inversions, especially of the lower boundary layer, are captured in a very satisfactory way by using the elevation scanning mode. To improve the quality of liquid water content measurements in clouds the authors incorporate a sophisticated target classification scheme developed within the European cloud observing network CloudNet. It allows the detailed discrimination between different types of backscatterers detected by cloud radar and ceilometer. Finally, to allow IPT application also to drizzling cases, an LWC profiling method is integrated. This technique classifies the detected hydrometeors into three different size classes using certain thresholds determined by radar reflectivity and/or ceilometer extinction profiles. By inclusion into IPT, the retrieved profiles are made consistent with the measurements of the microwave profiler and an LWC a priori profile. Results of IPT application to 13 days of the LAUNCH campaign are analyzed, and the importance of integrated profiling for model evaluation is underlined.

In situ measurements indicate the complexity and nonunique character of radar reflectivity–liquid water content (Z–LWC) relationships in stratocumulus and cumulus clouds. Parameters of empirical (statistical) Z–LWC dependences vary within a wide range. Respectively, the accuracy of retrieval algorithms remains low. This situation is partially related to the fact that empirical algorithms and parameters are often derived without a corresponding understanding of physical mechanisms responsible for the formation of the Z–LWC diagrams. In this study, the authors investigate the processes of formation of the Z–LWC relationships using a new trajectory ensemble model of the cloud-topped boundary layer (BL). In the model, the entire volume of the BL is covered by Lagrangian parcels advected by a turbulent-like velocity field. The time-dependent velocity field is generated by a turbulent model and obeys the correlation turbulent laws. Each Lagrangian parcel represents the “cloud parcel model” with an accurate description of processes of diffusion growth–evaporation of aerosols and droplets and droplet collisions. The fact that parcels are adjacent to each other allows one to calculate sedimentation of droplets and precipitation (drizzle) formation. The characteristic parcel size is 50 m; the number of parcels is 1840. The model calculates droplet size distributions (DSDs), as well as their moments (e.g., aerosol and drop concentration, mass content, radar reflectivity) in each parcel. In the course of the model integration, Z–LWC relationships are calculated for each parcel, as well as the scattering diagram including all parcels. The model reproduces in situ observed types of the Z–LWC relationships. It is shown that different regimes represent different stages of cloud evolution: diffusion growth, beginning of drizzle formation, and stage of heavy drizzle, respectively. The large scattering of the Z–LWC relationships is found to be an inherent property of any drizzling cloud. Different zones on the Z–LWC diagram are related to cloud volumes located at different levels within a cloud and having different DSD. This finding allows for improvement of retrieval algorithms.

A novel trajectory ensemble model of the cloud-topped boundary layer containing 1340 Lagrangian parcels moving with a turbulent-like flow with the observed statistical properties was applied to investigate the formation of the microphysical structure of stratocumulus clouds (Sc) in a nonmixing limit (when turbulent mixing between the parcels is not taken into account). The Sc observed in two research flights during the Second Dynamics and Chemistry of the Marine Stratocumulus field study (DYCOMS II)—RF01 (no drizzle) and RF07 (weak drizzle)—are simulated. The mechanisms leading to a high variability of droplet size distributions (DSDs) with different spectrum width and dispersion are discussed. Drizzle formation was investigated using the radar reflectivity–LWC and LWC–effective drop radius diagrams simulated by the model in the nondrizzle and drizzle cases. It is shown that in the RF07 case large cloud droplets that trigger drop collisions and drizzle formation form only in a small fraction (about 1%) of the parcels (which will be referred to as lucky parcels) in which LWC exceeds ∼1.5 g m−3. This value exceeds the horizontally averaged LWC maximum value of 0.9 g m−3 by two to three standard deviations, indicating a small amount of lucky parcels. In a nondrizzling cloud simulation this threshold is exceeded extremely rarely. The dependence of the threshold value of LWC on aerosol concentration is discussed. The lucky parcels (at least in the nonmixing limit) start their updraft in the vicinity of the surface, where the water vapor mixing ratio is maximum, and ascend to the highest levels close to the cloud top. It is shown that the lucky parcel tracks are related to the large eddies in the boundary layer, which indicates the substantial role of large eddies in drizzle formation.