The HD(CP)2 Observational Prototype Experiment (HOPE) was performed as a major 2-month field experiment in Jülich, Germany, in April and May 2013, followed by a smaller campaign in Melpitz, Germany, in September 2013. HOPE has been designed to provide an observational dataset for a critical evaluation of the new German community atmospheric icosahedral non-hydrostatic (ICON) model at the scale of the model simulations and further to provide information on land-surface–atmospheric boundary layer exchange, cloud and precipitation processes, as well as sub-grid variability and microphysical properties that are subject to parameterizations. HOPE focuses on the onset of clouds and precipitation in the convective atmospheric boundary layer. This paper summarizes the instrument set-ups, the intensive observation periods, and example results from both campaigns. HOPE-Jülich instrumentation included a radio sounding station, 4 Doppler lidars, 4 Raman lidars (3 of them provide temperature, 3 of them water vapour, and all of them particle backscatter data), 1 water vapour differential absorption lidar, 3 cloud radars, 5 microwave radiometers, 3 rain radars, 6 sky imagers, 99 pyranometers, and 5 sun photometers operated at different sites, some of them in synergy. The HOPE-Melpitz campaign combined ground-based remote sensing of aerosols and clouds with helicopter- and balloon-based in situ observations in the atmospheric column and at the surface. HOPE provided an unprecedented collection of atmospheric dynamical, thermodynamical, and micro- and macrophysical properties of aerosols, clouds, and precipitation with high spatial and temporal resolution within a cube of approximately 10 × 10 × 10 km3. HOPE data will significantly contribute to our understanding of boundary layer dynamics and the formation of clouds and precipitation. The datasets have been made available through a dedicated data portal. First applications of HOPE data for model evaluation have shown a general agreement between observed and modelled boundary layer height, turbulence characteristics, and cloud coverage, but they also point to significant differences that deserve further investigations from both the observational and the modelling perspective.

The Institute of Physics and Meteorology of the University of Hohenheim (UHOH) operates a scanning rotational Raman lidar (RRL) for high-resolution temperature and water vapor measurements. The measurement performance of the RRL was improved in several aspects. The statistical error of temperature measurements was reduced by up to 70% through optimization of the filter passbands for various solar background conditions. The optimization method, based on detailed simulations, was written for one specific wavelength and was not applicable to other Raman lidar systems. Therefore the simulation results were parametrized in respect to temperature and background level and expressed in units of wavenumbers. A new interference filter transmitting rotational Raman lines near the excitation wavelength was installed, resulting in a higher transmission and eliminating possible leakage signal. A detection channel for the vibrational Raman line of water vapor was added for the retrieval of water vapor mixing ratios during day-and nighttime. More than 300 hours of temperature and more than 200 hours of water vapor measurements were performed and the acquired profiles used in several publications. Atmospheric variance and higher order moment profiles of the daytime atmospheric boundary layer were derived. Das Institut fur Physik und Meteorologie der Universitat Hohenheim (UHOH) betreibt ein scannendes Rotations-Raman-Lidar (RRL) fur hochaufgeloste Messungen von Temperatur- und Wasserdampffeldern. Die Leistungsfahigkeit des Systems bezuglich der Genauigkeit und statistischer Messungenauigkeit konnte erheblich verbessert werden. Der statistische Messfehler wurde bis zu 70% reduziert durch die Abstimmung der detektierten Wellenlangenbereiche auf den Tageslichthintergrund. Die Optimierung basiert auf einer detaillierten Simulation der Rotations-Raman-Linien und der Filterkurven. Die Berechnungen wurden zuerst wellenlangenabhangig durchgefuhrt und waren daher nicht direkt ubertragbar auf andere Ramanlidarsysteme. Deshalb wurden die Ergebnisse der Simulation parametrisiert bezuglich der Temperatur und dem Signalhintergrund und inWellenzahleinheiten angegeben. Ein neuer Interferenzfilter fur den Wellenlangenbereich nahe der Anregungswellenlange wurde eingebaut. Dieser besitzt eine hohere Maximaltransmission und eine erhohte optische Dichte fur die Anregungswellenlange. Damit kann eine zusatzliches Fehlersignal in Bereichen mit hoher Ruckstreuung vermieden werden. Ein Empfangskanal fur das Vibrations-Raman-Signal von Wasserdampf ermoglicht die Messung desWasserdampfmischungverhaltnisses. Mehr als 300 Stunden Temperaturmessungen und 200 Stunden Wasserdampfmessungen wurden durchgefuhrt und waren die Basis fur mehrere Publikationen. Messdaten mit hoher zeitlicher Auflosung wurden verwendet um Varianzprofile und Profile der hoheren Momente in der planetaren Grenzschicht zu ermitteln.

The impact of assimilating lower‐tropospheric lidar temperature profiles into a numerical weather prediction (NWP) model was investigated. The profiles were measured with the Temperature Rotational Raman Lidar (TRRL) of the University of Hohenheim on 24 April 2013. The day showed the development of a typical daytime planetary boundary layer (PBL) with no optically thick clouds. The Weather Research and Forecasting (WRF) model was operated with 57 vertical levels covering Central Europe with 3 km horizontal resolution. Three different experiments were carried out with a rapid update cycle with hourly three‐dimensional variational data assimilation. The impact run (ALL_DA) was performed with the assimilation of conventional data and the additional assimilation of TRRL profiles between 0900 and 1800 UTC in a height range from about 500 to 3000 m above ground level with a vertical resolution of about 100 m. In CONV_DA and NO_DA, only conventional data and no data were assimilated, respectively. To consider the representativeness of the TRRL profiles, an observation error of 0.7 K was used for all heights. The assimilation was performed using the radiosonde operator. The TRRL data assimilation corrected the temperature profiles towards the lidar data. In the mean, the boundary‐layer height was improved by 60 m in ALL_DA compared to the TRRL data and the temperature gradient in the entrainment layer by 0.19 K (100 m)−1. While ALL_DA showed a root mean square error (RMSE) of 0.6 K compared to the TRRL data, the RMSE of CONV_DA was twice as large. Compared to data from radiosondes launched at the TRRL site, ALL_DA showed a significantly smaller RMSE than CONV_DA in two out of the four times radiosonde data were available. We conclude that the assimilation of TRRL data has great potential to close the critical gap of missing temperature observations in the lower troposphere.

We revisit the methodology of rotational Raman temperature measurements covering both lidar and non-range-resolved measurements, e.g., for aircraft control. The results of detailed optimization calculations are presented for the commonly used extraction of signals from the anti-Stokes branch. Different background conditions and realistic shapes of the filter transmission curves are taken into account. Practical uncertainties of the central passbands and widths are discussed. We found a simple parametrization for the optimum filter passband shifts depending on the atmospheric temperature range of interest and the background. The approximation errors of this parametrization are smaller than 2% for temperatures between 200 and 300 K and smaller than 4% between 180 and 200 K.

Atmospheric variables in the convective boundary layer (CBL), which are critical for turbulence parameterizations inweather and climatemodels, are assessed. These include entrainment fluxes, higher-ordermoments of humidity, potential temperature, and vertical wind, as well as dissipation rates. Theoretical relationships between the integral scales, gradients, and higher-order moments of atmospheric variables, fluxes, and dissipation rates are developed mainly focusing on the entrainment layer (EL) at the top of the CBL. These equations form the starting point for tests of and new approaches in CBL turbulence parameterizations. For the investigation of these relationships, an observational approach using a synergy of ground-based water vapor, temperature, and wind lidar systems is proposed. These systems measure instantaneous vertical profiles with high temporal and spatial resolution throughout the CBL including the EL. The resolution of these systems permits the simultaneous measurement of gradients and fluctuations of these atmospheric variables. For accurate analyses of the gradients and the shapes of turbulence profiles, the lidar system performances are very important. It is shown that each lidar profile can be characterized very well with respect to bias and system noise and that the constant bias has negligible effect on the measurement of turbulent fluctuations. It is demonstrated how different gradient relationships can be measured and tested with the proposed lidar synergy within operational measurements or new field campaigns. Particularly, a novel approach is introduced for measuring the rate of destruction of humidity and temperature variances, which is an important component of the variance budget equations.