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Ulrich Löhnert, S. Crewell, O. Krasnov, E. O’Connor, and H. Russchenberg

operational radiosonde network with its typically 12-hourly observations is by far not sufficient for evaluating model performance on small time (short term 0 ± 18 h) and spatial (model resolution <3 km) scale. Because satellite instruments are also not able to resolve BL variables well, strong efforts have been undertaken within the last decade to enhance the development of ground-based remote sensing instrumentation. However, no single instrument is capable to observe all relevant atmospheric variables

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Matthias Grzeschik, Hans-Stefan Bauer, Volker Wulfmeyer, Dirk Engelbart, Ulla Wandinger, Ina Mattis, Dietrich Althausen, Ronny Engelmann, Matthias Tesche, and Andrea Riede

optimized by 4DVAR of the lidar data. 5. Results and discussion a. Evaluation of initial fields Before data assimilation is performed, a comparison of the time–height cross sections of the lidar data with the ECMWF control data gives insight into the performance of the model with respect to the representation of the water vapor field. Figures 6  – 8 present the model results at the grid boxes of the lidar systems in comparison with the lidar data (see the top and middle panels). The coarse structure of

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Rod Frehlich, Yannick Meillier, and Michael L. Jensen

1. Introduction Measuring and modeling of the boundary layer is challenging, especially for the nighttime stable boundary layer (SBL). In particular, high-quality in situ measurements of profiles of mean and turbulent statistics of the nighttime SBL are logistically difficult using instrumented towers or instrumented research aircraft ( Tjernström 1993 ). A suite of fast response turbulence sensors attached to a tethered system can operate effectively under these nighttime conditions. The

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Daniela Nowak, Dominique Ruffieux, Judith L. Agnew, and Laurent Vuilleumier

fog layer was below the lower detection height of the cloud radar. b. Automatic detection of cloud-base and -top performances during the TUC experiment During the TUC campaign, human eye reports stated 200 stratus cloud or fog situations, divided into 110 stratus cloud and 90 fog observations ( Table 2 ). The efficiency of the automatic cloud detection with the ceilometer and cloud radar was evaluated using only the measurements performed concurrently with the operational synops. Since synop are

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P. C. S. Devara, P. E. Raj, K. K. Dani, G. Pandithurai, M. C. R. Kalapureddy, S. M. Sonbawne, Y. J. Rao, and S. K. Saha

and clouds is necessary mainly for obtaining better radiative forcing estimates—one of the major uncertainties in understanding the influence of aerosols and precursor gases on weather, climate change, and underlying processes—and for refining models for improving satellite data retrieval algorithms. In view of the importance of aerosols in tropical atmospheric processes ( Hansen et al. 2000 ), the availability of data describing their main properties is rather poor, in particular with respect to

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Laura Bianco, James M. Wilczak, and Allen B. White

1. Introduction The depth of the atmosphere’s turbulent planetary boundary layer (PBL) is well recognized as an important parameter for air quality monitoring and prediction studies, as well as for the evaluation of numerical weather prediction models. One potential method to routinely monitor the dynamically defined PBL depths uses high-resolution wind-profiling radars, as the maximum value of the radar-derived refractive index parameter C 2 n (which usually emerges at the inversion due to

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