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

clearly defined PBL at most hours, ambiguities may sometimes exist. These ambiguities can arise for example from weak levels of turbulence within the PBL, intermittent turbulence, and clouds. A particularly difficult period of the day is in the late afternoon with the collapse of the boundary layer. At this time the layer below the inversion is intermittently turbulent before it becomes fully decoupled from the inversion, and the depth of the PBL is nebulous. An automatic algorithm for detecting PBL

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Edwin F. Campos, Wayne Hocking, and Frédéric Fabry

equivalent reflectivity factors measured at the X band and the corresponding values derived from drop size distributions at ground. To validate our simulation, the rain signal measured by the VHF radar is also plotted as a dotted line in Fig. 6 . This rain signal was obtained from the algorithm described by Campos et al. (2006 , section 2.3). The application of this algorithm is presented in Fig. 7 for a profile of Doppler spectra measured by our VHF radar. In these examples, the crosses (linked by

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Rod Frehlich, Yannick Meillier, and Michael L. Jensen

-scale turbulence in these low-turbulence regimes. The development of eye-safe Doppler lidar ( Henderson et al. 1991 , 1993 ; Grund et al. 2001 ; Pearson and Collier 1999 ) with range resolution on the order of 20–70 m and new processing algorithms ( Banakh and Smalikho 1997 ; Frehlich 1997 , 2001a ; Frehlich and Cornman 2002 ; Frehlich et al. 1994 , 1997 , 1998 , 2006 ; Banta et al. 2003 , 2006 ; Davies et al. 2004 ; Lothon et al. 2006 ) have produced high-quality measurements of the boundary

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Danny E. Scipión, Phillip B. Chilson, Evgeni Fedorovich, and Robert D. Palmer

was used to calculate the Doppler moments from the resulting complex time series data (including the additive noise). First, the Doppler spectra were found and then the noise level was estimated using the algorithm described in Hildebrand and Sekhon (1974) . After the spectral levels were reduced to compensate for the estimated noise level, the first three moments (power, mean radial velocity, and spectrum width) were estimated following Doviak and Zrnić (1993) . An example of the spectral

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B. L. Cheong, R. D. Palmer, T-Y. Yu, K-F. Yang, M. W. Hoffman, S. J. Frasier, and F. J. Lopez-Dekker

reconstruct a two-dimensional, angular distribution of the atmospheric structure within the volume illuminated by the transmitted beam (e.g., Kudeki and Sürücü 1991 ; Palmer et al. 1998 ; Yu et al. 2000 ; Hélal et al. 2001 ). In addition, the angular distribution of radial velocity can be obtained by an efficient algorithm ( Cheong et al. 2004 ). CRI can be thought of as a beam-forming technique, in which a number of receiving beams can be synthesized simultaneously by coherently combining signals

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Eduardo Landulfo, Alexandros Papayannis, Ani Sobral Torres, Sandro Toshio Uehara, Lucila Maria Viola Pozzetti, Caio Alencar de Matos, Patricia Sawamura, Walter Morinobu Nakaema, and Wellington de Jesus

based on the solution of the basic lidar equation, taking into account the atmospheric solar background radiation correction ( Papayannis and Chourdakis 2002 ). The retrieval of the aerosol optical properties is based on the measurements of the aerosol backscatter coefficient β aer at 532 nm, up to an altitude of 5–6 km. The determination of the vertical profile of the aerosol backscatter coefficient relies on the lidar inversion technique following a modified Klett’s algorithm ( Klett 1985

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

and Cloud Profiling Systems and its Impact on High-Resolution Modeling (LAUNCH). Section 3 describes the improved IPT, with special emphasis on the target classification, the inclusion of the elevation scanning measurements of the MWP, and the incorporation of the K06 retrieval algorithm. We then show results of IPT application to simulated measurements in section 4 , making clear the potential of elevation scanning measurements for BL profiling. Section 5 shows experimental results

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

at 1500 m AGL (not shown), which is out of the limit chosen for the cloud radar top height detection algorithm (1200 m, the study focuses on fog and low clouds). Finally, the ceilometer shows a transition period from a higher cloud layer to well detected low stratus cloud between 2100 and 0000 UTC. Thus, it can be assumed that there was a transition period until 0400 UTC from a multilayer cloud situation in the lower troposphere to a well-defined low stratus cloud situation for the rest of the

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V. Bellantone, I. Carofalo, F. De Tomasi, M. R. Perrone, M. Santese, A. M. Tafuro, and A. Turnone

, 670, 870, 940, and 1020 nm, while measurements of sky radiance are made at 440, 670, 870, and 1020 nm. Holben et al. (1998 , 2001 ) give detailed descriptions of the instrument and data acquisition procedures. A flexible inversion algorithm, developed by Dubovik and King (2000) , is used to retrieve columnar aerosol parameters from direct-sun and diffuse-sky radiance measurements. A brief discussion on the accuracy of individual retrievals is reported in Dubovik et al. (2002) . Cloud

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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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